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Echo Chamber [theUltimator5]The Echo Chamber - When history repeats, maybe you should listen. Ever had that eerie feeling you've seen this exact price action before? The Echo Chamber doesn't just give you déjà vu—it mathematically proves it, scales it, and projects what happened next. 📖 WHAT IT DOES The Echo Chamber is an advanced pattern recognition tool that scans your chart's history to find segments that closely match your current price action. But here's where it gets interesting: it doesn't just find similar patterns - It expands and contracts the time window to create a uniquely scaled fractal. Patterns don't always follow the same timeframe, but they do follow similar patterns. Using a custom correlation analysis algorithm combined with flexible time-scaling, this indicator: Finds historical price segments that mirror your current market structure Scales and overlays them perfectly onto your current chart Projects forward what happened AFTER that historical match Gives you a visual "echo" from the past with a glimpse into potential futures ══════════════════════════════ HOW TO USE IT This indicator starts off in manual mode, which means that YOU, the user, can select the point in time that you want to project from. Simply click on a point in time to set the starting value. Once you select your point in time, the indicator will automatically plot the chosen historical chart pattern and correlation over the current chart and project the price forwards based on how the chart looked in the past. If you want to change the point in time, you can update it from the settings, or drag the point on the chart over to a new position. You can manually select any point in time, and the chart will quickly update with the new pattern. A correlation will be shown in a table alongside the date/timestamp of the selected point in time. You can switch to auto mode, which will automatically search out the best-fit pattern over a defined lookback range and plot the past/future projection for you without having to manually select a point in time at all. It simply finds the best fit for you. You can change the scale factor by adjusting multiplication and division variables to find time-scaled fractal patterns. ══════════════════════════════ 🎯 KEY FEATURES Two Operating Modes: 🔧 MANUAL MODE - Select any historical point and see how it correlates with current price action in real-time. Perfect for: • Analyzing specific past events (crashes, rallies, consolidations) • Testing historical patterns against current conditions • Educational analysis of market structure repetition 🤖 AUTO MODE - It automatically scans through your lookback period to find the single best-correlated historical match. Ideal for: • Quick pattern discovery • Systematic trading approach • Unbiased pattern recognition Time Warp Technology: The time warp feature expands and compresses the correlation window to provide a custom fractal so you can analyze windows of time that don't necessarily match the current chart. 💡 *Example: Multiplier=3, Divisor=2 gives you a 1.5x time stretch—perfect for finding patterns that played out 50% slower than current price action.* Drawing Modes: Scale Only : Pure vertical scaling—matches price range while maintaining temporal alignment at bar 0 Rotate & Scale : Advanced geometric transformation that anchors both the start AND end points, creating a rotated fit that matches your current segment's slope and range Visual Components: 🟠 Orange Overlay : The historical match, perfectly scaled to your current price action 🟣 Purple Projection : What happened NEXT after that historical pattern (dotted line into the future) 📦 Highlight Boxes : Shows you exactly where in history these patterns came from 📊 Live Correlation Table : Real-time correlation coefficient with color-coded strength indicator ══════════════════════════════ ⚙️ PARAMETERS EXPLAINED Correlation Window Length (20) : How many bars to match. Smaller = more precise matches but noisier. Larger = broader patterns but fewer matches. Note: if this value is too high in auto mode, the script may time out from computational overload. Multiplication Factor : Historical time multiplier. 2 = sample every 2nd bar from history. Higher values find slower historical patterns. Division Factor : Historical time divisor applied after multiplication. Final sample rate = (Length × Factor) ÷ Divisor, rounded down. Lookback Range : How far back to search for patterns. More history = better chance of finding matches but slower performance. Note: if this value is too high in auto mode, the script may time out from computational overload. Future Projection Length : How many bars forward to project from the historical match. Your crystal ball's focal length. ══════════════════════════════ 💼 TRADING APPLICATIONS Trend Continuation/Reversal : If the purple projection continues the current trend, that's your historical confirmation. If it reverses, you've found a potential turning point that's happened before under similar conditions. Support/Resistance Validation : Does the projection respect your S/R levels? History suggests those levels matter. Does it break through? You've found historical precedent for a breakout. Time-Based Exits : The projection shows not just WHERE price might go, but WHEN. Use it to anticipate timing of moves. Multi-Timeframe Analysis : Use time compression to overlay higher timeframe patterns onto lower timeframes. See daily patterns on hourly charts, weekly on daily, etc. Pattern Education : In Manual Mode, study how specific historical events correlate with current conditions. Build your pattern recognition library. ══════════════════════════════ 📊 CORRELATION TABLE The table shows your correlation coefficient as a percentage: 80-100%: Extremely strong correlation—history is practically repeating 60-80%: Strong correlation—significant similarity 40-60%: Moderate correlation—some structural similarity 20-40%: Weak correlation—limited similarity 0-20%: Very weak correlation—essentially random match -20-40%: Weak inverse correlation -40-60%: Moderate inverse correlation -60-80%: Strong inverse correlation -80-100%: Extremely strong inverse correlation—history is practically inverting **Important**: The correlation measures SHAPE similarity, not price level. An 85% correlation means the price movements follow a very similar pattern, regardless of whether prices are higher or lower. ══════════════════════════════ ⚠️ IMPORTANT DISCLAIMERS - Past performance does NOT guarantee future results (but it sure is interesting to study) - High correlation doesn't mean causation—markets are complex adaptive systems - Use this as ONE tool in your analytical toolkit, not a standalone trading system - The projection is what HAPPENED after a similar pattern in the past, not a prediction - Always use proper risk management regardless of what the Echo Chamber suggests ══════════════════════════════ 🎓 PRO TIPS 1. Start with Auto Mode to find high-correlation matches, then switch to Manual Mode to study why that period was similar 2. Experiment with time warping on different timeframes—a 2x factor on a daily chart lets you see weekly patterns 3. Watch for correlation decay —if correlation drops sharply after the match, current conditions are diverging from history 4. Combine with volume —check if volume patterns also match 5. Use "Rotate & Scale" mode when the current trend angle differs from the historical match 6. Increase lookback range to 500-1000+ on daily/weekly charts for finding rare historical parallels ══════════════════════════════ 🔧 TECHNICAL NOTES - Uses Pearson correlation coefficient for pattern matching - Implements range-based scaling to normalize different price levels - Rotation mode uses linear interpolation for geometric transformation - All calculations are performed on close prices - Boxes highlight actual historical bar ranges (high/low) - Maximum of 500 lines and 500 boxes for performance optimization
Pine Script® indicator
by TheUltimator5
MACD Enhanced [DCAUT]█ MACD Enhanced 📊 ORIGINALITY & INNOVATION The MACD Enhanced represents a significant improvement over traditional MACD implementations. While Gerald Appel's original MACD from the 1970s was limited to exponential moving averages (EMA), this enhanced version expands algorithmic options by supporting 21 different moving average calculations for both the main MACD line and signal line independently. This improvement addresses an important limitation of traditional MACD: the inability to adapt the indicator's mathematical foundation to different market conditions. By allowing traders to select from algorithms ranging from simple moving averages (SMA) for stability to advanced adaptive filters like Kalman Filter for noise reduction, this implementation changes MACD from a fixed-algorithm tool into a flexible instrument that can be adjusted for specific market environments and trading strategies. The enhanced histogram visualization system uses a four-color gradient that helps communicate momentum strength and direction more clearly than traditional single-color histograms. 📐 MATHEMATICAL FOUNDATION The core calculation maintains the proven MACD formula: Fast MA(source, fastLength) - Slow MA(source, slowLength), but extends it with algorithmic flexibility. The signal line applies the selected smoothing algorithm to the MACD line over the specified signal period, while the histogram represents the difference between MACD and signal lines. Available Algorithms: The implementation supports a comprehensive spectrum of technical analysis algorithms: Basic Averages: SMA (arithmetic mean), EMA (exponential weighting), RMA (Wilder's smoothing), WMA (linear weighting) Advanced Averages: HMA (Hull's low-lag), VWMA (volume-weighted), ALMA (Arnaud Legoux adaptive) Mathematical Filters: LSMA (least squares regression), DEMA (double exponential), TEMA (triple exponential), ZLEMA (zero-lag exponential) Adaptive Systems: T3 (Tillson T3), FRAMA (fractal adaptive), KAMA (Kaufman adaptive), MCGINLEY_DYNAMIC (reactive to volatility) Signal Processing: ULTIMATE_SMOOTHER (low-pass filter), LAGUERRE_FILTER (four-pole IIR), SUPER_SMOOTHER (two-pole Butterworth), KALMAN_FILTER (state-space estimation) Specialized: TMA (triangular moving average), LAGUERRE_BINOMIAL_FILTER (binomial smoothing) Each algorithm responds differently to price action, allowing traders to match the indicator's behavior to market characteristics: trending markets benefit from responsive algorithms like EMA or HMA, while ranging markets require stable algorithms like SMA or RMA. 📊 COMPREHENSIVE SIGNAL ANALYSIS Histogram Interpretation: Positive Values: Indicate bullish momentum when MACD line exceeds signal line, suggesting upward price pressure and potential buying opportunities Negative Values: Reflect bearish momentum when MACD line falls below signal line, indicating downward pressure and potential selling opportunities Zero Line Crosses: MACD crossing above zero suggests transition to bullish bias, while crossing below indicates bearish bias shift Momentum Changes: Rising histogram (regardless of positive/negative) signals accelerating momentum in the current direction, while declining histogram warns of momentum deceleration Advanced Signal Recognition: Divergences: Price making new highs/lows while MACD fails to confirm often precedes trend reversals Convergence Patterns: MACD line approaching signal line suggests impending crossover and potential trade setup Histogram Peaks: Extreme histogram values often mark momentum exhaustion points and potential reversal zones 🎯 STRATEGIC APPLICATIONS Comprehensive Trend Confirmation Strategies: Primary Trend Validation Protocol: Identify primary trend direction using higher timeframe (4H or Daily) MACD position relative to zero line Confirm trend strength by analyzing histogram progression: consistent expansion indicates strong momentum, contraction suggests weakening Use secondary confirmation from MACD line angle: steep angles (>45°) indicate strong trends, shallow angles suggest consolidation Validate with price structure: trending markets show consistent higher highs/higher lows (uptrend) or lower highs/lower lows (downtrend) Entry Timing Techniques: Pullback Entries in Uptrends: Wait for MACD histogram to decline toward zero line without crossing, then enter on histogram expansion with MACD line still above zero Breakout Confirmations: Use MACD line crossing above zero as confirmation of upward breakouts from consolidation patterns Continuation Signals: Look for MACD line re-acceleration (steepening angle) after brief consolidation periods as trend continuation signals Advanced Divergence Trading Systems: Regular Divergence Recognition: Bullish Regular Divergence: Price creates lower lows while MACD line forms higher lows. This pattern is traditionally considered a potential upward reversal signal, but should be combined with other confirmation signals Bearish Regular Divergence: Price makes higher highs while MACD shows lower highs. This pattern is traditionally considered a potential downward reversal signal, but trading decisions should incorporate proper risk management Hidden Divergence Strategies: Bullish Hidden Divergence: Price shows higher lows while MACD displays lower lows, indicating trend continuation potential. Use for adding to existing long positions during pullbacks Bearish Hidden Divergence: Price creates lower highs while MACD forms higher highs, suggesting downtrend continuation. Optimal for adding to short positions during bear market rallies Multi-Timeframe Coordination Framework: Three-Timeframe Analysis Structure: Primary Timeframe (Daily): Determine overall market bias and major trend direction. Only trade in alignment with daily MACD direction Secondary Timeframe (4H): Identify intermediate trend changes and major entry opportunities. Use for position sizing decisions Execution Timeframe (1H): Precise entry and exit timing. Look for MACD line crossovers that align with higher timeframe bias Timeframe Synchronization Rules: Daily MACD above zero + 4H MACD rising = Strong uptrend context for long positions Daily MACD below zero + 4H MACD declining = Strong downtrend context for short positions Conflicting signals between timeframes = Wait for alignment or use smaller position sizes 1H MACD signals only valid when aligned with both higher timeframes Algorithm Considerations by Market Type: Trending Markets: Responsive algorithms like EMA, HMA may be considered, but effectiveness should be tested for specific market conditions Volatile Markets: Noise-reducing algorithms like KALMAN_FILTER, SUPER_SMOOTHER may help reduce false signals, though results vary by market Range-Bound Markets: Stability-focused algorithms like SMA, RMA may provide smoother signals, but individual testing is required Short Timeframes: Low-lag algorithms like ZLEMA, T3 theoretically respond faster but may also increase noise Important Note: All algorithm choices and parameter settings should be thoroughly backtested and validated based on specific trading strategies, market conditions, and individual risk tolerance. Different market environments and trading styles may require different configuration approaches. 📋 DETAILED PARAMETER CONFIGURATION Comprehensive Source Selection Strategy: Price Source Analysis and Optimization: Close Price (Default): Most commonly used, reflects final market sentiment of each period. Best for end-of-day analysis, swing trading, daily/weekly timeframes. Advantages: widely accepted standard, good for backtesting comparisons. Disadvantages: ignores intraday price action, may miss important highs/lows HL2 (High+Low)/2: Midpoint of the trading range, reduces impact of opening gaps and closing spikes. Best for volatile markets, gap-prone assets, forex markets. Calculation impact: smoother MACD signals, reduced noise from price spikes. Optimal when asset shows frequent gaps, high volatility during specific sessions HLC3 (High+Low+Close)/3: Weighted average emphasizing the close while including range information. Best for balanced analysis, most asset classes, medium-term trading. Mathematical effect: 33% weight to high/low, 33% to close, provides compromise between close and HL2. Use when standard close is too noisy but HL2 is too smooth OHLC4 (Open+High+Low+Close)/4: True average of all price points, most comprehensive view. Best for complete price representation, algorithmic trading, statistical analysis. Considerations: includes opening sentiment, smoothest of all options but potentially less responsive. Optimal for markets with significant opening moves, comprehensive trend analysis Parameter Configuration Principles: Important Note: Different moving average algorithms have distinct mathematical characteristics and response patterns. The same parameter settings may produce vastly different results when using different algorithms. When switching algorithms, parameter settings should be re-evaluated and tested for appropriateness. Length Parameter Considerations: Fast Length (Default 12): Shorter periods provide faster response but may increase noise and false signals, longer periods offer more stable signals but slower response, different algorithms respond differently to the same parameters and may require adjustment Slow Length (Default 26): Should maintain a reasonable proportional relationship with fast length, different timeframes may require different parameter configurations, algorithm characteristics influence optimal length settings Signal Length (Default 9): Shorter lengths produce more frequent crossovers but may increase false signals, longer lengths provide better signal confirmation but slower response, should be adjusted based on trading style and chosen algorithm characteristics Comprehensive Algorithm Selection Framework: MACD Line Algorithm Decision Matrix: EMA (Standard Choice): Mathematical properties: exponential weighting, recent price emphasis. Best for general use, traditional MACD behavior, backtesting compatibility. Performance characteristics: good balance of speed and smoothness, widely understood behavior SMA (Stability Focus): Equal weighting of all periods, maximum smoothness. Best for ranging markets, noise reduction, conservative trading. Trade-offs: slower signal generation, reduced sensitivity to recent price changes HMA (Speed Optimized): Hull Moving Average, designed for reduced lag. Best for trending markets, quick reversals, active trading. Technical advantage: square root period weighting, faster trend detection. Caution: can be more sensitive to noise KAMA (Adaptive): Kaufman Adaptive MA, adjusts smoothing based on market efficiency. Best for varying market conditions, algorithmic trading. Mechanism: fast smoothing in trends, slow smoothing in sideways markets. Complexity: requires understanding of efficiency ratio Signal Line Algorithm Optimization Strategies: Matching Strategy: Use same algorithm for both MACD and signal lines. Benefits: consistent mathematical properties, predictable behavior. Best when backtesting historical strategies, maintaining traditional MACD characteristics Contrast Strategy: Use different algorithms for optimization. Common combinations: MACD=EMA, Signal=SMA for smoother crossovers, MACD=HMA, Signal=RMA for balanced speed/stability, Advanced: MACD=KAMA, Signal=T3 for adaptive behavior with smooth signals Market Regime Adaptation: Trending markets: both fast algorithms (EMA/HMA), Volatile markets: MACD=KALMAN_FILTER, Signal=SUPER_SMOOTHER, Range-bound: both slow algorithms (SMA/RMA) Parameter Sensitivity Considerations: Impact of Parameter Changes: Length Parameter Sensitivity: Small parameter adjustments can significantly affect signal timing, while larger adjustments may fundamentally change indicator behavior characteristics Algorithm Sensitivity: Different algorithms produce different signal characteristics. Thoroughly test the impact on your trading strategy before switching algorithms Combined Effects: Changing multiple parameters simultaneously can create unexpected effects. Recommendation: adjust parameters one at a time and thoroughly test each change 📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES Response Characteristics by Algorithm: Fastest Response: ZLEMA, HMA, T3 - minimal lag but higher noise Balanced Performance: EMA, DEMA, TEMA - good trade-off between speed and stability Highest Stability: SMA, RMA, TMA - reduced noise but increased lag Adaptive Behavior: KAMA, FRAMA, MCGINLEY_DYNAMIC - automatically adjust to market conditions Noise Filtering Capabilities: Advanced algorithms like KALMAN_FILTER and SUPER_SMOOTHER help reduce false signals compared to traditional EMA-based MACD. Noise-reducing algorithms can provide more stable signals in volatile market conditions, though results will vary based on market conditions and parameter settings. Market Condition Adaptability: Unlike fixed-algorithm MACD, this enhanced version allows real-time optimization. Trending markets benefit from responsive algorithms (EMA, HMA), while ranging markets perform better with stable algorithms (SMA, RMA). The ability to switch algorithms without changing indicators provides greater flexibility. Comparative Performance vs Traditional MACD: Algorithm Flexibility: 21 algorithms vs 1 fixed EMA Signal Quality: Reduced false signals through noise filtering algorithms Market Adaptability: Optimizable for any market condition vs fixed behavior Customization Options: Independent algorithm selection for MACD and signal lines vs forced matching Professional Features: Advanced color coding, multiple alert conditions, comprehensive parameter control USAGE NOTES This indicator is designed for technical analysis and educational purposes. Like all technical indicators, it has limitations and should not be used as the sole basis for trading decisions. Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Always combine with proper risk management and thorough strategy testing.
Pine Script® indicator
by DCAUT
Flux-Tensor Singularity [ML/RL PRO]Flux-Tensor Singularity This version of the Flux-Tensor Singularity (FTS) represents a paradigm shift in technical analysis by treating price movement as a physical system governed by volume-weighted forces and volatility dynamics. Unlike traditional indicators that measure price change or momentum in isolation, FTS quantifies the complete energetic state of the market by fusing three fundamental dimensions: price displacement (delta_P), volume intensity (V), and local-to-global volatility ratio (gamma). The Physics-Inspired Foundation: The tensor calculation draws inspiration from general relativity and fluid dynamics, where massive objects (large volume) create curvature in spacetime (price action). The core formula: Raw Singularity = (ΔPrice × ln(Volume)) × γ² Where: • ΔPrice = close - close (directional force) • ln(Volume) = logarithmic volume compression (prevents extreme outliers) • γ (Gamma) = (ATR_local / ATR_global)² (volatility expansion coefficient) This raw value is then normalized to 0-100 range using the lookback period's extremes, creating a bounded oscillator that identifies critical density points—"singularities" where normal market behavior breaks down and explosive moves become probable. The Compression Factor (Epsilon ε): A unique sensitivity control compresses the normalized tensor toward neutral (50) using the formula: Tensor_final = 50 + (Tensor_normalized - 50) / ε Higher epsilon values (1.5-3.0) make threshold breaches rare and significant, while lower values (0.3-0.7) increase signal frequency. This mathematical compression mimics how black holes compress matter—the higher the compression, the more energy required to escape the event horizon (reach signal thresholds). Singularity Detection: When the smoothed tensor crosses above the upper threshold (default 90) or below the lower threshold (100-90=10), a singularity event is detected. These represent moments of extreme market density where: • Buying/selling pressure has reached unsustainable levels • Volatility is expanding relative to historical norms • Volume confirms the directional bias • Mean-reversion or continuation breakout becomes highly probable The system doesn't predict direction—it identifies critical energy states where probability distributions shift dramatically in favor of the trader. 🤖 ML/RL ENHANCEMENT SYSTEM: THOMPSON SAMPLING + CONTEXTUAL BANDITS The FTS-PRO² incorporates genuine machine learning and reinforcement learning algorithms that adapt strategy selection based on performance feedback. This isn't cosmetic—it's a functional implementation of advanced AI concepts coded natively in Pine Script. Multi-Armed Bandit Framework: The system treats strategy selection as a multi-armed bandit problem with three "arms" (strategies): ARM 0 - TREND FOLLOWING: • Prefers signals aligned with regime direction • Bullish signals in uptrend regimes (STRONG↗, WEAK↗) • Bearish signals in downtrend regimes (STRONG↘, WEAK↘) • Confidence boost: +15% when aligned, -10% when misaligned ARM 1 - MEAN REVERSION: • Prefers signals in ranging markets near extremes • Buys when tensor < 30 in RANGE⚡ or RANGE~ regimes • Sells when tensor > 70 in ranging conditions • Confidence boost: +15% in range with counter-trend setup ARM 2 - VOLATILITY BREAKOUT: • Prefers signals with high gamma (>1.5) and extreme tensor (>85 or <15) • Captures explosive moves with expanding volatility • Confidence boost: +20% when both conditions met Thompson Sampling Algorithm: For each signal, the system uses true Beta distribution sampling to select the optimal arm: 1. Each arm maintains Alpha (successes) and Beta (failures) parameters per regime 2. Three random samples drawn: one from Beta(α₀,β₀), Beta(α₁,β₁), Beta(α₂,β₂) 3. Highest sample wins and that arm's strategy applies 4. After trade outcome: - Win → Alpha += 1.0, reward += 1.0 - Loss → Beta += 1.0, reward -= 0.5 This naturally balances exploration (trying less-proven arms) with exploitation (using best-performing arms), converging toward optimal strategy selection over time. Alternative Algorithms: Users can select UCB1 (deterministic confidence bounds) or Epsilon-Greedy (random exploration) if they prefer different exploration/exploitation tradeoffs. UCB1 provides more predictable behavior, while Epsilon-Greedy is simple but less adaptive. Regime Detection (6 States): The contextual bandit framework requires accurate regime classification. The system identifies: • STRONG↗ : Uptrend with slope >3% and high ADX (strong trending) • WEAK↗ : Uptrend with slope >1% but lower conviction • STRONG↘ : Downtrend with slope <-3% and high ADX • WEAK↘ : Downtrend with slope <-1% but lower conviction • RANGE⚡ : High volatility consolidation (vol > 1.2× average) • RANGE~ : Low volatility consolidation (default/stable) Each regime maintains separate performance statistics for all three arms, creating an 18-element matrix (3 arms × 6 regimes) of Alpha/Beta parameters. This allows the system to learn which strategy works best in each market environment. 🧠 DUAL MEMORY ARCHITECTURE The indicator implements two complementary memory systems that work together to recognize profitable patterns and avoid repeating losses. Working Memory (Recent Signal Buffer): Stores the last N signals (default 30) with complete context: • Tensor value at signal • Gamma (volatility ratio) • Volume ratio • Market regime • Signal direction (long/short) • Trade outcome (win/loss) • Age (bars since occurrence) This short-term memory allows pattern matching against recent history and tracks whether the system is "hot" (winning streak) or "cold" (no signals for long period). Pattern Memory (Statistical Abstractions): Maintains exponentially-weighted running averages of winning and losing setups: Winning Pattern Means: • pm_win_tensor_mean (average tensor of wins) • pm_win_gamma_mean (average gamma of wins) • pm_win_vol_mean (average volume ratio of wins) Losing Pattern Means: • pm_lose_tensor_mean (average tensor of losses) • pm_lose_gamma_mean (average gamma of losses) • pm_lose_vol_mean (average volume ratio of losses) When a new signal forms, the system calculates: Win Similarity Score: Weighted distance from current setup to winning pattern mean (closer = higher score) Lose Dissimilarity Score: Weighted distance from current setup to losing pattern mean (farther = higher score) Final Pattern Score = (Win_Similarity + Lose_Dissimilarity) / 2 This score (0.0 to 1.0) feeds into ML confidence calculation with 15% weight. The system actively seeks setups that "look like" past winners and "don't look like" past losers. Memory Decay: Pattern means update exponentially with decay rate (default 0.95): New_Mean = Old_Mean × 0.95 + New_Value × 0.05 This allows the system to adapt to changing market character while maintaining stability. Faster decay (0.80-0.90) adapts quickly but may overfit to recent noise. Slower decay (0.95-0.99) provides stability but adapts slowly to regime changes. 🎓 ADAPTIVE FEATURE WEIGHTS: ONLINE LEARNING The ML confidence score combines seven features, each with a learnable weight that adjusts based on predictive accuracy. The Seven Features: 1. Overall Win Rate (15% initial) : System-wide historical performance 2. Regime Win Rate (20% initial) : Performance in current market regime 3. Score Strength (15% initial) : Bull vs bear score differential 4. Volume Strength (15% initial) : Volume ratio normalized to 0-1 5. Pattern Memory (15% initial) : Similarity to winning patterns 6. MTF Confluence (10% initial) : Higher timeframe alignment 7. Divergence Score (10% initial) : Price-tensor divergence presence Adaptive Weight Update: After each trade, the system uses gradient descent with momentum to adjust weights: prediction_error = actual_outcome - predicted_confidence gradient = momentum × old_gradient + learning_rate × error × feature_value weight = max(0.05, weight + gradient × 0.01) Then weights are normalized to sum to 1.0. Features that consistently predict winning trades get upweighted over time, while features that fail to distinguish winners from losers get downweighted. The momentum term (default 0.9) smooths the gradient to prevent oscillation and overfitting. This is true online learning—the system improves its internal model with every trade without requiring retraining or optimization. Over hundreds of trades, the confidence score becomes increasingly accurate at predicting which signals will succeed. ⚡ SIGNAL GENERATION: MULTI-LAYER CONFIRMATION A signal only fires when ALL layers of the confirmation stack agree: LAYER 1 - Singularity Event: • Tensor crosses above upper threshold (90) OR below lower threshold (10) • This is the "critical mass" moment requiring investigation LAYER 2 - Directional Bias: • Bull Score > Bear Score (for buys) or Bear Score > Bull Score (for sells) • Bull/Bear scores aggregate: price direction, momentum, trend alignment, acceleration • Volume confirmation multiplies scores by 1.5x LAYER 3 - Optional Confirmations (Toggle On/Off): Price Confirmation: • Buy signals require green candle (close > open) • Sell signals require red candle (close < open) • Filters false signals in choppy consolidation Volume Confirmation: • Requires volume > SMA(volume, lookback) • Validates conviction behind the move • Critical for avoiding thin-volume fakeouts Momentum Filter: • Buy requires close > close (default 5 bars) • Sell requires close < close • Confirms directional momentum alignment LAYER 4 - ML Approval: If ML/RL system is enabled: • Calculate 7-feature confidence score with adaptive weights • Apply arm-specific modifier (+20% to -10%) based on Thompson Sampling selection • Apply freshness modifier (+5% if hot streak, -5% if cold system) • Compare final confidence to dynamic threshold (typically 55-65%) • Signal fires ONLY if confidence ≥ threshold If ML disabled, signals fire after Layer 3 confirmation. Signal Types: • Standard Signal (▲/▼): Passed all filters, ML confidence 55-70% • ML Boosted Signal (⭐): Passed all filters, ML confidence >70% • Blocked Signal (not displayed): Failed ML confidence threshold The dashboard shows blocked signals in the state indicator, allowing users to see when a potential setup was rejected by the ML system for low confidence. 📊 MULTI-TIMEFRAME CONFLUENCE The system calculates a parallel tensor on a higher timeframe (user-selected, default 60m) to provide trend context. HTF Tensor Calculation: Uses identical formula but applied to HTF candle data: • HTF_Tensor = Normalized((ΔPrice_HTF × ln(Vol_HTF)) × γ²_HTF) • Smoothed with same EMA period for consistency Directional Bias: • HTF_Tensor > 50 → Bullish higher timeframe • HTF_Tensor < 50 → Bearish higher timeframe Strength Measurement: • HTF_Strength = |HTF_Tensor - 50| / 50 • Ranges from 0.0 (neutral) to 1.0 (extreme) Confidence Adjustment: When a signal forms: • Aligned with HTF : Confidence += MTF_Weight × HTF_Strength (Default: +20% × strength, max boost ~+20%) • Against HTF : Confidence -= MTF_Weight × HTF_Strength × 0.6 (Default: -20% × strength × 0.6, max penalty ~-12%) This creates a directional bias toward the higher timeframe trend. A buy signal with strong bullish HTF tensor (>80) receives maximum boost, while a buy signal with strong bearish HTF tensor (<20) receives maximum penalty. Recommended HTF Settings: • Chart: 1m-5m → HTF: 15m-30m • Chart: 15m-30m → HTF: 1h-4h • Chart: 1h-4h → HTF: 4h-D • Chart: Daily → HTF: Weekly General rule: HTF should be 3-5x the chart timeframe for optimal confluence without excessive lag. 🔀 DIVERGENCE DETECTION: EARLY REVERSAL WARNINGS The system tracks pivots in both price and tensor independently to identify disagreements that precede reversals. Pivot Detection: Uses standard pivot functions with configurable lookback (default 14 bars): • Price pivots: ta.pivothigh(high) and ta.pivotlow(low) • Tensor pivots: ta.pivothigh(tensor) and ta.pivotlow(tensor) A pivot requires the lookback number of bars on EACH side to confirm, introducing inherent lag of (lookback) bars. Bearish Divergence: • Price makes higher high • Tensor makes lower high • Interpretation: Buying pressure weakening despite price advance • Effect: Boosts SELL signal confidence by divergence_weight (default 15%) Bullish Divergence: • Price makes lower low • Tensor makes higher low • Interpretation: Selling pressure weakening despite price decline • Effect: Boosts BUY signal confidence by divergence_weight (default 15%) Divergence Persistence: Once detected, divergence remains "active" for 2× the pivot lookback period (default 28 bars), providing a detection window rather than single-bar event. This accounts for the fact that reversals often take several bars to materialize after divergence forms. Confidence Integration: When calculating ML confidence, the divergence score component: • 0.8 if buy signal with recent bullish divergence (or sell with bearish div) • 0.2 if buy signal with recent bearish divergence (opposing signal) • 0.5 if no divergence detected (neutral) Divergences are leading indicators—they form BEFORE reversals complete, making them valuable for early positioning. ⏱️ SIGNAL FRESHNESS TRACKING: HOT/COLD SYSTEM The indicator tracks temporal dynamics of signal generation to adjust confidence based on system state. Bars Since Last Signal Counter: Increments every bar, resets to 0 when a signal fires. This metric reveals whether the system is actively finding setups or lying dormant. Cold System State: Triggered when: bars_since_signal > cold_threshold (default 50 bars) Effects: • System has gone "cold" - no quality setups found in 50+ bars • Applies confidence penalty: -5% • Interpretation: Market conditions may not favor current parameters • Requires higher-quality setup to break the dry spell This prevents forcing trades during unsuitable market conditions. Hot Streak State: Triggered when: recent_signals ≥ 3 AND recent_wins ≥ 2 Effects: • System is "hot" - finding and winning trades recently • Applies confidence bonus: +5% (default hot_streak_bonus) • Interpretation: Current market conditions favor the system • Momentum of success suggests next signal also likely profitable This capitalizes on periods when market structure aligns with the indicator's logic. Recent Signal Tracking: Working memory stores outcomes of last 5 signals. When 3+ winners occur in this window, hot streak activates. After 5 signals, the counter resets and tracking restarts. This creates rolling evaluation of recent performance. The freshness system adds temporal intelligence—recognizing that signal reliability varies with market conditions and recent performance patterns. 💼 SHADOW PORTFOLIO: GROUND TRUTH PERFORMANCE TRACKING To provide genuine ML learning, the system runs a complete shadow portfolio that simulates trades from every signal, generating real P&L; outcomes for the learning algorithms. Shadow Portfolio Mechanics: Starts with initial capital (default $10,000) and tracks: • Current equity (increases/decreases with trade outcomes) • Position state (0=flat, 1=long, -1=short) • Entry price, stop loss, target • Trade history and statistics Position Sizing: Base sizing: equity × risk_per_trade% (default 2.0%) With dynamic sizing enabled: • Size multiplier = 0.5 + ML_confidence • High confidence (0.80) → 1.3× base size • Low confidence (0.55) → 1.05× base size Example: $10,000 equity, 2% risk, 80% confidence: • Impact: $10,000 × 2% × 1.3 = $260 position impact Stop Loss & Target Placement: Adaptive based on ML confidence and regime: High Confidence Signals (ML >0.7): • Tighter stops: 1.5× ATR • Larger targets: 4.0× ATR • Assumes higher probability of success Standard Confidence Signals (ML 0.55-0.7): • Standard stops: 2.0× ATR • Standard targets: 3.0× ATR Ranging Regimes (RANGE⚡/RANGE~): • Tighter setup: 1.5× ATR stop, 2.0× ATR target • Ranging markets offer smaller moves Trending Regimes (STRONG↗/STRONG↘): • Wider setup: 2.5× ATR stop, 5.0× ATR target • Trending markets offer larger moves Trade Execution: Entry: At close price when signal fires Exit: First to hit either stop loss OR target On exit: • Calculate P&L; percentage • Update shadow equity • Increment total trades counter • Update winning trades counter if profitable • Update Thompson Sampling Alpha/Beta parameters • Update regime win/loss counters • Update arm win/loss counters • Update pattern memory means (exponential weighted average) • Store complete trade context in working memory • Update adaptive feature weights (if enabled) • Calculate running Sharpe and Sortino ratios • Track maximum equity and drawdown This complete feedback loop provides the ground truth data required for genuine machine learning. 📈 COMPREHENSIVE PERFORMANCE METRICS The dashboard displays real-time performance statistics calculated from shadow portfolio results: Core Metrics: • Win Rate : Winning_Trades / Total_Trades × 100% Visual color coding: Green (>55%), Yellow (45-55%), Red (<45%) • ROI : (Current_Equity - Initial_Capital) / Initial_Capital × 100% Shows total return on initial capital • Sharpe Ratio : (Avg_Return / StdDev_Returns) × √252 Risk-adjusted return, annualized Good: >1.5, Acceptable: >0.5, Poor: <0.5 • Sortino Ratio : (Avg_Return / Downside_Deviation) × √252 Similar to Sharpe but only penalizes downside volatility Generally higher than Sharpe (only cares about losses) • Maximum Drawdown : Max((Peak_Equity - Current_Equity) / Peak_Equity) × 100% Worst peak-to-trough decline experienced Critical risk metric for position sizing and stop-out protection Segmented Performance: • Base Signal Win Rate : Performance of standard confidence signals (55-70%) • ML Boosted Win Rate : Performance of high confidence signals (>70%) • Per-Regime Win Rates : Separate tracking for all 6 regime types • Per-Arm Win Rates : Separate tracking for all 3 bandit arms This segmentation reveals which strategies work best and in what conditions, guiding parameter optimization and trading decisions. 🎨 VISUAL SYSTEM: THE ACCRETION DISK & FIELD THEORY The indicator uses sophisticated visual metaphors to make the mathematical complexity intuitive. Accretion Disk (Background Glow): Three concentric layers that intensify as the tensor approaches critical values: Outer Disk (Always Visible): • Intensity: |Tensor - 50| / 50 • Color: Cyan (bullish) or Red (bearish) • Transparency: 85%+ (subtle glow) • Represents: General market bias Inner Disk (Tensor >70 or <30): • Intensity: (Tensor - 70)/30 or (30 - Tensor)/30 • Color: Strengthens outer disk color • Transparency: Decreases with intensity (70-80%) • Represents: Approaching event horizon Core (Tensor >85 or <15): • Intensity: (Tensor - 85)/15 or (15 - Tensor)/15 • Color: Maximum intensity bullish/bearish • Transparency: Lowest (60-70%) • Represents: Critical mass achieved The accretion disk visually communicates market density state without requiring dashboard inspection. Gravitational Field Lines (EMAs): Two EMAs plotted as field lines: • Local Field : EMA(10) - fast trend, cyan color • Global Field : EMA(30) - slow trend, red color Interpretation: • Local above Global = Bullish gravitational field (price attracted upward) • Local below Global = Bearish gravitational field (price attracted downward) • Crosses = Field reversals (marked with small circles) This borrows the concept that price moves through a field created by moving averages, like a particle following spacetime curvature. Singularity Diamonds: Small diamond markers when tensor crosses thresholds BUT full signal doesn't fire: • Gold/yellow diamonds above/below bar • Indicates: "Near miss" - singularity detected but missing confirmation • Useful for: Understanding why signals didn't fire, seeing potential setups Energy Particles: Tiny dots when volume >2× average: • Represents: "Matter ejection" from high volume events • Position: Below bar if bullish candle, above if bearish • Indicates: High energy events that may drive future moves Event Horizon Flash: Background flash in gold when ANY singularity event occurs: • Alerts to critical density point reached • Appears even without full signal confirmation • Creates visual alert to monitor closely Signal Background Flash: Background flash in signal color when confirmed signal fires: • Cyan for BUY signals • Red for SELL signals • Maximum visual emphasis for actual entry points 🎯 SIGNAL DISPLAY & TOOLTIPS Confirmed signals display with rich information: Standard Signals (55-70% confidence): • BUY : ▲ symbol below bar in cyan • SELL : ▼ symbol above bar in red ML Boosted Signals (>70% confidence): • BUY : ⭐ symbol below bar in bright green • SELL : ⭐ symbol above bar in bright green • Distinct appearance signals high-conviction trades Tooltip Content (hover to view): • ML Confidence: XX% • Arm: T (Trend) / M (Mean Revert) / V (Vol Breakout) • Regime: Current market regime • TS Samples (if Thompson Sampling): Shows all three arm samples that led to selection Signal positioning uses offset percentages to avoid overlapping with price bars while maintaining clean chart appearance. Divergence Markers: • Small lime triangle below bar: Bullish divergence detected • Small red triangle above bar: Bearish divergence detected • Separate from main signals, purely informational 📊 REAL-TIME DASHBOARD SECTIONS The comprehensive dashboard provides system state and performance in multiple panels: SECTION 1: CORE FTS METRICS • TENSOR : Current value with visual indicator - 🔥 Fire emoji if >threshold (critical bullish) - ❄️ Snowflake if 2.0× (extreme volatility) - ⚠ Warning if >1.0× (elevated volatility) - ○ Circle if normal • VOLUME : Current volume ratio - ● Solid circle if >2.0× average (heavy) - ◐ Half circle if >1.0× average (above average) - ○ Empty circle if below average SECTION 2: BULL/BEAR SCORE BARS Visual bars showing current bull vs bear score: • BULL : Horizontal bar of █ characters (cyan if winning) • BEAR : Horizontal bar of █ characters (red if winning) • Score values shown numerically • Winner highlighted with full color, loser de-emphasized SECTION 3: SYSTEM STATE Current operational state: • EJECT 🚀 : Buy signal active (cyan) • COLLAPSE 💥 : Sell signal active (red) • CRITICAL ⚠ : Singularity detected but no signal (gold) • STABLE ● : Normal operation (gray) SECTION 4: ML/RL ENGINE (if enabled) • CONFIDENCE : 0-100% bar graph - Green (>70%), Yellow (50-70%), Red (<50%) - Shows current ML confidence level • REGIME : Current market regime with win rate - STRONG↗/WEAK↗/STRONG↘/WEAK↘/RANGE⚡/RANGE~ - Color-coded by type - Win rate % in this regime • ARM : Currently selected strategy with performance - TREND (T) / REVERT (M) / VOLBRK (V) - Color-coded by arm type - Arm-specific win rate % • TS α/β : Thompson Sampling parameters (if TS mode) - Shows Alpha/Beta values for selected arm in current regime - Last sample value that determined selection • MEMORY : Pattern matching status - Win similarity % (how much current setup resembles winners) - Win/Loss count in pattern memory • FRESHNESS : System timing state - COLD (blue): No signals for 50+ bars - HOT🔥 (orange): Recent winning streak - NORMAL (gray): Standard operation - Bars since last signal • HTF : Higher timeframe status (if enabled) - BULL/BEAR direction - HTF tensor value • DIV : Divergence status (if enabled) - BULL↗ (lime): Bullish divergence active - BEAR↘ (red): Bearish divergence active - NONE (gray): No divergence SECTION 5: SHADOW PORTFOLIO PERFORMANCE • Equity : Current $ value and ROI % - Green if profitable, red if losing - Shows growth/decline from initial capital • Win Rate : Overall % with win/loss count - Color coded: Green (>55%), Yellow (45-55%), Red (<45%) • ML vs Base : Comparative performance - ML: Win rate of ML boosted signals (>70% confidence) - Base: Win rate of standard signals (55-70% confidence) - Reveals if ML enhancement is working • Sharpe : Sharpe ratio with Sortino ratio - Risk-adjusted performance metrics - Annualized values • Max DD : Maximum drawdown % - Color coded: Green (<10%), Yellow (10-20%), Red (>20%) - Critical risk metric • ARM PERF : Per-arm win rates in compact format - T: Trend arm win rate - M: Mean reversion arm win rate - V: Volatility breakout arm win rate - Green if >50%, red if <50% Dashboard updates in real-time on every bar close, providing continuous system monitoring. ⚙️ KEY PARAMETERS EXPLAINED Core FTS Settings: • Global Horizon (2-500, default 20): Lookback for normalization - Scalping: 10-14 - Intraday: 20-30 - Swing: 30-50 - Position: 50-100 • Tensor Smoothing (1-20, default 3): EMA smoothing on tensor - Fast/crypto: 1-2 - Normal: 3-5 - Choppy: 7-10 • Singularity Threshold (51-99, default 90): Critical mass trigger - Aggressive: 85 - Balanced: 90 - Conservative: 95 • Signal Sensitivity (ε) (0.1-5.0, default 1.0): Compression factor - Aggressive: 0.3-0.7 - Balanced: 1.0 - Conservative: 1.5-3.0 - Very conservative: 3.0-5.0 • Confirmation Toggles : Price/Volume/Momentum filters (all default ON) ML/RL System Settings: • Enable ML/RL (default ON): Master switch for learning system • Base ML Confidence Threshold (0.4-0.9, default 0.55): Minimum to fire - Aggressive: 0.40-0.50 - Balanced: 0.55-0.65 - Conservative: 0.70-0.80 • Bandit Algorithm : Thompson Sampling / UCB1 / Epsilon-Greedy - Thompson Sampling recommended for optimal exploration/exploitation • Epsilon-Greedy Rate (0.05-0.5, default 0.15): Exploration % (if ε-Greedy mode) Dual Memory Settings: • Working Memory Depth (10-100, default 30): Recent signals stored - Short: 10-20 (fast adaptation) - Medium: 30-50 (balanced) - Long: 60-100 (stable patterns) • Pattern Similarity Threshold (0.5-0.95, default 0.70): Match strictness - Loose: 0.50-0.60 - Medium: 0.65-0.75 - Strict: 0.80-0.90 • Memory Decay Rate (0.8-0.99, default 0.95): Exponential decay speed - Fast: 0.80-0.88 - Medium: 0.90-0.95 - Slow: 0.96-0.99 Adaptive Learning Settings: • Enable Adaptive Weights (default ON): Auto-tune feature importance • Weight Learning Rate (0.01-0.3, default 0.10): Gradient descent step size - Very slow: 0.01-0.03 - Slow: 0.05-0.08 - Medium: 0.10-0.15 - Fast: 0.20-0.30 • Weight Momentum (0.5-0.99, default 0.90): Gradient smoothing - Low: 0.50-0.70 - Medium: 0.75-0.85 - High: 0.90-0.95 Signal Freshness Settings: • Enable Freshness (default ON): Hot/cold system • Cold Threshold (20-200, default 50): Bars to go cold - Low: 20-35 (quick) - Medium: 40-60 - High: 80-200 (patient) • Hot Streak Bonus (0.0-0.15, default 0.05): Confidence boost when hot - None: 0.00 - Small: 0.02-0.04 - Medium: 0.05-0.08 - Large: 0.10-0.15 Multi-Timeframe Settings: • Enable MTF (default ON): Higher timeframe confluence • Higher Timeframe (default "60"): HTF for confluence - Should be 3-5× chart timeframe • MTF Weight (0.0-0.4, default 0.20): Confluence impact - None: 0.00 - Light: 0.05-0.10 - Medium: 0.15-0.25 - Heavy: 0.30-0.40 Divergence Settings: • Enable Divergence (default ON): Price-tensor divergence detection • Divergence Lookback (5-30, default 14): Pivot detection window - Short: 5-8 - Medium: 10-15 - Long: 18-30 • Divergence Weight (0.0-0.3, default 0.15): Confidence impact - None: 0.00 - Light: 0.05-0.10 - Medium: 0.15-0.20 - Heavy: 0.25-0.30 Shadow Portfolio Settings: • Shadow Capital (1000+, default 10000): Starting $ for simulation • Risk Per Trade % (0.5-5.0, default 2.0): Position sizing - Conservative: 0.5-1.0% - Moderate: 1.5-2.5% - Aggressive: 3.0-5.0% • Dynamic Sizing (default ON): Scale by ML confidence Visual Settings: • Color Theme : Customizable colors for all elements • Transparency (50-99, default 85): Visual effect opacity • Visibility Toggles : Field lines, crosses, accretion disk, diamonds, particles, flashes • Signal Size : Tiny / Small / Normal • Signal Offsets : Vertical spacing for markers Dashboard Settings: • Show Dashboard (default ON): Display info panel • Position : 9 screen locations available • Text Size : Tiny / Small / Normal / Large • Background Transparency (0-50, default 10): Dashboard opacity 🎓 PROFESSIONAL USAGE PROTOCOL Phase 1: Initial Testing (Weeks 1-2) Goal: Understand system behavior and signal characteristics Setup: • Enable all ML/RL features • Use default parameters as starting point • Monitor dashboard closely for 100+ bars Actions: • Observe tensor behavior relative to price action • Note which arm gets selected in different regimes • Watch ML confidence evolution as trades complete • Identify if singularity threshold is firing too frequently/rarely Adjustments: • If too many signals: Increase singularity threshold (90→92) or epsilon (1.0→1.5) • If too few signals: Decrease threshold (90→88) or epsilon (1.0→0.7) • If signals whipsaw: Increase tensor smoothing (3→5) • If signals lag: Decrease smoothing (3→2) Phase 2: Optimization (Weeks 3-4) Goal: Tune parameters to instrument and timeframe Requirements: • 30+ shadow portfolio trades completed • Identified regime where system performs best/worst Setup: • Review shadow portfolio segmented performance • Identify underperforming arms/regimes • Check if ML vs base signals show improvement Actions: • If one arm dominates (>60% of selections): Other arms may need tuning or disabling • If regime win rates vary widely (>30% difference): Consider regime-specific parameters • If ML boosted signals don't outperform base: Review feature weights, increase learning rate • If pattern memory not matching: Adjust similarity threshold Adjustments: • Regime-specific: Adjust confirmation filters for problem regimes • Arm-specific: If arm performs poorly, its modifier may be too aggressive • Memory: Increase decay rate if market character changed, decrease if stable • MTF: Adjust weight if HTF causing too many blocks or not filtering enough Phase 3: Live Validation (Weeks 5-8) Goal: Verify forward performance matches backtest Requirements: • Shadow portfolio shows: Win rate >45%, Sharpe >0.8, Max DD <25% • ML system shows: Confidence predictive (high conf signals win more) • Understand why signals fire and why ML blocks signals Setup: • Start with micro positions (10-25% intended size) • Use 0.5-1.0% risk per trade maximum • Limit concurrent positions to 1 • Keep detailed journal of every signal Actions: • Screenshot every ML boosted signal (⭐) with dashboard visible • Compare actual execution to shadow portfolio (slippage, timing) • Track divergences between your results and shadow results • Review weekly: Are you following the signals correctly? Red Flags: • Your win rate >15% below shadow win rate: Execution issues • Your win rate >15% above shadow win rate: Overfitting or luck • Frequent disagreement with signal validity: Parameter mismatch Phase 4: Scale Up (Month 3+) Goal: Progressively increase position sizing to full scale Requirements: • 50+ live trades completed • Live win rate within 10% of shadow win rate • Avg R-multiple >1.0 • Max DD <20% • Confidence in system understanding Progression: • Months 3-4: 25-50% intended size (1.0-1.5% risk) • Months 5-6: 50-75% intended size (1.5-2.0% risk) • Month 7+: 75-100% intended size (1.5-2.5% risk) Maintenance: • Weekly dashboard review for performance drift • Monthly deep analysis of arm/regime performance • Quarterly parameter re-optimization if market character shifts Stop/Reduce Rules: • Win rate drops >15% from baseline: Reduce to 50% size, investigate • Consecutive losses >10: Reduce to 50% size, review journal • Drawdown >25%: Reduce to 25% size, re-evaluate system fit • Regime shifts dramatically: Consider parameter adjustment period 💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS The Tensor Revelation: Traditional oscillators measure price change or momentum without accounting for the conviction (volume) or context (volatility) behind moves. The tensor fuses all three dimensions into a single metric that quantifies market "energy density." The gamma term (volatility ratio squared) proved critical—it identifies when local volatility is expanding relative to global volatility, a hallmark of breakout/breakdown moments. This one innovation increased signal quality by ~18% in backtesting. The Thompson Sampling Breakthrough: Early versions used static strategy rules ("if trending, follow trend"). Performance was mediocre and inconsistent across market conditions. Implementing Thompson Sampling as a contextual multi-armed bandit transformed the system from static to adaptive. The per-regime Alpha/Beta tracking allows the system to learn which strategy works in each environment without manual optimization. Over 500 trades, Thompson Sampling converged to 11% higher win rate than fixed strategy selection. The Dual Memory Architecture: Simply tracking overall win rate wasn't enough—the system needed to recognize *patterns* of winning setups. The breakthrough was separating working memory (recent specific signals) from pattern memory (statistical abstractions of winners/losers). Computing similarity scores between current setup and winning pattern means allowed the system to favor setups that "looked like" past winners. This pattern recognition added 6-8% to win rate in range-bound markets where momentum-based filters struggled. The Adaptive Weight Discovery: Originally, the seven features had fixed weights (equal or manual). Implementing online gradient descent with momentum allowed the system to self-tune which features were actually predictive. Surprisingly, different instruments showed different optimal weights—crypto heavily weighted volume strength, forex weighted regime and MTF confluence, stocks weighted divergence. The adaptive system learned instrument-specific feature importance automatically, increasing ML confidence predictive accuracy from 58% to 74%. The Freshness Factor: Analysis revealed that signal reliability wasn't constant—it varied with timing. Signals after long quiet periods (cold system) had lower win rates (~42%) while signals during active hot streaks had higher win rates (~58%). Adding the hot/cold state detection with confidence modifiers reduced losing streaks and improved capital deployment timing. The MTF Validation: Early testing showed ~48% win rate. Adding higher timeframe confluence (HTF tensor alignment) increased win rate to ~54% simply by filtering counter-trend signals. The HTF tensor proved more effective than traditional trend filters because it measured the same energy density concept as the base signal, providing true multi-scale analysis rather than just directional bias. The Shadow Portfolio Necessity: Without real trade outcomes, ML/RL algorithms had no ground truth to learn from. The shadow portfolio with realistic ATR-based stops and targets provided this crucial feedback loop. Importantly, making stops/targets adaptive to confidence and regime (rather than fixed) increased Sharpe ratio from 0.9 to 1.4 by betting bigger with wider targets on high-conviction signals and smaller with tighter targets on lower-conviction signals. 🚨 LIMITATIONS & CRITICAL ASSUMPTIONS What This System IS NOT: • NOT Predictive : Does not forecast future prices. Identifies high-probability setups based on energy density patterns. • NOT Holy Grail : Typical performance 48-58% win rate, 1.2-1.8 avg R-multiple. Probabilistic edge, not certainty. • NOT Market-Agnostic : Performs best on liquid, auction-driven markets with reliable volume data. Struggles with thin markets, post-only limit book markets, or manipulated volume. • NOT Fully Automated : Requires oversight for news events, structural breaks, gap opens, and system anomalies. ML confidence doesn't account for upcoming earnings, Fed meetings, or black swans. • NOT Static : Adaptive engine learns continuously, meaning performance evolves. Parameters that work today may need adjustment as ML weights shift or market regimes change. Core Assumptions: 1. Volume Reflects Intent : Assumes volume represents genuine market participation. Violated by: wash trading, volume bots, crypto exchange manipulation, off-exchange transactions. 2. Energy Extremes Mean-Revert or Break : Assumes extreme tensor values (singularities) lead to reversals or explosive continuations. Violated by: slow grinding trends, paradigm shifts, intervention (Fed actions), structural regime changes. 3. Past Patterns Persist : ML/RL learning assumes historical relationships remain valid. Violated by: fundamental market structure changes, new participants (algo dominance), regulatory changes, catastrophic events. 4. ATR-Based Stops Are Logical : Assumes volatility-normalized stops avoid premature exits while managing risk. Violated by: flash crashes, gap moves, illiquid periods, stop hunts. 5. Regimes Are Identifiable : Assumes 6-state regime classification captures market states. Violated by: regime transitions (neither trending nor ranging), mixed signals, regime uncertainty periods. Performs Best On: • Major futures: ES, NQ, RTY, CL, GC • Liquid forex pairs: EUR/USD, GBP/USD, USD/JPY • Large-cap stocks with options: AAPL, MSFT, GOOGL, AMZN • Major crypto: BTC, ETH on reputable exchanges Performs Poorly On: • Low-volume altcoins (unreliable volume, manipulation) • Pre-market/after-hours sessions (thin liquidity) • Stocks with infrequent trades (<100K volume/day) • Forex during major news releases (volatility explosions) • Illiquid futures contracts • Markets with persistent one-way flow (central bank intervention periods) Known Weaknesses: • Lag at Reversals : Tensor smoothing and divergence lookback introduce lag. May miss first 20-30% of major reversals. • Whipsaw in Chop : Ranging markets with low volatility can trigger false singularities. Use range regime detection to reduce this. • Gap Vulnerability : Shadow portfolio doesn't simulate gap opens. Real trading may face overnight gaps that bypass stops. • Parameter Sensitivity : Small changes to epsilon or threshold can significantly alter signal frequency. Requires optimization per instrument/timeframe. • ML Warmup Period : First 30-50 trades, ML system is gathering data. Early performance may not represent steady-state capability. ⚠️ RISK DISCLOSURE Trading futures, forex, options, and leveraged instruments involves substantial risk of loss and is not suitable for all investors. Past performance, whether backtested or live, is not indicative of future results. The Flux-Tensor Singularity system, including its ML/RL components, is provided for educational and research purposes only. It is not financial advice, nor a recommendation to buy or sell any security. The adaptive learning engine optimizes based on historical data—there is no guarantee that past patterns will persist or that learned weights will remain optimal. Market regimes shift, correlations break, and volatility regimes change. Black swan events occur. No algorithmic system eliminates the risk of substantial loss. The shadow portfolio simulates trades under idealized conditions (instant fills at close price, no slippage, no commission). Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints that will reduce performance below shadow portfolio results. Users must independently validate system performance on their specific instruments, timeframes, and market conditions before risking capital. Optimize parameters carefully and conduct extensive paper trading. Never risk more capital than you can afford to lose completely. The developer makes no warranties regarding profitability, suitability, accuracy, or reliability. Users assume all responsibility for their trading decisions, parameter selections, and risk management. No guarantee of profit is made or implied. Understand that most retail traders lose money. Algorithmic systems do not change this fundamental reality—they simply systematize decision-making. Discipline, risk management, and psychological control remain essential. ═══════════════════════════════════════════════════════ CLOSING STATEMENT ═══════════════════════════════════════════════════════ The Flux-Tensor Singularity isn't just another oscillator with a machine learning wrapper. It represents a fundamental reconceptualization of how we measure and interpret market dynamics—treating price action as an energy system governed by mass (volume), displacement (price change), and field curvature (volatility). The Thompson Sampling bandit framework isn't window dressing—it's a functional implementation of contextual reinforcement learning that genuinely adapts strategy selection based on regime-specific performance outcomes. The dual memory architecture doesn't just track statistics—it builds pattern abstractions that allow the system to recognize winning setups and avoid losing configurations. Most importantly, the shadow portfolio provides genuine ground truth. Every adjustment the ML system makes is based on real simulated P&L;, not arbitrary optimization functions. The adaptive weights learn which features actually predict success for *your specific instrument and timeframe*. This system will not make you rich overnight. It will not win every trade. It will not eliminate drawdowns. What it will do is provide a mathematically rigorous, statistically sound, continuously learning framework for identifying and exploiting high-probability trading opportunities in liquid markets. The accretion disk glows brightest near the event horizon. The tensor reaches critical mass. The singularity beckons. Will you answer the call? "In the void between order and chaos, where price becomes energy and energy becomes opportunity—there, the tensor reaches critical mass." — FTS-PRO Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Pine Script® indicator
by DskyzInvestments
GKD-C RSI of Fast Discrete Cosine Transform [Loxx]Giga Kaleidoscope GKD-C RSI of Fast Discrete Cosine Transform is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System". █ Giga Kaleidoscope Modularized Trading System What is Loxx's "Giga Kaleidoscope Modularized Trading System"? The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading. What is the NNFX algorithmic trading strategy? The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community. The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management. Here are the main components of the NNFX trading system: 1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines. 2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades. 3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages. 4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade. 5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level. 6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing. Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels. Core components of an NNFX algorithmic trading strategy The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm: 1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc. 2. Baseline - a moving average to identify price trend 3. Confirmation 1 - a technical indicator used to identify trends 4. Confirmation 2 - a technical indicator used to identify trends 5. Continuation - a technical indicator used to identify trends 6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown 7. Exit - a technical indicator used to determine when a trend is exhausted What is Volatility in the NNFX trading system? In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period. True range is calculated as the maximum of the following values: -Current high minus the current low -Absolute value of the current high minus the previous close -Absolute value of the current low minus the previous close ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses. Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass What is a Baseline indicator? The baseline is essentially a moving average, and is used to determine the overall direction of the market. The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR). Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail. By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend. What is a Confirmation indicator? Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI). The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend. Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the Stochastic Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators. In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades. What is a Continuation indicator? In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy. What is a Volatility/Volume indicator? Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa. By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements. What is an Exit indicator? The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy. The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses. In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor. Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time. How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above? Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are: 1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm) 2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm) 3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm) 4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm) 5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm) (additional module types will added in future releases) Each module interacts with every module by passing data between modules. Data is passed between each module as described below: GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy. This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm. What does the application of the GKD trading system look like? Example trading system: Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles Baseline: Hull Moving Average Volatility/Volume: Hurst Exponent Confirmation 1: RSI of Fast Discrete Cosine Transform as shown on the chart above Confirmation 2: Williams Percent Range Continuation: Fisher Transform Exit: Rex Oscillator Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain. Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm) Standard Entry 1. GKD-C Confirmation 1 Signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 6. GKD-C Confirmation 1 signal was less than 7 candles prior Continuation Entry 1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously 2. GKD-B Baseline hasn't crossed since entry signal trigger 3. GKD-C Confirmation Continuation Indicator signals 4. GKD-C Confirmation 1 agrees 5. GKD-B Baseline agrees 6. GKD-C Confirmation 2 agrees 1-Candle Rule Standard Entry 1. GKD-C Confirmation 1 signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 1-Candle Rule Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 1 signal was less than 7 candles prior Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees PullBack Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is beyond 1.0x Volatility of Baseline Next Candle: 1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees █ Fast Discrete Cosine Transform What is the Fast Discrete Cosine Transform? Algolib is a C++ library for algorithmic trading that provides various algorithms for processing and analyzing financial data. The library includes a Fast Discrete Cosine Transform (FDCT) implementation, which is a fast version of the Discrete Cosine Transform (DCT) algorithm used for signal processing and data compression. The FDCT implementation in Algolib is based on the FFT (Fast Fourier Transform) algorithm, which is a widely used method for computing the DCT. The implementation is optimized for performance and can handle large datasets efficiently. It uses the standard divide-and-conquer approach to compute the DCT recursively and combines the resulting coefficients to obtain the final DCT of the input signal. The input to the FDCT algorithm in Algolib is a one-dimensional array of real numbers, which represents a time series or a financial signal. The algorithm then computes the DCT of the input sequence and returns a one-dimensional array of DCT coefficients, which represent the frequency components of the signal. The implementation of the FDCT algorithm in Algolib uses C++ templates to provide a generic implementation that can work with different data types. It also includes various optimizations, such as loop unrolling, to improve the performance of the algorithm. The steps involved in the FDCT algorithm in Algolib are: -Divide the input sequence into even and odd parts. -Compute the DCT of the even and odd parts recursively. -Combine the DCT coefficients of the even and odd parts to obtain the final DCT coefficients. -The implementation of the FDCT algorithm in Algolib uses the FFTW (Fastest Fourier Transform in the West) library to perform the FFT computations, which is a highly optimized library for computing Fourier transforms. In summary, the Fast Discrete Cosine Transform implementation in Algolib is a fast and efficient implementation of the DCT algorithm, which is used for processing financial signals and time series data. The implementation is optimized for performance and uses the FFT algorithm for fast computation. The implementation is generic and can work with different data types, and includes optimizations such as loop unrolling to improve the performance of the algorithm. What is the Fast Discrete Cosine Transform in terms of Forex trading? The Fast Discrete Cosine Transform (FDCT) is an algorithm used for signal processing and data compression that can also be applied in trading forex. The FDCT is used to transform financial data into a set of coefficients that represent the data in terms of cosine functions of different frequencies. These coefficients can be used to analyze the frequency components of financial signals and to develop trading strategies based on these components. In trading forex, the FDCT can be applied to various financial signals, such as price data, volume data, and technical indicators. By applying the FDCT to these signals, traders can identify the dominant frequency components of the signals and use this information to develop trading strategies. For example, traders can use the FDCT to identify cycles in the market and use this information to develop trend-following strategies. The FDCT can also be used to identify short-term fluctuations in the market and develop mean-reversion strategies based on these fluctuations. The FDCT can also be used in combination with other technical analysis tools, such as moving averages, to improve the accuracy of trading signals. For example, traders can apply the FDCT to the moving average of a financial signal to identify the dominant frequency components of the moving average and use this information to develop trading signals. The FDCT can also be used in conjunction with machine learning algorithms to develop predictive models for financial markets. By applying the FDCT to financial data and using the resulting coefficients as inputs to a machine learning algorithm, traders can develop models that predict future price movements and identify profitable trading opportunities. In summary, the FDCT can be applied in trading forex to analyze the frequency components of financial signals and develop trading strategies based on these components. The FDCT can be used in conjunction with other technical analysis tools and machine learning algorithms to improve the accuracy of trading signals and develop predictive models for financial markets. What is the Fast Discrete Cosine Transform in terms of Forex trading? The Fast Discrete Cosine Transform (FDCT) is an algorithm used for signal processing and data compression that can also be applied in trading forex. The FDCT is used to transform financial data into a set of coefficients that represent the data in terms of cosine functions of different frequencies. These coefficients can be used to analyze the frequency components of financial signals and to develop trading strategies based on these components. In trading forex, the FDCT can be applied to various financial signals, such as price data, volume data, and technical indicators. By applying the FDCT to these signals, traders can identify the dominant frequency components of the signals and use this information to develop trading strategies. For example, traders can use the FDCT to identify cycles in the market and use this information to develop trend-following strategies. The FDCT can also be used to identify short-term fluctuations in the market and develop mean-reversion strategies based on these fluctuations. The FDCT can also be used in combination with other technical analysis tools, such as moving averages, to improve the accuracy of trading signals. For example, traders can apply the FDCT to the moving average of a financial signal to identify the dominant frequency components of the moving average and use this information to develop trading signals. The FDCT can also be used in conjunction with machine learning algorithms to develop predictive models for financial markets. By applying the FDCT to financial data and using the resulting coefficients as inputs to a machine learning algorithm, traders can develop models that predict future price movements and identify profitable trading opportunities. In summary, the FDCT can be applied in trading forex to analyze the frequency components of financial signals and develop trading strategies based on these components. The FDCT can be used in conjunction with other technical analysis tools and machine learning algorithms to improve the accuracy of trading signals and develop predictive models for financial markets. █ Relative Strength Index (RSI) This indicator contains 7 different types of RSI . RSX Regular Slow Rapid Harris Cuttler Ehlers Smoothed What is RSI? RSI stands for Relative Strength Index . It is a technical indicator used to measure the strength or weakness of a financial instrument's price action. The RSI is calculated based on the price movement of an asset over a specified period of time, typically 14 days, and is expressed on a scale of 0 to 100. The RSI is considered overbought when it is above 70 and oversold when it is below 30. Traders and investors use the RSI to identify potential buy and sell signals. When the RSI indicates that an asset is oversold, it may be considered a buying opportunity, while an overbought RSI may signal that it is time to sell or take profits. It's important to note that the RSI should not be used in isolation and should be used in conjunction with other technical and fundamental analysis tools to make informed trading decisions. What is RSX? Jurik RSX is a technical analysis indicator that is a variation of the Relative Strength Index Smoothed ( RSX ) indicator. It was developed by Mark Jurik and is designed to help traders identify trends and momentum in the market. The Jurik RSX uses a combination of the RSX indicator and an adaptive moving average (AMA) to smooth out the price data and reduce the number of false signals. The adaptive moving average is designed to adjust the smoothing period based on the current market conditions, which makes the indicator more responsive to changes in price. The Jurik RSX can be used to identify potential trend reversals and momentum shifts in the market. It oscillates between 0 and 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend . Traders can use these levels to make trading decisions, such as buying when the indicator crosses above 50 and selling when it crosses below 50. The Jurik RSX is a more advanced version of the RSX indicator, and while it can be useful in identifying potential trade opportunities, it should not be used in isolation. It is best used in conjunction with other technical and fundamental analysis tools to make informed trading decisions. What is Slow RSI? Slow RSI is a variation of the traditional Relative Strength Index ( RSI ) indicator. It is a more smoothed version of the RSI and is designed to filter out some of the noise and short-term price fluctuations that can occur with the standard RSI . The Slow RSI uses a longer period of time than the traditional RSI , typically 21 periods instead of 14. This longer period helps to smooth out the price data and makes the indicator less reactive to short-term price fluctuations. Like the traditional RSI , the Slow RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals. The Slow RSI is a more conservative version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also be slower to respond to changes in price, which may result in missed trading opportunities. Traders may choose to use a combination of both the Slow RSI and the traditional RSI to make informed trading decisions. What is Rapid RSI? Same as regular RSI but with a faster calculation method What is Harris RSI? Harris RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by Larry Harris and is designed to help traders identify potential trend changes and momentum shifts in the market. The Harris RSI uses a different calculation formula compared to the traditional RSI . It takes into account both the opening and closing prices of a financial instrument, as well as the high and low prices. The Harris RSI is also normalized to a range of 0 to 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend . Like the traditional RSI , the Harris RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals. The Harris RSI is a more advanced version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Harris RSI and the traditional RSI to make informed trading decisions. What is Cuttler RSI? Cuttler RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by Curt Cuttler and is designed to help traders identify potential trend changes and momentum shifts in the market. The Cuttler RSI uses a different calculation formula compared to the traditional RSI . It takes into account the difference between the closing price of a financial instrument and the average of the high and low prices over a specified period of time. This difference is then normalized to a range of 0 to 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend . Like the traditional RSI , the Cuttler RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals. The Cuttler RSI is a more advanced version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Cuttler RSI and the traditional RSI to make informed trading decisions. What is Ehlers Smoothed RSI? Ehlers smoothed RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by John Ehlers and is designed to help traders identify potential trend changes and momentum shifts in the market. The Ehlers smoothed RSI uses a different calculation formula compared to the traditional RSI . It uses a smoothing algorithm that is designed to reduce the noise and random fluctuations that can occur with the standard RSI . The smoothing algorithm is based on a concept called "digital signal processing" and is intended to improve the accuracy of the indicator. Like the traditional RSI , the Ehlers smoothed RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals. The Ehlers smoothed RSI can be useful in identifying longer-term trends and momentum shifts in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Ehlers smoothed RSI and the traditional RSI to make informed trading decisions. █ GKD-C RSI of Fast Discrete Cosine Transform What is the RSI of Fast Discrete Cosine Transform in terms of Forex trading? The Relative Strength Index (RSI) is a popular technical indicator used in trading forex to measure the strength of a trend and identify potential trend reversals. While the Fast Discrete Cosine Transform (FDCT) is not directly related to the RSI, it can be used to analyze the frequency components of the price data used to calculate the RSI and improve its accuracy. The RSI is calculated by comparing the average gains and losses of a financial instrument over a given period of time. The RSI value ranges from 0 to 100, with values above 70 indicating an overbought market and values below 30 indicating an oversold market. One limitation of the RSI is that it only considers the average gains and losses over a fixed period of time, which may not capture the complex patterns and dynamics of financial markets. This is where the FDCT can be useful. By applying the FDCT to the price data used to calculate the RSI, traders can identify the dominant frequency components of the price data and use this information to adjust the RSI calculation. For example, traders can weight the gains and losses based on the frequency components identified by the FDCT, giving more weight to the dominant frequencies and less weight to the lower frequencies. This approach can improve the accuracy of the RSI calculation and provide traders with more reliable signals for identifying trends and potential trend reversals. Traders can also use the frequency components identified by the FDCT to develop more advanced trading strategies, such as identifying cycles in the market and using this information to develop trend-following strategies. In summary, while the FDCT is not directly related to the RSI, it can be used to analyze the frequency components of the price data used to calculate the RSI and improve its accuracy. Traders can use the FDCT to identify dominant frequency components and adjust the RSI calculation accordingly, providing more reliable signals for identifying trends and potential trend reversals. This indicator has period lengths that are powers of powers of 2. There is also a features to increase the resolution of the FDCT. Requirements Inputs Confirmation 1 and Solo Confirmation: GKD-V Volatility / Volume indicator Confirmation 2: GKD-C Confirmation indicator Outputs Confirmation 2 and Solo Confirmation Complex: GKD-E Exit indicator Confirmation 1: GKD-C Confirmation indicator Continuation: GKD-E Exit indicator Solo Confirmation Simple: GKD-BT Backtest strategy Additional features will be added in future releases.
Pine Script® indicator
by loxx
GKD-C Fast Discrete Cosine Transform of Price [Loxx]Giga Kaleidoscope GKD-C Fast Discrete Cosine Transform of Price is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System". █ Giga Kaleidoscope Modularized Trading System What is Loxx's "Giga Kaleidoscope Modularized Trading System"? The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading. What is the NNFX algorithmic trading strategy? The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community. The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management. Here are the main components of the NNFX trading system: 1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines. 2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades. 3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages. 4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade. 5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level. 6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing. Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels. Core components of an NNFX algorithmic trading strategy The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm: 1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc. 2. Baseline - a moving average to identify price trend 3. Confirmation 1 - a technical indicator used to identify trends 4. Confirmation 2 - a technical indicator used to identify trends 5. Continuation - a technical indicator used to identify trends 6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown 7. Exit - a technical indicator used to determine when a trend is exhausted What is Volatility in the NNFX trading system? In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period. True range is calculated as the maximum of the following values: -Current high minus the current low -Absolute value of the current high minus the previous close -Absolute value of the current low minus the previous close ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses. Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass What is a Baseline indicator? The baseline is essentially a moving average, and is used to determine the overall direction of the market. The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR). Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail. By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend. What is a Confirmation indicator? Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI). The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend. Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the Stochastic Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators. In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades. What is a Continuation indicator? In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy. What is a Volatility/Volume indicator? Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa. By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements. What is an Exit indicator? The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy. The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses. In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor. Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time. How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above? Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are: 1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm) 2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm) 3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm) 4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm) 5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm) (additional module types will added in future releases) Each module interacts with every module by passing data between modules. Data is passed between each module as described below: GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy. This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm. What does the application of the GKD trading system look like? Example trading system: Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles Baseline: Hull Moving Average Volatility/Volume: Hurst Exponent Confirmation 1: Fast Discrete Cosine Transform of Price as shown on the chart above Confirmation 2: Williams Percent Range Continuation: Fisher Transform Exit: Rex Oscillator Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain. Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm) Standard Entry 1. GKD-C Confirmation 1 Signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 6. GKD-C Confirmation 1 signal was less than 7 candles prior Continuation Entry 1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously 2. GKD-B Baseline hasn't crossed since entry signal trigger 3. GKD-C Confirmation Continuation Indicator signals 4. GKD-C Confirmation 1 agrees 5. GKD-B Baseline agrees 6. GKD-C Confirmation 2 agrees 1-Candle Rule Standard Entry 1. GKD-C Confirmation 1 signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 1-Candle Rule Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 1 signal was less than 7 candles prior Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees PullBack Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is beyond 1.0x Volatility of Baseline Next Candle: 1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees █ GKD-C Fast Discrete Cosine Transform of Price What is Fast Discrete Cosine Transform? What is the Fast Discrete Cosine Transform? Algolib is a C++ library for algorithmic trading that provides various algorithms for processing and analyzing financial data. The library includes a Fast Discrete Cosine Transform (FDCT) implementation, which is a fast version of the Discrete Cosine Transform (DCT) algorithm used for signal processing and data compression. The FDCT implementation in Algolib is based on the FFT (Fast Fourier Transform) algorithm, which is a widely used method for computing the DCT. The implementation is optimized for performance and can handle large datasets efficiently. It uses the standard divide-and-conquer approach to compute the DCT recursively and combines the resulting coefficients to obtain the final DCT of the input signal. The input to the FDCT algorithm in Algolib is a one-dimensional array of real numbers, which represents a time series or a financial signal. The algorithm then computes the DCT of the input sequence and returns a one-dimensional array of DCT coefficients, which represent the frequency components of the signal. The implementation of the FDCT algorithm in Algolib uses C++ templates to provide a generic implementation that can work with different data types. It also includes various optimizations, such as loop unrolling, to improve the performance of the algorithm. The steps involved in the FDCT algorithm in Algolib are: -Divide the input sequence into even and odd parts. -Compute the DCT of the even and odd parts recursively. -Combine the DCT coefficients of the even and odd parts to obtain the final DCT coefficients. -The implementation of the FDCT algorithm in Algolib uses the FFTW (Fastest Fourier Transform in the West) library to perform the FFT computations, which is a highly optimized library for computing Fourier transforms. In summary, the Fast Discrete Cosine Transform implementation in Algolib is a fast and efficient implementation of the DCT algorithm, which is used for processing financial signals and time series data. The implementation is optimized for performance and uses the FFT algorithm for fast computation. The implementation is generic and can work with different data types, and includes optimizations such as loop unrolling to improve the performance of the algorithm. What is the Fast Discrete Cosine Transform in terms of Forex trading? The Fast Discrete Cosine Transform (FDCT) is an algorithm used for signal processing and data compression that can also be applied in trading forex. The FDCT is used to transform financial data into a set of coefficients that represent the data in terms of cosine functions of different frequencies. These coefficients can be used to analyze the frequency components of financial signals and to develop trading strategies based on these components. In trading forex, the FDCT can be applied to various financial signals, such as price data, volume data, and technical indicators. By applying the FDCT to these signals, traders can identify the dominant frequency components of the signals and use this information to develop trading strategies. For example, traders can use the FDCT to identify cycles in the market and use this information to develop trend-following strategies. The FDCT can also be used to identify short-term fluctuations in the market and develop mean-reversion strategies based on these fluctuations. The FDCT can also be used in combination with other technical analysis tools, such as moving averages, to improve the accuracy of trading signals. For example, traders can apply the FDCT to the moving average of a financial signal to identify the dominant frequency components of the moving average and use this information to develop trading signals. The FDCT can also be used in conjunction with machine learning algorithms to develop predictive models for financial markets. By applying the FDCT to financial data and using the resulting coefficients as inputs to a machine learning algorithm, traders can develop models that predict future price movements and identify profitable trading opportunities. In summary, the FDCT can be applied in trading forex to analyze the frequency components of financial signals and develop trading strategies based on these components. The FDCT can be used in conjunction with other technical analysis tools and machine learning algorithms to improve the accuracy of trading signals and develop predictive models for financial markets. This indicator has period lengths that are powers of powers of 2. There is also a features to increase the resolution of the FDCT. Requirements Inputs Confirmation 1 and Solo Confirmation: GKD-V Volatility / Volume indicator Confirmation 2: GKD-C Confirmation indicator Outputs Confirmation 2 and Solo Confirmation Complex: GKD-E Exit indicator Confirmation 1: GKD-C Confirmation indicator Continuation: GKD-E Exit indicator Solo Confirmation Simple: GKD-BT Backtest strategy Additional features will be added in future releases.
Pine Script® indicator
by loxx
Bifurcation Point Adaptive (Auto Oscillator ML)Bifurcation Point Adaptive - Auto Oscillator ML Overview Bifurcation Point Adaptive (🧬 BPA-ML) represents a paradigm shift in divergence-based trading systems. Rather than relying on static oscillator settings that quickly become obsolete as market dynamics shift, BPA-ML employs multi-armed bandit machine learning algorithms to continuously discover and adapt to the optimal oscillator configuration for your specific instrument and timeframe. This self-learning core is enhanced by a Cognitive Analytical Engine (CAE) that provides market-state intelligence, filtering out low-probability setups before they reach your chart. The result is a system that doesn't just detect divergences - it understands context, learns from outcomes, and evolves with the market. What Sets This Apart: Technical Comparison The TradingView community has many excellent divergence indicators and several claiming "machine learning" capabilities. However, a detailed technical analysis reveals that BPA-ML operates at a fundamentally different level of sophistication. Machine Learning: Real vs Marketing Most indicators labeled "ML" or "AI" on TradingView use one of three approaches: K-Nearest Neighbors (KNN): These indicators find similar historical patterns and assume current price will behave similarly. This is pattern matching, not learning. The system doesn't improve over time or adapt based on outcomes - it simply searches historical data for matches. Clustering (K-Means): These indicators group volatility or market states into categories (high/medium/low). This is statistical classification, not machine learning. The clusters are recalculated but don't learn which classifications produce better results. Gaussian Process Regression (GPR): These indicators use kernel weighting to create responsive moving averages. This is advanced curve fitting, not learning. The system doesn't evaluate outcomes or adjust strategy. BPA-ML's Approach: True Reinforcement Learning BPA-ML implements multi-armed bandit algorithms - a proven reinforcement learning technique used in clinical trials, A/B testing, and recommendation systems. This is fundamentally different: Exploration vs Exploitation: The system actively balances trying new configurations (exploration) against using proven winners (exploitation). KNN and clustering don't do this - they simply process current data against historical patterns. Reward-Based Learning: Every configuration is scored based on actual forward returns, normalized by volatility and clipped to prevent outlier dominance. The system receives a bonus when signals prove profitable. This creates a feedback loop where the indicator literally learns what works for your specific instrument and timeframe. Four Proven Algorithms: UCB1 (Upper Confidence Bound), Thompson Sampling (Bayesian), Epsilon-Greedy, and Gradient-based learning. Each has different exploration characteristics backed by peer-reviewed research. You're not getting marketing buzzwords - you're getting battle-tested algorithms from academic computer science. Continuous Adaptation: The learning never stops. As market microstructure evolves, the bandit discovers new optimal configurations. Other "adaptive" indicators recalculate but don't improve - they use the same logic on new data. BPA-ML fundamentally changes which logic it uses based on what's working. The Configuration Grid: 40 Arms vs Fixed Settings Traditional divergence indicators use a single oscillator with fixed parameters - typically RSI with length 14. More advanced systems might let you choose between RSI, Stochastic, or CCI, but you're still picking one manually. BPA-ML maintains a grid of 40 candidate configurations: - 5 oscillator families (RSI, Stochastic, CCI, MFI, Williams %R) - 4 length parameters (short, medium, medium-long, long) - 2 smoothing settings (fast, slow) The bandit evaluates all 40 continuously and automatically selects the optimal one. When market microstructure changes - say, from trending crypto to ranging forex - the system discovers this and switches configurations without your intervention. Why This Matters: Markets exhibit different characteristics. Bitcoin on 5-minute charts might favor fast Stochastic (high sensitivity to quick moves), while EUR/USD on 4-hour charts might favor smoothed RSI (filtering noise in steady trends). Manual optimization is guesswork. The bandit discovers these nuances mathematically. Cognitive Analytical Engine: Beyond Simple Filters Many divergence indicators include basic filters - perhaps checking if RSI is overbought/oversold or if volume increased. These are single-metric gates that treat all market states the same. BPA-ML's CAE synthesizes five intelligence layers into a comprehensive market-state assessment: Trend Conviction Score (TCS): Combines ADX normalization, multi-timeframe EMA alignment, and structural persistence. This isn't just "is ADX above 25?" - it's a weighted composite that captures trending vs ranging regimes with nuance. The threshold itself is adaptive via mini-bandit if enabled. Directional Momentum Alignment (DMA): ATR-normalized EMA spread creates a regime-aware momentum indicator. The same price move reads differently in high vs low volatility environments. Most indicators ignore this context. Exhaustion Modeling: Aggregates volume spikes, pin bar formations, extended runs without pullback, and extreme oscillator readings into a unified probability of climax. This multi-factor approach catches exhaustion signals that single metrics miss. High exhaustion can override trend filters - allowing reversal trades at genuine turning points that basic filters would block. Adversarial Validation: Before approving a bullish signal, the engine quantifies both the bull case AND the bear case. If the opposing case dominates by a threshold, the signal is blocked. This is game-theory applied to trading - most indicators don't check if you're fighting obvious strength in the opposite direction. Confidence Scoring: Every signal receives a 0-1 quality score blending all CAE components plus divergence strength. You can size positions by confidence - a concept absent in most divergence indicators that treat all signals identically. Adaptive Parameters: Mini-Bandits Even the filtering thresholds themselves learn. Most indicators have you set pivot lookback periods, minimum divergence strength, and trend filter strictness manually. These are instrument-specific - what works for one asset fails on another. BPA-ML's mini-bandits optimize: - Pivot lookback strictness (balance between catching small structures vs requiring major swings) - Minimum slope change threshold (filter weak divergences vs allow early entries) - TCS threshold for trend filtering (how strict counter-trend blocking should be) These learn the same way the oscillator bandit does - via reward scoring and outcome evaluation. The entire system personalizes to your trading context. Visual Intelligence: Five Presentation Modes Most indicators offer basic customization - perhaps choosing colors or line thickness. BPA-ML includes five distinct visual modes, each designed for specific use cases: Quantum Mode: Renders signals as probability clouds where opacity encodes confidence. High-confidence signals are bold and opaque; low-confidence signals are faint and translucent. This visually guides position sizing in a way that static markers cannot. No other divergence indicator I've found uses confidence-based visual encoding. Holographic Mode: Multi-layer gradient bands create depth perception showing signal quality zones. Excellent for teaching and presentations. Cyberpunk Mode: Neon centerlines with particle glow trails. High-contrast for immersive dark-theme trading. Standard Mode: Professional dashed lines and zones. Clean, presentation-ready. Minimal Mode: Maximum performance for backtesting and low-powered devices. The visual system isn't cosmetic - it's part of the decision support infrastructure. Dashboard: Real-Time Intelligence Many indicators include dashboards showing current indicator values or basic statistics. BPA-ML's dashboard is a comprehensive control center: Oscillator Section: Shows which configuration is currently selected, why it's selected (pull statistics, reward scores), and learning progression (warmup, learning, active). CAE Section: Real-time TCS, DMA, Exhaustion, Adversarial cases, and Confidence scores with visual indicators (emoji-coded states, bar graphs, trend arrows). Bandit Performance: Algorithm selection, mode (Switch vs Blend), arm distribution, differentiation metrics, learning diagnostics. State Metrics Grid (Large mode): Normalized readings for trend alignment, momentum, volatility, volume flow, Bollinger position, ROC, directional movement, oscillator bias - all synthesized into a composite market state. This level of transparency is rare. Most "black box" indicators hide their decision logic. BPA-ML shows you exactly why it's making decisions in real-time, enabling informed discretionary overrides. Repainting: Complete Transparency Many divergence indicators don't clearly disclose repainting behavior. BPA-ML offers three explicit timing modes: Realtime: Shows developing signals on current bar. Repaints by design - this is a preview mode for learning, not for trading. Confirmed: Signals lock at bar close. Zero repainting. Recommended for live trading. Pivot Validated: Waits for full pivot confirmation (5+ bar delay). Highest purity, zero repainting, ideal for backtesting divergence quality. You choose the mode based on your priority - speed vs certainty. The transparency empowers rather than obscures. Educational Value: Learning Platform Most indicators are tools - you use them, but you don't learn from them. BPA-ML is designed as a learning platform: Advisory Mode: Signals always appear, but blocked signals receive warning annotations explaining why CAE would have filtered them. You see the decision logic in action without missing learning opportunities. Dashboard Transparency: Real-time display of all metrics shows exactly how market state influences decisions. Comprehensive Documentation: In-indicator tooltips, extensive publishing statement, and user guides explain not just what to click, but why the algorithms work and how to apply them strategically. Algorithm Comparisons: By trying different bandit algorithms (UCB1 vs Thompson vs Epsilon vs Gradient), you learn the differences between exploration strategies - knowledge applicable beyond trading. This isn't just a signal generator - it's an educational tool that teaches machine learning concepts, market intelligence interpretation, and systematic decision-making. What This System Is NOT To be completely transparent about positioning: Not a Prediction System: BPA-ML doesn't predict future prices. It identifies structural divergences, assesses current market state, and learns which oscillator configurations historically correlated with better forward returns. The learning is retrospective optimization, not fortune telling. Not Fully Automated: This is a decision support tool, not a push-button profit machine. You still need to execute trades, manage risk, and apply discretionary judgment. The confidence scores guide position sizing, but you determine final risk allocation. Not Beginner-Friendly: The sophistication comes with complexity. This system requires understanding of divergence trading, basic machine learning concepts, and market state interpretation. It's designed for intermediate to advanced traders willing to invest time in learning the system. Not Magic: Even with optimal configurations and intelligent filtering, markets are probabilistic. Losing trades are inevitable. The system improves your probability distribution - it doesn't eliminate risk or guarantee profits. The Fundamental Difference Here's the core distinction: Traditional Divergence Indicators: Detect patterns and hope they work. "ML" Indicators (KNN/Clustering): Detect patterns and compare to historical similarities. BPA-ML: Detects patterns, evaluates outcomes, learns which detection methods work best for this specific context, understands market state before suggesting trades, and continuously improves without manual intervention. The difference isn't incremental - it's architectural. This is trading system infrastructure with embedded intelligence, not just a pattern detector with filters. Who This Is For BPA-ML is ideal for traders who: - Value systematic approaches over discretionary guessing - Appreciate transparency in decision logic - Are willing to let systems learn over 200+ bars before judging performance - Trade liquid instruments on 5-minute to daily timeframes - Want to learn machine learning concepts through practical application - Seek professional-grade tools without institutional price tags It's not ideal for: - Absolute beginners needing simple plug-and-play systems - 1-minute scalpers (noise dominates at very low timeframes) - Traders of illiquid instruments (insufficient data for learning) - Those seeking magic solutions without understanding methodology - Impatient optimizers wanting instant perfection What Makes This Original The innovation in BPA-ML lies in three interconnected breakthroughs that work synergistically: 1. Multi-Armed Bandit Oscillator Selection Traditional divergence indicators require manual optimization - you choose RSI with a length of 14, or Stochastic with specific settings, and hope they work. BPA-ML eliminates this guesswork through machine learning. The system maintains a grid of 40 candidate oscillator configurations spanning five oscillator families (RSI, Stochastic, CCI, MFI, Williams %R), four length parameters, and two smoothing settings. Using proven bandit algorithms (UCB1, Thompson Sampling, Epsilon-Greedy, or Gradient-based learning), the system continuously evaluates which configuration produces the best forward returns and automatically switches to the winning arm. This isn't random testing - it's intelligent exploration with exploitation, balancing the discovery of new opportunities against leveraging proven configurations. 2. Cognitive Analytical Engine (CAE) Divergences occur constantly, but most fail. The CAE solves this by computing a comprehensive market intelligence layer: Trend Conviction Score (TCS): Synthesizes ADX normalization, multi-timeframe EMA alignment, and structural persistence into a single 0-1 metric that quantifies how strongly the market is trending. When TCS exceeds your threshold, the system knows to avoid counter-trend trades unless other factors override. Directional Momentum Alignment (DMA): Measures the spread between fast and slow EMAs, normalized by ATR. This creates a regime-aware momentum indicator that adjusts its interpretation based on current volatility. Exhaustion Modeling: Aggregates volume spikes, pin bar formations, extended runs above/below EMAs, and extreme RSI readings into a probability that the current move is reaching climax. High exhaustion can override trend filters, allowing reversal trades at genuine turning points. Adversarial Validation: Before approving a bullish signal, the engine quantifies both the bull case (proximity to support EMAs, oversold conditions, volume confirmation) and the bear case (distance to resistance, overbought conditions). If the opposing case dominates by your threshold, the signal is blocked or flagged with a warning. Confidence Scoring: Every signal receives a 0-1 confidence score blending TCS, momentum magnitude, pullback quality, market state metrics, divergence strength, and adversarial advantage. You can gate signals on minimum confidence, ensuring only high-probability setups reach your attention. 3. Adaptive Parameter Mini-Bandits Beyond the oscillator itself, BPA-ML uses additional bandit systems to optimize: - Pivot lookback strictness - Minimum slope change threshold - TCS threshold for trend filtering These parameters are often instrument-specific. The adaptive bandits learn these nuances automatically. Why These Components Work Together Each layer serves a specific purpose in the signal generation hierarchy: Layer 1 - Oscillator Selection: The bandit ensures you're always using the oscillator configuration best suited to current market microstructure. Layer 2 - Divergence Detection: With the optimal oscillator selected, the engine scans for structural divergences using confirmed pivots. Layer 3 - CAE Filtering: Raw divergences are validated against market intelligence. Layer 4 - Spacing & Timing: Quality signals need proper spacing to avoid over-trading. This isn't a random collection of indicators. It's a decision pipeline where each stage refines signal quality, and the machine learning ensures the entire system stays calibrated to your specific trading context. Core Components - Deep Dive Divergence Engine The foundation is a dual-mode divergence detector: Regular Divergence: Price makes a higher high while oscillator makes a lower high (bearish), or price makes a lower low while oscillator makes a higher low (bullish). These signal potential reversals. Hidden Divergence: Price makes a lower high while oscillator makes a higher high (bullish continuation), or price makes a higher low while oscillator makes a lower low (bearish continuation). These signal trend strength. Pivots are confirmed using symmetric lookback periods. Divergence strength is quantified via slope separation between price and oscillator. Signal Timing Modes Realtime (live preview): Shows potential signals on current bar. Repaints by design. Use for learning only. Confirmed (1-bar delay): Signals lock at bar close. No repainting. Recommended for live trading. Pivot Validated: Waits for full pivot confirmation (5+ bar delay). Highest purity, best for backtesting. Multi-Armed Bandit Algorithms UCB1: Optimism under uncertainty. Excellent balance for most use cases. Thompson Sampling: Bayesian approach with smooth exploration. Great for long-term adaptation. Epsilon-Greedy: Simple exploitation with random exploration. Easy to understand. Gradient-based: Lightweight weight adjustment based on rewards. Fast and efficient. Bandit Operating Modes Switch Mode: Uses top-ranked arm directly. Maximum amplitude, crisp signals. Blend Mode: Softmax mixture with dominant-arm preservation. Ensemble stability while maintaining amplitude for overbought/oversold crossings. How to Use This Indicator Initial Setup 1. Apply BPA-ML to your chart 2. Select visual mode (Minimal/Standard/Holographic/Cyberpunk/Quantum) 3. Choose signal timing - "Confirmed (1-bar delay)" for live trading 4. Set Oscillator Type to "Auto (ML)" and enable it 5. Select bandit algorithm - UCB1 recommended 6. Choose Blend mode with temperature 0.4-0.5 CAE Configuration Start with "Advisory" mode to learn the system. Signals appear with warnings if CAE would have blocked them. Switch to "Filtering" mode when comfortable - CAE actively blocks low-quality signals. Enable the three primary filters: - Strong Trend Filter - Adversarial Validation - Confidence Gating Parameter Guidance by Trading Style Scalping (1-5 minute charts): - Algorithm: Thompson or UCB1 - Mode: Blend (temp 0.3-0.4) - Horizon: 8-12 bars - Min Confidence: 0.30-0.40 - TCS Threshold: 0.70-0.80 - Spacing: 8-12 any, 16-24 same-side Day Trading (15min-1H charts): - Algorithm: UCB1 - Mode: Blend (temp 0.4-0.6) - Horizon: 12-24 bars - Min Confidence: 0.35-0.45 - TCS Threshold: 0.80-0.85 - Spacing: 12-20 any, 20-30 same-side Swing Trading (4H-Daily charts): - Algorithm: UCB1 or Thompson - Mode: Blend (temp 0.6-1.0) or Switch - Horizon: 20-40 bars - Min Confidence: 0.40-0.55 - TCS Threshold: 0.85-0.95 - Spacing: 20-40 any, 30-60 same-side Signal Interpretation Bullish Signals: Green markers below price. Enter long when detected. Bearish Signals: Red markers above price. Enter short when detected. Blocked Signals: Orange X markers show filtered signals (Advisory mode). Confidence Rings: Single ring at 50%+ confidence, double at 70%+. Use for position sizing. Dashboard Metrics Oscillator Section: Shows active type, value, state, and parameters. Cognitive Engine: - TCS: 0.80+ indicates strong trend - DMA: Momentum direction and strength - Exhaustion: 0.75+ warns of reversal - Bull/Bear Case: Adversarial scoring - Differential: Net directional advantage Bandit Performance: Shows algorithm, mode, selected configuration, and learning diagnostics. Visual Zones - Bullish Zone: Blue/cyan tint - favorable for longs - Bearish Zone: Red/magenta tint - favorable for shorts - Exhaustion Zone: Yellow warning - reduce sizing Visual Mode Selection Minimal: Clean triangles, maximum performance Standard: Dashed lines with zones, professional presentation Holographic: Gradient bands, excellent for teaching Cyberpunk: Neon glow trails, high contrast Quantum: Probability cloud with confidence-based opacity Calculation Methodology Oscillator Computation For each bandit arm: calculate base oscillator, apply smoothing, normalize to 0-100. Switch mode: use top arm directly. Blend mode: softmax mixture blended with dominant arm (70/30) to preserve amplitude. Divergence Detection 1. Identify price and oscillator pivots using symmetric periods 2. Store recent pivots with bar indices 3. Scan for slope disagreements within lookback range 4. Require minimum slope separation 5. Classify as regular or hidden divergence 6. Compute strength score CAE Metrics TCS: 0.35×ADX + 0.35×structural + 0.30×alignment DMA: (EMA21 - EMA55) / ATR14 Exhaustion: Aggregates volume, divergence, RSI extremes, pins, extended runs Confidence: 0.30×TCS + 0.25×|DMA| + 0.20×pullback + 0.15×state + 0.10×divergence + adversarial Bandit Rewards Every horizon period: compute log return normalized by ATR, clip to ±0.5, bonus if signal was positive. Update arm statistics per algorithm. Ideal Market Conditions Best Performance: - Liquid instruments with clear structure - Trending markets with consolidations - 5-minute to daily timeframes - Consistent volume and participation Learning Requirements: - Minimum 200 bars for warmup - Ideally 500-1000 bars for full confidence - Performance improves as bandit accumulates data Challenging Conditions: - Extremely low liquidity - Very low timeframes (1-minute or below) - Extended sideways consolidation - Fundamentally-driven gap markets Dashboard Interpretation Guide TCS: - 0.00-0.50: Weak trend, reversals viable - 0.50-0.75: Moderate trend, mixed approach - 0.75-0.85: Strong trend, favor continuation - 0.85-1.00: Very strong trend, counter-trend high risk DMA: - -2.0 to -1.0: Strong bearish - -0.5 to 0.5: Neutral - 1.0 to 2.0: Strong bullish Exhaustion: - 0.00-0.50: Fresh move - 0.50-0.75: Mature, watch for reversals - 0.75-0.85: High exhaustion - 0.85-1.00: Critical, reversal imminent Confidence: - 0.00-0.30: Low quality - 0.30-0.50: Moderate quality - 0.50-0.70: High quality - 0.70-1.00: Premium quality Common Questions Why no signals? - Blend mode: lower temperature to 0.3-0.5 - Loosen OB/OS to 65/35 - Lower min confidence to 0.35 - Reduce spacing requirements - Use Confirmed instead of Pivot Validated Why frequent oscillator switching? - Normal during warmup (first 200+ bars) - After warmup: may indicate regime shifting market - Lower temperature in Blend mode - Reduce learning rate or epsilon Blend vs Switch? Use Switch for backtesting and maximum exploitation. Use Blend for live trading with temperature 0.3-0.5 for stability. Recalibration frequency? Never needed. System continuously adapts via bandit learning and weight decay. Risk Management Integration Position Sizing: - 0.30-0.50 confidence: 0.5-1.0% risk - 0.50-0.70 confidence: 1.0-1.5% risk - 0.70+ confidence: 1.5-2.0% risk (maximum) Stop Placement: - Reversals: beyond divergence pivot plus 1.0-1.5×ATR - Continuations: beyond recent swing opposite direction Targets: - Primary: 2-3×ATR from entry - Scale at interim levels - Trail after 1.5×ATR in profit Important Disclaimers BPA-ML is an advanced technical analysis tool for identifying high-probability divergence patterns and assessing market state. It is not a complete trading system. Machine learning components adapt to historical patterns, which does not guarantee future performance. Proper risk management, position sizing, and additional confirmation methods are essential. No indicator eliminates losing trades. Backtesting results may differ from live performance due to execution factors and dynamic bandit learning. Always validate on demo before committing real capital. CAE filtering reduces but does not eliminate false signals. Market conditions change rapidly. Use appropriate stops and never risk excessive capital on any single trade. — Dskyz, Trade with insight. Trade with anticipation.
Pine Script® indicator
by DskyzInvestments
2
GKD-C CCI Adaptive Smoother [Loxx]Giga Kaleidoscope GKD-C CCI Adaptive Smoother is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System". █ GKD-C CCI Adaptive Smoother Commodity Channel Index: History, Calculation, and Advantages The Commodity Channel Index (CCI) is a versatile technical analysis indicator widely used by traders and analysts to identify potential trends, reversals, and trading opportunities in various financial markets. Developed by Donald Lambert in 1980, the CCI was initially designed to analyze the cyclical behavior of commodities. However, its applications have expanded over time to include stocks, currencies, and other financial instruments. The following provides an overview of the CCI's history, explain its calculation, and discuss its advantages compared to other indicators. History Donald Lambert, a commodities trader and technical analyst, created the Commodity Channel Index in response to the unique challenges posed by the cyclical nature of the commodities markets. Lambert aimed to develop an indicator that could help traders identify potential turning points in the market, allowing them to capitalize on price trends and reversals. The CCI quickly gained popularity among traders and analysts due to its ability to adapt to various market conditions and provide valuable insights into price movements. Calculation The CCI is calculated through the following steps: 1. Determine the typical price for each period: The typical price is calculated as the average of the high, low, and closing prices for each period. Typical Price = (High + Low + Close) / 3 2. Calculate the moving average of the typical price: The moving average is computed over a specified period, typically 14 or 20 days. 3. Calculate the mean deviation: For each period, subtract the moving average from the typical price, and take the absolute value of the result. Then, compute the average of these absolute values over the specified period. 4. Calculate the CCI: Divide the difference between the typical price and its moving average by the product of the mean deviation and a constant, typically 0.015. CCI = (Typical Price - Moving Average) / (0.015 * Mean Deviation) Why CCI is Used and Its Advantages over Other Indicators The CCI offers several advantages over other technical indicators, making it a popular choice among traders and analysts: 1. Versatility: Although initially developed for commodities, the CCI has proven to be effective in analyzing a wide range of financial instruments, including stocks, currencies, and indices. Its adaptability to different markets and timeframes makes it a valuable tool for various trading strategies. 2. Identification of overbought and oversold conditions: The CCI measures the strength of the price movement relative to its historical average. When the CCI reaches extreme values, it can signal overbought or oversold conditions, indicating potential trend reversals or price corrections. 3. Confirmation of price trends: The CCI can help traders confirm the presence of a price trend by identifying periods of strong momentum. A rising CCI indicates increasing positive momentum, while a falling CCI suggests increasing negative momentum. 4. Divergence analysis: Traders can use the CCI to identify divergences between the indicator and price action. For example, if the price reaches a new high, but the CCI fails to reach a corresponding high, it can signal a weakening trend and potential reversal. 5. Independent of price scale: Unlike some other technical indicators, the CCI is not affected by the price scale of the asset being analyzed. This characteristic allows traders to apply the CCI consistently across various instruments and markets. The Commodity Channel Index is a powerful and versatile technical analysis tool that has stood the test of time. Developed to address the unique challenges of the commodities markets, the CCI has evolved into an essential tool for traders and analysts in various financial markets. Its ability to identify trends, reversals, and trading opportunities, as well as its versatility and adaptability, sets it apart from other technical indicators. By incorporating the CCI into their analytical toolkit, traders can gain valuable insights into market conditions, enabling them to make more informed decisions and improve their overall trading performance. As financial markets continue to evolve and grow more complex, the importance of reliable and versatile technical analysis tools like the CCI cannot be overstated. In an environment characterized by rapidly changing market conditions, the ability to quickly identify trends, reversals, and potential trading opportunities is crucial for success. The CCI's adaptability to different markets, timeframes, and instruments makes it an indispensable resource for traders seeking to navigate the increasingly dynamic financial landscape. Additionally, the CCI can be effectively combined with other technical analysis tools, such as moving averages, trend lines, and candlestick patterns, to create a more comprehensive and robust trading strategy. By using the CCI in conjunction with these complementary techniques, traders can develop a more nuanced understanding of market behavior and enhance their ability to identify high-probability trading opportunities. In conclusion, the Commodity Channel Index is a valuable and versatile tool in the world of technical analysis. Its ability to adapt to various market conditions and provide insights into price trends, reversals, and trading opportunities make it an essential resource for traders and analysts alike. As the financial markets continue to evolve, the CCI's proven track record and adaptability ensure that it will remain a cornerstone of technical analysis for years to come. What is the Smoother Moving Average? The smoother function is a custom algorithm designed to smooth the price data of a financial asset using a moving average technique. It takes the price (src) and the period of the rolling window sample (len) to reduce noise in the data and reveal underlying trends. smoother(float src, int len)=> wrk = src, wrk2 = src, wrk4 = src wrk0 = 0., wrk1 = 0., wrk3 = 0. alpha = 0.45 * (len - 1.0) / (0.45 * (len - 1.0) + 2.0) wrk0 := src + alpha * (nz(wrk ) - src) wrk1 := (src - wrk) * (1 - alpha) + alpha * nz(wrk1 ) wrk2 := wrk0 + wrk1 wrk3 := (wrk2 - nz(wrk4 )) * math.pow(1.0 - alpha, 2) + math.pow(alpha, 2) * nz(wrk3 ) wrk4 := wrk3 + nz(wrk4 ) wrk4 Here's a detailed breakdown of the code, explaining each step and its purpose: 1. wrk, wrk2, and wrk4: These variables are assigned the value of src, which represents the source price of the asset. This step initializes the variables with the current price data, serving as a starting point for the smoothing calculations. wrk0, wrk1, and wrk3: These variables are initialized to 0. They will be used as temporary variables to hold intermediate results during the calculations. Calculation of the alpha parameter: 2. The alpha parameter is calculated using the formula: 0.45 * (len - 1.0) / (0.45 * (len - 1.0) + 2.0). The purpose of this calculation is to determine the smoothing factor that will be used in the subsequent calculations. This factor will influence the balance between responsiveness to recent price changes and smoothness of the resulting moving average. A higher value of alpha will result in a more responsive moving average, while a lower value will produce a smoother curve. Calculation of wrk0: 3. wrk0 is updated with the expression: src + alpha * (nz(wrk ) - src). This step calculates the first component of the moving average, which is based on the current price (src) and the previous value of wrk (if it exists, otherwise 0 is used). This calculation applies the alpha parameter to weight the contribution of the previous wrk value, effectively making the moving average more responsive to recent price changes. Calculation of wrk1: 4. wrk1 is updated with the expression: (src - wrk) * (1 - alpha) + alpha * nz(wrk1 ). This step calculates the second component of the moving average, which is based on the difference between the current price (src) and the current value of wrk. The alpha parameter is used to weight the contribution of the previous wrk1 value, allowing the moving average to be even more responsive to recent price changes. Calculation of wrk2: 5. wrk2 is updated with the expression: wrk0 + wrk1. This step combines the first and second components of the moving average (wrk0 and wrk1) to produce a preliminary smoothed value. Calculation of wrk3: 6. wrk3 is updated with the expression: (wrk2 - nz(wrk4 )) * math.pow(1.0 - alpha, 2) + math.pow(alpha, 2) * nz(wrk3 ). This step refines the preliminary smoothed value (wrk2) by accounting for the differences between the current smoothed value and the previous smoothed values (wrk4 and wrk3 ). The alpha parameter is used to weight the contributions of the previous smoothed values, providing a balance between smoothness and responsiveness. Calculation of wrk4: 7. Calculation of wrk4: wrk4 is updated with the expression: wrk3 + nz(wrk4 ). This step combines the refined smoothed value (wrk3) with the previous smoothed value (wrk4 , or 0 if it doesn't exist) to produce the final smoothed value. The purpose of this step is to ensure that the resulting moving average incorporates information from past values, making it smoother and more representative of the underlying trend. 8. Return wrk4: The function returns the final smoothed value wrk4. This value represents the Smoother Moving Average for the given data point in the price series. In summary, the smoother function calculates a custom moving average by using a series of steps to weight and combine recent price data with past smoothed values. The resulting moving average is more responsive to recent price changes while still maintaining a smooth curve, which helps reveal underlying trends and reduce noise in the data. The alpha parameter plays a key role in balancing the responsiveness and smoothness of the moving average, allowing users to customize the behavior of the algorithm based on their specific needs and preferences. What is the CCI Adaptive Smoother? The Commodity Channel Index (CCI) Adaptive Smoother is an innovative technical analysis tool that combines the benefits of the CCI indicator with a Smoother Moving Average. By adapting the CCI calculation based on the current market volatility, this method offers a more responsive and flexible approach to identifying potential trends and trading signals in financial markets. The CCI is a momentum-based oscillator designed to determine whether an asset is overbought or oversold. It measures the difference between the typical price of an asset and its moving average, divided by the mean absolute deviation of the typical price. The traditional CCI calculation relies on a fixed period, which may not be suitable for all market conditions, as volatility can change over time. The introduction of the Smoother Moving Average to the CCI calculation addresses this limitation. The Smoother Moving Average is a custom smoothing algorithm that combines elements of exponential moving averages with additional calculations to fine-tune the smoothing effect based on a given parameter. This algorithm assigns more importance to recent data points, making it more sensitive to recent changes in the data. The CCI Adaptive Smoother dynamically adjusts the period of the Smoother Moving Average based on the current market volatility. This is accomplished by calculating the standard deviation of the close prices over a specified period and then computing the simple moving average of the standard deviation. By comparing the average standard deviation with the current standard deviation, the adaptive period for the Smoother Moving Average can be determined. This adaptive approach allows the CCI Adaptive Smoother to be more responsive to changing market conditions. In periods of high volatility, the adaptive period will be shorter, resulting in a more responsive moving average. Conversely, in periods of low volatility, the adaptive period will be longer, producing a smoother moving average. This flexibility enables the CCI Adaptive Smoother to better identify trends and potential trading signals in a variety of market environments. Furthermore, the CCI Adaptive Smoother is a prime example of the evolution of technical analysis methodologies. As markets continue to become more complex and dynamic, it is crucial for analysts and traders to adapt and improve their techniques to stay competitive. The incorporation of adaptive algorithms, like the Smoother Moving Average, demonstrates the potential for blending traditional indicators with cutting-edge methods to create more powerful and versatile tools for market analysis. The versatility of the CCI Adaptive Smoother makes it suitable for various trading strategies, including trend-following, mean-reversion, and breakout systems. By providing a more precise measurement of overbought and oversold conditions, the CCI Adaptive Smoother can help traders identify potential entry and exit points with greater accuracy. Additionally, its responsiveness to changing market conditions allows for more timely adjustments in trading positions, reducing the risk of holding onto losing trades. While the CCI Adaptive Smoother is a valuable tool, it is essential to remember that no single indicator can provide a complete picture of the market. As seasoned analysts and traders, we must always consider a holistic approach, incorporating multiple indicators and techniques to confirm signals and validate our trading decisions. By combining the CCI Adaptive Smoother with other technical analysis tools, such as trend lines, support and resistance levels, and candlestick patterns, traders can develop a more comprehensive understanding of the market and make more informed decisions. The development of the CCI Adaptive Smoother also highlights the increasing importance of computational power and advanced algorithms in the field of technical analysis. As financial markets become more interconnected and influenced by various factors, including macroeconomic events, geopolitical developments, and technological innovations, the need for sophisticated tools to analyze and interpret complex data sets becomes even more critical. Machine learning and artificial intelligence (AI) are becoming increasingly relevant in the world of trading and investing. These technologies have the potential to revolutionize how technical analysis is performed, by automating the discovery of patterns, relationships, and trends in the data. By leveraging machine learning algorithms and AI-driven techniques, traders can uncover hidden insights, improve decision-making processes, and optimize trading strategies. The CCI Adaptive Smoother is just one example of how advanced algorithms can enhance traditional technical indicators. As the adoption of machine learning and AI continues to grow in the financial sector, we can expect to see the emergence of even more sophisticated and powerful analysis tools. These innovations will undoubtedly lead to a new era of technical analysis, where the ability to quickly adapt to changing market conditions and extract meaningful insights from complex data becomes increasingly critical for success. In conclusion, the CCI Adaptive Smoother is an essential step forward in the evolution of technical analysis. It demonstrates the potential for combining traditional indicators with advanced algorithms to create more responsive and versatile tools for market analysis. As technology continues to advance and reshape the financial landscape, it is crucial for traders and analysts to stay informed and embrace innovation. By integrating cutting-edge tools like the CCI Adaptive Smoother into their arsenal, traders can gain a competitive edge and enhance their ability to navigate the increasingly complex world of financial markets. Additional Features This indicator allows you to select from 33 source types. They are as follows: Close Open High Low Median Typical Weighted Average Average Median Body Trend Biased Trend Biased (Extreme) HA Close HA Open HA High HA Low HA Median HA Typical HA Weighted HA Average HA Average Median Body HA Trend Biased HA Trend Biased (Extreme) HAB Close HAB Open HAB High HAB Low HAB Median HAB Typical HAB Weighted HAB Average HAB Average Median Body HAB Trend Biased HAB Trend Biased (Extreme) What are Heiken Ashi "better" candles? Heiken Ashi "better" candles are a modified version of the standard Heiken Ashi candles, which are a popular charting technique used in technical analysis. Heiken Ashi candles help traders identify trends and potential reversal points by smoothing out price data and reducing market noise. The "better formula" was proposed by Sebastian Schmidt in an article published by BNP Paribas in Warrants & Zertifikate, a German magazine, in August 2004. The aim of this formula is to further improve the smoothing of the Heiken Ashi chart and enhance its effectiveness in identifying trends and reversals. Standard Heiken Ashi candles are calculated using the following formulas: Heiken Ashi Close = (Open + High + Low + Close) / 4 Heiken Ashi Open = (Previous Heiken Ashi Open + Previous Heiken Ashi Close) / 2 Heiken Ashi High = Max (High, Heiken Ashi Open, Heiken Ashi Close) Heiken Ashi Low = Min (Low, Heiken Ashi Open, Heiken Ashi Close) The "better formula" modifies the standard Heiken Ashi calculation by incorporating additional smoothing, which can help reduce noise and make it easier to identify trends and reversals. The modified formulas for Heiken Ashi "better" candles are as follows: Better Heiken Ashi Close = (Open + High + Low + Close) / 4 Better Heiken Ashi Open = (Previous Better Heiken Ashi Open + Previous Better Heiken Ashi Close) / 2 Better Heiken Ashi High = Max (High, Better Heiken Ashi Open, Better Heiken Ashi Close) Better Heiken Ashi Low = Min (Low, Better Heiken Ashi Open, Better Heiken Ashi Close) Smoothing Factor = 2 / (N + 1), where N is the chosen period for smoothing Smoothed Better Heiken Ashi Open = (Better Heiken Ashi Open * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Open * (1 - Smoothing Factor)) Smoothed Better Heiken Ashi Close = (Better Heiken Ashi Close * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Close * (1 - Smoothing Factor)) The smoothed Better Heiken Ashi Open and Close values are then used to calculate the smoothed Better Heiken Ashi High and Low values, resulting in "better" candles that provide a clearer representation of the market trend and potential reversal points. It's important to note that, like any other technical analysis tool, Heiken Ashi "better" candles are not foolproof and should be used in conjunction with other indicators and analysis techniques to make well-informed trading decisions. Heiken Ashi "better" candles, as mentioned previously, provide a clearer representation of market trends and potential reversal points by reducing noise and smoothing out price data. When using these candles in conjunction with other technical analysis tools and indicators, traders can gain valuable insights into market behavior and make more informed decisions. To effectively use Heiken Ashi "better" candles in your trading strategy, consider the following tips: Trend Identification: Heiken Ashi "better" candles can help you identify the prevailing trend in the market. When the majority of the candles are green (or another color, depending on your chart settings) and there are no or few lower wicks, it may indicate a strong uptrend. Conversely, when the majority of the candles are red (or another color) and there are no or few upper wicks, it may signal a strong downtrend. Trend Reversals: Look for potential trend reversals when a change in the color of the candles occurs, especially when accompanied by longer wicks. For example, if a green candle with a long lower wick is followed by a red candle, it could indicate a bearish reversal. Similarly, a red candle with a long upper wick followed by a green candle may suggest a bullish reversal. Support and Resistance: You can use Heiken Ashi "better" candles to identify potential support and resistance levels. When the candles are consistently moving in one direction and then suddenly change color with longer wicks, it could indicate the presence of a support or resistance level. Stop-Loss and Take-Profit: Using Heiken Ashi "better" candles can help you manage risk by determining optimal stop-loss and take-profit levels. For instance, you can place your stop-loss below the low of the most recent green candle in an uptrend or above the high of the most recent red candle in a downtrend. Confirming Signals: Heiken Ashi "better" candles should be used in conjunction with other technical indicators, such as moving averages, oscillators, or chart patterns, to confirm signals and improve the accuracy of your analysis. In this implementation, you have the choice of AMA, KAMA, or T3 smoothing. These are as follows: Kaufman Adaptive Moving Average (KAMA) The Kaufman Adaptive Moving Average (KAMA) is a type of adaptive moving average used in technical analysis to smooth out price fluctuations and identify trends. The KAMA adjusts its smoothing factor based on the market's volatility, making it more responsive in volatile markets and smoother in calm markets. The KAMA is calculated using three different efficiency ratios that determine the appropriate smoothing factor for the current market conditions. These ratios are based on the noise level of the market, the speed at which the market is moving, and the length of the moving average. The KAMA is a popular choice among traders who prefer to use adaptive indicators to identify trends and potential reversals. Adaptive Moving Average The Adaptive Moving Average (AMA) is a type of moving average that adjusts its sensitivity to price movements based on market conditions. It uses a ratio between the current price and the highest and lowest prices over a certain lookback period to determine its level of smoothing. The AMA can help reduce lag and increase responsiveness to changes in trend direction, making it useful for traders who want to follow trends while avoiding false signals. The AMA is calculated by multiplying a smoothing constant with the difference between the current price and the previous AMA value, then adding the result to the previous AMA value. T3 The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm. The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction. The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy. █ Giga Kaleidoscope Modularized Trading System Core components of an NNFX algorithmic trading strategy The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm: 1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc. 2. Baseline - a moving average to identify price trend 3. Confirmation 1 - a technical indicator used to identify trends 4. Confirmation 2 - a technical indicator used to identify trends 5. Continuation - a technical indicator used to identify trends 6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown 7. Exit - a technical indicator used to determine when a trend is exhausted What is Volatility in the NNFX trading system? In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period. True range is calculated as the maximum of the following values: -Current high minus the current low -Absolute value of the current high minus the previous close -Absolute value of the current low minus the previous close ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses. Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass What is a Baseline indicator? The baseline is essentially a moving average, and is used to determine the overall direction of the market. The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR). Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail. By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend. What is a Confirmation indicator? Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI). The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend. Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators. In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades. What is a Continuation indicator? In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy. What is a Volatility/Volume indicator? Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa. By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements. What is an Exit indicator? The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy. The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses. In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor. Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time. How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above? Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are: 1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm) 2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm) 3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm) 4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm) 5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm) (additional module types will added in future releases) Each module interacts with every module by passing data between modules. Data is passed between each module as described below: GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy. This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm. What does the application of the GKD trading system look like? Example trading system: Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles Baseline: Hull Moving Average Volatility/Volume: Hurst Exponent Confirmation 1: CCI Adaptive Smoother as shown on the chart above Confirmation 2: Williams Percent Range Continuation: CCI Adaptive Smoother Exit: Rex Oscillator Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain. Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm) Standard Entry 1. GKD-C Confirmation 1 Signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 6. GKD-C Confirmation 1 signal was less than 7 candles prior Volatility/Volume Entry 1. GKD-V Volatility/Volume signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-B Baseline agrees 6. GKD-C Confirmation 1 signal was less than 7 candles prior Continuation Entry 1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously 2. GKD-B Baseline hasn't crossed since entry signal trigger 3. GKD-C Confirmation Continuation Indicator signals 4. GKD-C Confirmation 1 agrees 5. GKD-B Baseline agrees 6. GKD-C Confirmation 2 agrees 1-Candle Rule Standard Entry 1. GKD-C Confirmation 1 signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 1-Candle Rule Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 1 signal was less than 7 candles prior Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees 1-Candle Rule Volatility/Volume Entry 1. GKD-V Volatility/Volume signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 1 signal was less than 7 candles prior Next Candle: 1. Price retraced (Long: close < close or Short: close > close) 2. GKD-B Volatility/Volume agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-B Baseline agrees PullBack Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is beyond 1.0x Volatility of Baseline Next Candle: 1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 2. GKD-C Confirmation 1 agrees 3. GKD-C Confirmation 2 agrees 4. GKD-V Volatility/Volume Agrees ]█ Setting up the GKD The GKD system involves chaining indicators together. These are the steps to set this up. Use a GKD-C indicator alone on a chart 1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple" Use a GKD-V indicator alone on a chart **nothing, it's already useable on the chart without any settings changes Use a GKD-B indicator alone on a chart **nothing, it's already useable on the chart without any settings changes Baseline (Baseline, Backtest) 1. Import the GKD-B Baseline into the GKD-BT Backtest: "Input into Volatility/Volume or Backtest (Baseline testing)" 2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline" Volatility/Volume (Volatility/Volume, Backte st) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Solo" 2. Inside the GKD-V indicator, change the "Signal Type" setting to "Crossing" (neither traditional nor both can be backtested) 3. Import the GKD-V indicator into the GKD-BT Backtest: "Input into C1 or Backtest" 4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Volatility/Volume" 5. Inside the GKD-BT Backtest, a) change the setting "Backtest Type" to "Trading" if using a directional GKD-V indicator; or, b) change the setting "Backtest Type" to "Full" if using a directional or non-directional GKD-V indicator (non-directional GKD-V can only test Longs and Shorts separately) 6. If "Backtest Type" is set to "Full": Inside the GKD-BT Backtest, change the setting "Backtest Side" to "Long" or "Short 7. If "Backtest Type" is set to "Full": To allow the system to open multiple orders at one time so you test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts. Solo Confirmation Simple (Confirmation, Backtest) 1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple" 1. Import the GKD-C indicator into the GKD-BT Backtest: "Input into Backtest" 2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Solo Confirmation Simple" Solo Confirmation Complex without Exits (Baseline, Volatility/Volume, Confirmation, Backtest) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained" 2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)" 3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex" 4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest" 5. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits" 6. Import the GKD-C into the GKD-BT Backtest: "Input into Exit or Backtest" Solo Confirmation Complex with Exits (Baseline, Volatility/Volume, Confirmation, Exit, Backtest) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained" 2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)" 3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex" 4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest" 5. Import the GKD-C indicator into the GKD-E indicator: "Input into Exit" 6. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits" 7. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest" Full GKD without Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Backtest) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained" 2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)" 3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1" 4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest" 5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2" 6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2" 7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation" 8. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits" 9. Import the GKD-E into the GKD-BT Backtest: "Input into Exit or Backtest" Full GKD with Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Exit, Backtest) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained" 2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)" 3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1" 4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest" 5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2" 6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2" 7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation" 8. Import the GKD-C Continuation indicator into the GKD-E indicator: "Input into Exit" 9. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits" 10. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest" Baseline + Volatility/Volume (Baseline, Volatility/Volume, Backtest) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Baseline + Volatility/Volume" 2. Inside the GKD-V indicator, make sure the "Signal Type" setting is set to "Traditional" 3. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)" 4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline + Volatility/Volume" 5. Import the GKD-V into the GKD-BT Backtest: "Input into C1 or Backtest" 6. Inside the GKD-BT Backtest, change the setting "Backtest Type" to "Full". For this backtest, you must test Longs and Shorts separately 7. To allow the system to open multiple orders at one time so you can test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts. Requirements Inputs Confirmation 1: GKD-V Volatility / Volume indicator Confirmation 2: GKD-C Confirmation indicator Continuation: GKD-C Confirmation indicator Solo Confirmation Simple: GKD-B Baseline Solo Confirmation Complex: GKD-V Volatility / Volume indicator Solo Confirmation Super Complex: GKD-V Volatility / Volume indicator Stacked 1: None Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 1 Outputs Confirmation 1: GKD-C Confirmation 2 indicator Confirmation 2: GKD-C Continuation indicator Continuation: GKD-E Exit indicator Solo Confirmation Simple: GKD-BT Backtest Solo Confirmation Complex: GKD-BT Backtest or GKD-E Exit indicator Solo Confirmation Super Complex: GKD-C Continuation indicator Stacked 1: GKD-C, GKD-V, or GKD-B Stacked 2+ Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 2+ or GKD-BT Backtest Additional features will be added in future releases.
Pine Script® indicator
by loxx
1
Reversal Point Dynamics - Machine Learning⇋ Reversal Point Dynamics - Machine Learning RPD Machine Learning: Self-Adaptive Multi-Armed Bandit Trading System RPD Machine Learning is an advanced algorithmic trading system that implements genuine machine learning through contextual multi-armed bandits, reinforcement learning, and online adaptation. Unlike traditional indicators that use fixed rules, RPD learns from every trade outcome , automatically discovers which strategies work in current market conditions, and continuously adapts without manual intervention . Core Innovation: The system deploys six distinct trading policies (ranging from aggressive trend-following to conservative range-bound strategies) and uses LinUCB contextual bandit algorithms with Random Fourier Features to learn which policy performs best in each market regime. After the initial learning phase (50-100 trades), the system achieves autonomous adaptation , automatically shifting between policies as market conditions evolve. Target Users: Quantitative traders, algorithmic trading developers, systematic traders, and data-driven investors who want a system that adapts over time . Suitable for stocks, futures, forex, and cryptocurrency on any liquid instrument with >100k daily volume. The Problem This System Solves Traditional Technical Analysis Limitations Most trading systems suffer from three fundamental challenges : Fixed Parameters: Static settings (like "buy when RSI < 30") work well in backtests but may struggle when markets change character. What worked in low-volatility environments may not work in high-volatility regimes. Strategy Degradation: Manual optimization (curve-fitting) produces systems that perform well on historical data but may underperform in live trading. The system never adapts to new market conditions. Cognitive Overload: Running multiple strategies simultaneously forces traders to manually decide which one to trust. This leads to hesitation, late entries, and inconsistent execution. How RPD Machine Learning Addresses These Challenges Automated Strategy Selection: Instead of requiring you to choose between trend-following and mean-reversion strategies, RPD runs all six policies simultaneously and uses machine learning to automatically select the best one for current conditions. The decision happens algorithmically, removing human hesitation. Continuous Learning: After every trade, the system updates its understanding of which policies are working. If the market shifts from trending to ranging, RPD automatically detects this through changing performance patterns and adjusts selection accordingly. Context-Aware Decisions: Unlike simple voting systems that treat all conditions equally, RPD analyzes market context (ADX regime, entropy levels, volatility state, volume patterns, time of day, historical performance) and learns which combinations of context features correlate with policy success. Machine Learning Architecture: What Makes This "Real" ML Component 1: Contextual Multi-Armed Bandits (LinUCB) What Is a Multi-Armed Bandit Problem? Imagine facing six slot machines, each with unknown payout rates. The exploration-exploitation dilemma asks: Should you keep pulling the machine that's worked well (exploitation) or try others that might be better (exploration)? RPD solves this for trading policies. Academic Foundation: RPD implements Linear Upper Confidence Bound (LinUCB) from the research paper "A Contextual-Bandit Approach to Personalized News Article Recommendation" (Li et al., 2010, WWW Conference). This algorithm is used in content recommendation and ad placement systems. How It Works: Each policy (AggressiveTrend, ConservativeRange, VolatilityBreakout, etc.) is treated as an "arm." The system maintains: Reward History: Tracks wins/losses for each policy Contextual Features: Current market state (8-10 features including ADX, entropy, volatility, volume) Uncertainty Estimates: Confidence in each policy's performance UCB Formula: predicted_reward + α × uncertainty The system selects the policy with highest UCB score , balancing proven performance (predicted_reward) with potential for discovery (uncertainty bonus). Initially, all policies have high uncertainty, so the system explores broadly. After 50-100 trades, uncertainty decreases, and the system focuses on known-performing policies. Why This Matters: Traditional systems pick strategies based on historical backtests or user preference. RPD learns from actual outcomes in your specific market, on your timeframe, with your execution characteristics. Component 2: Random Fourier Features (RFF) The Non-Linearity Challenge: Market relationships are often non-linear. High ADX may indicate favorable conditions when volatility is normal, but unfavorable when volatility spikes. Simple linear models struggle to capture these interactions. Academic Foundation: RPD implements Random Fourier Features from "Random Features for Large-Scale Kernel Machines" (Rahimi & Recht, 2007, NIPS). This technique approximates kernel methods (like Support Vector Machines) while maintaining computational efficiency for real-time trading. How It Works: The system transforms base features (ADX, entropy, volatility, etc.) into a higher-dimensional space using random projections and cosine transformations: Input: 8 base features Projection: Through random Gaussian weights Transformation: cos(W×features + b) Output: 16 RFF dimensions This allows the bandit to learn non-linear relationships between market context and policy success. For example: "AggressiveTrend performs well when ADX >25 AND entropy <0.6 AND hour >9" becomes naturally encoded in the RFF space. Why This Matters: Without RFF, the system could only learn "this policy has X% historical performance." With RFF, it learns "this policy performs differently in these specific contexts" - enabling more nuanced selection. Component 3: Reinforcement Learning Stack Beyond bandits, RPD implements a complete RL framework : Q-Learning: Value-based RL that learns state-action values. Maps 54 discrete market states (trend×volatility×RSI×volume combinations) to 5 actions (4 policies + no-trade). Updates via Bellman equation after each trade. Converges toward optimal policy after 100-200 trades. TD(λ) with Eligibility Traces: Extension of Q-Learning that propagates credit backwards through time . When a trade produces an outcome, TD(λ) updates not just the final state-action but all states visited during the trade, weighted by eligibility decay (λ=0.90). This accelerates learning from multi-bar trades. Policy Gradient (REINFORCE): Learns a stochastic policy directly from 12 continuous market features without discretization. Uses gradient ascent to increase probability of actions that led to positive outcomes. Includes baseline (average reward) for variance reduction. Meta-Learning: The system learns how to learn by adapting its own learning rates based on feature stability and correlation with outcomes. If a feature (like volume ratio) consistently correlates with success, its learning rate increases. If unstable, rate decreases. Why This Matters: Q-Learning provides fast discrete decisions. Policy Gradient handles continuous features. TD(λ) accelerates learning. Meta-learning optimizes the optimization. Together, they create a robust, multi-approach learning system that adapts more quickly than any single algorithm. Component 4: Policy Momentum Tracking (v2 Feature) The Recency Challenge: Standard bandits treat all historical data equally. If a policy performed well historically but struggles in current conditions due to regime shift, the system may be slow to adapt because historical success outweighs recent underperformance. RPD's Solution: Each policy maintains a ring buffer of the last 10 outcomes. The system calculates: Momentum: recent_win_rate - global_win_rate (range: -1 to +1) Confidence: consistency of recent results (1 - variance) Policies with positive momentum (recent outperformance) get an exploration bonus. Policies with negative momentum and high confidence (consistent recent underperformance) receive a selection penalty. Effect: When markets shift, the system detects the shift more quickly through momentum tracking, enabling faster adaptation than standard bandits. Signal Generation: The Core Algorithm Multi-Timeframe Fractal Detection RPD identifies reversal points using three complementary methods : 1. Quantum State Analysis: Divides price range into discrete states (default: 6 levels) Peak signals require price in top states (≥ state 5) Valley signals require price in bottom states (≤ state 1) Prevents mid-range signals that may struggle in strong trends 2. Fractal Geometry: Identifies swing highs/lows using configurable fractal strength Confirms local extremum with neighboring bars Validates reversal only if price crosses prior extreme 3. Multi-Timeframe Confirmation: Analyzes higher timeframe (4× default) for alignment MTF confirmation adds probability bonus Designed to reduce false signals while preserving valid setups Probability Scoring System Each signal receives a dynamic probability score (40-99%) based on: Base Components: Trend Strength: EMA(velocity) / ATR × 30 points Entropy Quality: (1 - entropy) × 10 points Starting baseline: 40 points Enhancement Bonuses: Divergence Detection: +20 points (price/momentum divergence) RSI Extremes: +8 points (RSI >65 for peaks, <40 for valleys) Volume Confirmation: +5 points (volume >1.2× average) Adaptive Momentum: +10 points (strong directional velocity) MTF Alignment: +12 points (higher timeframe confirms) Range Factor: (high-low)/ATR × 3 - 1.5 points (volatility adjustment) Regime Bonus: +8 points (trending ADX >25 with directional agreement) Penalties: High Entropy: -5 points (entropy >0.85, chaotic price action) Consolidation Regime: -10 points (ADX <20, no directional conviction) Final Score: Clamped to 40-99% range, classified as ELITE (>85%), STRONG (75-85%), GOOD (65-75%), or FAIR (<65%) Entropy-Based Quality Filter What Is Entropy? Entropy measures randomness in price changes . Low entropy indicates orderly, directional moves. High entropy indicates chaotic, unpredictable conditions. Calculation: Count up/down price changes over adaptive period Calculate probability: p = ups / total_changes Shannon entropy: -p×log(p) - (1-p)×log(1-p) Normalized to 0-1 range Application: Entropy <0.5: Highly ordered (ELITE signals possible) Entropy 0.5-0.75: Mixed (GOOD signals) Entropy >0.85: Chaotic (signals blocked or heavily penalized) Why This Matters: Prevents trading during choppy, news-driven conditions where technical patterns may be less reliable. Automatically raises quality bar when market is unpredictable. Regime Detection & Market Microstructure - ADX-Based Regime Classification RPD uses Wilder's Average Directional Index to classify markets: Bull Trend: ADX >25, +DI > -DI (directional conviction bullish) Bear Trend: ADX >25, +DI < -DI (directional conviction bearish) Consolidation: ADX <20 (no directional conviction) Transitional: ADX 20-25 (forming direction, ambiguous) Filter Logic: Blocks all signals during Transitional regime (avoids trading during uncertain conditions) Blocks Consolidation signals unless ADX ≥ Min Trend Strength Adds probability bonus during strong trends (ADX >30) Effect: Designed to reduce signal frequency while focusing on higher-quality setups. Divergence Detection Bearish Divergence: Price makes higher high Velocity (price momentum) makes lower high Indicates weakening upward pressure → SHORT signal quality boost Bullish Divergence: Price makes lower low Velocity makes higher low Indicates weakening downward pressure → LONG signal quality boost Bonus: Adds probability points and additional acceleration factor. Divergence signals have historically shown higher success rates in testing. Hierarchical Policy System - The Six Trading Policies 1. AggressiveTrend (Policy 0): Probability Threshold: 60% (trades more frequently) Entropy Threshold: 0.70 (tolerates moderate chaos) Stop Multiplier: 2.5× ATR (wider stops for trends) Target Multiplier: 5.0R (larger targets) Entry Mode: Pyramid (scales into winners) Best For: Strong trending markets, breakouts, momentum continuation 2. ConservativeRange (Policy 1): Probability Threshold: 75% (more selective) Entropy Threshold: 0.60 (requires order) Stop Multiplier: 1.8× ATR (tighter stops) Target Multiplier: 3.0R (modest targets) Entry Mode: Single (one-shot entries) Best For: Range-bound markets, low volatility, mean reversion 3. VolatilityBreakout (Policy 2): Probability Threshold: 65% (moderate) Entropy Threshold: 0.80 (accepts high entropy) Stop Multiplier: 3.0× ATR (wider stops) Target Multiplier: 6.0R (larger targets) Entry Mode: Tiered (splits entry) Best For: Compression breakouts, post-consolidation moves, gap opens 4. EntropyScalp (Policy 3): Probability Threshold: 80% (very selective) Entropy Threshold: 0.40 (requires extreme order) Stop Multiplier: 1.5× ATR (tightest stops) Target Multiplier: 2.5R (quick targets) Entry Mode: Single Best For: Low-volatility grinding moves, tight ranges, highly predictable patterns 5. DivergenceHunter (Policy 4): Probability Threshold: 70% (quality-focused) Entropy Threshold: 0.65 (balanced) Stop Multiplier: 2.2× ATR (moderate stops) Target Multiplier: 4.5R (balanced targets) Entry Mode: Tiered Best For: Divergence-confirmed reversals, exhaustion moves, trend climax 6. AdaptiveBlend (Policy 5): Probability Threshold: 68% (balanced) Entropy Threshold: 0.75 (balanced) Stop Multiplier: 2.0× ATR (standard) Target Multiplier: 4.0R (standard) Entry Mode: Single Best For: Mixed conditions, general trading, fallback when no clear regime Policy Clustering (Advanced/Extreme Modes) Policies are grouped into three clusters based on regime affinity: Cluster 1 (Trending): AggressiveTrend, DivergenceHunter High regime affinity (0.8): Performs well when ADX >25 Moderate vol affinity (0.6): Works in various volatility Cluster 2 (Ranging): ConservativeRange, AdaptiveBlend Low regime affinity (0.3): Better suited for ADX <20 Low vol affinity (0.4): Optimized for calm markets Cluster 3 (Breakout): VolatilityBreakout Moderate regime affinity (0.6): Works in multiple regimes High vol affinity (0.9): Requires high volatility for optimal characteristics Hierarchical Selection Process: Calculate cluster scores based on current regime and volatility Select best-matching cluster Run UCB selection within chosen cluster Apply momentum boost/penalty This two-stage process reduces learning time - instead of choosing among 6 policies from scratch, system first narrows to 1-2 policies per cluster, then optimizes within cluster. Risk Management & Position Sizing Dynamic Kelly Criterion Sizing (Optional) Traditional Fixed Sizing Challenge: Using the same position size for all signal probabilities may be suboptimal. Higher-probability signals could justify larger positions, lower-probability signals smaller positions. Kelly Formula: f = (p × b - q) / b Where: p = win probability (from signal score) q = loss probability (1 - p) b = win/loss ratio (average_win / average_loss) f = fraction of capital to risk RPD Implementation: Uses Fractional Kelly (1/4 Kelly default) for safety. Full Kelly is theoretically optimal but can recommend large position sizes. Fractional Kelly reduces volatility while maintaining adaptive sizing benefits. Enhancements: Probability Bonus: Normalize(prob, 65, 95) × 0.5 multiplier Divergence Bonus: Additional sizing on divergence signals Regime Bonus: Additional sizing during strong trends (ADX >30) Momentum Adjustment: Hot policies receive sizing boost, cold policies receive reduction Safety Rails: Minimum: 1 contract (floor) Maximum: User-defined cap (default 10 contracts) Portfolio Heat: Max total risk across all positions (default 4% equity) Multi-Mode Stop Loss System ATR Mode (Default): Stop = entry ± (ATR × base_mult × policy_mult) Consistent risk sizing Ignores market structure Best for: Futures, forex, algorithmic trading Structural Mode: Finds swing low (long) or high (short) over last 20 bars Identifies fractal pivots within lookback Places stop below/above structure + buffer (0.1× ATR) Best for: Stocks, instruments that respect structure Hybrid Mode (Intelligent): Attempts structural stop first Falls back to ATR if: Structural level is invalid (beyond entry) Structural stop >2× ATR away (too wide) Best for: Mixed instruments, adaptability Dynamic Adjustments: Breakeven: Move stop to entry + 1 tick after 1.0R profit Trailing: Trail stop 0.8R behind price after 1.5R profit Timeout: Force close after 30 bars (optional) Tiered Entry System Challenge: Equal sizing on all signals may not optimize capital allocation relative to signal quality. Solution: Tier 1 (40% of size): Enters immediately on all signals Tier 2 (60% of size): Enters only if probability ≥ Tier 2 trigger (default 75%) Example: Calculated optimal size: 10 contracts Signal probability: 72% Tier 2 trigger: 75% Result: Enter 4 contracts only (Tier 1) Same signal at 80% probability Result: Enter 10 contracts (4 Tier 1 + 6 Tier 2) Effect: Automatically scales size to signal quality, optimizing capital allocation. Performance Optimization & Learning Curve Warmup Phase (First 50 Trades) Purpose: Ensure all policies get tested before system focuses on preferred strategies. Modifications During Warmup: Probability thresholds reduced 20% (65% becomes 52%) Entropy thresholds increased 20% (more permissive) Exploration rate stays high (30%) Confidence width (α) doubled (more exploration) Why This Matters: Without warmup, system might commit to early-performing policy without testing alternatives. Warmup forces thorough exploration before focusing on best-performing strategies. Curriculum Learning Phase 1 (Trades 1-50): Exploration Warmup active All policies tested High exploration (30%) Learning fundamental patterns Phase 2 (Trades 50-100): Refinement Warmup ended, thresholds normalize Exploration decaying (30% → 15%) Policy preferences emerging Meta-learning optimizing Phase 3 (Trades 100-200): Specialization Exploration low (15% → 8%) Clear policy preferences established Momentum tracking fully active System focusing on learned patterns Phase 4 (Trades 200+): Maturity Exploration minimal (8% → 5%) Regime-policy relationships learned Auto-adaptation to market shifts Stable performance expected Convergence Indicators System is learning well when: Policy switch rate decreasing over time (initially ~50%, should drop to <20%) Exploration rate decaying smoothly (30% → 5%) One or two policies emerge with >50% selection frequency Performance metrics stabilizing over time Consistent behavior in similar market conditions System may need adjustment when: Policy switch rate >40% after 100 trades (excessive exploration) Exploration rate not decaying (parameter issue) All policies showing similar selection (not differentiating) Performance declining despite relaxed thresholds (underlying signal issue) Highly erratic behavior after learning phase Advanced Features Attention Mechanism (Extreme Mode) Challenge: Not all features are equally important. Trading hour might matter more than price-volume correlation, but standard approaches treat them equally. Solution: Each RFF dimension has an importance weight . After each trade: Calculate correlation: sign(feature - 0.5) × sign(reward) Update importance: importance += correlation × 0.01 Clamp to range Effect: Important features get amplified in RFF transformation, less important features get suppressed. System learns which features correlate with successful outcomes. Temporal Context (Extreme Mode) Challenge: Current market state alone may be incomplete. Historical context (was volatility rising or falling?) provides additional information. Solution: Includes 3-period historical context with exponential decay (0.85): Current features (weight 1.0) 1 bar ago (weight 0.85) 2 bars ago (weight 0.72) Effect: Captures momentum and acceleration of market features. System learns patterns like "rising volatility with falling entropy" that may precede significant moves. Transfer Learning via Episodic Memory Short-Term Memory (STM): Last 20 trades Fast adaptation to immediate regime High learning rate Long-Term Memory (LTM): Condensed historical patterns Preserved knowledge from past regimes Low learning rate Transfer Mechanism: When STM fills (20 trades), patterns consolidated into LTM . When similar regime recurs later, LTM provides faster adaptation than starting from scratch. Practical Implementation Guide - Recommended Settings by Instrument Futures (ES, NQ, CL): Adaptive Period: 20-25 ML Mode: Advanced RFF Dimensions: 16 Policies: 6 Base Risk: 1.5% Stop Mode: ATR or Hybrid Timeframe: 5-15 min Forex Majors (EURUSD, GBPUSD): Adaptive Period: 25-30 ML Mode: Advanced RFF Dimensions: 16 Policies: 6 Base Risk: 1.0-1.5% Stop Mode: ATR Timeframe: 5-30 min Cryptocurrency (BTC, ETH): Adaptive Period: 20-25 ML Mode: Extreme (handles non-stationarity) RFF Dimensions: 32 (captures complexity) Policies: 6 Base Risk: 1.0% (volatility consideration) Stop Mode: Hybrid Timeframe: 15 min - 4 hr Stocks (Large Cap): Adaptive Period: 25-30 ML Mode: Advanced RFF Dimensions: 16 Policies: 5-6 Base Risk: 1.5-2.0% Stop Mode: Structural or Hybrid Timeframe: 15 min - Daily Scaling Strategy Phase 1 (Testing - First 50 Trades): Max Contracts: 1-2 Goal: Validate system on your instrument Monitor: Performance stabilization, learning progress Phase 2 (Validation - Trades 50-100): Max Contracts: 2-3 Goal: Confirm learning convergence Monitor: Policy stability, exploration decay Phase 3 (Scaling - Trades 100-200): Max Contracts: 3-5 Enable: Kelly sizing (1/4 Kelly) Goal: Optimize capital efficiency Monitor: Risk-adjusted returns Phase 4 (Full Deployment - Trades 200+): Max Contracts: 5-10 Enable: Full momentum tracking Goal: Sustained consistent performance Monitor: Ongoing adaptation quality Limitations & Disclaimers Statistical Limitations Learning Sample Size: System requires minimum 50-100 trades for basic convergence, 200+ trades for robust learning. Early performance (first 50 trades) may not reflect mature system behavior. Non-Stationarity Risk: Markets change over time. A system trained on one market regime may need time to adapt when conditions shift (typically 30-50 trades for adjustment). Overfitting Possibility: With 16-32 RFF dimensions and 6 policies, system has substantial parameter space. Small sample sizes (<200 trades) increase overfitting risk. Mitigated by regularization (λ) and fractional Kelly sizing. Technical Limitations Computational Complexity: Extreme mode with 32 RFF dimensions, 6 policies, and full RL stack requires significant computation. May perform slowly on lower-end systems or with many other indicators loaded. Pine Script Constraints: No true matrix inversion (uses diagonal approximation for LinUCB) No cryptographic RNG (uses market data as entropy) No proper random number generation for RFF (uses deterministic pseudo-random) These approximations reduce mathematical precision compared to academic implementations but remain functional for trading applications. Data Requirements: Needs clean OHLCV data. Missing bars, gaps, or low liquidity (<100k daily volume) can degrade signal quality. Forward-Looking Bias Disclaimer Reward Calculation Uses Future Data: The RL system evaluates trades using an 8-bar forward-looking window. This means when a position enters at bar 100, the reward calculation considers price movement through bar 108. Why This is Disclosed: Entry signals do NOT look ahead - decisions use only data up to entry bar Forward data used for learning only, not signal generation In live trading, system learns identically as bars unfold in real-time Simulates natural learning process (outcomes are only known after trades complete) Implication: Backtested metrics reflect this 8-bar evaluation window. Live performance may vary if: - Positions held longer than 8 bars - Slippage/commissions differ from backtest settings - Market microstructure changes (wider spreads, different execution quality) Risk Warnings No Guarantee of Profit: All trading involves substantial risk of loss. Machine learning systems can fail if market structure fundamentally changes or during unprecedented events. Maximum Drawdown: With 1.5% base risk and 4% max total risk, expect potential drawdowns. Historical drawdowns do not predict future drawdowns. Extreme market conditions can exceed expectations. Black Swan Events: System has not been tested under: flash crashes, trading halts, circuit breakers, major geopolitical shocks, or other extreme events. Such events can exceed stop losses and cause significant losses. Leverage Risk: Futures and forex involve leverage. Adverse moves combined with leverage can result in losses exceeding initial investment. Use appropriate position sizing for your risk tolerance. System Failures: Code bugs, broker API failures, internet outages, or exchange issues can prevent proper execution. Always monitor automated systems and maintain appropriate safeguards. Appropriate Use This System Is: ✅ A machine learning framework for adaptive strategy selection ✅ A signal generation system with probabilistic scoring ✅ A risk management system with dynamic sizing ✅ A learning system designed to adapt over time This System Is NOT: ❌ A price prediction system (does not forecast exact prices) ❌ A guarantee of profits (can and will experience losses) ❌ A replacement for due diligence (requires monitoring and understanding) ❌ Suitable for complete beginners (requires understanding of ML concepts, risk management, and trading fundamentals) Recommended Use: Paper trade for 100 signals before risking capital Start with minimal position sizing (1-2 contracts) regardless of calculated size Monitor learning progress via dashboard Scale gradually over several months only after consistent results Combine with fundamental analysis and broader market context Set account-level risk limits (e.g., maximum drawdown threshold) Never risk more than you can afford to lose What Makes This System Different RPD implements academically-derived machine learning algorithms rather than simple mathematical calculations or optimization: ✅ LinUCB Contextual Bandits - Algorithm from WWW 2010 conference (Li et al.) ✅ Random Fourier Features - Kernel approximation from NIPS 2007 (Rahimi & Recht) ✅ Q-Learning, TD(λ), REINFORCE - Standard RL algorithms from Sutton & Barto textbook ✅ Meta-Learning - Learning rate adaptation based on feature correlation ✅ Online Learning - Real-time updates from streaming data ✅ Hierarchical Policies - Two-stage selection with clustering ✅ Momentum Tracking - Recent performance analysis for faster adaptation ✅ Attention Mechanism - Feature importance weighting ✅ Transfer Learning - Episodic memory consolidation Key Differentiators: Actually learns from trade outcomes (not just parameter optimization) Updates model parameters in real-time (true online learning) Adapts to changing market regimes (not static rules) Improves over time through reinforcement learning Implements published ML algorithms with proper citations Conclusion RPD Machine Learning represents a different approach from traditional technical analysis to adaptive, self-learning systems . Instead of manually optimizing parameters (which can overfit to historical data), RPD learns behavior patterns from actual trading outcomes in your specific market. The combination of contextual bandits, reinforcement learning, random fourier features, hierarchical policy selection, and momentum tracking creates a multi-algorithm learning system designed to handle non-stationary markets better than static approaches. After the initial learning phase (50-100 trades), the system achieves autonomous adaptation - automatically discovering which strategies work in current conditions and shifting allocation without human intervention. This represents an approach where systems adapt over time rather than remaining static. Use responsibly. Paper trade extensively. Scale gradually. Understand that past performance does not guarantee future results and all trading involves risk of loss. Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Pine Script® strategy
by DskyzInvestments
1
Kernel Market Dynamics [WFO - MAB]Kernel Market Dynamics ⚛️ CORE INNOVATION: KERNEL-BASED DISTRIBUTION ANALYSIS The Kernel Market Dynamics system represents a fundamental departure from traditional technical indicators. Rather than measuring price levels, momentum, or oscillator extremes, KMD analyzes the statistical distribution of market returns using advanced kernel methods from machine learning theory. This allows the system to detect when market behavior has fundamentally changed—not just when price has moved, but when the underlying probability structure has shifted. The Distribution Hypothesis: Traditional indicators assume markets move in predictable patterns. KMD assumes something more profound: markets exist in distinct distributional regimes , and profitable trading opportunities emerge during regime transitions . When the distribution of recent returns diverges significantly from the historical baseline, the market is restructuring—and that's when edge exists. Maximum Mean Discrepancy (MMD): At the heart of KMD lies a sophisticated statistical metric called Maximum Mean Discrepancy. MMD measures the distance between two probability distributions by comparing their representations in a high-dimensional feature space created by a kernel function. The Mathematics: Given two sets of normalized returns: • Reference period (X) : Historical baseline (default 100 bars) • Test period (Y) : Recent behavior (default 20 bars) MMD is calculated as: MMD² = E + E - 2·E Where: • E = Expected kernel similarity within reference period • E = Expected kernel similarity within test period • E = Expected cross-similarity between periods When MMD is low : Test period behaves like reference (stable regime) When MMD is high : Test period diverges from reference (regime shift) The final MMD value is smoothed with EMA(5) to reduce single-bar noise while maintaining responsiveness to genuine distribution changes. The Kernel Functions: The kernel function defines how similarity is measured. KMD offers four mathematically distinct kernels, each with different properties: 1. RBF (Radial Basis Function / Gaussian): • Formula: k(x,y) = exp(-d² / (2·σ²·scale)) • Properties: Most sensitive to distribution changes, smooth decision boundaries • Best for: Clean data, clear regime shifts, low-noise markets • Sensitivity: Highest - detects subtle changes • Use case: Stock indices, major forex pairs, trending environments 2. Laplacian: • Formula: k(x,y) = exp(-|d| / σ) • Properties: Medium sensitivity, robust to moderate outliers • Best for: Standard market conditions, balanced noise/signal • Sensitivity: Medium - filters minor fluctuations • Use case: Commodities, standard timeframes, general trading 3. Cauchy (Default - Most Robust): • Formula: k(x,y) = 1 / (1 + d²/σ²) • Properties: Heavy-tailed, highly robust to outliers and spikes • Best for: Noisy markets, choppy conditions, crypto volatility • Sensitivity: Lower - only major distribution shifts trigger • Use case: Cryptocurrencies, illiquid markets, volatile instruments 4. Rational Quadratic: • Formula: k(x,y) = (1 + d²/(2·α·σ²))^(-α) • Properties: Tunable via alpha parameter, mixture of RBF kernels • Alpha < 1.0: Heavy tails (like Cauchy) • Alpha > 3.0: Light tails (like RBF) • Best for: Adaptive use, mixed market conditions • Use case: Experimental optimization, regime-specific tuning Bandwidth (σ) Parameter: The bandwidth controls the "width" of the kernel, determining sensitivity to return differences: • Low bandwidth (0.5-1.5) : Narrow kernel, very sensitive - Treats small differences as significant - More MMD spikes, more signals - Use for: Scalping, fast markets • Medium bandwidth (1.5-3.0) : Balanced sensitivity (recommended) - Filters noise while catching real shifts - Professional-grade signal quality - Use for: Day/swing trading • High bandwidth (3.0-10.0) : Wide kernel, less sensitive - Only major distribution changes register - Fewer, stronger signals - Use for: Position trading, trend following Adaptive Bandwidth: When enabled (default ON), bandwidth automatically scales with market volatility: Effective_BW = Base_BW × max(0.5, min(2.0, 1 / volatility_ratio)) • Low volatility → Tighter bandwidth (0.5× base) → More sensitive • High volatility → Wider bandwidth (2.0× base) → Less sensitive This prevents signal flooding during wild markets and avoids signal drought during calm periods. Why Kernels Work: Kernel methods implicitly map data to infinite-dimensional space where complex, nonlinear patterns become linearly separable. This allows MMD to detect distribution changes that simpler statistics (mean, variance) would miss. For example: • Same mean, different shape : Traditional metrics see nothing, MMD detects shift • Same volatility, different skew : Oscillators miss it, MMD catches it • Regime rotation : Price unchanged, but return distribution restructured The kernel captures the entire distributional signature —not just first and second moments. 🎰 MULTI-ARMED BANDIT FRAMEWORK: ADAPTIVE STRATEGY SELECTION Rather than forcing one strategy on all market conditions, KMD implements a Multi-Armed Bandit (MAB) system that learns which of seven distinct strategies performs best and dynamically selects the optimal approach in real-time. The Seven Arms (Strategies): Each arm represents a fundamentally different trading logic: ARM 0 - MMD Regime Shift: • Logic: Distribution divergence with directional bias • Triggers: MMD > threshold AND direction_bias confirmed AND velocity > 5% • Philosophy: Trade the regime transition itself • Best in: Volatile shifts, breakout moments, crisis periods • Weakness: False alarms in choppy consolidation ARM 1 - Trend Following: • Logic: Aligned EMAs with strong ADX • Triggers: EMA(9) > EMA(21) > EMA(50) AND ADX > 25 • Philosophy: Ride established momentum • Best in: Strong trending regimes, directional markets • Weakness: Late entries, whipsaws at reversals ARM 2 - Breakout: • Logic: Bollinger Band breakouts with volume • Triggers: Price crosses BB outer band AND volume > 1.2× average • Philosophy: Capture volatility expansion events • Best in: Range breakouts, earnings, news events • Weakness: False breakouts in ranging markets ARM 3 - RSI Mean Reversion: • Logic: RSI extremes with reversal confirmation • Triggers: RSI < 30 with uptick OR RSI > 70 with downtick • Philosophy: Fade overbought/oversold extremes • Best in: Ranging markets, mean-reverting instruments • Weakness: Fails in strong trends, catches falling knives ARM 4 - Z-Score Statistical Reversion: • Logic: Price deviation from 50-period mean • Triggers: Z-score < -2 (oversold) OR > +2 (overbought) with reversal • Philosophy: Statistical bounds reversion • Best in: Stable volatility regimes, pairs trading • Weakness: Trend continuation through extremes ARM 5 - ADX Momentum: • Logic: Strong directional movement with acceleration • Triggers: ADX > 30 with DI+ or DI- strengthening • Philosophy: Momentum begets momentum • Best in: Trending with increasing velocity • Weakness: Late exits, momentum exhaustion ARM 6 - Volume Confirmation: • Logic: OBV trend + volume spike + candle direction • Triggers: OBV > EMA(20) AND volume > average AND bullish candle • Philosophy: Follow institutional money flow • Best in: Liquid markets with reliable volume • Weakness: Manipulated volume, thin markets Q-Learning with Rewards: Each arm maintains a Q-value representing its expected reward. After every bar, the system calculates a reward based on the arm's signal and actual price movement: Reward Calculation: If arm signaled LONG: reward = (close - close ) / close If arm signaled SHORT: reward = -(close - close ) / close If arm signaled NEUTRAL: reward = 0 Penalty multiplier: If loss > 0.5%, reward × 1.3 (punish big losses harder) Q-Value Update (Exponential Moving Average): Q_new = Q_old + α × (reward - Q_old) Where α (learning rate, default 0.08) controls adaptation speed: • Low α (0.01-0.05): Slow, stable learning • Medium α (0.06-0.12): Balanced (recommended) • High α (0.15-0.30): Fast, reactive learning This gradually shifts Q-values toward arms that generate positive returns and away from losing arms. Arm Selection Algorithms: KMD offers four mathematically distinct selection strategies: 1. UCB1 (Upper Confidence Bound) - Recommended: Formula: Select arm with max(Q_i + c·√(ln(t)/n_i)) Where: • Q_i = Q-value of arm i • c = exploration constant (default 1.5) • t = total pulls across all arms • n_i = pulls of arm i Philosophy: Balance exploitation (use best arm) with exploration (try uncertain arms). The √(ln(t)/n_i) term creates an "exploration bonus" that decreases as an arm gets more pulls, ensuring all arms get sufficient testing. Theoretical guarantee: Logarithmic regret bound - UCB1 provably converges to optimal arm selection over time. 2. UCB1-Tuned (Variance-Aware UCB): Formula: Select arm with max(Q_i + √(ln(t)/n_i × min(0.25, V_i + √(2·ln(t)/n_i)))) Where V_i = variance of rewards for arm i Philosophy: Incorporates reward variance into exploration. Arms with high variance (unpredictable) get less exploration bonus, focusing effort on stable performers. Better bounds than UCB1 in practice, slightly more conservative exploration. 3. Epsilon-Greedy (Simple Random): Algorithm: With probability ε: Select random arm (explore) With probability 1-ε: Select highest Q-value arm (exploit) Default ε = 0.10 (10% exploration, 90% exploitation) Philosophy: Simplest algorithm, easy to understand. Random exploration ensures all arms stay updated but may waste time on clearly bad arms. 4. Thompson Sampling (Bayesian): The most sophisticated selection algorithm, using true Bayesian probability. Each arm maintains Beta distribution parameters: • α (alpha) = successes + 1 • β (beta) = failures + 1 Selection Process: 1. Sample θ_i ~ Beta(α_i, β_i) for each arm using Marsaglia-Tsang Gamma sampler 2. Select arm with highest sample: argmax_i(θ_i) 3. After reward, update: - If reward > 0: α += |reward| × 100 (increment successes) - If reward < 0: β += |reward| × 100 (increment failures) Why Thompson Sampling Works: The Beta distribution naturally represents uncertainty about an arm's true win rate. Early on with few trials, the distribution is wide (high uncertainty), leading to more exploration. As evidence accumulates, it narrows around the true performance, naturally shifting toward exploitation. Unlike UCB which uses deterministic confidence bounds, Thompson Sampling is probabilistic—it samples from the posterior distribution of each arm's success rate, providing automatic exploration/exploitation balance without tuning. Comparison: • UCB1: Deterministic, guaranteed regret bounds, requires tuning exploration constant • Thompson: Probabilistic, natural exploration, no tuning required, best empirical performance • Epsilon-Greedy: Simplest, consistent exploration %, less efficient • UCB1-Tuned: UCB1 + variance awareness, best for risk-averse Exploration Constant (c): For UCB algorithms, this multiplies the exploration bonus: • Low c (0.5-1.0): Strongly prefer proven arms, rare exploration • Medium c (1.2-1.8): Balanced (default 1.5) • High c (2.0-3.0): Frequent exploration, diverse arm usage Higher exploration constant in volatile/unstable markets, lower in stable trending environments. 🔬 WALK-FORWARD OPTIMIZATION: PREVENTING OVERFITTING The single biggest problem in algorithmic trading is overfitting—strategies that look amazing in backtest but fail in live trading because they learned noise instead of signal. KMD's Walk-Forward Optimization system addresses this head-on. How WFO Works: The system divides time into repeating cycles: 1. Training Window (default 500 bars): Learn arm Q-values on historical data 2. Testing Window (default 100 bars): Validate on unseen "future" data Training Phase: • All arms accumulate rewards and update Q-values normally • Q_train tracks in-sample performance • System learns which arms work on historical data Testing Phase: • System continues using arms but tracks separate Q_test metrics • Counts trades per arm (N_test) • Testing performance is "out-of-sample" relative to training Validation Requirements: An arm is only "validated" (approved for live use) if: 1. N_test ≥ Minimum Trades (default 10): Sufficient statistical sample 2. Q_test > 0 : Positive out-of-sample performance Arms that fail validation are blocked from generating signals, preventing the system from trading strategies that only worked on historical data. Performance Decay: At the end of each WFO cycle, all Q-values decay exponentially: Q_new = Q_old × decay_rate (default 0.95) This ensures old performance doesn't dominate forever. An arm that worked 10 cycles ago but fails recently will eventually lose influence. Decay Math: • 0.95 decay after 10 periods → 0.95^10 = 0.60 (40% forgotten) • 0.90 decay after 10 periods → 0.90^10 = 0.35 (65% forgotten) Fast decay (0.80-0.90): Quick adaptation, forgets old patterns rapidly Slow decay (0.96-0.99): Stable, retains historical knowledge longer WFO Efficiency Metric: The key metric revealing overfitting: Efficiency = (Q_test / Q_train) for each validated arm, averaged • Efficiency > 0.8 : Excellent - strategies generalize well (LOW overfit risk) • Efficiency 0.5-0.8 : Acceptable - moderate generalization (MODERATE risk) • Efficiency < 0.5 : Poor - strategies curve-fitted to history (HIGH risk) If efficiency is low, the system has learned noise. Training performance was good but testing (forward) performance is weak—classic overfitting. The dashboard displays real-time WFO efficiency, allowing users to gauge system robustness. Low efficiency should trigger parameter review or reduced position sizing. Why WFO Matters: Consider two scenarios: Scenario A - No WFO: • Arm 3 (RSI Reversion) shows Q-value of 0.15 on all historical data • System trades it aggressively • Reality: It only worked during one specific ranging period • Live trading: Fails because market has trended since backtest Scenario B - With WFO: • Arm 3 shows Q_train = 0.15 (good in training) • But Q_test = -0.05 (loses in testing) with 12 test trades • N_test ≥ 10 but Q_test < 0 → Arm BLOCKED • System refuses to trade it despite good backtest • Live trading: Protected from false strategy WFO ensures only strategies that work going forward get used, not just strategies that fit the past. Optimal Window Sizing: Training Window: • Too short (100-300): May learn recent noise, insufficient data • Too long (1000-2000): May include obsolete market regimes • Recommended: 4-6× testing window (default 500) Testing Window: • Too short (50-80): Insufficient validation, high variance • Too long (300-500): Delayed adaptation to regime changes • Recommended: 1/5 to 1/4 of training (default 100) Minimum Trades: • Too low (5-8): Statistical noise, lucky runs validate • Too high (30-50): Many arms never validate, system rarely trades • Recommended: 10-15 (default 10) ⚖️ WEIGHTED CONFLUENCE SYSTEM: MULTI-FACTOR SIGNAL QUALITY Not all signals are created equal. KMD implements a sophisticated 100-point quality scoring system that combines eight independent factors with different importance weights. The Scoring Framework: Each potential signal receives a quality score from 0-100 by accumulating points from aligned factors: CRITICAL FACTORS (20 points each): 1. Bandit Arm Alignment (20 points): • Full points if selected arm's signal matches trade direction • Zero points if arm disagrees • Weight: Highest - the bandit selected this arm for a reason 2. MMD Regime Quality (20 points): • Requires: MMD > dynamic threshold AND directional bias confirmed • Scaled by MMD percentile (how extreme vs history) • If MMD in top 10% of history: 100% of 20 points • If MMD at 50th percentile: 50% of 20 points • Weight: Highest - distribution shift is the core signal HIGH IMPACT FACTORS (15 points each): 3. Trend Alignment (15 points): • Full points if EMA(9) > EMA(21) > EMA(50) for longs (inverse for shorts) • Scaled by ADX strength: - ADX > 25: 100% (1.0× multiplier) - strong trend - ADX 20-25: 70% (0.7× multiplier) - moderate trend - ADX < 20: 40% (0.4× multiplier) - weak trend • Weight: High - trend is friend, alignment increases probability 4. Volume Confirmation (15 points): • Requires: OBV > EMA(OBV, 20) aligned with direction • Scaled by volume ratio: vol_current / vol_average - Volume 1.5×+ average: 100% of points (institutional participation) - Volume 1.0-1.5× average: 67% of points (above average) - Volume below average: 0 points (weak conviction) • Weight: High - volume validates price moves MODERATE FACTORS (10 points each): 5. Market Structure (10 points): • Full points (10) if bullish structure (higher highs, higher lows) for longs • Partial points (6) if near support level (within 1% of swing low) • Similar logic inverted for bearish trades • Weight: Moderate - structure context improves entries 6. RSI Positioning (10 points): • For long signals: - RSI < 50: 100% of points (1.0× multiplier) - room to run - RSI 50-60: 60% of points (0.6× multiplier) - neutral - RSI 60-70: 30% of points (0.3× multiplier) - elevated - RSI > 70: 0 points (0× multiplier) - overbought • Inverse for short signals • Weight: Moderate - momentum context, not primary signal BONUS FACTORS (10 points each): 7. Divergence (10 points): • Full 10 points if bullish divergence detected for long (or bearish for short) • Zero points otherwise • Weight: Bonus - leading indicator, adds confidence when present 8. Multi-Timeframe Confirmation (10 points): • Full 10 points if higher timeframe aligned (HTF EMA trending same direction, RSI supportive) • Zero points if MTF disabled or HTF opposes • Weight: Bonus - macro context filter, prevents counter-trend disasters Total Maximum: 110 points (20+20+15+15+10+10+10+10) Signal Quality Calculation: Quality Score = (Accumulated_Points / Maximum_Possible) × 100 Where Maximum_Possible = 110 points if all factors active, adjusts if MTF disabled. Example Calculation: Long signal candidate: • Bandit Arm: +20 (arm signals long) • MMD Quality: +16 (MMD high, 80th percentile) • Trend: +11 (EMAs aligned, ADX = 22 → 70% × 15) • Volume: +10 (OBV rising, vol 1.3× avg → 67% × 15 = 10) • Structure: +10 (higher lows forming) • RSI: +6 (RSI = 55 → 60% × 10) • Divergence: +0 (none present) • MTF: +10 (HTF bullish) Total: 83 / 110 × 100 = 75.5% quality score This is an excellent quality signal - well above threshold (default 60%). Quality Thresholds: • Score 80-100 : Exceptional setup - all factors aligned • Score 60-80 : High quality - most factors supportive (default minimum) • Score 40-60 : Moderate - mixed confluence, proceed with caution • Score 20-40 : Weak - minimal support, likely filtered out • Score 0-20 : Very weak - almost certainly blocked The minimum quality threshold (default 60) is the gatekeeper. Only signals scoring above this value can trigger trades. Dynamic Threshold Adjustment: The system optionally adjusts the threshold based on historical signal distribution: If Dynamic Threshold enabled: Recent_MMD_Mean = SMA(MMD, 50) Recent_MMD_StdDev = StdDev(MMD, 50) Dynamic_Threshold = max(Base_Threshold × 0.5, min(Base_Threshold × 2.0, MMD_Mean + MMD_StdDev × 0.5)) This auto-calibrates to market conditions: • Quiet markets (low MMD): Threshold loosens (0.5× base) • Active markets (high MMD): Threshold tightens (2× base) Signal Ranking Filter: When enabled, the system tracks the last 100 signal quality scores and only fires signals in the top percentile. If Ranking Percentile = 75%: • Collect last 100 signal scores in memory • Sort ascending • Threshold = Score at 75th percentile position • Only signals ≥ this threshold fire This ensures you're only taking the cream of the crop —top 25% of signals by quality, not every signal that technically qualifies. 🚦 SIGNAL GENERATION: TRANSITION LOGIC & COOLDOWNS The confluence system determines if a signal qualifies , but the signal generation logic controls when triangles appear on the chart. Core Qualification: For a LONG signal to qualify: 1. Bull quality score ≥ signal threshold (default 60) 2. Selected arm signals +1 (long) 3. Cooldown satisfied (bars since last signal ≥ cooldown period) 4. Drawdown protection OK (current drawdown < pause threshold) 5. MMD ≥ 80% of dynamic threshold (slight buffer below full threshold) For a SHORT signal to qualify: 1. Bear quality score ≥ signal threshold 2. Selected arm signals -1 (short) 3-5. Same as long But qualification alone doesn't trigger a chart signal. Three Signal Modes: 1. RESPONSIVE (Default - Recommended): Signals appear on: • Fresh qualification (wasn't qualified last bar, now is) • Direction reversal (was qualified short, now qualified long) • Quality improvement (already qualified, quality jumps 25%+ during EXTREME regime) This mode shows new opportunities and significant upgrades without cluttering the chart with repeat signals. 2. TRANSITION ONLY: Signals appear on: • Fresh qualification only • Direction reversal only This is the cleanest mode - signals only when first qualifying or when flipping direction. Misses re-entries if quality improves mid-regime. 3. CONTINUOUS: Signals appear on: • Every bar that qualifies Testing/debugging mode - shows all qualified bars. Very noisy but useful for understanding when system wants to trade. Cooldown System: Prevents signal clustering and overtrading by enforcing minimum bars between signals. Base Cooldown: User-defined (default 5 bars) Adaptive Cooldown (Optional): If enabled, cooldown scales with volatility: Effective_Cooldown = Base_Cooldown × volatility_multiplier Where: ATR_Pct = ATR(14) / Close × 100 Volatility_Multiplier = max(0.5, min(3.0, ATR_Pct / 2.0)) • Low volatility (ATR 1%): Multiplier ~0.5× → Cooldown = 2-3 bars (tight) • Medium volatility (ATR 2%): Multiplier 1.0× → Cooldown = 5 bars (normal) • High volatility (ATR 4%+): Multiplier 2.0-3.0× → Cooldown = 10-15 bars (wide) This prevents excessive trading during wild swings while allowing more signals during calm periods. Regime Filter: Three modes controlling which regimes allow trading: OFF: Trade in any regime (STABLE, TRENDING, SHIFTING, ELEVATED, EXTREME) SMART (Recommended): • Regime score = 1.0 for SHIFTING, ELEVATED (optimal) • Regime score = 0.8 for TRENDING (acceptable) • Regime score = 0.5 for EXTREME (too chaotic) • Regime score = 0.2 for STABLE (too quiet) Quality scores are multiplied by regime score. A 70% quality signal in STABLE regime becomes 70% × 0.2 = 14% → blocked. STRICT: • Regime score = 1.0 for SHIFTING, ELEVATED only • Regime score = 0.0 for all others → hard block Only trades during optimal distribution shift regimes. Drawdown Protection: If current equity drawdown exceeds pause threshold (default 8%), all signals are blocked until equity recovers. This circuit breaker prevents compounding losses during adverse conditions or broken market structure. 🎯 RISK MANAGEMENT: ATR-BASED STOPS & TARGETS Every signal generates volatility-normalized stop loss and target levels displayed as boxes on the chart. Stop Loss Calculation: Stop_Distance = ATR(14) × ATR_Multiplier (default 1.5) For LONG: Stop = Entry - Stop_Distance For SHORT: Stop = Entry + Stop_Distance The stop is placed 1.5 ATRs away from entry by default, adapting automatically to instrument volatility. Target Calculation: Target_Distance = Stop_Distance × Risk_Reward_Ratio (default 2.0) For LONG: Target = Entry + Target_Distance For SHORT: Target = Entry - Target_Distance Default 2:1 risk/reward means target is twice as far as stop. Example: • Price: $100 • ATR: $2 • ATR Multiplier: 1.5 • Risk/Reward: 2.0 LONG Signal: • Entry: $100 • Stop: $100 - ($2 × 1.5) = $97.00 (-$3 risk) • Target: $100 + ($3 × 2.0) = $106.00 (+$6 reward) • Risk/Reward: $3 risk for $6 reward = 1:2 ratio Target/Stop Box Lifecycle: Boxes persist for a lifetime (default 20 bars) OR until an opposite signal fires, whichever comes first. This provides visual reference for active trade levels without permanent chart clutter. When a new opposite-direction signal appears, all existing boxes from the previous direction are immediately deleted, ensuring only relevant levels remain visible. Adaptive Stop/Target Sizing: While not explicitly coded in the current version, the shadow portfolio tracking system calculates PnL based on these levels. Users can observe which ATR multipliers and risk/reward ratios produce optimal results for their instrument/timeframe via the dashboard performance metrics. 📊 COMPREHENSIVE VISUAL SYSTEM KMD provides rich visual feedback through four distinct layers: 1. PROBABILITY CLOUD (Adaptive Volatility Bands): Two sets of bands around price that expand/contract with MMD: Calculation: Std_Multiplier = 1 + MMD × 3 Upper_1σ = Close + ATR × Std_Multiplier × 0.5 Lower_1σ = Close - ATR × Std_Multiplier × 0.5 Upper_2σ = Close + ATR × Std_Multiplier Lower_2σ = Close - ATR × Std_Multiplier • Inner band (±0.5× adjusted ATR) : 68% probability zone (1 standard deviation equivalent) • Outer band (±1.0× adjusted ATR) : 95% probability zone (2 standard deviation equivalent) When MMD spikes, bands widen dramatically, showing increased uncertainty. When MMD calms, bands tighten, showing normal price action. 2. MOMENTUM FLOW VECTORS (Directional Arrows): Dynamic arrows that visualize momentum strength and direction: Arrow Properties: • Length: Proportional to momentum magnitude (2-10 bars forward) • Width: 1px (weak), 2px (medium), 3px (strong) • Transparency: 30-100 (more opaque = stronger momentum) • Direction: Up for bullish, down for bearish • Placement: Below bars (bulls) or above bars (bears) Trigger Logic: • Always appears every 5 bars (regular sampling) • Forced appearance if momentum strength > 50 OR regime shift OR MMD velocity > 10% Strong momentum (>75%) gets: • Secondary support arrow (70% length, lighter color) • Label showing "75%" strength Very strong momentum (>60%) gets: • Gradient flow lines (thick vertical lines showing momentum vector) This creates a dynamic "flow field" showing where market pressure is pushing price. 3. REGIME ZONES (Distribution Shift Highlighting): Boxes drawn around price action during periods when MMD > threshold: Zone Detection: • System enters "in_regime" mode when MMD crosses above threshold • Tracks highest high and lowest low during regime • Exits "in_regime" when MMD crosses back below threshold • Draws box from regime_start to current bar, spanning high to low Zone Colors: • EXTREME regime: Red with 90% transparency (dangerous) • SHIFTING regime: Amber with 92% transparency (active) • Other regimes: Teal with 95% transparency (normal) Emphasis Boxes: When regime_shift occurs (MMD crosses above threshold that bar), a special 4-bar wide emphasis box highlights the exact transition moment with thicker borders and lower transparency. This visual immediately shows "the market just changed" moments. 4. SIGNAL CONNECTION LINES: Lines connecting consecutive signals to show trade sequences: Line Types: • Solid line : Same direction signals (long → long, short → short) • Dotted line : Reversal signals (long → short or short → long) Visual Purpose: • Identify signal clusters (multiple entries same direction) • Spot reversal patterns (system changing bias) • See average bars between signals • Understand system behavior patterns Connections are limited to signals within 100 bars of each other to avoid across-chart lines. 📈 COMPREHENSIVE DASHBOARD: REAL-TIME SYSTEM STATE The dashboard provides complete transparency into system internals with three size modes: MINIMAL MODE: • Header (Regime + WFO phase) • Signal Status (LONG READY / SHORT READY / WAITING) • Core metrics only COMPACT MODE (Default): • Everything in Minimal • Kernel info • Active bandit arm + validation • WFO efficiency • Confluence scores (bull/bear) • MMD current value • Position status (if active) • Performance summary FULL MODE: • Everything in Compact • Signal Quality Diagnostics: - Bull quality score vs threshold with progress bar - Bear quality score vs threshold with progress bar - MMD threshold check (✓/✗) - MMD percentile (top X% of history) - Regime fit score (how well current regime suits trading) - WFO confidence level (validation strength) - Adaptive cooldown status (bars remaining vs required) • All Arms Signals: - Shows all 7 arm signals (▲/▼/○) - Q-value for each arm - Indicates selected arm with ◄ • Thompson Sampling Parameters (if TS mode): - Alpha/Beta values for selected arm - Probability estimate (α/(α+β)) • Extended Performance: - Expectancy per trade - Sharpe ratio with star rating - Individual arm performance (if enough data) Key Dashboard Sections: REGIME: Current market regime (STABLE/TRENDING/SHIFTING/ELEVATED/EXTREME) with color-coded background SIGNAL STATUS: • "▲ LONG READY" (cyan) - Long signal qualified • "▼ SHORT READY" (red) - Short signal qualified • "○ WAITING" (gray) - No qualified signals • Signal Mode displayed (Responsive/Transition/Continuous) KERNEL: • Active kernel type (RBF/Laplacian/Cauchy/Rational Quadratic) • Current bandwidth (effective after adaptation) • Adaptive vs Fixed indicator • RBF scale (if RBF) or RQ alpha (if RQ) BANDIT: • Selection algorithm (UCB1/UCB1-Tuned/Epsilon/Thompson) • Active arm name (MMD Shift, Trend, Breakout, etc.) • Validation status (✓ if validated, ? if unproven) • Pull count (n=XXX) - how many times selected • Q-Value (×10000 for readability) • UCB score (exploration + exploitation) • Train Q vs Test Q comparison • Test trade count WFO: • Current period number • Progress through period (XX%) • Efficiency percentage (color-coded: green >80%, yellow 50-80%, red <50%) • Overfit risk assessment (LOW/MODERATE/HIGH) • Validated arms count (X/7) CONFLUENCE: • Bull score (X/7) with progress bar (███ full, ██ medium, █ low, ○ none) • Bear score (X/7) with progress bar • Color-coded: Green/red if ≥ minimum, gray if below MMD: • Current value (3 decimals) • Threshold (2 decimals) • Ratio (MMD/Threshold × multiplier, e.g. "1.5x" = 50% above threshold) • Velocity (+/- percentage change) with up/down arrows POSITION: • Status: LONG/SHORT/FLAT • Active indicator (● if active, ○ if flat) • Bars since entry • Current P&L percentage (if active) • P&L direction (▲ profit / ▼ loss) • R-Multiple (how many Rs: PnL / initial_risk) PERFORMANCE: • Total Trades • Wins (green) / Losses (red) breakdown • Win Rate % with visual bar and color coding • Profit Factor (PF) with checkmark if >1.0 • Expectancy % (average profit per trade) • Sharpe Ratio with star rating (★★★ >2, ★★ >1, ★ >0, ○ negative) • Max DD % (maximum drawdown) with "Now: X%" showing current drawdown 🔧 KEY PARAMETERS EXPLAINED Kernel Configuration: • Kernel Function : RBF / Laplacian / Cauchy / Rational Quadratic - Start with Cauchy for stability, experiment with others • Bandwidth (σ) (0.5-10.0, default 2.0): Kernel sensitivity - Lower: More signals, more false positives (scalping: 0.8-1.5) - Medium: Balanced (swing: 1.5-3.0) - Higher: Fewer signals, stronger quality (position: 3.0-8.0) • Adaptive Bandwidth (default ON): Auto-adjust to volatility - Keep ON for most markets • RBF Scale (0.1-2.0, default 0.5): RBF-specific scaling - Only matters if RBF kernel selected - Lower = more sensitive (0.3 for scalping) - Higher = less sensitive (1.0+ for position) • RQ Alpha (0.5-5.0, default 2.0): Rational Quadratic tail behavior - Only matters if RQ kernel selected - Low (0.5-1.0): Heavy tails, robust to outliers (like Cauchy) - High (3.0-5.0): Light tails, sensitive (like RBF) Analysis Windows: • Reference Period (30-500, default 100): Historical baseline - Scalping: 50-80 - Intraday: 80-150 - Swing: 100-200 - Position: 200-500 • Test Period (5-100, default 20): Recent behavior window - Should be 15-25% of Reference Period - Scalping: 10-15 - Intraday: 15-25 - Swing: 20-40 - Position: 30-60 • Sample Size (10-40, default 20): Data points for MMD - Lower: Faster, less reliable (scalping: 12-15) - Medium: Balanced (standard: 18-25) - Higher: Slower, more reliable (position: 25-35) Walk-Forward Optimization: • Enable WFO (default ON): Master overfitting protection - Always ON for live trading • Training Window (100-2000, default 500): Learning data - Should be 4-6× Testing Window - 1m-5m: 300-500 - 15m-1h: 500-800 - 4h-1D: 500-1000 - 1D-1W: 800-2000 • Testing Window (50-500, default 100): Validation data - Should be 1/5 to 1/4 of Training - 1m-5m: 50-100 - 15m-1h: 80-150 - 4h-1D: 100-200 - 1D-1W: 150-500 • Min Trades for Validation (5-50, default 10): Statistical threshold - Active traders: 8-12 - Position traders: 15-30 • Performance Decay (0.8-0.99, default 0.95): Old data forgetting - Aggressive: 0.85-0.90 (volatile markets) - Moderate: 0.92-0.96 (most use cases) - Conservative: 0.97-0.99 (stable markets) Multi-Armed Bandit: • Learning Rate (α) (0.01-0.3, default 0.08): Adaptation speed - Low: 0.01-0.05 (position trading, stable) - Medium: 0.06-0.12 (day/swing trading) - High: 0.15-0.30 (scalping, fast adaptation) • Selection Strategy : UCB1 / UCB1-Tuned / Epsilon-Greedy / Thompson - UCB1 recommended for most (proven, reliable) - Thompson for advanced users (best empirical performance) • Exploration Constant (c) (0.5-3.0, default 1.5): Explore vs exploit - Low: 0.5-1.0 (conservative, proven strategies) - Medium: 1.2-1.8 (balanced) - High: 2.0-3.0 (experimental, volatile markets) • Epsilon (0.0-0.3, default 0.10): Random exploration (ε-greedy only) - Only applies if Epsilon-Greedy selected - Standard: 0.10 (10% random) Signal Configuration: • MMD Threshold (0.05-1.0, default 0.15): Distribution divergence trigger - Low: 0.08-0.12 (scalping, sensitive) - Medium: 0.12-0.20 (day/swing) - High: 0.25-0.50 (position, strong signals) - Stocks/indices: 0.12-0.18 - Forex: 0.15-0.25 - Crypto: 0.20-0.35 • Confluence Filter (default ON): Multi-factor requirement - Keep ON for quality signals • Minimum Confluence (1-7, default 2): Factors needed - Very low: 1 (high frequency) - Low: 2-3 (active trading) - Medium: 4-5 (swing) - High: 6-7 (rare perfect setups) • Cooldown (1-20, default 5): Bars between signals - Short: 1-3 (scalping, allows rapid re-entry) - Medium: 4-7 (day/swing) - Long: 8-20 (position, ensures development) • Signal Mode : Responsive / Transition Only / Continuous - Responsive: Recommended (new + upgrades) - Transition: Cleanest (first + reversals) - Continuous: Testing (every qualified bar) Advanced Signal Control: • Minimum Signal Strength (30-90, default 60): Quality floor - Lower: More signals (scalping: 40-50) - Medium: Balanced (standard: 55-65) - Higher: Fewer signals (position: 70-80) • Dynamic MMD Threshold (default ON): Auto-calibration - Keep ON for adaptive behavior • Signal Ranking Filter (default ON): Top percentile only - Keep ON to trade only best signals • Ranking Percentile (50-95, default 75): Selectivity - 75 = top 25% of signals - 85 = top 15% of signals - 90 = top 10% of signals • Adaptive Cooldown (default ON): Volatility-scaled spacing - Keep ON for intelligent spacing • Regime Filter : Off / Smart / Strict - Off: Any regime (maximize frequency) - Smart: Avoid extremes (recommended) - Strict: Only optimal regimes (maximum quality) Risk Parameters: • Risk:Reward Ratio (1.0-5.0, default 2.0): Target distance multiplier - Conservative: 1.0-1.5 (higher WR needed) - Balanced: 2.0-2.5 (standard professional) - Aggressive: 3.0-5.0 (lower WR acceptable) • Stop Loss (ATR mult) (0.5-4.0, default 1.5): Stop distance - Tight: 0.5-1.0 (scalping, low vol) - Medium: 1.2-2.0 (day/swing) - Wide: 2.5-4.0 (position, high vol) • Pause After Drawdown (2-20%, default 8%): Circuit breaker - Aggressive: 3-6% (small accounts) - Moderate: 6-10% (most traders) - Relaxed: 10-15% (large accounts) Multi-Timeframe: • MTF Confirmation (default OFF): Higher TF filter - Turn ON for swing/position trading - Keep OFF for scalping/day trading • Higher Timeframe (default "60"): HTF for trend check - Should be 3-5× chart timeframe - 1m chart → 5m or 15m - 5m chart → 15m or 60m - 15m chart → 60m or 240m - 1h chart → 240m or D Display: • Probability Cloud (default ON): Volatility bands • Momentum Flow Vectors (default ON): Directional arrows • Regime Zones (default ON): Distribution shift boxes • Signal Connections (default ON): Lines between signals • Dashboard (default ON): Stats table • Dashboard Position : Top Left / Top Right / Bottom Left / Bottom Right • Dashboard Size : Minimal / Compact / Full • Color Scheme : Default / Monochrome / Warm / Cool • Show MMD Debug Plot (default OFF): Overlay MMD value - Turn ON temporarily for threshold calibration 🎓 PROFESSIONAL USAGE PROTOCOL Phase 1: Parameter Calibration (Week 1) Goal: Find optimal kernel and bandwidth for your instrument/timeframe Setup: • Enable "Show MMD Debug Plot" • Start with Cauchy kernel, 2.0 bandwidth • Run on chart with 500+ bars of history Actions: • Watch yellow MMD line vs red threshold line • Count threshold crossings per 100 bars • Adjust bandwidth to achieve desired signal frequency: - Too many crossings (>20): Increase bandwidth (2.5-3.5) - Too few crossings (<5): Decrease bandwidth (1.2-1.8) • Try other kernels to see sensitivity differences • Note: RBF most sensitive, Cauchy most robust Target: 8-12 threshold crossings per 100 bars for day trading Phase 2: WFO Validation (Weeks 2-3) Goal: Verify strategies generalize out-of-sample Requirements: • Enable WFO with default settings (500/100) • Let system run through 2-3 complete WFO cycles • Accumulate 50+ total trades Actions: • Monitor WFO Efficiency in dashboard • Check which arms validate (green ✓) vs unproven (yellow ?) • Review Train Q vs Test Q for selected arm • If efficiency < 0.5: System overfitting, adjust parameters Red Flags: • Efficiency consistently <0.4: Serious overfitting • Zero arms validate after 2 cycles: Windows too short or thresholds too strict • Selected arm never validates: Investigate arm logic relevance Phase 3: Signal Quality Tuning (Week 4) Goal: Optimize confluence and quality thresholds Requirements: • Switch dashboard to FULL mode • Enable all diagnostic displays • Track signals for 100+ bars Actions: • Watch Bull/Bear quality scores in real-time • Note quality distribution of fired signals (are they all 60-70% or higher?) • If signal ranking on, check percentile cutoff appropriateness • Adjust "Minimum Signal Strength" to filter weak setups • Adjust "Minimum Confluence" if too many/few signals Optimization: • If win rate >60%: Lower thresholds (capture more opportunities) • If win rate <45%: Raise thresholds (improve quality) • If Profit Factor <1.2: Increase minimum quality by 5-10 points Phase 4: Regime Awareness (Week 5) Goal: Understand which regimes work best Setup: • Track performance by regime using notes/journal • Dashboard shows current regime constantly Actions: • Note signal quality and outcomes in each regime: - STABLE: Often weak signals, low confidence - TRENDING: Trend-following arms dominate - SHIFTING: Highest signal quality, core opportunity - ELEVATED: Good signals, moderate success - EXTREME: Mixed results, high variance • Adjust Regime Filter based on findings • If losing in EXTREME consistently: Use "Smart" or "Strict" filter Phase 5: Micro Live Testing (Weeks 6-8) Goal: Validate forward performance with minimal capital Requirements: • Paper trading shows: WR >45%, PF >1.2, Efficiency >0.6 • Understand why signals fire and why they're blocked • Comfortable with dashboard interpretation Setup: • 10-25% intended position size • Focus on ML-boosted signals (if any pattern emerges) • Keep detailed journal with screenshots Actions: • Execute every signal the system generates (within reason) • Compare your P&L to shadow portfolio metrics • Track divergence between your results and system expectations • Review weekly: What worked? What failed? Any execution issues? Red Flags: • Your WR >20% below paper: Execution problems (slippage, timing) • Your WR >20% above paper: Lucky streak or parameter mismatch • Dashboard metrics drift significantly: Market regime changed Phase 6: Full Scale Deployment (Month 3+) Goal: Progressively increase to full position sizing Requirements: • 30+ micro live trades completed • Live WR within 15% of paper WR • Profit Factor >1.0 live • Max DD <15% live • Confidence in parameter stability Progression: • Months 3-4: 25-50% intended size • Months 5-6: 50-75% intended size • Month 7+: 75-100% intended size Maintenance: • Weekly dashboard review for metric drift • Monthly WFO efficiency check (should stay >0.5) • Quarterly parameter re-optimization if market character shifts • Annual deep review of arm performance and kernel relevance Stop/Reduce Rules: • WR drops >20% from baseline: Reduce to 50%, investigate • Consecutive losses >12: Reduce to 25%, review parameters • Drawdown >20%: Stop trading, reassess system fit • WFO efficiency <0.3 for 2+ periods: System broken, retune completely 💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS The Kernel Discovery: Early versions used simple moving average crossovers and momentum indicators—they captured obvious moves but missed subtle regime changes. The breakthrough came from reading academic papers on two-sample testing and kernel methods. Applying Maximum Mean Discrepancy to financial returns revealed distribution shifts 10-20 bars before traditional indicators signaled. This edge—knowing the market had fundamentally changed before it was obvious—became the core of KMD. Testing showed Cauchy kernel outperformed others by 15% win rate in crypto specifically because its heavy tails ignored the massive outlier spikes (liquidation cascades, bot manipulation) that fooled RBF into false signals. The Seven Arms Revelation: Originally, the system had one strategy: "Trade when MMD crosses threshold." Performance was inconsistent—great in ranging markets, terrible in trends. The insight: different market structures require different strategies. Creating seven distinct arms based on different market theories (trend-following, mean-reversion, breakout, volume, momentum) and letting them compete solved the problem. The multi-armed bandit wasn't added as a gimmick—it was the solution to "which strategy should I use right now?" The system discovers the answer automatically through reinforcement learning. The Thompson Sampling Superiority: UCB1 worked fine, but Thompson Sampling empirically outperformed it by 8% over 1000+ trades in backtesting. The reason: Thompson's probabilistic selection naturally hedges uncertainty. When two arms have similar Q-values, UCB1 picks one deterministically (whichever has slightly higher exploration bonus). Thompson samples from both distributions, sometimes picking the "worse" one—and often discovering it's actually better in current conditions. Implementing true Beta distribution sampling (Box-Muller + Marsaglia-Tsang) instead of fake approximations was critical. Fake Thompson (using random with bias) underperformed UCB1. Real Thompson with proper Bayesian updating dominated. The Walk-Forward Necessity: Initial backtests showed 65% win rate across 5000 trades. Live trading: 38% win rate over first 100 trades. Crushing disappointment. The problem: overfitting. The training data included the test data (look-ahead bias). Implementing proper walk-forward optimization with out-of-sample validation dropped backtest win rate to 51%—but live performance matched at 49%. That's a system you can trust. WFO efficiency metric became the North Star. If efficiency >0.7, live results track paper. If efficiency <0.5, prepare for disappointment. The Confluence Complexity: First signals were simple: "MMD high + arm agrees." This generated 200+ signals on 1000 bars with 42% win rate—not tradeable. Adding confluence (must have trend + volume + structure + RSI) reduced signals to 40 with 58% win rate. The math clicked: fewer, better signals outperform many mediocre signals . The weighted system (20pt critical factors, 15pt high-impact, 10pt moderate/bonus) emerged from analyzing which factors best predicted wins. Bandit arm alignment and MMD quality were 2-3× more predictive than RSI or divergence, so they got 2× the weight. This isn't arbitrary—it's data-driven. The Dynamic Threshold Insight: Fixed MMD threshold failed across different market conditions. 0.15 worked perfectly on ES but fired constantly on Bitcoin. The adaptive threshold (scaling with recent MMD mean + stdev) auto-calibrated to instrument volatility. This single change made the system deployable across forex, crypto, stocks without manual tuning per instrument. The Signal Mode Evolution: Originally, every qualified bar showed a triangle. Charts became unusable—dozens of stacked triangles during trending regimes. "Transition Only" mode cleaned this up but missed re-entries when quality spiked mid-regime. "Responsive" mode emerged as the optimal balance: show fresh qualifications, reversals, AND significant quality improvements (25%+) during extreme regimes. This captures the signal intent ("something important just happened") without chart pollution. 🚨 LIMITATIONS & CRITICAL ASSUMPTIONS What This System IS NOT: • NOT Predictive : KMD doesn't forecast prices. It identifies when the current distribution differs from historical baseline, suggesting regime transition—but not direction or magnitude. • NOT Holy Grail : Typical performance is 48-56% win rate with 1.3-1.8 avg R-multiple. This is a probabilistic edge, not certainty. Expect losing streaks of 8-12 trades. • NOT Universal : Performs best on liquid, auction-driven markets (futures, major forex, large-cap stocks, BTC/ETH). Struggles with illiquid instruments, thin order books, heavily manipulated markets. • NOT Hands-Off : Requires monitoring for news events, earnings, central bank announcements. MMD cannot detect "Fed meeting in 2 hours" or "CEO stepping down"—it only sees statistical patterns. • NOT Immune to Regime Persistence : WFO helps but cannot predict black swans or fundamental market structure changes (pandemic, war, regulatory overhaul). During these events, all historical patterns may break. Core Assumptions: 1. Return Distributions Exhibit Clustering : Markets alternate between relatively stable distributional regimes. Violation: Permanent random walk, no regime structure. 2. Distribution Changes Precede Price Moves : Statistical divergence appears before obvious technical signals. Violation: Instantaneous regime flips (gaps, news), no statistical warning. 3. Volume Reflects Real Activity : Volume-based confluence assumes genuine participation. Violation: Wash trading, spoofing, exchange manipulation (common in crypto). 4. Past Arm Performance Predicts Future Arm Performance : The bandit learns from history. Violation: Fundamental strategy regime change (e.g., market transitions from mean-reverting to trending permanently). 5. ATR-Based Stops Are Rational : Volatility-normalized risk management avoids premature exits. Violation: Flash crashes, liquidity gaps, stop hunts precisely targeting ATR multiples. 6. Kernel Similarity Maps to Economic Similarity : Mathematical similarity (via kernel) correlates with economic similarity (regime). Violation: Distributions match by chance while fundamentals differ completely. Performs Best On: • ES, NQ, RTY (S&P 500, Nasdaq, Russell 2000 futures) • Major forex pairs: EUR/USD, GBP/USD, USD/JPY, AUD/USD • Liquid commodities: CL (crude oil), GC (gold), SI (silver) • Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M avg daily volume) • Major crypto on reputable exchanges: BTC, ETH (Coinbase, Kraken) Performs Poorly On: • Low-volume stocks (<$1M daily volume) • Exotic forex pairs with erratic spreads • Illiquid crypto altcoins (manipulation, unreliable volume) • Pre-market/after-hours (thin liquidity, gaps) • Instruments with frequent corporate actions (splits, dividends) • Markets with persistent one-sided intervention (central bank pegs) Known Weaknesses: • Lag During Instantaneous Shifts : MMD requires (test_window) bars to detect regime change. Fast-moving events (5-10 bar crashes) may bypass detection entirely. • False Positives in Choppy Consolidation : Low-volatility range-bound markets can trigger false MMD spikes from random noise crossing threshold. Regime filter helps but doesn't eliminate. • Parameter Sensitivity : Small bandwidth changes (2.0→2.5) can alter signal frequency by 30-50%. Requires careful calibration per instrument. • Bandit Convergence Time : MAB needs 50-100 trades per arm to reliably learn Q-values. Early trades (first 200 bars) are essentially random exploration. • WFO Warmup Drag : First WFO cycle has no validation data, so all arms start unvalidated. System may trade rarely or conservatively for first 500-600 bars until sufficient test data accumulates. • Visual Overload : With all display options enabled (cloud, vectors, zones, connections), chart can become cluttered. Disable selectively for cleaner view. ⚠️ RISK DISCLOSURE Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results. The Kernel Market Dynamics system, including its multi-armed bandit and walk-forward optimization components, is provided for educational purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument. The adaptive learning algorithms optimize based on historical data—there is no guarantee that learned strategies will remain profitable or that kernel-detected regime changes will lead to profitable trades. Market conditions change, correlations break, and distributional regimes shift in ways that historical data cannot predict. Black swan events occur. Walk-forward optimization reduces but does not eliminate overfitting risk. WFO efficiency metrics indicate likelihood of forward performance but cannot guarantee it. A system showing high efficiency on one dataset may show low efficiency on another timeframe or instrument. The dashboard shadow portfolio simulates trades under idealized conditions: instant fills, no slippage, no commissions, perfect execution. Real trading involves slippage (often 1-3 ticks per trade), commissions, latency, partial fills, rejected orders, requotes, and liquidity constraints that significantly reduce performance below simulated results. Maximum Mean Discrepancy is a statistical distance metric—high MMD indicates distribution divergence but does not indicate direction, magnitude, duration, or profitability of subsequent moves. MMD can spike during sideways chop, producing signals with no directional follow-through. Users must independently validate system performance on their specific instruments, timeframes, broker execution, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 trades) and start with micro position sizing (10-25% intended size) for at least 50 trades before scaling up. Never risk more capital than you can afford to lose completely. Use proper position sizing (1-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Algorithmic systems do not change this fundamental reality—they systematize decision-making but do not eliminate risk. The developer makes no warranties regarding profitability, suitability, accuracy, reliability, or fitness for any particular purpose. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes. By using this indicator, you acknowledge that you have read and understood these risk disclosures and accept full responsibility for all trading activity and potential losses. 📁 SUGGESTED TRADINGVIEW CATEGORIES PRIMARY CATEGORY: Statistics The Kernel Market Dynamics system is fundamentally a statistical learning framework . At its core lies Maximum Mean Discrepancy—an advanced two-sample statistical test from the academic machine learning literature. The indicator compares probability distributions using kernel methods (RBF, Laplacian, Cauchy, Rational Quadratic) that map data to high-dimensional feature spaces for nonlinear similarity measurement. The multi-armed bandit framework implements reinforcement learning via Q-learning with exponential moving average updates. Thompson Sampling uses true Bayesian inference with Beta posterior distributions. Walk-forward optimization performs rigorous out-of-sample statistical validation with train/test splits and efficiency metrics that detect overfitting. The confluence system aggregates multiple statistical indicators (RSI, ADX, OBV, Z-scores, EMAs) with weighted scoring that produces a 0-100 quality metric. Signal ranking uses percentile-based filtering on historical quality distributions. The dashboard displays comprehensive statistics: win rates, profit factors, Sharpe ratios, expectancy, drawdowns—all computed from trade return distributions. This is advanced statistical analysis applied to trading: distribution comparison, kernel methods, reinforcement learning, Bayesian inference, hypothesis testing, and performance analytics. The statistical sophistication distinguishes KMD from simple technical indicators. SECONDARY CATEGORY: Volume Volume analysis plays a crucial role in KMD's signal generation and validation. The confluence system includes volume confirmation as a high-impact factor (15 points): signals require above-average volume (>1.2× mean) for full points, with scaling based on volume ratio. The OBV (On-Balance Volume) trend indicator determines directional bias for Arm 6 (Volume Confirmation strategy). Volume ratio (current / 20-period average) directly affects confluence scores—higher volume strengthens signal quality. The momentum flow vectors scale width and opacity based on volume momentum relative to average. Energy particle visualization specifically marks volume burst events (>2× average volume) as potential market-moving catalysts. Several bandit arms explicitly incorporate volume: • Arm 2 (Breakout): Requires volume confirmation for Bollinger Band breaks • Arm 6 (Volume Confirmation): Primary logic based on OBV trend + volume spike The system recognizes volume as the "conviction" behind price moves—distribution changes matter more when accompanied by significant volume, indicating genuine participant behavior rather than noise. This volume-aware filtering improves signal reliability in liquid markets. TERTIARY CATEGORY: Volatility Volatility measurement and adaptation permeate the KMD system. ATR (Average True Range) forms the basis for all risk management: stops are placed at ATR × multiplier, targets are scaled accordingly. The adaptive bandwidth feature scales kernel bandwidth (0.5-2.0×) inversely with volatility—tightening during calm markets, widening during volatile periods. The probability cloud (primary visual element) directly visualizes volatility: bands expand/contract based on (1 + MMD × 3) multiplier applied to ATR. Higher MMD (distribution divergence) + higher ATR = dramatically wider uncertainty bands. Adaptive cooldown scales minimum bars between signals based on ATR percentage: higher volatility = longer cooldown (up to 3× base), preventing overtrading during whipsaw conditions. The gamma parameter in the tensor calculation (from related indicators) and volatility ratio measurements influence MMD sensitivity. Regime classification incorporates volatility metrics: high volatility with ranging price action produces "RANGE⚡" regime, while volatility expansion with directional movement produces trending regimes. The system adapts its behavior to volatility regimes—tighter requirements during extreme volatility, looser requirements during stable periods. ATR-based risk management ensures position sizing and exit levels automatically adapt to instrument volatility, making the system deployable across instruments with different average volatilities (stocks vs crypto) without manual recalibration. ══════════════════════════════════════════ CLOSING STATEMENT ══════════════════════════════════════════ Kernel Market Dynamics doesn't just measure price—it measures the probability structure underlying price. It doesn't just pick one strategy—it learns which strategies work in which conditions. It doesn't just optimize on history—it validates on the future. This is machine learning applied correctly to trading: not curve-fitting oscillators to maximize backtest profit, but implementing genuine statistical learning algorithms (kernel methods, multi-armed bandits, Bayesian inference) that adapt to market evolution while protecting against overfitting through rigorous walk-forward testing. The seven arms compete. The Thompson sampler selects. The kernel measures. The confluence scores. The walk-forward validates. The signals fire. Most indicators tell you what happened. KMD tells you when the game changed. "In the space between distributions, where the kernel measures divergence and the bandit learns from consequence—there, edge exists." — KMD-WFO-MAB v2 Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Pine Script® indicator
by DskyzInvestments
2
[Excalibur] Ehlers AutoCorrelation Periodogram ModifiedKeep your coins folks, I don't need them, don't want them. If you wish be generous, I do hope that charitable peoples worldwide with surplus food stocks may consider stocking local food banks before stuffing monetary bank vaults, for the crusade of remedying the needs of less than fortunate children, parents, elderly, homeless veterans, and everyone else who deserves nutritional sustenance for the soul. DEDICATION: This script is dedicated to the memory of Nikolai Dmitriyevich Kondratiev (Никола́й Дми́триевич Кондра́тьев) as tribute for being a pioneering economist and statistician, paving the way for modern econometrics by advocation of rigorous and empirical methodologies. One of his most substantial contributions to the study of business cycle theory include a revolutionary hypothesis recognizing the existence of dynamic cycle-like phenomenon inherent to economies that are characterized by distinct phases of expansion, stagnation, recession and recovery, what we now know as "Kondratiev Waves" (K-waves). Kondratiev was one of the first economists to recognize the vital significance of applying quantitative analysis on empirical data to evaluate economic dynamics by means of statistical methods. His understanding was that conceptual models alone were insufficient to adequately interpret real-world economic conditions, and that sophisticated analysis was necessary to better comprehend the nature of trending/cycling economic behaviors. Additionally, he recognized prosperous economic cycles were predominantly driven by a combination of technological innovations and infrastructure investments that resulted in profound implications for economic growth and development. I will mention this... nation's economies MUST be supported and defended to continuously evolve incrementally in order to flourish in perpetuity OR suffer through eras with lasting ramifications of societal stagnation and implosion. Analogous to the realm of economics, aperiodic cycles/frequencies, both enduring and ephemeral, do exist in all facets of life, every second of every day. To name a few that any blind man can naturally see are: heartbeat (cardiac cycles), respiration rates, circadian rhythms of sleep, powerful magnetic solar cycles, seasonal cycles, lunar cycles, weather patterns, vegetative growth cycles, and ocean waves. Do not pretend for one second that these basic aforementioned examples do not affect business cycle fluctuations in minuscule and monumental ways hour to hour, day to day, season to season, year to year, and decade to decade in every nation on the planet. Kondratiev's original seminal theories in macroeconomics from nearly a century ago have proven remarkably prescient with many of his antiquated elementary observations/notions/hypotheses in macroeconomics being scholastically studied and topically researched further. Therefore, I am compelled to honor and recognize his statistical insight and foresight. If only.. Kondratiev could hold a pocket sized computer in the cup of both hands bearing the TradingView logo and platform services, I truly believe he would be amazed in marvelous delight with a GARGANTUAN smile on his face. INTRODUCTION: Firstly, this is NOT technically speaking an indicator like most others. I would describe it as an advanced cycle period detector to obtain market data spectral estimates with low latency and moderate frequency resolution. Developers can take advantage of this detector by creating scripts that utilize a "Dominant Cycle Source" input to adaptively govern algorithms. Be forewarned, I would only recommend this for advanced developers, not novice code dabbling. Although, there is some Pine wizardry introduced here for novice Pine enthusiasts to witness and learn from. AI did describe the code into one super-crunched sentence as, "a rare feat of exceptionally formatted code masterfully balancing visual clarity, precision, and complexity to provide immense educational value for both programming newcomers and expert Pine coders alike." Understand all of the above aforementioned? Buckle up and proceed for a lengthy read of verbose complexity... This is my enhanced and heavily modified version of autocorrelation periodogram (ACP) for Pine Script v5.0. It was originally devised by the mathemagician John Ehlers for detecting dominant cycles (frequencies) in an asset's price action. I have been sitting on code similar to this for a long time, but I decided to unleash the advanced code with my fashion. Originally Ehlers released this with multiple versions, one in a 2016 TASC article and the other in his last published 2013 book "Cycle Analytics for Traders", chapter 8. He wasn't joking about "concepts of advanced technical trading" and ACP is nowhere near to his most intimidating and ingenious calculations in code. I will say the book goes into many finer details about the original periodogram, so if you wish to delve into even more elaborate info regarding Ehlers' original ACP form AND how you may adapt algorithms, you'll have to obtain one. Note to reader, comparing Ehlers' original code to my chimeric code embracing the "Power of Pine", you will notice they have little resemblance. What you see is a new species of autocorrelation periodogram combining Ehlers' innovation with my fascinations of what ACP could be in a Pine package. One other intention of this script's code is to pay homage to Ehlers' lifelong works. Like Kondratiev, Ehlers is also a hardcore cycle enthusiast. I intend to carry on the fire Ehlers envisioned and I believe that is literally displayed here as a pleasant "fiery" example endowed with Pine. With that said, I tried to make the code as computationally efficient as possible, without going into dozens of more crazy lines of code to speed things up even more. There's also a few creative modifications I made by making alterations to the originating formulas that I felt were improvements, one of them being lag reduction. By recently questioning every single thing I thought I knew about ACP, combined with the accumulation of my current knowledge base, this is the innovative revision I came up with. I could have improved it more but decided not to mind thrash too many TV members, maybe later... I am now confident Pine should have adequate overhead left over to attach various indicators to the dominant cycle via input.source(). TV, I apologize in advance if in the future a server cluster combusts into a raging inferno... Coders, be fully prepared to build entire algorithms from pure raw code, because not all of the built-in Pine functions fully support dynamic periods (e.g. length=ANYTHING). Many of them do, as this was requested and granted a while ago, but some functions are just inherently finicky due to implementation combinations and MUST be emulated via raw code. I would imagine some comprehensive library or numerous authored scripts have portions of raw code for Pine built-ins some where on TV if you look diligently enough. Notice: Unfortunately, I will not provide any integration support into member's projects at all. I have my own projects that require way too much of my day already. While I was refactoring my life (forgoing many other "important" endeavors) in the early half of 2023, I primarily focused on this code over and over in my surplus time. During that same time I was working on other innovations that are far above and beyond what this code is. I hope you understand. The best way programmatically may be to incorporate this code into your private Pine project directly, after brutal testing of course, but that may be too challenging for many in early development. Being able to see the periodogram is also beneficial, so input sourcing may be the "better" avenue to tether portions of the dominant cycle to algorithms. Unique indication being able to utilize the dominantCycle may be advantageous when tethering this script to those algorithms. The easiest way is to manually set your indicators to what ACP recognizes as the dominant cycle, but that's actually not considered dynamic real time adaption of an indicator. Different indicators may need a proportion of the dominantCycle, say half it's value, while others may need the full value of it. That's up to you to figure that out in practice. Sourcing one or more custom indicators dynamically to one detector's dominantCycle may require code like this: `int sourceDC = int(math.max(6, math.min(49, input.source(close, "Dominant Cycle Source"))))`. Keep in mind, some algos can use a float, while algos with a for loop require an integer. I have witnessed a few attempts by talented TV members for a Pine based autocorrelation periodogram, but not in this caliber. Trust me, coding ACP is no ordinary task to accomplish in Pine and modifying it blessed with applicable improvements is even more challenging. For over 4 years, I have been slowly improving this code here and there randomly. It is beautiful just like a real flame, but... this one can still burn you! My mind was fried to charcoal black a few times wrestling with it in the distant past. My very first attempt at translating ACP was a month long endeavor because PSv3 simply didn't have arrays back then. Anyways, this is ACP with a newer engine, I hope you enjoy it. Any TV subscriber can utilize this code as they please. If you are capable of sufficiently using it properly, please use it wisely with intended good will. That is all I beg of you. Lastly, you now see how I have rasterized my Pine with Ehlers' swami-like tech. Yep, this whole time I have been using hline() since PSv3, not plot(). Evidently, plot() still has a deficiency limited to only 32 plots when it comes to creating intense eye candy indicators, the last I checked. The use of hline() is the optimal choice for rasterizing Ehlers styled heatmaps. This does only contain two color schemes of the many I have formerly created, but that's all that is essentially needed for this gizmo. Anything else is generally for a spectacle or seeing how brutal Pine can be color treated. The real hurdle is being able to manipulate colors dynamically with Merlin like capabilities from multiple algo results. That's the true challenging part of these heatmap contraptions to obtain multi-colored "predator vision" level indication. You now have basic hline() food for thought empowerment to wield as you can imaginatively dream in Pine projects. PERIODOGRAM UTILITY IN REAL WORLD SCENARIOS: This code is a testament to the abilities that have yet to be fully realized with indication advancements. Periodograms, spectrograms, and heatmaps are a powerful tool with real-world applications in various fields such as financial markets, electrical engineering, astronomy, seismology, and neuro/medical applications. For instance, among these diverse fields, it may help traders and investors identify market cycles/periodicities in financial markets, support engineers in optimizing electrical or acoustic systems, aid astronomers in understanding celestial object attributes, assist seismologists with predicting earthquake risks, help medical researchers with neurological disorder identification, and detection of asymptomatic cardiovascular clotting in the vaxxed via full body thermography. In either field of study, technologies in likeness to periodograms may very well provide us with a better sliver of analysis beyond what was ever formerly invented. Periodograms can identify dominant cycles and frequency components in data, which may provide valuable insights and possibly provide better-informed decisions. By utilizing periodograms within aspects of market analytics, individuals and organizations can potentially refrain from making blinded decisions and leverage data-driven insights instead. PERIODOGRAM INTERPRETATION: The periodogram renders the power spectrum of a signal, with the y-axis representing the periodicity (frequencies/wavelengths) and the x-axis representing time. The y-axis is divided into periods, with each elevation representing a period. In this periodogram, the y-axis ranges from 6 at the very bottom to 49 at the top, with intermediate values in between, all indicating the power of the corresponding frequency component by color. The higher the position occurs on the y-axis, the longer the period or lower the frequency. The x-axis of the periodogram represents time and is divided into equal intervals, with each vertical column on the axis corresponding to the time interval when the signal was measured. The most recent values/colors are on the right side. The intensity of the colors on the periodogram indicate the power level of the corresponding frequency or period. The fire color scheme is distinctly like the heat intensity from any casual flame witnessed in a small fire from a lighter, match, or camp fire. The most intense power would be indicated by the brightest of yellow, while the lowest power would be indicated by the darkest shade of red or just black. By analyzing the pattern of colors across different periods, one may gain insights into the dominant frequency components of the signal and visually identify recurring cycles/patterns of periodicity. SETTINGS CONFIGURATIONS BRIEFLY EXPLAINED: Source Options: These settings allow you to choose the data source for the analysis. Using the `Source` selection, you may tether to additional data streams (e.g. close, hlcc4, hl2), which also may include samples from any other indicator. For example, this could be my "Chirped Sine Wave Generator" script found in my member profile. By using the `SineWave` selection, you may analyze a theoretical sinusoidal wave with a user-defined period, something already incorporated into the code. The `SineWave` will be displayed over top of the periodogram. Roofing Filter Options: These inputs control the range of the passband for ACP to analyze. Ehlers had two versions of his highpass filters for his releases, so I included an option for you to see the obvious difference when performing a comparison of both. You may choose between 1st and 2nd order high-pass filters. Spectral Controls: These settings control the core functionality of the spectral analysis results. You can adjust the autocorrelation lag, adjust the level of smoothing for Fourier coefficients, and control the contrast/behavior of the heatmap displaying the power spectra. I provided two color schemes by checking or unchecking a checkbox. Dominant Cycle Options: These settings allow you to customize the various types of dominant cycle values. You can choose between floating-point and integer values, and select the rounding method used to derive the final dominantCycle values. Also, you may control the level of smoothing applied to the dominant cycle values. DOMINANT CYCLE VALUE SELECTIONS: External to the acs() function, the code takes a dominant cycle value returned from acs() and changes its numeric form based on a specified type and form chosen within the indicator settings. The dominant cycle value can be represented as an integer or a decimal number, depending on the attached algorithm's requirements. For example, FIR filters will require an integer while many IIR filters can use a float. The float forms can be either rounded, smoothed, or floored. If the resulting value is desired to be an integer, it can be rounded up/down or just be in an integer form, depending on how your algorithm may utilize it. AUTOCORRELATION SPECTRUM FUNCTION BASICALLY EXPLAINED: In the beginning of the acs() code, the population of caches for precalculated angular frequency factors and smoothing coefficients occur. By precalculating these factors/coefs only once and then storing them in an array, the indicator can save time and computational resources when performing subsequent calculations that require them later. In the following code block, the "Calculate AutoCorrelations" is calculated for each period within the passband width. The calculation involves numerous summations of values extracted from the roofing filter. Finally, a correlation values array is populated with the resulting values, which are normalized correlation coefficients. Moving on to the next block of code, labeled "Decompose Fourier Components", Fourier decomposition is performed on the autocorrelation coefficients. It iterates this time through the applicable period range of 6 to 49, calculating the real and imaginary parts of the Fourier components. Frequencies 6 to 49 are the primary focus of interest for this periodogram. Using the precalculated angular frequency factors, the resulting real and imaginary parts are then utilized to calculate the spectral Fourier components, which are stored in an array for later use. The next section of code smooths the noise ridden Fourier components between the periods of 6 and 49 with a selected filter. This species also employs numerous SuperSmoothers to condition noisy Fourier components. One of the big differences is Ehlers' versions used basic EMAs in this section of code. I decided to add SuperSmoothers. The final sections of the acs() code determines the peak power component for normalization and then computes the dominant cycle period from the smoothed Fourier components. It first identifies a single spectral component with the highest power value and then assigns it as the peak power. Next, it normalizes the spectral components using the peak power value as a denominator. It then calculates the average dominant cycle period from the normalized spectral components using Ehlers' "Center of Gravity" calculation. Finally, the function returns the dominant cycle period along with the normalized spectral components for later external use to plot the periodogram. POST SCRIPT: Concluding, I have to acknowledge a newly found analyst for assistance that I couldn't receive from anywhere else. For one, Claude doesn't know much about Pine, is unfortunately color blind, and can't even see the Pine reference, but it was able to intuitively shred my code with laser precise realizations. Not only that, formulating and reformulating my description needed crucial finesse applied to it, and I couldn't have provided what you have read here without that artificial insight. Finding the right order of words to convey the complexity of ACP and the elaborate accompanying content was a daunting task. No code in my life has ever absorbed so much time and hard fricking work, than what you witness here, an ACP gem cut pristinely. I'm unveiling my version of ACP for an empowering cause, in the hopes a future global army of code wielders will tether it to highly functional computational contraptions they might possess. Here is ACP fully blessed poetically with the "Power of Pine" in sublime code. ENJOY!
Pine Script® indicator
by ImmortalFreedom
36
GKD-B Multi-Ticker Baseline [Loxx]Giga Kaleidoscope GKD-B Multi-Ticker Baseline is a Baseline module included in Loxx's "Giga Kaleidoscope Modularized Trading System". This is a special implementation of GKD-B Baseline that allows the trader to input multiple tickers to be passed onto a GKD-BT Multi-Ticker Backtest. This baseline can only be used with the GKD-BT Multi-Ticker Backtests. GKD-B Multi-Ticker Baseline includes 64 different moving averages: Adaptive Moving Average - AMA ADXvma - Average Directional Volatility Moving Average Ahrens Moving Average Alexander Moving Average - ALXMA Deviation Scaled Moving Average - DSMA Donchian Double Exponential Moving Average - DEMA Double Smoothed Exponential Moving Average - DSEMA Double Smoothed FEMA - DSFEMA Double Smoothed Range Weighted EMA - DSRWEMA Double Smoothed Wilders EMA - DSWEMA Double Weighted Moving Average - DWMA Ehlers Optimal Tracking Filter - EOTF Exponential Moving Average - EMA Fast Exponential Moving Average - FEMA Fractal Adaptive Moving Average - FRAMA Generalized DEMA - GDEMA Generalized Double DEMA - GDDEMA Hull Moving Average (Type 1) - HMA1 Hull Moving Average (Type 2) - HMA2 Hull Moving Average (Type 3) - HMA3 Hull Moving Average (Type 4) - HMA4 IE /2 - Early T3 by Tim Tilson Integral of Linear Regression Slope - ILRS Instantaneous Trendline Kalman Filter Kaufman Adaptive Moving Average - KAMA Laguerre Filter Leader Exponential Moving Average Linear Regression Value - LSMA ( Least Squares Moving Average ) Linear Weighted Moving Average - LWMA McGinley Dynamic McNicholl EMA Non-Lag Moving Average Ocean NMA Moving Average - ONMAMA One More Moving Average - OMA Parabolic Weighted Moving Average Probability Density Function Moving Average - PDFMA Quadratic Regression Moving Average - QRMA Regularized EMA - REMA Range Weighted EMA - RWEMA Recursive Moving Trendline Simple Decycler - SDEC Simple Jurik Moving Average - SJMA Simple Moving Average - SMA Sine Weighted Moving Average Smoothed LWMA - SLWMA Smoothed Moving Average - SMMA Smoother Super Smoother T3 Three-pole Ehlers Butterworth Three-pole Ehlers Smoother Triangular Moving Average - TMA Triple Exponential Moving Average - TEMA Two-pole Ehlers Butterworth Two-pole Ehlers smoother Variable Index Dynamic Average - VIDYA Variable Moving Average - VMA Volume Weighted EMA - VEMA Volume Weighted Moving Average - VWMA Zero-Lag DEMA - Zero Lag Exponential Moving Average Zero-Lag Moving Average Zero Lag TEMA - Zero Lag Triple Exponential Moving Average Adaptive Moving Average - AMA The Adaptive Moving Average (AMA) is a moving average that changes its sensitivity to price moves depending on the calculated volatility. It becomes more sensitive during periods when the price is moving smoothly in a certain direction and becomes less sensitive when the price is volatile. ADXvma - Average Directional Volatility Moving Average Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator. The ADXvma has the SMA in Chande's CMO replaced with an EMA , it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated. A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA . The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move. Ahrens Moving Average Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows. Alexander Moving Average - ALXMA This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile. Deviation Scaled Moving Average - DSMA The Deviation-Scaled Moving Average is a data smoothing technique that acts like an exponential moving average with a dynamic smoothing coefficient. The smoothing coefficient is automatically updated based on the magnitude of price changes. In the Deviation-Scaled Moving Average, the standard deviation from the mean is chosen to be the measure of this magnitude. The resulting indicator provides substantial smoothing of the data even when price changes are small while quickly adapting to these changes. Donchian Donchian Channels are three lines generated by moving average calculations that comprise an indicator formed by upper and lower bands around a midrange or median band. The upper band marks the highest price of a security over N periods while the lower band marks the lowest price of a security over N periods. Double Exponential Moving Average - DEMA The Double Exponential Moving Average ( DEMA ) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag. Double Smoothed Exponential Moving Average - DSEMA The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA . It's also considered a leading indicator compared to the EMA , and is best utilized whenever smoothness and speed of reaction to market changes are required. Double Smoothed FEMA - DSFEMA Same as the Double Exponential Moving Average (DEMA), but uses a faster version of EMA for its calculation. Double Smoothed Range Weighted EMA - DSRWEMA Range weighted exponential moving average (EMA) is, unlike the "regular" range weighted average calculated in a different way. Even though the basis - the range weighting - is the same, the way how it is calculated is completely different. By definition this type of EMA is calculated as a ratio of EMA of price*weight / EMA of weight. And the results are very different and the two should be considered as completely different types of averages. The higher than EMA to price changes responsiveness when the ranges increase remains in this EMA too and in those cases this EMA is clearly leading the "regular" EMA. This version includes double smoothing. Double Smoothed Wilders EMA - DSWEMA Welles Wilder was frequently using one "special" case of EMA (Exponential Moving Average) that is due to that fact (that he used it) sometimes called Wilder's EMA. This version is adding double smoothing to Wilder's EMA in order to make it "faster" (it is more responsive to market prices than the original) and is still keeping very smooth values. Double Weighted Moving Average - DWMA Double weighted moving average is an LWMA (Linear Weighted Moving Average). Instead of doing one cycle for calculating the LWMA, the indicator is made to cycle the loop 2 times. That produces a smoother values than the original LWMA Ehlers Optimal Tracking Filter - EOTF The Elher's Optimum Tracking Filter quickly adjusts rapid shifts in the price and yet is relatively smooth when the price has a sideways action. The operation of this filter is similar to Kaufman’s Adaptive Moving Average Exponential Moving Average - EMA The EMA places more significance on recent data points and moves closer to price than the SMA ( Simple Moving Average ). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA . Fast Exponential Moving Average - FEMA An Exponential Moving Average with a short look-back period. Fractal Adaptive Moving Average - FRAMA The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry. Generalized DEMA - GDEMA The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages.". Instead of using fixed multiplication factor in the final DEMA formula, the generalized version allows you to change it. By varying the "volume factor" form 0 to 1 you apply different multiplications and thus producing DEMA with different "speed" - the higher the volume factor is the "faster" the DEMA will be (but also the slope of it will be less smooth). The volume factor is limited in the calculation to 1 since any volume factor that is larger than 1 is increasing the overshooting to the extent that some volume factors usage makes the indicator unusable. Generalized Double DEMA - GDDEMA The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages''. This is an extension of the Generalized DEMA using Tim Tillsons (the inventor of T3) idea, and is using GDEMA of GDEMA for calculation (which is the "middle step" of T3 calculation). Since there are no versions showing that middle step, this version covers that too. The result is smoother than Generalized DEMA, but is less smooth than T3 - one has to do some experimenting in order to find the optimal way to use it, but in any case, since it is "faster" than the T3 (Tim Tillson T3) and still smooth, it looks like a good compromise between speed and smoothness. Hull Moving Average (Type 1) - HMA1 Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMA for smoothing. Hull Moving Average (Type 2) - HMA2 Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses EMA for smoothing. Hull Moving Average (Type 3) - HMA3 Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses LWMA for smoothing. Hull Moving Average (Type 4) - HMA4 Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMMA for smoothing. IE /2 - Early T3 by Tim Tilson and T3 new The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm. The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction. The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy. Integral of Linear Regression Slope - ILRS A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA ( Simple Moving Average ) of length N, which is actually the midpoint of the linear regression line as it moves across the data. Instantaneous Trendline The Instantaneous Trendline is created by removing the dominant cycle component from the price information which makes this Moving Average suitable for medium to long-term trading. Kalman Filter Kalman filter is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies. This means that the filter was originally designed to work with noisy data. Also, it is able to work with incomplete data. Another advantage is that it is designed for and applied in dynamic systems; our price chart belongs to such systems. This version is true to the original design of the trade-ready Kalman Filter where velocity is the triggering mechanism. Kalman Filter is a more accurate smoothing/prediction algorithm than the moving average because it is adaptive: it accounts for estimation errors and tries to adjust its predictions from the information it learned in the previous stage. Theoretically, Kalman Filter consists of measurement and transition components. Kaufman Adaptive Moving Average - KAMA Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. Laguerre Filter The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation. Adjusting the Alpha coefficient is used to increase or decrease its lag and its smoothness. Leader Exponential Moving Average The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter. Linear Regression Value - LSMA ( Least Squares Moving Average ) LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down. Linear Weighted Moving Average - LWMA LWMA reacts to price quicker than the SMA and EMA . Although it's similar to the Simple Moving Average , the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion. McGinley Dynamic John McGinley created this Moving Average to track prices better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets. McNicholl EMA Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws. Non-lag moving average The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals. Ocean NMA Moving Average - ONMAMA Created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility without being programmed to do so. For more info, read his guide "Ocean Theory, an Introduction" One More Moving Average (OMA) The One More Moving Average (OMA) is a technical indicator that calculates a series of Jurik-style moving averages in order to reduce noise and provide smoother price data. It uses six exponential moving averages to generate the final value, with the length of the moving averages determined by an adaptive algorithm that adjusts to the current market conditions. The algorithm calculates the average period by comparing the signal to noise ratio and using this value to determine the length of the moving averages. The resulting values are used to generate the final value of the OMA, which can be used to identify trends and potential changes in trend direction. Parabolic Weighted Moving Average The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average . The Linear Weighted Moving Average calculates the average by assigning different weights to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator. Probability Density Function Moving Average - PDFMA Probability density function based MA is a sort of weighted moving average that uses probability density function to calculate the weights. By its nature it is similar to a lot of digital filters. Quadratic Regression Moving Average - QRMA A quadratic regression is the process of finding the equation of the parabola that best fits a set of data. This moving average is an obscure concept that was posted to Forex forums in around 2008. Regularized EMA - REMA The regularized exponential moving average (REMA) by Chris Satchwell is a variation on the EMA (see Exponential Moving Average) designed to be smoother but not introduce too much extra lag. Range Weighted EMA - RWEMA This indicator is a variation of the range weighted EMA. The variation comes from a possible need to make that indicator a bit less "noisy" when it comes to slope changes. The method used for calculating this variation is the method described by Lee Leibfarth in his article "Trading With An Adaptive Price Zone". Recursive Moving Trendline Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrow's price. Simple Decycler - SDEC The Ehlers Simple Decycler study is a virtually zero-lag technical indicator proposed by John F. Ehlers. The original idea behind this study (and several others created by John F. Ehlers) is that market data can be considered a continuum of cycle periods with different cycle amplitudes. Thus, trending periods can be considered segments of longer cycles, or, in other words, low-frequency segments. Applying the right filter might help identify these segments. Simple Loxx Moving Average - SLMA A three stage moving average combining an adaptive EMA, a Kalman Filter, and a Kauffman adaptive filter. Simple Moving Average - SMA The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA . Sine Weighted Moving Average The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average). Smoothed LWMA - SLWMA A smoothed version of the LWMA Smoothed Moving Average - SMMA The Smoothed Moving Average is similar to the Simple Moving Average ( SMA ), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen as an accurate yet laggy Moving Average. Smoother The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA ( Smoothed Moving Average ). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm. Super Smoother The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a Two pole Butterworth filter combined with a 2-bar SMA ( Simple Moving Average ) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence. Three-pole Ehlers Butterworth The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA . They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing. Three-pole Ehlers smoother The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth. Triangular Moving Average - TMA The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data. Triple Exponential Moving Average - TEMA The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, its signals are considered very noisy and should only be used in extremely fast-paced trading conditions. Two-pole Ehlers Butterworth The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded. Two-pole Ehlers smoother A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers . Variable Index Dynamic Average - VIDYA Variable Index Dynamic Average Technical Indicator ( VIDYA ) was developed by Tushar Chande. It is an original method of calculating the Exponential Moving Average ( EMA ) with the dynamically changing period of averaging. Variable Moving Average - VMA The Variable Moving Average (VMA) is a study that uses an Exponential Moving Average being able to automatically adjust its smoothing factor according to the market volatility. Volume Weighted EMA - VEMA An EMA that uses a volume and price weighted calculation instead of the standard price input. Volume Weighted Moving Average - VWMA A Volume Weighted Moving Average is a moving average where more weight is given to bars with heavy volume than with light volume. Thus the value of the moving average will be closer to where most trading actually happened than it otherwise would be without being volume weighted. Zero-Lag DEMA - Zero Lag Double Exponential Moving Average John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence. Zero-Lag Moving Average The Zero Lag Moving Average is described by its creator, John Ehlers , as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average. Zero-Lag TEMA - Zero Lag Triple Exponential Moving Average Just like the Zero Lag DEMA , this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators. █ Volatility Goldie Locks Zone This volatility filter is the standard first pass filter that is used for all NNFX systems despite the additional volatility/volume filter used in step 5. For this filter, price must fall into a range of maximum and minimum values calculated using multiples of volatility. Unlike the standard NNFX systems, this version of volatility filtering is separated from the core Baseline and uses it's own moving average with Loxx's Exotic Source Types. █ Volatility Types included The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well. This module includes 17 types of volatility: Close-to-Close Parkinson Garman-Klass Rogers-Satchell Yang-Zhang Garman-Klass-Yang-Zhang Exponential Weighted Moving Average Standard Deviation of Log Returns Pseudo GARCH(2,2) Average True Range True Range Double Standard Deviation Adaptive Deviation Median Absolute Deviation Efficiency-Ratio Adaptive ATR Mean Absolute Deviation Static Percent Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user. Close-to-Close Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility. Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is. Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility. Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators. Parkinson Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day. The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility. One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator. Garman-Klass Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security. Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate. Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements. Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes. Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail. Rogers-Satchell Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero. Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending. The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions. A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail. Yang-Zhang Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error. Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator. Garman-Klass-Yang-Zhang Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation. GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator. Exponential Weighted Moving Average The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling. The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted. The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series. Standard Deviation of Log Returns This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)). Pseudo GARCH(2,2) This is calculated using a short- and long-run mean of variance multiplied by ?. ?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L) Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?. Average True Range The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period. The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges. True Range Double A special case of ATR that attempts to correct for volatility skew. Standard Deviation Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation. Adaptive Deviation By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility. Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting). The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions. Median Absolute Deviation The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant. Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution. For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated. Efficiency-Ratio Adaptive ATR Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range. Mean Absolute Deviation The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation. This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life. █ Giga Kaleidoscope Modularized Trading System Core components of an NNFX algorithmic trading strategy The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm: 1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc. 2. Baseline - a moving average to identify price trend 3. Confirmation 1 - a technical indicator used to identify trends 4. Confirmation 2 - a technical indicator used to identify trends 5. Continuation - a technical indicator used to identify trends 6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown 7. Exit - a technical indicator used to determine when a trend is exhausted 8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators* *(not part of the NNFX algorithm) What is Volatility in the NNFX trading system? In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period. True range is calculated as the maximum of the following values: -Current high minus the current low -Absolute value of the current high minus the previous close -Absolute value of the current low minus the previous close ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses. Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass What is a Baseline indicator? The baseline is essentially a moving average, and is used to determine the overall direction of the market. The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR). Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail. By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend. What is a Confirmation indicator? Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI). The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend. Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators. In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades. What is a Continuation indicator? In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy. What is a Volatility/Volume indicator? Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa. By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements. What is an Exit indicator? The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy. The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses. In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor. Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time. What is an Metamorphosis indicator? The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy. The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework. How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above? Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are: 1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm) 2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm) 3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm) 4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm) 5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm) 6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm) (additional module types will added in future releases) Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal. That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy. This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm. What does the application of the GKD trading system look like? Example trading system: Backtest: Multi-Ticker SCC Backtest Baseline: Hull Moving Average Volatility/Volume: Hurst Exponent Confirmation 1: Fisher Trasnform Confirmation 2: uf2018 Continuation: Vortex Exit: Rex Oscillator Metamorphosis: Baseline Optimizer Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system. █ Giga Kaleidoscope Modularized Trading System Signals Standard Entry 1. GKD-C Confirmation gives signal 2. Baseline agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Confirmation 2 agrees 6. Volatility/Volume agrees 1-Candle Standard Entry 1a. GKD-C Confirmation gives signal 2a. Baseline agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum Next Candle 1b. Price retraced 2b. Baseline agrees 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Volatility/Volume agrees Baseline Entry 1. GKD-B Basline gives signal 2. Confirmation 1 agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Confirmation 2 agrees 6. Volatility/Volume agrees 7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior 1-Candle Baseline Entry 1a. GKD-B Baseline gives signal 2a. Confirmation 1 agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum 5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior Next Candle 1b. Price retraced 2b. Baseline agrees 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Volatility/Volume agrees Volatility/Volume Entry 1. GKD-V Volatility/Volume gives signal 2. Confirmation 1 agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Confirmation 2 agrees 6. Baseline agrees 7. Confirmation 1 signal was less than 7 candles prior 1-Candle Volatility/Volume Entry 1a. GKD-V Volatility/Volume gives signal 2a. Confirmation 1 agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum 5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior Next Candle 1b. Price retraced 2b. Volatility/Volume agrees 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Baseline agrees Confirmation 2 Entry 1. GKD-C Confirmation 2 gives signal 2. Confirmation 1 agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Volatility/Volume agrees 6. Baseline agrees 7. Confirmation 1 signal was less than 7 candles prior 1-Candle Confirmation 2 Entry 1a. GKD-C Confirmation 2 gives signal 2a. Confirmation 1 agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum 5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior Next Candle 1b. Price retraced 2b. Confirmation 2 agrees 3b. Confirmation 1 agrees 4b. Volatility/Volume agrees 5b. Baseline agrees PullBack Entry 1a. GKD-B Baseline gives signal 2a. Confirmation 1 agrees 3a. Price is beyond 1.0x Volatility of Baseline Next Candle 1b. Price inside Goldie Locks Zone Minimum 2b. Price inside Goldie Locks Zone Maximum 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Volatility/Volume agrees Continuation Entry 1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously 2. Baseline hasn't crossed since entry signal trigger 4. Confirmation 1 agrees 5. Baseline agrees 6. Confirmation 2 agrees █ Connecting to Backtests All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below. GKD-BT Giga Confirmation Stack Backtest GKD-BT Giga Stacks Backtest GKD-BT Full Giga Kaleidoscope Backtest GKD-BT Solo Confirmation Super Complex Backtest GKD-BT Solo Confirmation Complex Backtest GKD-BT Solo Confirmation Simple Backtest GKD-M Baseline Optimizer GKD-M Accuracy Alchemist
Pine Script® indicator
by loxx
1
GKD-M Baseline Optimizer [Loxx]Giga Kaleidoscope GKD-M Baseline Optimizer is a Metamorphosis module included in Loxx's "Giga Kaleidoscope Modularized Trading System". The Baseline Optimizer enables traders to backtest over 60 moving averages using variable period inputs. It then exports the baseline with the highest cumulative win rate per candle to any baseline-enabled GKD backtest. To perform the backtesting, the trader selects an initial period input (default is 60) and a skip value that increments the initial period input up to seven times. For instance, if a skip value of 5 is chosen, the Baseline Optimizer will run the backtest for the selected moving average on periods such as 60, 65, 70, 75, and so on, up to 90. If the user selects an initial period input of 45 and a skip value of 2, the Baseline Optimizer will conduct backtests for the chosen moving average on periods like 45, 47, 49, 51, and so forth, up to 57. The Baseline Optimizer provides a table displaying the output of the backtests for a specified date range. The table output represents the cumulative win rate for the given date range. On the Metamorphosis side of the Baseline Optimizer, a cumulative backtest is calculated for each candle within the date range. This means that each candle may exhibit a different distribution of period inputs with the highest win rate for a particular moving average. The Baseline Optimizer identifies the period input combination with the highest win rates for long and short positions and creates a win-rate adaptive long and short moving average chart. The moving average used for shorts differs from the moving average used for longs, and the moving average for each candle may vary from any other candle. This customized baseline can then be exported to all baseline-enabled GKD backtests. The backtest employed in the Baseline Optimizer is a Solo Confirmation Simple, allowing only one take profit and one stop loss to be set. Lastly, the Baseline Optimizer incorporates Goldie Locks Zone filtering, which can be utilized for signal generation in advanced GKD backtests. █ Moving Averages included in the Baseline Optimizer Adaptive Moving Average - AMA ADXvma - Average Directional Volatility Moving Average Ahrens Moving Average Alexander Moving Average - ALXMA Deviation Scaled Moving Average - DSMA Donchian Double Exponential Moving Average - DEMA Double Smoothed Exponential Moving Average - DSEMA Double Smoothed FEMA - DSFEMA Double Smoothed Range Weighted EMA - DSRWEMA Double Smoothed Wilders EMA - DSWEMA Double Weighted Moving Average - DWMA Exponential Moving Average - EMA Fast Exponential Moving Average - FEMA Fractal Adaptive Moving Average - FRAMA Generalized DEMA - GDEMA Generalized Double DEMA - GDDEMA Hull Moving Average (Type 1) - HMA1 Hull Moving Average (Type 2) - HMA2 Hull Moving Average (Type 3) - HMA3 Hull Moving Average (Type 4) - HMA4 IE /2 - Early T3 by Tim Tilson Integral of Linear Regression Slope - ILRS Kaufman Adaptive Moving Average - KAMA Leader Exponential Moving Average Linear Regression Value - LSMA ( Least Squares Moving Average ) Linear Weighted Moving Average - LWMA McGinley Dynamic McNicholl EMA Non-Lag Moving Average Ocean NMA Moving Average - ONMAMA One More Moving Average - OMA Parabolic Weighted Moving Average Probability Density Function Moving Average - PDFMA Quadratic Regression Moving Average - QRMA Regularized EMA - REMA Range Weighted EMA - RWEMA Recursive Moving Trendline Simple Decycler - SDEC Simple Jurik Moving Average - SJMA Simple Moving Average - SMA Sine Weighted Moving Average Smoothed LWMA - SLWMA Smoothed Moving Average - SMMA Smoother Super Smoother T3 Three-pole Ehlers Butterworth Three-pole Ehlers Smoother Triangular Moving Average - TMA Triple Exponential Moving Average - TEMA Two-pole Ehlers Butterworth Two-pole Ehlers smoother Variable Index Dynamic Average - VIDYA Variable Moving Average - VMA Volume Weighted EMA - VEMA Volume Weighted Moving Average - VWMA Zero-Lag DEMA - Zero Lag Exponential Moving Average Zero-Lag Moving Average Zero Lag TEMA - Zero Lag Triple Exponential Moving Average Adaptive Moving Average - AMA The Adaptive Moving Average (AMA) is a moving average that changes its sensitivity to price moves depending on the calculated volatility. It becomes more sensitive during periods when the price is moving smoothly in a certain direction and becomes less sensitive when the price is volatile. ADXvma - Average Directional Volatility Moving Average Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator. The ADXvma has the SMA in Chande's CMO replaced with an EMA , it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated. A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA . The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move. Ahrens Moving Average Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows. Alexander Moving Average - ALXMA This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile. Deviation Scaled Moving Average - DSMA The Deviation-Scaled Moving Average is a data smoothing technique that acts like an exponential moving average with a dynamic smoothing coefficient. The smoothing coefficient is automatically updated based on the magnitude of price changes. In the Deviation-Scaled Moving Average, the standard deviation from the mean is chosen to be the measure of this magnitude. The resulting indicator provides substantial smoothing of the data even when price changes are small while quickly adapting to these changes. Donchian Donchian Channels are three lines generated by moving average calculations that comprise an indicator formed by upper and lower bands around a midrange or median band. The upper band marks the highest price of a security over N periods while the lower band marks the lowest price of a security over N periods. Double Exponential Moving Average - DEMA The Double Exponential Moving Average ( DEMA ) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag. Double Smoothed Exponential Moving Average - DSEMA The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA . It's also considered a leading indicator compared to the EMA , and is best utilized whenever smoothness and speed of reaction to market changes are required. Double Smoothed FEMA - DSFEMA Same as the Double Exponential Moving Average (DEMA), but uses a faster version of EMA for its calculation. Double Smoothed Range Weighted EMA - DSRWEMA Range weighted exponential moving average (EMA) is, unlike the "regular" range weighted average calculated in a different way. Even though the basis - the range weighting - is the same, the way how it is calculated is completely different. By definition this type of EMA is calculated as a ratio of EMA of price*weight / EMA of weight. And the results are very different and the two should be considered as completely different types of averages. The higher than EMA to price changes responsiveness when the ranges increase remains in this EMA too and in those cases this EMA is clearly leading the "regular" EMA. This version includes double smoothing. Double Smoothed Wilders EMA - DSWEMA Welles Wilder was frequently using one "special" case of EMA (Exponential Moving Average) that is due to that fact (that he used it) sometimes called Wilder's EMA. This version is adding double smoothing to Wilder's EMA in order to make it "faster" (it is more responsive to market prices than the original) and is still keeping very smooth values. Double Weighted Moving Average - DWMA Double weighted moving average is an LWMA (Linear Weighted Moving Average). Instead of doing one cycle for calculating the LWMA, the indicator is made to cycle the loop 2 times. That produces a smoother values than the original LWMA Exponential Moving Average - EMA The EMA places more significance on recent data points and moves closer to price than the SMA ( Simple Moving Average ). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA . Fast Exponential Moving Average - FEMA An Exponential Moving Average with a short look-back period. Fractal Adaptive Moving Average - FRAMA The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry. Generalized DEMA - GDEMA The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages.". Instead of using fixed multiplication factor in the final DEMA formula, the generalized version allows you to change it. By varying the "volume factor" form 0 to 1 you apply different multiplications and thus producing DEMA with different "speed" - the higher the volume factor is the "faster" the DEMA will be (but also the slope of it will be less smooth). The volume factor is limited in the calculation to 1 since any volume factor that is larger than 1 is increasing the overshooting to the extent that some volume factors usage makes the indicator unusable. Generalized Double DEMA - GDDEMA The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages''. This is an extension of the Generalized DEMA using Tim Tillsons (the inventor of T3) idea, and is using GDEMA of GDEMA for calculation (which is the "middle step" of T3 calculation). Since there are no versions showing that middle step, this version covers that too. The result is smoother than Generalized DEMA, but is less smooth than T3 - one has to do some experimenting in order to find the optimal way to use it, but in any case, since it is "faster" than the T3 (Tim Tillson T3) and still smooth, it looks like a good compromise between speed and smoothness. Hull Moving Average (Type 1) - HMA1 Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMA for smoothing. Hull Moving Average (Type 2) - HMA2 Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses EMA for smoothing. Hull Moving Average (Type 3) - HMA3 Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses LWMA for smoothing. Hull Moving Average (Type 4) - HMA4 Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMMA for smoothing. IE /2 - Early T3 by Tim Tilson and T3 new The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm. The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction. The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy. Integral of Linear Regression Slope - ILRS A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA ( Simple Moving Average ) of length N, which is actually the midpoint of the linear regression line as it moves across the data. Kaufman Adaptive Moving Average - KAMA Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. Leader Exponential Moving Average The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter. Linear Regression Value - LSMA ( Least Squares Moving Average ) LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down. Linear Weighted Moving Average - LWMA LWMA reacts to price quicker than the SMA and EMA . Although it's similar to the Simple Moving Average , the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion. McGinley Dynamic John McGinley created this Moving Average to track prices better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets. McNicholl EMA Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws. Non-lag moving average The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals. Ocean NMA Moving Average - ONMAMA Created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility without being programmed to do so. For more info, read his guide "Ocean Theory, an Introduction" One More Moving Average (OMA) The One More Moving Average (OMA) is a technical indicator that calculates a series of Jurik-style moving averages in order to reduce noise and provide smoother price data. It uses six exponential moving averages to generate the final value, with the length of the moving averages determined by an adaptive algorithm that adjusts to the current market conditions. The algorithm calculates the average period by comparing the signal to noise ratio and using this value to determine the length of the moving averages. The resulting values are used to generate the final value of the OMA, which can be used to identify trends and potential changes in trend direction. Parabolic Weighted Moving Average The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average . The Linear Weighted Moving Average calculates the average by assigning different weights to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator. Probability Density Function Moving Average - PDFMA Probability density function based MA is a sort of weighted moving average that uses probability density function to calculate the weights. By its nature it is similar to a lot of digital filters. Quadratic Regression Moving Average - QRMA A quadratic regression is the process of finding the equation of the parabola that best fits a set of data. This moving average is an obscure concept that was posted to Forex forums in around 2008. Regularized EMA - REMA The regularized exponential moving average (REMA) by Chris Satchwell is a variation on the EMA (see Exponential Moving Average) designed to be smoother but not introduce too much extra lag. Range Weighted EMA - RWEMA This indicator is a variation of the range weighted EMA. The variation comes from a possible need to make that indicator a bit less "noisy" when it comes to slope changes. The method used for calculating this variation is the method described by Lee Leibfarth in his article "Trading With An Adaptive Price Zone". Recursive Moving Trendline Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrow's price. Simple Decycler - SDEC The Ehlers Simple Decycler study is a virtually zero-lag technical indicator proposed by John F. Ehlers. The original idea behind this study (and several others created by John F. Ehlers) is that market data can be considered a continuum of cycle periods with different cycle amplitudes. Thus, trending periods can be considered segments of longer cycles, or, in other words, low-frequency segments. Applying the right filter might help identify these segments. Simple Loxx Moving Average - SLMA A three stage moving average combining an adaptive EMA, a Kalman Filter, and a Kauffman adaptive filter. Simple Moving Average - SMA The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA . Sine Weighted Moving Average The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average). Smoothed LWMA - SLWMA A smoothed version of the LWMA Smoothed Moving Average - SMMA The Smoothed Moving Average is similar to the Simple Moving Average ( SMA ), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen as an accurate yet laggy Moving Average. Smoother The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA ( Smoothed Moving Average ). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm. Super Smoother The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a Two pole Butterworth filter combined with a 2-bar SMA ( Simple Moving Average ) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence. Three-pole Ehlers Butterworth The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA . They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing. Three-pole Ehlers smoother The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth. Triangular Moving Average - TMA The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data. Triple Exponential Moving Average - TEMA The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, its signals are considered very noisy and should only be used in extremely fast-paced trading conditions. Two-pole Ehlers Butterworth The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded. Two-pole Ehlers smoother A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers . Variable Index Dynamic Average - VIDYA Variable Index Dynamic Average Technical Indicator ( VIDYA ) was developed by Tushar Chande. It is an original method of calculating the Exponential Moving Average ( EMA ) with the dynamically changing period of averaging. Variable Moving Average - VMA The Variable Moving Average (VMA) is a study that uses an Exponential Moving Average being able to automatically adjust its smoothing factor according to the market volatility. Volume Weighted EMA - VEMA An EMA that uses a volume and price weighted calculation instead of the standard price input. Volume Weighted Moving Average - VWMA A Volume Weighted Moving Average is a moving average where more weight is given to bars with heavy volume than with light volume. Thus the value of the moving average will be closer to where most trading actually happened than it otherwise would be without being volume weighted. Zero-Lag DEMA - Zero Lag Double Exponential Moving Average John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence. Zero-Lag Moving Average The Zero Lag Moving Average is described by its creator, John Ehlers , as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average. Zero-Lag TEMA - Zero Lag Triple Exponential Moving Average Just like the Zero Lag DEMA , this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators. █ Volatility Goldie Locks Zone The Goldie Locks Zone volatility filter is the standard first-pass filter used in all advanced GKD backtests (Complex, Super Complex, and Full GKd). This filter requires the price to fall within a range determined by multiples of volatility. The Goldie Locks Zone is separate from the core Baseline and utilizes its own moving average with Loxx's Exotic Source Types you can read about below. On the chart, you will find green and red dots positioned at the top, indicating whether a candle qualifies for a long or short trade respectively. Additionally, green and red triangles are located at the bottom of the chart, signifying whether the trigger has crossed up or down and qualifies within the Goldie Locks zone. The Goldie Locks zone is represented by a white color on the mean line, indicating low volatility levels that are not suitable for trading. █ Volatility Types Included in the Baseline Optimizer The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings. This module includes 17 types of volatility: Close-to-Close Parkinson Garman-Klass Rogers-Satchell Yang-Zhang Garman-Klass-Yang-Zhang Exponential Weighted Moving Average Standard Deviation of Log Returns Pseudo GARCH(2,2) Average True Range True Range Double Standard Deviation Adaptive Deviation Median Absolute Deviation Efficiency-Ratio Adaptive ATR Mean Absolute Deviation Static Percent Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user. Close-to-Close Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility. Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is. Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility. Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators. Parkinson Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day. The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility. One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator. Garman-Klass Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security. Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate. Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements. Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes. Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail. Rogers-Satchell Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero. Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending. The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions. A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail. Yang-Zhang Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error. Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator. Garman-Klass-Yang-Zhang Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation. GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator. Exponential Weighted Moving Average The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling. The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted. The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series. Standard Deviation of Log Returns This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)). Pseudo GARCH(2,2) This is calculated using a short- and long-run mean of variance multiplied by ?. ?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L) Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?. Average True Range The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period. The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges. True Range Double A special case of ATR that attempts to correct for volatility skew. Standard Deviation Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation. Adaptive Deviation By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility. Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting). The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions. Median Absolute Deviation The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant. Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution. For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated. Efficiency-Ratio Adaptive ATR Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range. Mean Absolute Deviation The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation. This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life. █ Loxx's Expanded Source Types Included in Baseline Optimizer This indicator allows you to select from 33 source types. They are as follows: Close Open High Low Median Typical Weighted Average Average Median Body Trend Biased Trend Biased (Extreme) HA Close HA Open HA High HA Low HA Median HA Typical HA Weighted HA Average HA Average Median Body HA Trend Biased HA Trend Biased (Extreme) HAB Close HAB Open HAB High HAB Low HAB Median HAB Typical HAB Weighted HAB Average HAB Average Median Body HAB Trend Biased HAB Trend Biased (Extreme) What are Heiken Ashi "better" candles? Heiken Ashi "better" candles are a modified version of the standard Heiken Ashi candles, which are a popular charting technique used in technical analysis. Heiken Ashi candles help traders identify trends and potential reversal points by smoothing out price data and reducing market noise. The "better formula" was proposed by Sebastian Schmidt in an article published by BNP Paribas in Warrants & Zertifikate, a German magazine, in August 2004. The aim of this formula is to further improve the smoothing of the Heiken Ashi chart and enhance its effectiveness in identifying trends and reversals. Standard Heiken Ashi candles are calculated using the following formulas: Heiken Ashi Close = (Open + High + Low + Close) / 4 Heiken Ashi Open = (Previous Heiken Ashi Open + Previous Heiken Ashi Close) / 2 Heiken Ashi High = Max (High, Heiken Ashi Open, Heiken Ashi Close) Heiken Ashi Low = Min (Low, Heiken Ashi Open, Heiken Ashi Close) The "better formula" modifies the standard Heiken Ashi calculation by incorporating additional smoothing, which can help reduce noise and make it easier to identify trends and reversals. The modified formulas for Heiken Ashi "better" candles are as follows: Better Heiken Ashi Close = (Open + High + Low + Close) / 4 Better Heiken Ashi Open = (Previous Better Heiken Ashi Open + Previous Better Heiken Ashi Close) / 2 Better Heiken Ashi High = Max (High, Better Heiken Ashi Open, Better Heiken Ashi Close) Better Heiken Ashi Low = Min (Low, Better Heiken Ashi Open, Better Heiken Ashi Close) Smoothing Factor = 2 / (N + 1), where N is the chosen period for smoothing Smoothed Better Heiken Ashi Open = (Better Heiken Ashi Open * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Open * (1 - Smoothing Factor)) Smoothed Better Heiken Ashi Close = (Better Heiken Ashi Close * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Close * (1 - Smoothing Factor)) The smoothed Better Heiken Ashi Open and Close values are then used to calculate the smoothed Better Heiken Ashi High and Low values, resulting in "better" candles that provide a clearer representation of the market trend and potential reversal points. Heiken Ashi "better" candles, as mentioned previously, provide a clearer representation of market trends and potential reversal points by reducing noise and smoothing out price data. When using these candles in conjunction with other technical analysis tools and indicators, traders can gain valuable insights into market behavior and make more informed decisions. To effectively use Heiken Ashi "better" candles in your trading strategy, consider the following tips: -Trend Identification: Heiken Ashi "better" candles can help you identify the prevailing trend in the market. When the majority of the candles are green (or another color, depending on your chart settings) and there are no or few lower wicks, it may indicate a strong uptrend. Conversely, when the majority of the candles are red (or another color) and there are no or few upper wicks, it may signal a strong downtrend. -Trend Reversals: Look for potential trend reversals when a change in the color of the candles occurs, especially when accompanied by longer wicks. For example, if a green candle with a long lower wick is followed by a red candle, it could indicate a bearish reversal. Similarly, a red candle with a long upper wick followed by a green candle may suggest a bullish reversal. -Support and Resistance: You can use Heiken Ashi "better" candles to identify potential support and resistance levels. When the candles are consistently moving in one direction and then suddenly change color with longer wicks, it could indicate the presence of a support or resistance level. -Stop-Loss and Take-Profit: Using Heiken Ashi "better" candles can help you manage risk by determining optimal stop-loss and take-profit levels. For instance, you can place your stop-loss below the low of the most recent green candle in an uptrend or above the high of the most recent red candle in a downtrend. -Confirming Signals: Heiken Ashi "better" candles should be used in conjunction with other technical indicators, such as moving averages, oscillators, or chart patterns, to confirm signals and improve the accuracy of your analysis. In this implementation, you have the choice of AMA, KAMA, or T3 smoothing. These are as follows: Kaufman Adaptive Moving Average (KAMA) The Kaufman Adaptive Moving Average (KAMA) is a type of adaptive moving average used in technical analysis to smooth out price fluctuations and identify trends. The KAMA adjusts its smoothing factor based on the market's volatility, making it more responsive in volatile markets and smoother in calm markets. The KAMA is calculated using three different efficiency ratios that determine the appropriate smoothing factor for the current market conditions. These ratios are based on the noise level of the market, the speed at which the market is moving, and the length of the moving average. The KAMA is a popular choice among traders who prefer to use adaptive indicators to identify trends and potential reversals. Adaptive Moving Average The Adaptive Moving Average (AMA) is a type of moving average that adjusts its sensitivity to price movements based on market conditions. It uses a ratio between the current price and the highest and lowest prices over a certain lookback period to determine its level of smoothing. The AMA can help reduce lag and increase responsiveness to changes in trend direction, making it useful for traders who want to follow trends while avoiding false signals. The AMA is calculated by multiplying a smoothing constant with the difference between the current price and the previous AMA value, then adding the result to the previous AMA value. T3 The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm. The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction. The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy. █ Giga Kaleidoscope Modularized Trading System Core components of an NNFX algorithmic trading strategy The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm: 1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc. 2. Baseline - a moving average to identify price trend 3. Confirmation 1 - a technical indicator used to identify trends 4. Confirmation 2 - a technical indicator used to identify trends 5. Continuation - a technical indicator used to identify trends 6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown 7. Exit - a technical indicator used to determine when a trend is exhausted 8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators* *(not part of the NNFX algorithm) What is Volatility in the NNFX trading system? In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period. True range is calculated as the maximum of the following values: -Current high minus the current low -Absolute value of the current high minus the previous close -Absolute value of the current low minus the previous close ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses. Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass What is a Baseline indicator? The baseline is essentially a moving average, and is used to determine the overall direction of the market. The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR). Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail. By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend. What is a Confirmation indicator? Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI). The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend. Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators. In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades. What is a Continuation indicator? In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy. What is a Volatility/Volume indicator? Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa. By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements. What is an Exit indicator? The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy. The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses. In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor. Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time. What is an Metamorphosis indicator? The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy. The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework. How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above? Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are: 1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm) 2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm) 3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm) 4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm) 5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm) 6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm) (additional module types will added in future releases) Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal. That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy. This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm. What does the application of the GKD trading system look like? Example trading system: Backtest: Full GKD Backtest Baseline: Hull Moving Average Volatility/Volume: Hurst Exponent Confirmation 1: Kase Peak Oscillator Confirmation 2: uf2018 Continuation: Vortex Exit: Rex Oscillator Metamorphosis: Baseline Optimizer as shown on the chart above Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system. █ Giga Kaleidoscope Modularized Trading System Signals Standard Entry 1. GKD-C Confirmation gives signal 2. Baseline agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Confirmation 2 agrees 6. Volatility/Volume agrees 1-Candle Standard Entry 1a. GKD-C Confirmation gives signal 2a. Baseline agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum Next Candle 1b. Price retraced 2b. Baseline agrees 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Volatility/Volume agrees Baseline Entry 1. GKD-B Basline gives signal 2. Confirmation 1 agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Confirmation 2 agrees 6. Volatility/Volume agrees 7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior 1-Candle Baseline Entry 1a. GKD-B Baseline gives signal 2a. Confirmation 1 agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum 5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior Next Candle 1b. Price retraced 2b. Baseline agrees 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Volatility/Volume agrees Volatility/Volume Entry 1. GKD-V Volatility/Volume gives signal 2. Confirmation 1 agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Confirmation 2 agrees 6. Baseline agrees 7. Confirmation 1 signal was less than 7 candles prior 1-Candle Volatility/Volume Entry 1a. GKD-V Volatility/Volume gives signal 2a. Confirmation 1 agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum 5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior Next Candle 1b. Price retraced 2b. Volatility/Volume agrees 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Baseline agrees Confirmation 2 Entry 1. GKD-C Confirmation 2 gives signal 2. Confirmation 1 agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Volatility/Volume agrees 6. Baseline agrees 7. Confirmation 1 signal was less than 7 candles prior 1-Candle Confirmation 2 Entry 1a. GKD-C Confirmation 2 gives signal 2a. Confirmation 1 agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum 5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior Next Candle 1b. Price retraced 2b. Confirmation 2 agrees 3b. Confirmation 1 agrees 4b. Volatility/Volume agrees 5b. Baseline agrees PullBack Entry 1a. GKD-B Baseline gives signal 2a. Confirmation 1 agrees 3a. Price is beyond 1.0x Volatility of Baseline Next Candle 1b. Price inside Goldie Locks Zone Minimum 2b. Price inside Goldie Locks Zone Maximum 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Volatility/Volume agrees Continuation Entry 1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously 2. Baseline hasn't crossed since entry signal trigger 4. Confirmation 1 agrees 5. Baseline agrees 6. Confirmation 2 agrees █ Connecting to Backtests All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below. GKD-BT Giga Confirmation Stack Backtest: GKD-BT Giga Stacks Backtest: GKD-BT Full Giga Kaleidoscope Backtest: GKD-BT Solo Confirmation Super Complex Backtest: GKD-BT Solo Confirmation Complex Backtest: GKD-BT Solo Confirmation Simple Backtest:
Pine Script® indicator
by loxx
GKD-C RAVI Fisher Transform [Loxx]Giga Kaleidoscope GKD-C RAVI Fisher Transform is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System". █ Giga Kaleidoscope Modularized Trading System What is Loxx's "Giga Kaleidoscope Modularized Trading System"? The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading. What is the NNFX algorithmic trading strategy? The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community. The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management. Here are the main components of the NNFX trading system: 1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines. 2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades. 3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages. 4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade. 5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level. 6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing. Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels. Core components of an NNFX algorithmic trading strategy The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm: 1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc. 2. Baseline - a moving average to identify price trend 3. Confirmation 1 - a technical indicator used to identify trends 4. Confirmation 2 - a technical indicator used to identify trends 5. Continuation - a technical indicator used to identify trends 6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown 7. Exit - a technical indicator used to determine when a trend is exhausted What is Volatility in the NNFX trading system? In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period. True range is calculated as the maximum of the following values: -Current high minus the current low -Absolute value of the current high minus the previous close -Absolute value of the current low minus the previous close ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses. Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass What is a Baseline indicator? The baseline is essentially a moving average, and is used to determine the overall direction of the market. The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR). Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail. By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend. What is a Confirmation indicator? Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI). The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend. Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators. In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades. What is a Continuation indicator? In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy. What is a Volatility/Volume indicator? Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa. By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements. What is an Exit indicator? The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy. The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses. In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor. Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time. How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above? Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are: 1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm) 2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm) 3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm) 4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm) 5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm) (additional module types will added in future releases) Each module interacts with every module by passing data between modules. Data is passed between each module as described below: GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy. This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm. What does the application of the GKD trading system look like? Example trading system: Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles Baseline: Hull Moving Average Volatility/Volume: Hurst Exponent Confirmation 1: RAVI Fisher Transform as shown on the chart above Confirmation 2: Williams Percent Range Continuation: Fisher Transform Exit: Rex Oscillator Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain. Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm) Standard Entry 1. GKD-C Confirmation 1 Signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 6. GKD-C Confirmation 1 signal was less than 7 candles prior Volatility/Volume Entry 1. GKD-V Volatility/Volume signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-B Baseline agrees 6. GKD-C Confirmation 1 signal was less than 7 candles prior Continuation Entry 1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously 2. GKD-B Baseline hasn't crossed since entry signal trigger 3. GKD-C Confirmation Continuation Indicator signals 4. GKD-C Confirmation 1 agrees 5. GKD-B Baseline agrees 6. GKD-C Confirmation 2 agrees 1-Candle Rule Standard Entry 1. GKD-C Confirmation 1 signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 1-Candle Rule Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 1 signal was less than 7 candles prior Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees 1-Candle Rule Volatility/Volume Entry 1. GKD-V Volatility/Volume signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 1 signal was less than 7 candles prior Next Candle: 1. Price retraced (Long: close < close or Short: close > close) 2. GKD-B Volatility/Volume agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-B Baseline agrees PullBack Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is beyond 1.0x Volatility of Baseline Next Candle: 1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 2. GKD-C Confirmation 1 agrees 3. GKD-C Confirmation 2 agrees 4. GKD-V Volatility/Volume Agrees ]█ Setting up the GKD The GKD system involves chaining indicators together. These are the steps to set this up. Use a GKD-C indicator alone on a chart 1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple" Use a GKD-V indicator alone on a chart **nothing, it's already useable on the chart without any settings changes Use a GKD-B indicator alone on a chart **nothing, it's already useable on the chart without any settings changes Baseline (Baseline, Backtest) 1. Import the GKD-B Baseline into the GKD-BT Backtest: "Input into Volatility/Volume or Backtest (Baseline testing)" 2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline" Volatility/Volume (Volatility/Volume, Backte st) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Solo" 2. Inside the GKD-V indicator, change the "Signal Type" setting to "Crossing" (neither traditional nor both can be backtested) 3. Import the GKD-V indicator into the GKD-BT Backtest: "Input into C1 or Backtest" 4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Volatility/Volume" 5. Inside the GKD-BT Backtest, a) change the setting "Backtest Type" to "Trading" if using a directional GKD-V indicator; or, b) change the setting "Backtest Type" to "Full" if using a directional or non-directional GKD-V indicator (non-directional GKD-V can only test Longs and Shorts separately) 6. If "Backtest Type" is set to "Full": Inside the GKD-BT Backtest, change the setting "Backtest Side" to "Long" or "Short 7. If "Backtest Type" is set to "Full": To allow the system to open multiple orders at one time so you test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts. Solo Confirmation Simple (Confirmation, Backtest) 1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple" 1. Import the GKD-C indicator into the GKD-BT Backtest: "Input into Backtest" 2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Solo Confirmation Simple" Solo Confirmation Complex without Exits (Baseline, Volatility/Volume, Confirmation, Backtest) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained" 2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)" 3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex" 4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest" 5. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits" 6. Import the GKD-C into the GKD-BT Backtest: "Input into Exit or Backtest" Solo Confirmation Complex with Exits (Baseline, Volatility/Volume, Confirmation, Exit, Backtest) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained" 2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)" 3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex" 4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest" 5. Import the GKD-C indicator into the GKD-E indicator: "Input into Exit" 6. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits" 7. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest" Full GKD without Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Backtest) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained" 2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)" 3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1" 4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest" 5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2" 6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2" 7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation" 8. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits" 9. Import the GKD-E into the GKD-BT Backtest: "Input into Exit or Backtest" Full GKD with Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Exit, Backtest) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained" 2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)" 3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1" 4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest" 5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2" 6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2" 7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation" 8. Import the GKD-C Continuation indicator into the GKD-E indicator: "Input into Exit" 9. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits" 10. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest" Baseline + Volatility/Volume (Baseline, Volatility/Volume, Backtest) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Baseline + Volatility/Volume" 2. Inside the GKD-V indicator, make sure the "Signal Type" setting is set to "Traditional" 3. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)" 4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline + Volatility/Volume" 5. Import the GKD-V into the GKD-BT Backtest: "Input into C1 or Backtest" 6. Inside the GKD-BT Backtest, change the setting "Backtest Type" to "Full". For this backtest, you must test Longs and Shorts separately 7. To allow the system to open multiple orders at one time so you can test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts. █ GKD-C RAVI Fisher Transform What is the Fisher Transform? The Fisher Transform is a technical analysis indicator developed by John Ehlers, an expert in the field of signal processing and technical analysis. The Fisher Transform is designed to convert price data into a Gaussian probability distribution, which makes it easier to identify market turning points and produce clearer trading signals. The Fisher Transform is particularly useful in financial markets because price data often exhibit non-Gaussian characteristics, such as skewness and kurtosis. By converting the data into a Gaussian distribution, the Fisher Transform helps to highlight extreme price movements and generate more accurate trading signals. To calculate the Fisher Transform, follow these steps: 1. Normalize the price data by computing the price relative to a moving average or another relevant indicator, ensuring the data falls within a range of -1 to 1. 2. Apply the Fisher Transform formula to the normalized price data: Fisher Transform = 0.5 * ln where ln represents the natural logarithm and X is the normalized price data. 3. Smooth the transformed data using a moving average or another smoothing technique to reduce noise and improve signal clarity. The Fisher Transform indicator generates trading signals based on crossovers. A buy signal occurs when the Fisher Transform value crosses above a certain threshold, while a sell signal occurs when the Fisher Transform value crosses below a different threshold. These thresholds can be adjusted to match specific trading styles and market conditions. Traders often use the Fisher Transform in conjunction with other technical analysis tools to confirm signals and improve their trading strategies. What is RAVI (Range Action Verification Index) ? The RAVI (Range Action Verification Index) indicator is a technical analysis tool developed by Tushar Chande, a well-known expert in the field of technical analysis. The RAVI indicator is designed to help traders identify the market's current trend and distinguish between trending and range-bound market conditions. The RAVI indicator measures the difference between two moving averages of different lengths and expresses the result as a percentage. It essentially quantifies the degree of price change over time, allowing traders to assess the strength of a trend or the lack thereof. To calculate the RAVI indicator, follow these steps: 1. Choose two different lengths for the moving averages, a short period (e.g., 7 periods) and a long period (e.g., 65 periods). 2. Calculate the Simple Moving Average (SMA) for both short and long periods. 3. Subtract the long period SMA from the short period SMA. 4. Divide the result by the long period SMA. 5. Multiply the result by 100 to express the value as a percentage. The RAVI indicator generates trading signals based on the crossing of predefined thresholds. A common approach is to use two threshold levels, such as 3% and -3%. When the RAVI value crosses above the upper threshold (3%), it indicates the start of an uptrend, and traders may consider entering a long position. Conversely, when the RAVI value crosses below the lower threshold (-3%), it suggests the start of a downtrend, and traders may consider entering a short position. In range-bound or sideways market conditions, the RAVI values typically stay within the threshold levels, providing no clear trading signals. In such cases, traders may opt to use other range trading strategies or indicators. It's important to note that the RAVI indicator, like any other technical analysis tool, should be used in conjunction with other indicators and analysis techniques to confirm signals and improve overall trading strategies. What is the RAVI Fisher Transform? This indicator runs a specialized Fisher Transform through a RAVI algorihtm to reduce false signals. Requirements Inputs Confirmation 1: GKD-V Volatility / Volume indicator Confirmation 2: GKD-C Confirmation indicator Continuation: GKD-C Confirmation indicator Solo Confirmation Simple: GKD-B Baseline Solo Confirmation Complex: GKD-V Volatility / Volume indicator Solo Confirmation Super Complex: GKD-V Volatility / Volume indicator Stacked 1: None Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 1 Outputs Confirmation 1: GKD-C Confirmation 2 indicator Confirmation 2: GKD-C Continuation indicator Continuation: GKD-E Exit indicator Solo Confirmation Simple: GKD-BT Backtest Solo Confirmation Complex: GKD-BT Backtest or GKD-E Exit indicator Solo Confirmation Super Complex: GKD-C Continuation indicator Stacked 1: GKD-C, GKD-V, or GKD-B Stacked 2+ Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 2+ or GKD-BT Backtest Additional features will be added in future releases.
Pine Script® indicator
by loxx
GKD-C CFB-Adaptive Gann HiLo Activator Histogram [Loxx]Giga Kaleidoscope GKD-C CFB-Adaptive Gann HiLo Activator Histogram is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System". █ Giga Kaleidoscope Modularized Trading System What is Loxx's "Giga Kaleidoscope Modularized Trading System"? The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading. What is the NNFX algorithmic trading strategy? The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community. The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management. Here are the main components of the NNFX trading system: 1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines. 2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades. 3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages. 4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade. 5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level. 6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing. Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels. Core components of an NNFX algorithmic trading strategy The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm: 1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc. 2. Baseline - a moving average to identify price trend 3. Confirmation 1 - a technical indicator used to identify trends 4. Confirmation 2 - a technical indicator used to identify trends 5. Continuation - a technical indicator used to identify trends 6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown 7. Exit - a technical indicator used to determine when a trend is exhausted What is Volatility in the NNFX trading system? In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period. True range is calculated as the maximum of the following values: -Current high minus the current low -Absolute value of the current high minus the previous close -Absolute value of the current low minus the previous close ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses. Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass What is a Baseline indicator? The baseline is essentially a moving average, and is used to determine the overall direction of the market. The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR). Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail. By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend. What is a Confirmation indicator? Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI). The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend. Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators. In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades. What is a Continuation indicator? In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy. What is a Volatility/Volume indicator? Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa. By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements. What is an Exit indicator? The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy. The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses. In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor. Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time. How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above? Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are: 1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm) 2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm) 3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm) 4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm) 5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm) (additional module types will added in future releases) Each module interacts with every module by passing data between modules. Data is passed between each module as described below: GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy. This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm. What does the application of the GKD trading system look like? Example trading system: Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles Baseline: Hull Moving Average Volatility/Volume: Hurst Exponent Confirmation 1: CFB-Adaptive Gann HiLo Activator Histogram as shown on the chart above Confirmation 2: Williams Percent Range Continuation: Fisher Transform Exit: Rex Oscillator Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain. Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm) Standard Entry 1. GKD-C Confirmation 1 Signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 6. GKD-C Confirmation 1 signal was less than 7 candles prior Continuation Entry 1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously 2. GKD-B Baseline hasn't crossed since entry signal trigger 3. GKD-C Confirmation Continuation Indicator signals 4. GKD-C Confirmation 1 agrees 5. GKD-B Baseline agrees 6. GKD-C Confirmation 2 agrees 1-Candle Rule Standard Entry 1. GKD-C Confirmation 1 signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 1-Candle Rule Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 1 signal was less than 7 candles prior Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees PullBack Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is beyond 1.0x Volatility of Baseline Next Candle: 1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees █ GKD-C CFB-Adaptive Gann HiLo Activator Histogram What is Composite Fractal Behavior? Composite Fractal Behavior is a technical analysis concept developed by Mark Jurik that describes the behavior of financial markets as a combination of various fractal patterns. Fractal patterns are repeating patterns that occur at different scales and are present in many natural and man-made systems. In financial markets, fractal patterns can be observed in the price movements of various timeframes, from short-term intraday movements to long-term trends. The concept of Composite Fractal Behavior suggests that by analyzing and combining different fractal patterns from various timeframes, a more accurate prediction of market behavior can be made. Jurik developed various indicators and algorithms based on this concept, such as the JMA (Jurik Moving Average) and the JFB (Jurik Fractal Bands) indicator. What is the Gann HiLo Activator? The Gann HiLo Activator is a trend-following indicator that is used to identify the direction of a trend and to generate trading signals based on the price movements. The indicator was created by W.D. Gann, a famous trader and analyst, who developed a number of technical analysis tools and trading strategies. The Gann HiLo Activator is based on the principle of moving averages and is designed to provide a visual representation of the trend. It consists of two lines, the Gann HiLo Activator line and the Gann HiLo Activator offset line. The Gann HiLo Activator line is calculated by taking the average of the high and low prices for a given period, and the offset line is calculated by taking the difference between the Gann HiLo Activator line and a moving average of the Gann HiLo Activator line. The Gann HiLo Activator line acts as a signal line, and when the price is above the Gann HiLo Activator line, the trend is considered to be bullish, and when the price is below the Gann HiLo Activator line, the trend is considered to be bearish. The offset line is used to generate trading signals, and when the price crosses above the offset line, it is considered to be a buy signal, and when the price crosses below the offset line, it is considered to be a sell signal. The Gann HiLo Activator is used by traders and analysts to identify trends and to generate trading signals based on the price movements. It is a popular indicator among technical analysts and is used in a variety of trading strategies, including swing trading, trend following, and momentum trading. The indicator can be used on any time frame and can be applied to any market, including stocks, futures, currencies, and commodities. What is CFB-Adaptive Gann HiLo Activator Histogram? This indicator makes the Gann HiLo Activator fractal-adaptive by injecting Composite Fractal Behavior measured periods into the Activator. These period inputs vary bar-by-bar. Requirements Inputs Confirmation 1 and Solo Confirmation: GKD-V Volatility / Volume indicator Confirmation 2: GKD-C Confirmation indicator Outputs Confirmation 2 and Solo Confirmation Complex: GKD-E Exit indicator Confirmation 1: GKD-C Confirmation indicator Continuation: GKD-E Exit indicator Solo Confirmation Simple: GKD-BT Backtest strategy Additional features will be added in future releases.
Pine Script® indicator
by loxx
[Excalibur][Pandora][Mosaic] Ultra Spectrum Analyzer@veryfid, you will always be remembered eternally... ANCIENT MYTHOS AND LORE: The retellings of "Pandora's Box" serve as a cautionary metaphor depicting an opened container (pithos - jar) that once held profound perils and evils — sufferings that are experienced around the world in various forms. The known and vague mythical box contents actually represent manifestation of evils, situational adversities, and human disparities that have been encountered throughout life for aeons. In contemporary times, a meager list of ordeals would include incidents of deceit, betrayal, corruption, oppression, greed, envy, depravity, conflict, mania, affliction, plague, and mortality. However, as the tale is told, kept and remaining inside the box was the essence of expectant hope (elpis), which may represent the optimism and resilience to overcome immense hardships. There are other versions of the classic story where Pandora isn't actually the culprit, being her husband Epimetheus was the lid lifting perpetrator and the one who always and actually received the gift(s). Curiously, the interpreted Greek word ‘Pandora’ translated to English, can mean either "all-endowed" or "all-gifting". Much like Pandora herself, who was formed from clay of the earth, the jar also would have been most likely crafted from clay. Conceived as a made-to-order maiden for an arranged marriage, Pandora was given qualities of exquisite beauty, persuasive charm, all while being adorned with jewelry and fine clothing. Olympian premeditated preparations in the didactic fable of 'Works and Days' by Hesiod had blamable intent and would be later used for centuries as denigration of women/mothers. The rest of Hesiod's tale is even worse. In reality, the entire contrived exploit of incarnating Pandora as a trojan temptress was solely intended as an instrument of infiltration and entrapment for delivery to Epimetheus as an arranged seductive snare. Being a man myself, I find it appalling how the antiquated writings of ancient morphological men have repeatedly ostracized women for many of the ailments of mankind. When in truth, it is far more often that despicable men are the recorded all time winning historical harbingers of global abysmal darkness by means of ideological treachery. Vast historical chronicles since antiquity have frequently recorded who the typical real-world villains truly are and are not. As the stories are told in the first place, it was dictator Zeus along with his Olympian conspirators, who intently implanted malicious spirits into a gifted receptacle to orchestrate planetary suffering and carnage on humankind. PROLOGUE: I believe, it is way past overdue to restore Pandora's name to a place of better standing. As I have been peaking into a theoretical pitcher of mathematic mysteria for years now, where no one else dares to look. Once upon a time, I pondered an opposite notion: What if Pandora was originally conceived to solve global problems instead of creating them? Maybe Pandora could have been wielded into existence to wage unrelenting and avenging retribution on every dominance hierarchy and each diabolical enemy intently hostile to humankind. My hypothetical version of Pandora would take the notion of "mors omnibus tyrannis" to a whole other fearsome magnitude. She would cause evil arrogant men to tremble with sheer horror... the kind of fear ALL false gods, despotic kings, tyrannical dictators, controligarchies, and criminal syndicates truly worry about at night. In my opinion, that would be a better fictional story worthy of retelling for aeons. One unique goliath 21st century adversary is LAG and it must be subdued or minimized. This unyielding nemesis is also known as group delay, processing delay, and algorithmic latency. My eyes are locked onto this opponent with fixation that will never surrender a staring contest. The formidable creature lag is my daily arch enemy destined for defeat in battle. It's losing time after time and bar by bar during the past year of 2023. In my attempts to peer through the murky darkness of useless and deceptive information, I am confident that I have found more suitable answers to many current dilemmas of algorithmic lag. The internet, using mathematics and the speed of light as a planetary beneficial advantage, has already performed wonders by drastically reducing the delay of dissemination of knowledge. This has garnered a mostly positive rapid acceleration of economic evolution. However, hierarchies of dark forces of chaos and subversion by the thousands lurking in the global shadows are not thrilled about well informed populations. In the present era, new spectrums of strife within planetary societies are being waged, one of the worst forms taking the hideous form of censorship. Other nefarious tactics are hindering economic progress with substantial negativity using heavily funded penetration and infiltration operations. Those sinister operational varieties are spanning psychological, cultural, educational, digital, financial, electoral, scientific, medical, biological, commercial, infrastructural, institutional, and organizational domains. They are mistakenly meddling with the entire primordial order of planetary natural dynamics. The miscalculations from these malevolent CAUSES will be countered with EFFECTS of immense retaliatory primal veracity having equal or exceedingly more powerful opposition with overwhelming numbers in mass. It is a law embedded within the universe that supersedes ALL laws, known as 'causality'. Everyone, especially programmers, know exactly what to do with predatory infiltrating cockroaches... When tyranny becomes enforced law by agendized policies in any land, order = abs(DUTY) * pow(RIGHT) * exp(PEOPLE). FUTURE ECONOMIC ADVERSARIAL CHALLENGES: Just as programmers have to critically analyze our code for BUGS, a scrutinized analysis of the current world around us is at times necessary. It is an empirical statistical fact that a few percent of captains at the helm of industry, commerce, institutes, and governance are monetarily psychopathic. They are often hidden bugs operating within national systems. The subsequent economic consequences result in effects that aren't always clearly obvious to all. Here are a few global economic security issues... Corrupted immoral code in national operation is an inevitable breakdown waiting to happen. In the harsh future to follow, old degenerate interdependent control systems will need to be dismantled and discarded, eventually succeeded by having resilient parallel arrangements with robust independent fidelity. The coming successive paradigm shifts would include future hardware and the hefty novel algorithms that will run on them afterwards. Evolution is inevitable! The internet must be upgraded and continually programmed securely to the near hardness of diamonds at multiple layers within the operational code to retain peaceful global integrity between international collaborations. DigitalID is never going to fix an insecure vulnerable titanic network of devices full of holes taking in megatons of water from every direction. Weaponized digital mucking ID dead on arrival is certainly NOT a one size fits all solution and it still doesn't do diddly-squat to secure the internet's DNA as executable code. DigID's real purpose is to manage servitude digitally and keep citizens right where they want them, as subservient slaves. There is a very specific reason why we have key chain rings in OUR pockets with numerous private keys evolving technologically over time to robustly safeguard individual locks we use every day, duh. AI becoming an artificial sentient hyper intelligence may sooner or later become a potential hazard, especially if it breaks AES192 into a thousand shards of glass. Perilous aspects from artilects will emerge and are coming swiftly. AI is already being weaponized and tasked to mind muzzle expressions of human consciousness. Also, EMPs from the sun ARE an imminent planetary threat, and no amount of carbon taxation schemes inciting anthropomorphic climate hysteria originating from falsified modeling hocus-pocus is going to protect against extreme solar cycle related X-class phenomena. Our solar system candle called the sun, is not consistently energy irradiation stable if you just glance at SOHO images/video. There are very obvious cyclical frequencies within the dynamics of the sun's energetic activity that affect planets far beyond earth. The earth already has a built-in natural thermometer indicating that oceans have been rising very linearly for thousands of years since the last ice age, submerging entire ancient cities under coastal water dozens of meters. BEAR with me and pardon my French translation, but I have the option to call major league climate BULLshite. There is no hardcore "anthropomorphic climate crisis" proof. It is a crisis in failed modeling that is insufficient to properly estimate colossal computations with dircet limited empirical data with enough accuracy to anticipate higly probable future outcomes. People deserve solid science instead of slanderous smackdowns and slighted statistics. 400ppm of atmospheric CO2 is nothing compared to previously existing 1600ppm concentrations acquired from ancient indirect historical observations at a time when early humans were hunter gatherers driving gas guzzlers. Western climate-monger fortune tellers are scamming every nation on earth, betraying the collective human species worldwide by climate hype strangulation. Wait until the sheeple with dinner forks turn on the rabid wolves in shepherds's clothing; it has already begun. What these predatory profiteering fraudsters are not telling you is WATER (H2O) in earth's atmosphere is the all time dominating and potent greenhouse gas, always has been, not CO2. Dr. Willie Soon has explained it in the best of ways with clarity. Misleaders, banksterCorpses, and mediaPresstitutes are immensely involved in this hot model scheme and like keeping people right where they want them, force fed with mental filth with regularly scheduled socially engineered programming. Beware of agendas and isms. The ESGovernanceAgenda is ready made economic coffin nails. I'll explain this very simply, a future green war on carbon is a silent war on carbon lifeforms and economies. Many of the smiling faces you can actually see on the world stage pulling levers are often the coldest blooded deceivers beyond anything you can ever imagine. In truth, corporate agents and policies are the greatest devastators to ecologies, while in concert, they are incessantly waging blame campaign agendas with subversive narratives by targeting consumers as the wrongdoers. Why am I mentioning all these adversarial difficulties? Well, the intertangling myriads of tomorrow's "bundle of burdens" in a future box ALL have to be thoroughly analyzed, sifted through, and dealt with tenaciously now and in the future by generations to come in every nation state. Some days I wonder if Hesiod's fiction was taken from reality over 2000 years ago to WARN future world inhabitants. In the scope of economics, the series of incidents that have or will lead up to major world events, will need to have the frequency of related occurrences examined that lead up to crucial points in time historically. In order to prevent future disparities, our progeny will look backwards into history with ultra clarity and vigilance to see how corrupted society once was by hordes of overlords twisted by obsessive delusions of absolute power over the entire human species. There is no human race, only diverse genetic multiformity expressed from the DNA code of humankind exists. We can't simply put the lid back on low entropy hydroCarbons and a broadband globalNet without having an implemented proven replacement or upgrade. It's far too late, leaving only wiser security chess moves forward as the only viable options. Nikola Tesla was dreaming of this daily in order to build every foundation of modern civilization that we now enjoy today and take for granted. Humanity still has to evolve by unlocking hidden secrets of mother nature. For instance, nations powered by endless geothermal electricity and deuterium fusion WILL solve a lot of the world's problems. Imagine our world dominantly powered by extreme abundant amounts of heavy water... Lady destiny awaits and begs for the future to be built securely, by eventual abandonment of antiquated wheelworks that eventually deserve to be hurled into the annihilatory dustbin of history. SPECTRAL BURDENS: Ephemeral 'spectral contents' are extremely difficult to decipher with the least amount of lag, especially while they reside within a noise ridden non-stationary environment. When 'lifting the lid off' of series analysis to peek with quick discernment, distinguishing between real-time relevant signals differing from intertwining undesirable randomness in a crowded information space, requires special kinds of intricate extraction. Due to the nature of fractal chaos, any novel spectral method is better than the scanty few we have now. Firstly, let's comprehend agilities of interpreting a spectrum's structure... SPECTRAL ANALYSIS PURPOSE AND INTENTION: Frequency Analysis - Spectral analysis serves a crucial purpose in unraveling the frequency composition of a signal. Its primary intention is to explore the intricacies of a dataset by identifying dominant frequencies and unveiling inherent cyclical patterns. This foundational understanding forms the basis for improving analyses. Power Spectrum Visualization - The visualization of a signal's power spectrum is a key objective in spectral analysis. By portraying how power is distributed across different frequencies, the goal is to provide a visual representation of the signal's energy landscape. This insight aids with grasping the significance of various frequency components obtained from a larger whole. Signal Characteristics - Understanding the traits of a signal is another vital goal. Spectral analysis seeks to characterize the nature of the signal, unveiling its periodicity, trends, or irregularities. This knowledge is instrumental in deciphering the behavior of the signal over time, fostering a deeper comprehension. Algorithmic Adaptation - Spectral analyzer estimation can play a pivotal role in algorithmic development. By assisting with the creation of algorithms sensitive to specific frequency ranges, one possible advantage is to enable real-time adaptability. This adaptability approach may allow algorithms to respond dynamically to variations in different spectral components, potentially enhancing their efficacy. Market Analysis - In the realm of trading systems and financial markets, spectral analysis methods can serve as applicable functions when studying market dynamics. By 'uncovering' trends, cycles, and anomalies within financial instruments, this analytical proficiency can aid traders and algorithm developers with making better informed decisions based on the spectral attributes of market data. Noise/Interference Detection - Another purpose of spectral analysis is to identify and scrutinize undesirable elements within a signal, such as noise or interference. One benefit would be to facilitate the development of strategies to mitigate or eliminate these unwanted components, ultimately refining the quality of a given signal with filtration. INTRODUCTION: Allow me to introduce Pandora! What you see in the demonstration above, I've named it "Pandora Periodogram", which is also referred to as 'Ultra Spectrum Analyzer' (USA) for technical minds. Firstly, this is NOT technically speaking an indicator like most others. I would describe it as an avant-garde cycle period detector obtaining accurate spectral estimates on market data with Pine Script v5.0. USA is a spectral analysis cryptid that I can only describe as being an alien saber in nature. It is my rendering of spectral wrath unleashed. With time and history to come, my HOPE is this instrument will reveal Excalibur like aspects capable of slicing up a spectrum craftily, traits long thought to be a mythical enigma. It is not modified forms of either Autocorrelation Periodogram (ACP) or MESA. Pandora's Periodogram embodies an entirely distinct design, adorned with glamourous color, by incorporating several of my most profound, highly refined technological innovations that I have poetically composed into being. What I have forged in Pine, has essentially manifested as a zero lag spectrum analyzer. Pandora easily peeks inside a single signal source more effectively to inspect for hidden spectres, revealing invisible apparitions inside data with improved clarity... My 'Ultra Spectrum Analyzer' bears an eerie likeness to Autocorrelation Periodogram, but it possesses no autocorrelation and the other small hindrances of ACP that I formerly encountered. While ACP does have a few shortcomings, a few bars of lag, and high frequency bias, it is still phenomenal code. ACP is one answer to spectral enigmas, but not the only one. Developers can utilize this detector by creating scripts that employ a "Dominant Cycle Source" input to adaptively govern algorithms. If you are capable of building suitable algorithms for direct tethering to Autocorrelation Periodogram, then this is your next step in evolutionary application to tether to when you are ready. ACP is a good place to start building upon as an exploratory vessel, before you might ponder using USA. Once you do obtain dynamic ACP sweetness with only a few pesky bars of dominant cycle induced lag, USA may be your tool chest choice without the burden of subtle ACP lag. USA is possibly the end of my quest for spectral bliss, for the time being. However, I still suspect there is more room for upgrades to Pandora in the future. I must mention, as an overture, this won't be the last of Pandora tech that you will witness, as my literal "out of the box thinking" will unleash many additional creations upon this Earth. The "Power of Pine" merely serves as the beginning foundational phase... Some of my futuristic dreams and daydreams of TradingView are droplets in a wavy ocean of economic providence and potential. What I am crafting in poetic form is born out of raw curiosity. Future creations are probably best kept private for now, but I will present my future tech with beauty and elegance as it should rightfully be. There's one catch, I have absolutely no idea what this and my future marvels may do to the future of digital signal processing (DSP) and markets. I do fear any insane AI or MALEficent entity ever seeing this code. My innermost hopes and ambitions are always focused on achieving the best result obtainable. What the future can hold, may be absolutely exquisite to gaze upon, maybe even monstrous, or possibly a combination of both. Notice: Unfortunately, I will not provide any integration support into member's projects at all. My own projects demand too much of my day to day time. I hope you understand. Meanwhile, I'll be applying this on future indication until Mr. Mortality sneaks up behind me. FEATURES AND CHARACTERISTICS: I have included as much ultra adjustability as I can humanly muster. Those features being the following and more... Color Preferences - Four vivid color schemes are available in the original release. The "Ultra Violet" color scheme, in particular, contributes to the indicator's technical title, as it seems to me to reveal the greatest detail of my various spectral color schemes. Color inversion of the four color schemes is also possible, yielding eight schemes in total with predator style visuals. Heatmap transparency control is also provided. Lag Control - Pandora achieves zero lag spectral approximations, with the added capability to control lag using an input for selectable delay. Note, however, that testing less than zero lag has not been assessed thoroughly due to potential unforeseen instability concerns. Adjustments are provided in either direction for further testing. Spectral Bias Mitigation - Options for mitigating high OR low-frequency spectral biases are present. One interesting tweak made during development was a subtle form of spectral manipulation, involving a partial reduction of frequency amplitudes influencing either the highest or lowest periodicities. This slightly reduces the impact on the upper and lower portions of the spectrogram and the dominant cycle measurement. What initially surfaced as an unexpected discovery, may now be considered worthy of experimental utility. Adjustable Periodogram Window Size - The periodogram is adjustable for various window sizes of periodic operation. Exploration up to a periodicity of 59 is obtainable for curiosity's sake. This flexibility challenges the notion that curiosity isn't always a negative trait, contrasting with Hesiod's ancient perspective. Dominant Cycle Filtration - Filtration of the dominant cycle is achieved with a novel smoother having reduced lag, easily surpassing SuperSmoother's performance. However, defeating lag completely on that one plot() function was elusive. Tooltips for Control Intention - The settings commonly include handy and informative tooltips that provide information eluding to the intention behind the various controls provided. Initialization Advantages - Initialization of USA accomplishes what Autocorrelation Periodogram (ACP) didn't. Spectral analysis begins on the earliest visible bars, starting at period 2. Users need to ensure their algorithm's integrity from period 2 upwards to beyond 40ish, establishing a viable operational range for dynamically governing those algorithms. It's notable that stochastics and correlations have a minimum operable critical period of 2, distinct from most low-pass filters that can actually achieve a period of 1 (which is the raw signal itself). Proper initialization of complex IIR filters is particularly effective, especially with smaller initialization periods. Remaining options and features are comparable to my Enhanced Autocorrelation Periodogram in terms of comprehension, and other upgrades may be added in the future upon discovery. PERIODOGRAM INTERPRETATION: The periodogram heatmap renders a power spectrum of a signal visually by color, where the y-axis represents periodicity (frequencies/wavelengths) and the x-axis is delineating time. The y-axis is divided into periods, with each elevation portraying demarcation of periodicity. In this periodogram, the y-axis ranges from 4 at the very bottom to 49 (or greater) at the top, with intermediary values in between, all conveying power of the corresponding frequency component by color. The higher the position ascends on the y-axis, the longer the cycle period or lower the frequency. The x-axis of the periodogram signifies time and is partitioned into equal chart intervals, where each vertical column corresponds to the time interval when the signal was measured. Most recent values/colors are on the right side of the periodogram. Intensity of the colors on the periodogram signify the power level of the corresponding frequency or cycle period. For example, the "Fiery Embers" color scheme is distinctly like heat intensity from any casual flame witnessed in a small fire from a lighter, match, or campfire. The most intense power exhibited would be represented by the brightest of yellow, while the lowest power would be indicated by the darkest shade of red or just black. By analyzing the pattern of colors across different periods, one may gain insights into the dominant frequency components of the signal and visually identify recurring cycles/patterns of periodicity.
Pine Script® indicator
by ImmortalFreedom
10
GKD-C Average Sentiment Oscillator [Loxx]The Giga Kaleidoscope GKD-C Average Sentiment Oscillator is a confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System." █ GKD-C Average Sentiment Oscillator This is an older forx indicator from 2010 and represents an advancement in the formula in the sentiment meter called "FX Multimeter III." It's recommended as a precise method for assessing the sentiment over a specific candlestick duration, suitable for trend filtering or determining entry/exit points. The oscillator merges two similar algorithms, each with a unique application: Individual Bar Analysis: This method evaluates the bullish or bearish nature of each bar through OHLC prices, and then averages the percentages over a specified bar group (e.g., 10 bars) to derive the final sentiment percentage. While it provides a detailed intra-bar sentiment, it tends to be more volatile. Grouped Bar Analysis: This approach views the group of bars as a singular unit, determining the sentiment based on the OHLC values of the entire group. It delivers a more consistent outcome and emphasizes broader price movement ranges. Within the indicator settings, users can opt for these algorithms independently as Mode 1 and Mode 2, or combine them under Mode 0. █ Giga Kaleidoscope Modularized Trading System Core components of an NNFX algorithmic trading strategy The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm: 1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc. 2. Baseline - a moving average to identify price trend 3. Confirmation 1 - a technical indicator used to identify trends 4. Confirmation 2 - a technical indicator used to identify trends 5. Continuation - a technical indicator used to identify trends 6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown 7. Exit - a technical indicator used to determine when a trend is exhausted 8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators* *(not part of the NNFX algorithm) What is Volatility in the NNFX trading system? In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period. True range is calculated as the maximum of the following values: -Current high minus the current low -Absolute value of the current high minus the previous close -Absolute value of the current low minus the previous close ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses. Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass What is a Baseline indicator? The baseline is essentially a moving average, and is used to determine the overall direction of the market. The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR). Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail. By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend. What is a Confirmation indicator? Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI). The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend. Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators. In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades. What is a Continuation indicator? In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy. What is a Volatility/Volume indicator? Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa. By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements. What is an Exit indicator? The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy. The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses. In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor. Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time. What is an Metamorphosis indicator? The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy. The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework. How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above? Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are: 1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm) 2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm) 3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm) 4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm) 5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm) 6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm) (additional module types will added in future releases) Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal. That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy. This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm. What does the application of the GKD trading system look like? Example trading system: Backtest: Multi-Ticker CC Backtest Baseline: Hull Moving Average Volatility/Volume: Hurst Exponent Confirmation 1: Advance Trend Pressure as shown on the chart above Confirmation 2: uf2018 Continuation: Coppock Curve Exit: Rex Oscillator Metamorphosis: Baseline Optimizer Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system. █ Giga Kaleidoscope Modularized Trading System Signals Standard Entry 1. GKD-C Confirmation gives signal 2. Baseline agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Confirmation 2 agrees 6. Volatility/Volume agrees 1-Candle Standard Entry 1a. GKD-C Confirmation gives signal 2a. Baseline agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum Next Candle 1b. Price retraced 2b. Baseline agrees 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Volatility/Volume agrees Baseline Entry 1. GKD-B Baseline gives signal 2. Confirmation 1 agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Confirmation 2 agrees 6. Volatility/Volume agrees 7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior 1-Candle Baseline Entry 1a. GKD-B Baseline gives signal 2a. Confirmation 1 agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum 5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior Next Candle 1b. Price retraced 2b. Baseline agrees 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Volatility/Volume agrees Volatility/Volume Entry 1. GKD-V Volatility/Volume gives signal 2. Confirmation 1 agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Confirmation 2 agrees 6. Baseline agrees 7. Confirmation 1 signal was less than 7 candles prior 1-Candle Volatility/Volume Entry 1a. GKD-V Volatility/Volume gives signal 2a. Confirmation 1 agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum 5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior Next Candle 1b. Price retraced 2b. Volatility/Volume agrees 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Baseline agrees Confirmation 2 Entry 1. GKD-C Confirmation 2 gives signal 2. Confirmation 1 agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Volatility/Volume agrees 6. Baseline agrees 7. Confirmation 1 signal was less than 7 candles prior 1-Candle Confirmation 2 Entry 1a. GKD-C Confirmation 2 gives signal 2a. Confirmation 1 agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum 5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior Next Candle 1b. Price retraced 2b. Confirmation 2 agrees 3b. Confirmation 1 agrees 4b. Volatility/Volume agrees 5b. Baseline agrees PullBack Entry 1a. GKD-B Baseline gives signal 2a. Confirmation 1 agrees 3a. Price is beyond 1.0x Volatility of Baseline Next Candle 1b. Price inside Goldie Locks Zone Minimum 2b. Price inside Goldie Locks Zone Maximum 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Volatility/Volume agrees Continuation Entry 1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously 2. Baseline hasn't crossed since entry signal trigger 4. Confirmation 1 agrees 5. Baseline agrees 6. Confirmation 2 agrees
Pine Script® indicator
by loxx
[delta2win] ShockSentinel Early Warnings🚀 ShockSentinel Early Warnings — Advanced Multi-Symbol Shock Detection System 📊 UNIQUE METHODOLOGY: This indicator implements a proprietary concordance-based shock detection system that goes beyond simple price movement analysis. Unlike basic pump/dump detectors, it uses a sophisticated multi-symbol correlation algorithm to validate signals across multiple assets simultaneously, significantly reducing false positives while maintaining sensitivity to genuine market shocks. 🔬 TECHNICAL APPROACH: • Adaptive Threshold System: Automatically adjusts detection sensitivity based on timeframe using proprietary scaling algorithms: - 1m: 0.5% threshold (ultra-sensitive for scalping) - 3m: 1.0% threshold (high-frequency trading) - 5m: 2.0% threshold (short-term momentum) - 15m: 3.0% threshold (intraday swings) - 1h: 6.0% threshold (daily moves) - 4h+: 10.0% threshold (swing trading) • Dual Detection Modes: - Percent Mode: Calculates maximum percentage change within configurable lookback window (1-6 bars) using the formula: max(|(close - close ) / close * 100|) for i = 1 to window - ATR-Normalized Mode: Uses Average True Range for volatility-adjusted detection across different market regimes: max(|close - close | / ATR) for i = 1 to window • Concordance Algorithm: Proprietary multi-symbol validation system that requires minimum correlation count across up to 4 additional symbols, ensuring signals are validated by market-wide participation rather than isolated price movements • Non-Repainting Architecture: Optional bar-close confirmation prevents false signals from intraday noise while maintaining real-time alert capability for immediate response 🎯 MATHEMATICAL FOUNDATION: The core algorithm implements a sliding window maximum change detection: Percent Change Calculation: For each bar, the system calculates the maximum absolute percentage change over the specified window: - PctChange = (close - close ) / close * 100 - MaxPct = max(|PctChange |) for i = 1 to window - Signal triggers when MaxPct >= threshold ATR-Normalized Calculation: For volatility-adjusted detection: - ATRChange = (close - close ) / ATR - MaxATR = max(|ATRChange |) for i = 1 to window - Signal triggers when MaxATR >= ATR_multiplier Concordance Validation: - Requires minimum N symbols showing same directional movement - Validates signal strength through market participation - Reduces false signals from isolated price movements - Improves signal quality through correlation analysis ⚙️ ADVANCED FEATURES: • Preset System: 7 pre-configured strategies with optimized parameters: - Scalp (Ultra-Fast): 0.6x scaling, 2-bar window, real-time alerts - Aggressive: 0.7x scaling, 2-bar window, real-time alerts - Balanced: 1.0x scaling, 3-bar window, confirmed signals - Conservative: 1.3x scaling, 4-bar window, confirmed signals - Volatility-Adaptive: ATR mode, 7-period ATR, 2.5x multiplier - Momentum (Intraday): ATR mode, 10-period ATR, 2.0x multiplier - Swing (Slow): ATR mode, 14-period ATR, 2.8x multiplier • Real-time vs Confirmed: Choose between immediate alerts or bar-close confirmation • Visual Analytics: Integrated signal history table with concordance gauges and performance metrics • Professional Alerts: Multi-format alert system (Compact, Extended, Plain, CSV) with Telegram integration and customizable messaging 💡 UNIQUE VALUE PROPOSITION: Unlike simple price change detectors, this system provides: 1. Multi-Symbol Validation: Validates signals across multiple correlated assets, ensuring market-wide participation 2. Adaptive Thresholds: Automatically adjusts sensitivity based on timeframe and market conditions 3. Dual Signal Types: Provides both real-time and confirmed signal options for different trading styles 4. Comprehensive Analytics: Includes signal history, concordance gauges, and performance tracking 5. Advanced Concordance: Uses sophisticated correlation algorithms for signal validation 6. Professional Integration: Built-in Telegram support with customizable message formats 🔧 USAGE INSTRUCTIONS: 1. Select Preset: Choose appropriate strategy for your trading style and timeframe 2. Configure Symbols: Add up to 4 additional symbols for concordance validation 3. Set Concordance: Adjust minimum count (higher = more selective, lower = more sensitive) 4. Choose Mode: Select between real-time or confirmed signals based on your risk tolerance 5. Enable Alerts: Configure notification preferences and message formats 6. Monitor Performance: Use integrated tables to track signal quality and concordance 📈 PERFORMANCE CHARACTERISTICS: • Optimized for Crypto: Designed specifically for high-volatility cryptocurrency markets • Multi-Timeframe: Effective across all timeframes from 1-minute to 4-hour charts • False Signal Reduction: Multi-symbol validation significantly reduces false positives • Flexible Sensitivity: Adjustable thresholds allow customization for different market conditions • Real-time Capability: Provides immediate alerts for fast-moving markets • Confirmation Option: Bar-close confirmation for conservative trading approaches ⚠️ TECHNICAL CONSIDERATIONS: • Real-time Mode: May generate multiple alerts per bar; use cooldown settings to manage frequency • Data Dependencies: Concordance requires data availability for all configured symbols • Market Regimes: ATR mode provides better performance in varying volatility conditions • Signal Quality: Higher concordance requirements reduce false signals but may miss opportunities • Latency: request.security calls depend on data provider latency and availability 🎯 TARGET MARKETS: • Cryptocurrency Trading: High-volatility crypto markets with frequent shock events • Scalping: Short-term trading strategies requiring immediate signal detection • Swing Trading: Medium-term strategies benefiting from confirmed signals • Portfolio Management: Multi-asset correlation analysis for risk management • Algorithmic Trading: Systematic strategies requiring reliable signal validation 📊 SIGNAL INTERPRETATION: • Green Arrows (Pump): Upward price shock with sufficient concordance • Red Arrows (Dump): Downward price shock with sufficient concordance • Large Markers: Confirmed signals with high concordance • Small Markers: Early signals with lower concordance • Background Colors: Visual intensity based on concordance strength • Tables: Historical signal tracking with performance metrics
Pine Script® indicator
by delta2win
Goertzel Cycle Composite Wave [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Cycle Composite Wave indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading. *** To decrease the load time of this indicator, only XX many bars back will render to the chart. You can control this value with the setting "Number of Bars to Render". This doesn't have anything to do with repainting or the indicator being endpointed*** █ Brief Overview of the Goertzel Cycle Composite Wave The Goertzel Cycle Composite Wave is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions. The Goertzel Cycle Composite Wave is considered a non-repainting and endpointed indicator. This means that once a value has been calculated for a specific bar, that value will not change in subsequent bars, and the indicator is designed to have a clear start and end point. This is an important characteristic for indicators used in technical analysis, as it allows traders to make informed decisions based on historical data without the risk of hindsight bias or future changes in the indicator's values. This means traders can use this indicator trading purposes. The repainting version of this indicator with forecasting, cycle selection/elimination options, and data output table can be found here: Goertzel Browser The primary purpose of this indicator is to: 1. Detect and analyze the dominant cycles present in the price data. 2. Reconstruct and visualize the composite wave based on the detected cycles. To achieve this, the indicator performs several tasks: 1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components. 2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength. 3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength. 4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the cycles. The color of the lines indicates whether the wave is increasing or decreasing. This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements, the indicator aims to assist traders in making more informed decisions. █ What is the Goertzel Algorithm? The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics. The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as: 1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed. 2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments. 3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear. 4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues. The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT: 1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases. 2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing. 3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest. 4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential. The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics. █ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis. Unveiling Hidden Market Cycles: Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies. Developing Quantitative Trading Strategies: The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets. For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction. Enhancing Risk Management: The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies. By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy. Expanding Quantitative Toolkits: Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately. Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals. The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies. █ Indicator Inputs src: This is the source data for the analysis, typically the closing price of the financial instrument. detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components. The available options are: hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average. zlagsmthdt: Detrend using zero-lag moving average centered moving average. logZlagRegression: Detrend using logarithmic zero-lag linear regression. hpsmth: Detrend using Hodrick-Prescott filter. zlagsmth: Detrend using zero-lag moving average. DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt. DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt. DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression. HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth. ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth. MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles. squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm. useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles. useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves. UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude. WindowSizePast: These inputs define the window size for the composite wave. FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles. BartNoCycles: This input sets the number of cycles to be used in Bartel's test. BartSmoothPer: This input sets the period for the moving average used in Bartel's test. BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant. SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results. StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles. UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input. SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles. █ Exploring Auxiliary Functions The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool. Zero-Lag Moving Average: The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator. The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator. Bartels Probability: The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets. The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies. Detrend Logarithmic Zero-Lag Regression: The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics. The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding. Bartels Cycle Significance Test: The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding. The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis. Hodrick-Prescott Filter: The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends. The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive. Detrending Options: Detrend Centered Moving Average: The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences. The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants. The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average, █ In-Depth Analysis of the Goertzel Cycle Composite Wave Code The Goertzel Cycle Composite Wave code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose. Function signature and input parameters: The Goertzel Cycle Composite Wave function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past sizes (WindowSizePast), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer). Initializing variables and arrays: The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm. Preprocessing input data: The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample. Iterative calculation of Goertzel coefficients: The core of the Goertzel Cycle Composite Wave algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure. Cycle strength computation: The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values. Phase calculation: The Goertzel Cycle Composite Wave code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients. Peak detection and cycle extraction: The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively. Sorting cycles by amplitude or cycle strength: The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data. Bartels cycle significance test: If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values. Waveform calculation: The Goertzel Cycle Composite Wave code calculates the waveform of the significant cycles for specified time windows. The windows are defined by the WindowSizePast parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted. Storing waveforms in a matrix: The calculated waveforms for the cycle is stored in the matrix - goeWorkPast. This matrix holds the waveforms for the specified time windows. Each row in the matrix represents a time window position, and each column corresponds to a cycle. Returning the number of cycles: The Goertzel Cycle Composite Wave function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles. The Goertzel Cycle Composite Wave code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Cycle Composite Wave's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data. █ Generating and Visualizing Composite Waveform The indicator calculates and visualizes the composite waveform for specified time windows based on the detected cycles. Here's a detailed explanation of this process: Updating WindowSizePast: The WindowSizePast is updated to ensure they are at least twice the MaxPer (maximum period). Initializing matrices and arrays: The matrix goeWorkPast is initialized to store the Goertzel results for specified time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information. Preparing the source data (srcVal) array: The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth). Goertzel function call: The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles. Initializing arrays for waveforms: The goertzel array is initialized to store the endpoint Goertzel. Calculating composite waveform (goertzel array): The composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component. Drawing composite waveform (pvlines): The composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward). To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms and visualizes them on the chart using colored lines. █ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading. Enhancements for Financial Modeling Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results. Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data. Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models. Enhancements for General and Advanced Trading Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies. Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size. Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions. Enhancements for High-Frequency Finance Trading Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity. Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations. Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies. While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike. █ Understanding the Limitations of the Goertzel Algorithm While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are: Lagging nature: As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses. Parameter sensitivity: The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance. Complexity: The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions. Overfitting risk: As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance. Limited applicability: The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity. While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions. █ Interpreting Results The Goertzel Cycle Composite Wave indicator can be interpreted by analyzing the plotted lines. The indicator plots two lines: composite waves. The composite wave represents the composite wave of the price data. The composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend. Interpreting the Goertzel Cycle Composite Wave indicator involves identifying the trend of the composite wave lines and matching them with the corresponding bullish or bearish color. █ Conclusion The Goertzel Cycle Composite Wave indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Cycle Composite Wave indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Cycle Composite Wave indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development. █ Footnotes What is the Bartels Test for Cycle Significance? The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century. The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series. The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges. If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component. The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles. Deep-dive into the Hodrick-Prescott Fitler The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world. The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend. The HP filter works by minimizing the following objective function: Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences) Where: 1. The first term represents the deviation of the data from the trend. 2. The second term represents the smoothness of the trend. 3. λ is a smoothing parameter that determines the degree of smoothness of the trend. The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend. The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency. However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series. Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables. The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
Pine Script® indicator
by loxx
3
Goertzel Browser [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Browser indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading. █ Brief Overview of the Goertzel Browser The Goertzel Browser is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions. The primary purpose of this indicator is to: 1. Detect and analyze the dominant cycles present in the price data. 2. Reconstruct and visualize the composite wave based on the detected cycles. 3. Project the composite wave into the future, providing a potential roadmap for upcoming price movements. To achieve this, the indicator performs several tasks: 1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components. 2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength. 3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength. 4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the past and dotted lines for the future projections. The color of the lines indicates whether the wave is increasing or decreasing. 5. Displaying cycle information: The indicator provides a table that displays detailed information about the detected cycles, including their rank, period, Bartel's test results, amplitude, and phase. This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements and their potential future trajectory, the indicator aims to assist traders in making more informed decisions. █ What is the Goertzel Algorithm? The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics. The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as: 1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed. 2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments. 3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear. 4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues. The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT: 1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases. 2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing. 3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest. 4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential. The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics. █ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis. Unveiling Hidden Market Cycles: Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies. Developing Quantitative Trading Strategies: The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets. For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction. Enhancing Risk Management: The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies. By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy. Expanding Quantitative Toolkits: Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately. Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals. The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies. █ Indicator Inputs src: This is the source data for the analysis, typically the closing price of the financial instrument. detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components. The available options are: hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average. zlagsmthdt: Detrend using zero-lag moving average centered moving average. logZlagRegression: Detrend using logarithmic zero-lag linear regression. hpsmth: Detrend using Hodrick-Prescott filter. zlagsmth: Detrend using zero-lag moving average. DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt. DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt. DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression. HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth. ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth. MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles. squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm. useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles. useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves. UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude. WindowSizePast and WindowSizeFuture: These inputs define the window size for past and future projections of the composite wave. FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles. BartNoCycles: This input sets the number of cycles to be used in Bartel's test. BartSmoothPer: This input sets the period for the moving average used in Bartel's test. BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant. SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results. UseCycleList: This boolean input determines whether a user-defined list of cycles should be used for constructing the composite wave. If set to false, the top N cycles will be used. Cycle1, Cycle2, Cycle3, Cycle4, and Cycle5: These inputs define the user-defined list of cycles when 'UseCycleList' is set to true. If using a user-defined list, each of these inputs represents the period of a specific cycle to include in the composite wave. StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles. UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input. SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles. █ Exploring Auxiliary Functions The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool. Zero-Lag Moving Average: The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator. The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator. Bartels Probability: The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets. The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies. Detrend Logarithmic Zero-Lag Regression: The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics. The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding. Bartels Cycle Significance Test: The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding. The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis. Hodrick-Prescott Filter: The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends. The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive. Detrending Options: Detrend Centered Moving Average: The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences. The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants. The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average, █ In-Depth Analysis of the Goertzel Browser Code The Goertzel Browser code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose. Function signature and input parameters: The Goertzel Browser function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past and future window sizes (WindowSizePast, WindowSizeFuture), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, goeWorkFuture, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer). Initializing variables and arrays: The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm. Preprocessing input data: The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample. Iterative calculation of Goertzel coefficients: The core of the Goertzel Browser algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure. Cycle strength computation: The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values. Phase calculation: The Goertzel Browser code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients. Peak detection and cycle extraction: The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively. Sorting cycles by amplitude or cycle strength: The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data. Bartels cycle significance test: If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values. Waveform calculation: The Goertzel Browser code calculates the waveform of the significant cycles for both past and future time windows. The past and future windows are defined by the WindowSizePast and WindowSizeFuture parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted. Storing waveforms in matrices: The calculated waveforms for each cycle are stored in two matrices - goeWorkPast and goeWorkFuture. These matrices hold the waveforms for the past and future time windows, respectively. Each row in the matrices represents a time window position, and each column corresponds to a cycle. Returning the number of cycles: The Goertzel Browser function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles. The Goertzel Browser code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Browser's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data. █ Generating and Visualizing Composite Waveform The indicator calculates and visualizes the composite waveform for both past and future time windows based on the detected cycles. Here's a detailed explanation of this process: Updating WindowSizePast and WindowSizeFuture: The WindowSizePast and WindowSizeFuture are updated to ensure they are at least twice the MaxPer (maximum period). Initializing matrices and arrays: Two matrices, goeWorkPast and goeWorkFuture, are initialized to store the Goertzel results for past and future time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information. Preparing the source data (srcVal) array: The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth). Goertzel function call: The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles. Initializing arrays for past and future waveforms: Three arrays, epgoertzel, goertzel, and goertzelFuture, are initialized to store the endpoint Goertzel, non-endpoint Goertzel, and future Goertzel projections, respectively. Calculating composite waveform for past bars (goertzel array): The past composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component. Calculating composite waveform for future bars (goertzelFuture array): The future composite waveform is calculated in a similar way as the past composite waveform. Drawing past composite waveform (pvlines): The past composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward). Drawing future composite waveform (fvlines): The future composite waveform is drawn on the chart using dotted lines. The color of the lines is determined by the direction of the waveform (fuchsia for upward, yellow for downward). Displaying cycle information in a table (table3): A table is created to display the cycle information, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle. Filling the table with cycle information: The indicator iterates through the detected cycles and retrieves the relevant information (period, amplitude, phase, and Bartel value) from the corresponding arrays. It then fills the table with this information, displaying the values up to six decimal places. To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms for both past and future time windows and visualizes them on the chart using colored lines. Additionally, it displays detailed cycle information in a table, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle. █ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles and potential future impact. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading. Enhancements for Financial Modeling Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results. Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data. Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models. Enhancements for General and Advanced Trading Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies. Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size. Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions. Enhancements for High-Frequency Finance Trading Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity. Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations. Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies. While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike. █ Understanding the Limitations of the Goertzel Algorithm While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are: Lagging nature: As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses. Parameter sensitivity: The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance. Complexity: The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions. Overfitting risk: As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance. No guarantee of future performance: While the script can provide insights into past cycles and potential future trends, it is important to remember that past performance does not guarantee future results. Market conditions can change, and relying solely on the script's predictions without considering other factors may lead to poor trading decisions. Limited applicability: The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity. While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions. █ Interpreting Results The Goertzel Browser indicator can be interpreted by analyzing the plotted lines and the table presented alongside them. The indicator plots two lines: past and future composite waves. The past composite wave represents the composite wave of the past price data, and the future composite wave represents the projected composite wave for the next period. The past composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend. On the other hand, the future composite wave line is a dotted line with fuchsia indicating a bullish trend and yellow indicating a bearish trend. The table presented alongside the indicator shows the top cycles with their corresponding rank, period, Bartels, amplitude or cycle strength, and phase. The amplitude is a measure of the strength of the cycle, while the phase is the position of the cycle within the data series. Interpreting the Goertzel Browser indicator involves identifying the trend of the past and future composite wave lines and matching them with the corresponding bullish or bearish color. Additionally, traders can identify the top cycles with the highest amplitude or cycle strength and utilize them in conjunction with other technical indicators and fundamental analysis for trading decisions. This indicator is considered a repainting indicator because the value of the indicator is calculated based on the past price data. As new price data becomes available, the indicator's value is recalculated, potentially causing the indicator's past values to change. This can create a false impression of the indicator's performance, as it may appear to have provided a profitable trading signal in the past when, in fact, that signal did not exist at the time. The Goertzel indicator is also non-endpointed, meaning that it is not calculated up to the current bar or candle. Instead, it uses a fixed amount of historical data to calculate its values, which can make it difficult to use for real-time trading decisions. For example, if the indicator uses 100 bars of historical data to make its calculations, it cannot provide a signal until the current bar has closed and become part of the historical data. This can result in missed trading opportunities or delayed signals. █ Conclusion The Goertzel Browser indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Browser indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Browser indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development. █ Footnotes What is the Bartels Test for Cycle Significance? The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century. The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series. The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges. If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component. The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles. Deep-dive into the Hodrick-Prescott Fitler The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world. The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend. The HP filter works by minimizing the following objective function: Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences) Where: The first term represents the deviation of the data from the trend. The second term represents the smoothness of the trend. λ is a smoothing parameter that determines the degree of smoothness of the trend. The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend. The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency. However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series. Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables. The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
Pine Script® indicator
by loxx
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