*SSS 50% HTF** SSS 50% - HTF (Higher Timeframe) -D/W/M/Q Edition **
This indicator is based on Sara Strat Sniper's - 50% Rule for trading Outside Bars and helps you visual the method quickly.
This indicator allows you to select: Outside: Day/Week/Month/Quarter Warning Levels with Previous High and Low Levels available within the indicators settings.
Please note - Daily warning and high/low levels are set to the indicators default. Feel free to change and save as required. Any issues let me know. See known issues below.
** Indicator Setup Requirements **
For Indicator to correctly display the 50% price level you need to setup your chart correctly. Right-click your chart background, select Settings, select Scales and Tick “Indicator Last Value Label". Now your 50% Price level will be displayed.
Known Issues:
This indicator does not work when you try to view a lower timeframes levels on a higher timeframe chart.
Example 1: Selecting Daily Warning, High and Low Levels will not be displayed correctly on a Weekly timeframe and above etc. The Daily selection however will be displayed correctly on all lower timeframes from the 1 minute chart to the Daily.
Example 2: Selecting Weekly Warning, High and Low Levels will not be displayed correctly on a Monthly timeframe and above etc. The Weekly selection however will be displayed correctly on all lower timeframes from the 1 minute chart to the Weekly. Please apply these same rules for all other timeframe indicator selections.
Monthly Warning, High and Low levels will not work correctly on a Weekly chart when the current Weekly candlestick is forming between the end of a previous Month and start of new Month. Once the first Weekly candle of a new Month closes the Monthly levels will now be displayed correctly. Please note, selecting the Monthly levels when viewing a Monthly chart will work as normal and Monthly levels displayed on a Weekly chart will be corrected once the first Weeks candle has closed. I believe this error is caused by a pine script repainting issue when assessing historical price data which is out of my limited scripting brains control.
Search in scripts for "weekly"
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.
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CLOSING STATEMENT
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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.
HADES Timecycle SMTWhat this indicator tracks
1) Time‑cycles based on QT (Micro → 90‑minute → Daily → Weekly)
HADESSMT segments the trading day and week into recurring phases and paints them directly on your chart:
real time plotting of SSMTs for Micro cycles, when Q1 and Q2 highs or lows are different for correlated assets. same for the 90‑minute quarters , Daily cycles and Weekly cycles
2) SSMT : The script continuously compares your chart to a correlated instrument and highlights cycle‑scoped SMT divergences :
Scopes: Micro, 90m, Daily, Weekly.
The tool draws compact slanted segments between consecutive cycle highs/lows and places a small label with the scope tag (e.g., 90m, D, W, Mic.) and the comparison ticker.
Table summary: A docked panel logs Bullish/Bearish SMT currently active per scope.
In plain English: when two tightly related markets fail to confirm each other’s new extremes inside the same cycle window, HADESSMT calls that out visually and in the table.
3) PSP /scanner (👁️)
A compact scanner runs on 240m, 60m, and 15m composite views of your chosen inter‑market set and tags bars with an eye icon (👁️):
👁️ below price → a bullish turning‑point signature.
👁️ above price → a bearish turning‑point signature.
Events are logged in the table (e.g., “60m Bullish PSP 👁️”).
Treat PSP tags as context—they’re not trade signals by themselves. They often add confluence when they align with SSMT and cycle boundaries.
4) “True Open” levels
includes a daily open line that marks midnight open for the day.
Inter‑market sets (Triads & Dyads)
HADESSMT automatically picks a comparison instrument based on what you’re charting. Two mechanisms exist:
Triads (auto‑pairing):
FX: EUR + GBP ↔ inverse DXY
Metals: Gold + Silver
US Indices: NQ + YM + ES
You can show one or both comparison legs.
Tip: If you don’t see SMT labels, ensure your symbol belongs to one of the configured sets or customize the tickers in Triad inputs.
On‑chart visuals you’ll see
Thin slanted SMT markers between successive cycle extremes with a small scope/ticker tag.
👁️ PSP labels on higher‑timeframe bars.
True‑Open lines labeled 00:00 (daily) .
Summary table (right side by default) containing:
The comparison ticker currently in use,
Any active Bullish/Bearish SMT per scope,
Recent PSP 👁️ calls at 240/60/15 minutes.
How to use it (practical flow)
Pick your market & ensure a comparison exists (Triad ).
Important: HADESSMT is a context engine, not a trade system. Use your own risk management and confirmation.
Triad– enable one/both SMT comparisons; edit the default tickers if your broker symbols differ.
Timezone – UTC offset (default -4) to align cycle splits with your session.
Micro features appear on charts ≤ 5m.
90‑minute features are designed for charts ≤ 30m.
Daily features prefer charts ≤ 3h.
Weekly features render reliably on daily charts and below.
(If a layer doesn’t appear, you may be on a timeframe above its designed threshold.)
FAQs
Why don’t I see SMT on my market?
Ensure the symbol is in one of the configured Triad sets, or add your own correlated ticker(s).
What exactly is PSP?
A compact pressure/turn signature across your inter‑market set. It’s presented as an 👁️ tag and a table entry (bullish/bearish). The internal detection specifics are intentionally abstracted.
Gabriel's Squeeze Momentum📊 Gabriel’s Squeeze Momentum — Deluxe Volatility + Momentum Suite
An advanced, all-in-one squeeze & momentum framework that times volatility compression/expansion and trend shifts, with optional CVD (cumulative volume delta) momentum, ATR zone context, Discontinued Signal Lines (DSL) scalps, Colored DMI trend label, Williams VIX Fix (WVF) low-volatility exhaustion pings, Buff’s VTTI/VPCI volume confirmation, and real-time divergence detection.
What it does:
Discover Squeezes. They occur when volatility contracts, often preceding significant price moves.
Measures momentum with a fast, ATR-normalized linear regression—optionally on Price or CVD—so you see direction and “how hard it’s pushing.”
🧭 Signal Legend ~ Colors the squeeze so you instantly know regime:
🟡 / 🟣 (Tight/Very Tight): Coiled spring; prepare a plan.
🔴 / ⚫ = (Regular/Wide): Watch for Divergences between Price and Momentum.
🟢 (Fired): Expansion started; trade with momentum cross and bias.
Adds context bands at ±1/±2/±3 ATR (“trend / expansion / OB-OS”) to filter late or weak signals.
DSL (Discontinued Signal Lines) give early scalp flips on momentum vs. adaptive bands.
DMI label & triangles communicate trend strength and whether +DI / −DI is in control.
Williams VIX Fix flags capitulation/exhaustion style spikes (with optional VIX proxy).
VTTI/VPCI modules confirm when volume aligns with price trend or contradicts it.
Divergences (regular & hidden) auto-draw with optional live (may repaint) or on-close.
🎢 Squeeze Momentum — How the Logic Works 🎢
The Squeeze Momentum model is built on the principle of volatility compression and expansion. In markets, periods of low volatility are often followed by explosive moves, while high volatility eventually contracts. The “squeeze” seeks to identify these compression phases and prepare traders for the likely expansion that follows.
This indicator achieves that by comparing Bollinger Bands (BB) to Keltner Channels (KC).
Bands: Bollinger vs. Keltner
Bollinger Bands (BB): Calculated using a Simple Moving Average (SMA) of price and standard deviations (σ) of the closing price. The bands expand and contract depending on volatility.
Keltner Channels (KC): Built from an SMA plus/minus multiples of the Average True Range (ATR). Unlike some simplified squeeze indicators that approximate ATR, this implementation uses a true ATR-based KC, ensuring accuracy across different assets and timeframes.
By comparing whether the Bollinger Bands are inside or outside the Keltner Channels, the indicator identifies different squeeze regimes, each representing a distinct volatility environment.
📦 Regime Colors
The squeeze states are color-coded for quick interpretation:
🔹Wide Squeeze (⚫): BB inside KC with a high ATR multiplier. Extremely low volatility, often before major expansion.
🔹Normal Squeeze (🔴): BB inside KC with a moderate ATR multiplier (about 25% more sensitive than Wide). Typical compression setting.
🔹Narrow Squeeze (🟡): BB inside KC with a lower ATR multiplier (about 50% more sensitive than Wide). Signals tighter compression.
🔹Very Narrow Squeeze (🟣): BB inside KC with the lowest ATR multiplier (100% more sensitive than Wide). Indicates extreme coiling.
🔹Fired Squeeze (🟢): BB break outside KC. Marks the release of volatility and potential trend acceleration.
This multi-layered system improves upon classical SQZPRO by using precisely calculated Keltner Channels and multiple sensitivity levels, giving traders more granular information about volatility states.
🔒 Multi-Timeframe Support
The indicator automatically adjusts squeeze thresholds for different timeframes — hourly, 4-hour, daily, weekly, and monthly charts. Each regime has been manually tuned for its timeframe, allowing traders to use the same tool whether scalping, swing trading, or holding longer-term positions.
🎯 Momentum Core
Detecting a squeeze is only half the equation — the indicator also includes a momentum engine to determine direction and strength.
Price momentum is measured as the distance of Close from its Highest High and Lowest Low range, smoothed with a Simple Moving Average, and refined with Linear Regression.
This value is then divided by ATR, normalizing momentum relative to volatility.
Optionally, CVD Mode (Cumulative Volume Delta ÷ Volume) can replace price momentum for assets where order-flow and volume dynamics dominate (e.g., crypto).
🦆 Signal Line
Momentum is paired with a Simple Moving Average signal line:
🔹Bullish: Momentum > Signal.
🔹Bearish: Momentum < Signal.
This crossover logic provides directional bias and filters for false squeezes.
🚀 When to Use Price vs. CVD
CVD Mode (Crypto, FX with tick volume): Best for assets with strong volume/order-flow signals.
Price Mode (Equities, Commodities, Higher TFs): Best for assets with irregular or thin volume data.
🛢️ATR Zones (context filter) 🛢️
Its design is straightforward yet effective: it measures the difference between the current price from its highest highs, lowest lows, and a moving average over a chosen period, then expresses that difference in terms of the Average True Range (ATR) over the same period. By normalizing price deviations against volatility, ATR provides a clear sense of how far and how fast price is moving relative to its “normal” range.
Interpreting the Zone
Positive Values: When it is above zero, price is trading above its HH, LL, and moving average, suggesting bullish momentum. The higher the value, the stronger the momentum relative to volatility.
Negative Values: When the Momentum is below zero, price is trading below its HH, LL, and moving average, signaling bearish momentum. The deeper the reading, the stronger the downside pressure.
Magnitude Matters: Because the Momentum is expressed in ATR units, traders can immediately gauge whether the move is small (less than 1 ATR), moderate (1–2 ATRs), or extreme (3+ ATRs). This makes it especially useful for assessing overbought or oversold conditions in a normalized way.
Strengths:
🔹Volatility-Normalized: Unlike simple squeeze momentum oscillators that have different OB/OS levels, this Momentum adjusts for volatility. This makes signals more consistent across assets with different volatility profiles.
🔹Simplicity:
±1 ATR: trending zone (bulls above +1, bears below −1)
±2 ATR: expansion (keep, add, or trail). Stretch/risk of mean reversion.
±3 ATR: potential exhaustion/mean-revert zone.
🔹Momentum Clarity: By framing momentum in ATR terms, it is easier to distinguish between a small deviation from trend and a genuinely significant move. Sometimes it is a good sign that it trend to ±3/2 ATR, looks for similar directional moves.
Color: The script shades +2/+3 (OB) and −2/−3 (OS) areas and provides swing alerts at ±1 ATR.
💚 What Are Discontinued Signal Lines (DSL)? 💚
In technical analysis, one of the most common tools for smoothing out noisy data is the signal line. This concept appears in many indicators, such as the MACD or stochastic oscillator, where the raw value of an indicator is compared to a smoothed version of itself. The signal line acts as a lagging filter, making it easier to identify shifts in momentum, crossovers, and directional changes.
While useful, the classic signal line approach has limitations. By design, a single smoothed line introduces lag, which means traders may receive signals later than ideal. Additionally, a one-size-fits-all smoothing process often struggles to adapt to different levels of volatility or rapidly changing market conditions.
This is where Discontinued Signal Lines (DSL) come in. DSL is an advanced extension of the traditional signal line concept. Instead of relying on just one smoothed comparison, DSL employs multiple adaptive lines that adjust dynamically to the current state of the indicator. These adaptive lines effectively “discontinue” the dependence on a single, fixed smoothing method, producing a more flexible and nuanced representation of market conditions.
How DSL Works?
Traditional Signal Line: Compares an the Momentum against its own moving average. Provides crossover signals when the raw indicator value moves above or below the smoothed line.
Strength: reduces noise. Weakness: delayed signals and limited adaptability.
DSL Extension: Uses multiple adaptive lines that respond differently to the indicator’s current behavior. Instead of one static moving average, the DSL approach creates faster and slower “reaction lines.” These lines adapt dynamically, capturing acceleration or deceleration in the indicator’s state.
Result: Traders see how momentum is evolving across multiple adaptive thresholds. This reduces false signals and improves responsiveness in volatile conditions.
Benefits of Discontinued Signal Lines
🔹Nuanced Trend Detection
DSL doesn’t just flag when momentum changes direction—it shows the quality of that shift, highlighting whether it is gaining strength, losing steam, or consolidating.
🔹Adaptability Across Markets
Because DSL adjusts to the Momentum’s own dynamics, it works well across different asset classes and timeframes, from equities and futures to forex and crypto.
🔹Earlier Signal Recognition
Multiple adaptive lines allow traders to spot developing trends earlier than with a single smoothed signal line, without being overwhelmed by raw indicator noise.
🔹Better Confirmation
DSL is particularly useful for confirmation. If both adaptive lines agree then a fill is applied in the direction, confidence in the trend is higher as the color turns bull/bear.
🔹Practical Uses
Momentum Trading: Spot acceleration or deceleration in trend strength.
Trend Confirmation: Verify whether a breakout has momentum behind it.
Noise Filtering: Smooth out erratic moves while retaining adaptability.
⚖️ Colored Directional Movement Index (CDMI) ⚖️
The Directional Movement Index (DMI), created by J. Welles Wilder, is one of the most respected trend-following indicators in technical analysis. It is actually a family of three separate indicators combined into one: the +DI (Positive Directional Indicator), the –DI (Negative Directional Indicator), and the ADX (Average Directional Index). Together, they measure not only whether the market is trending but also the strength of that trend. Traders have used the DMI for decades to identify trend direction, gauge momentum, and filter out periods of market noise.
However, despite its reliability, the traditional DMI can be challenging to interpret. Reading three separate lines at once and extracting meaningful signals requires both experience and careful observation. This complexity often discourages newer traders from fully utilizing its power.
The Colored Directional Movement Index (CDMI) is a modern reinterpretation of Wilder’s classic tool. It condenses the same information into a single visual line while using color, shape, and density to communicate what’s happening beneath the surface. The goal is simple: make the DMI’s insights faster to read, easier to act upon, and more intuitive to integrate into trading decisions.
Key Features of CDMI
🔹Color Scale for Trend Strength
The main triangle changes its base color depending on the strength of the DI reading. Dark Red or Green, colors correspond to stronger trends, while faded Gray or lighter yellow tones signal weaker or fading trends. This makes it visually clear when the market is consolidating versus trending strongly.
🔹Color Density for Momentum
Beyond strength, the CDMI uses color density to represent momentum in the trend’s strength. If the ADX is rising (trend gaining momentum), the triangles grows more darker. If the ADX is falling (trend losing momentum), the triangle becomes paler. This provides an instant sense of whether a trend is accelerating or decelerating.
🔹Directional Triangles for Trend Direction
To replace the separate +DI and –DI lines, the CDMI plots small triangle shapes along the bottom axis. An upward-facing triangle indicates that +DI is dominant, confirming bullish direction. A downward-facing triangle signals –DI dominance, confirming bearish direction. This way, both strength and direction are shown without the clutter of multiple overlapping lines.
🔹Label Display for Detailed Values
For traders who want precise data alongside the visuals, CDMI includes a label that shows:
Current trend strength (ADX value).
Current +DI and –DI values.
Momentum status of the ADX (rising or falling).
Historical values of DMI readings, so traders can track how the indicator has evolved over time.
Tooltips are also available to explain “How to read the colored DMI line”, making this version more beginner-friendly.
Why CDMI Matters
The CDMI retains the proven reliability of Wilder’s DMI while solving its biggest drawback—interpretation difficulty. Instead of juggling three separate plots, traders get a single, information-rich line supplemented with intuitive shapes and labels. This streamlined format makes trend verification, momentum analysis, and signal confirmation much faster.
For trading applications, the CDMI can help:
Confirm Entries by showing whether the market is trending strongly enough to justify a position.
Avoid False Signals by filtering out periods of low ADX (weak trend).
Enhance Timing by tracking momentum shifts in trend strength.
By simplifying the complexity of the original DMI into an elegant, color-coded tool, the CDMI makes one of technical analysis’ most advanced indicators practical for everyday use.
😅 The VIX, the Williams Vix Fix, and Market Bottoms 😎
The VIX, formally known as the CBOE Volatility Index, has long been considered one of the most reliable indicators for spotting major market bottoms. Often referred to as the “fear gauge,” it measures the market’s expectation of volatility in the S&P 500 over the next 30 days. When fear grips investors and volatility spikes, the VIX rises sharply. Historically, these moments of extreme fear often coincide with powerful buying opportunities, as markets have a tendency to rebound once panic selling exhausts itself.
Larry Williams, a well-known trader and author, developed the Williams Vix Fix as a way to replicate the insights of the VIX across any tradable asset. While the VIX itself is tied specifically to S&P 500 options, Williams wanted a tool that could capture similar panic-driven dynamics in stocks, futures, forex, and other markets where the VIX is not directly applicable. His “fix” uses price action and volatility formulas to approximate the same emotional extremes reflected in the official VIX, creating almost identical results in practice. This makes the Williams Vix Fix a powerful addition to the trader’s toolbox, allowing the same principle that works on U.S. equities to be applied universally.
One of the most important characteristics of both the VIX and the Williams Vix Fix is that they are far more reliable at signaling market bottoms than market tops. The reason is psychological as much as it is mathematical. At market bottoms, fear and panic are widespread. Retail investors often capitulate, selling in a frenzy as prices drop. This panic drives volatility higher, producing the spikes we see in the VIX. At the same time, professional traders and institutions—those with larger capital and more disciplined strategies—tend to step in when volatility is stretched. They buy when others are fearful, using the panic of retail investors as an opportunity to acquire assets at discounted prices. This confluence of retail panic and institutional buying power is what makes the VIX such a strong bottom-finding tool.
In contrast, at market tops, the dynamic is very different. Tops tend not to be marked by panic or fear. Instead, they form quietly as enthusiasm fades, liquidity dries up, and buying interest wanes. Investors are often complacent, assuming prices will continue to rise, while professional money begins distributing their positions. Because there is no surge in fear, volatility remains muted, and the VIX does not offer a clear warning. This is why traders who rely on the VIX or the Williams Vix Fix must understand its limitations: it is exceptional for detecting bottoms but less useful for anticipating tops.
For traders, the lesson is straightforward. When you see the VIX or Williams Vix Fix spiking to extreme levels, it often indicates a high-probability environment for a rebound. These tools should not be used in isolation, but when combined with support levels, sentiment indicators, and market breadth, they can provide some of the most reliable bottom-fishing signals available. While no indicator is perfect, few have stood the test of time as consistently as the VIX—and thanks to Williams’ adaptation, its power can now be applied to nearly every market.
Indicator Signals (Great in risk-off charts):
🔹Flags spike events (tops/bottoms) with both original and filtered (AE/FE) criteria.
🔹Great as a risk overlay: tighten stops into AE/FE, or require “no spike” to enter.
🤯 Volume Comfirmation: VTTI & VPCI (Buff Dormeier) 🤯
Volume Trend Technical Indicator (VTTI)
The Volume Trend Technical Indicator (VTTI) is a momentum-style tool that analyzes how volume trends interact with price movement. Unlike basic volume measures that simply report how many shares or contracts were traded, the VTTI evaluates whether volume is expanding or contracting in the same direction as the prevailing price trend. The underlying logic is that healthy trends are supported by rising volume, while weakening trends often occur on shrinking volume.
At its core, VTTI looks at the rate of change in volume compared to price movements. By smoothing and normalizing these relationships, the indicator helps traders determine whether momentum is accelerating, decelerating, or diverging.
Rising VTTI: Suggests that volume is confirming the current price trend, strengthening the case for continuation. Flips BG Green after crossing it's signal.
Falling VTTI: Indicates that the trend may be losing participation, often a sign of possible consolidation or reversal. Flips BG Red after crossing it's signal.
Traders often use VTTI to filter entries and exits. For example, if price breaks out but VTTI does not rise above zero, the breakout may lack conviction. On the other hand, when both price and VTTI are aligned, probability of continuation improves.
Volume Price Confirmation Indicator (VPCI)
The Volume Price Confirmation Indicator (VPCI), developed by Buff Dormeier, takes the relationship between price and volume a step further. While traditional indicators like On-Balance Volume (OBV) or Chaikin Money Flow look at cumulative patterns, VPCI breaks price and volume into trend and volatility components and then recombines them to measure how well they confirm each other.
In essence, VPCI asks: “Does volume confirm what price is signaling?”
The formula integrates:
Price Trend Component – whether the market is trending upward or downward.
Volume Trend Component – whether trading activity supports that price trend.
Volatility Adjustments – to account for irregular swings.
The resulting oscillator fluctuates around a zero line:
Positive VPCI: Indicates that price and volume trends are in agreement (bullish confirmation).
Negative VPCI: Suggests that price and volume are diverging (bearish warning or false move).
Crossovers of Zero: Can serve as potential buy or sell signals, depending on context.
A key strength of VPCI is its sensitivity to divergence. When prices continue rising but VPCI begins falling, it often foreshadows a weakening rally. Conversely, a rising VPCI during a flat or down market can highlight early accumulation.
VTTI (Entry Signal) vs. VPCI (Exit Signal)
While both indicators study price-volume dynamics, their focus differs:
VTTI is simpler, emphasizing the trend of volume relative to price for momentum confirmation.
VPCI is more advanced, decomposing both price and volume into multiple components to produce a nuanced oscillator.
Used together, they provide complementary insights. VTTI helps quickly spot whether volume is supporting a move, while VPCI offers deeper confirmation and highlights subtle divergences.
Note: The Up/Down Volume Alert works better on the 4 HR, for Daily scalps or 30 minute for HR scalps. Intraday it's 2/10 minute.
🦅 Divergence toolkit 🦅
Divergences in Technical Analysis
Divergence occurs when the price action of an asset moves in one direction while a technical indicator, such as RSI, MACD, or Momentum, moves in the opposite direction. This disagreement between price and indicator often signals a shift in underlying market dynamics. Traders use divergences to anticipate either potential reversals or continuations in trends.
There are two main types of divergences: regular divergences, which typically precede reversals, and hidden divergences, which suggest continuation of the current trend.
Regular Divergence (Reversal Signals)
A regular divergence occurs when price and indicator disagree during a trend extension. These divergences signal that momentum is no longer fully supporting the current trend and that a reversal may be imminent.
🔹Regular Bullish Divergence
Price Action: Forms a lower low.
Indicator: Forms a higher low.
Interpretation: Price is making new lows, but the indicator is gaining strength. This suggests that selling pressure is weakening, and a reversal to the upside may occur.
Example: RSI rising while price dips to fresh lows.
🔹Regular Bearish Divergence
Price Action: Forms a higher high.
Indicator: Forms a lower high.
Interpretation: Price is reaching new highs, but the indicator shows weakening momentum. This implies that buying pressure is fading, warning of a potential downside reversal.
Example: MACD histogram falling while price makes higher highs.
Regular divergences are often spotted near the end of trends and are most powerful when aligned with key support/resistance levels or overbought/oversold conditions.
Hidden Divergence (Continuation Signals)
A hidden divergence occurs during retracements within a trend. Unlike regular divergences, hidden divergences suggest that the prevailing trend still has strength and is likely to continue.
🔹Hidden Bullish Divergence
Price Action: Forms a higher low.
Indicator: Forms a lower low.
Interpretation: Price is retracing within an uptrend, but the indicator is overshooting downward. This shows that momentum remains intact, supporting continuation upward.
🔹Hidden Bearish Divergence
Price Action: Forms a lower high.
Indicator: Forms a higher high.
Interpretation: Price is retracing within a downtrend, while the indicator overshoots upward. This indicates that bearish momentum remains strong, supporting continuation downward.
Hidden divergences often appear during pullbacks, helping traders time entries in the direction of the prevailing trend.
Practical Use of Divergences
🔹Trend Reversal Alerts – Regular divergences are early warnings that a trend may be ending.
🔹Trend Continuation Signals – Hidden divergences help confirm that retracements are simply pauses, not full reversals.
🔹Confluence with Other Tools – Divergences are more reliable when combined with support/resistance, candlestick patterns, or volume analysis.
🔹Multi-Timeframe Analysis – Spotting divergences on higher timeframes often produces stronger signals.
🕭🔔🛎️ Alert 🛎️🔔🕭
🔹Squeeze
🟢 Fired Squeeze
⚫ Low (Wide) Squeeze / 🔴 Normal / 🟡 Tight / 🟣 Very Tight
🔹Momentum
🐂 Bullish Trend Reversal (Crossover of Momentum and Signal from sub −2)
🐻 Bearish Trend Reversal (Crossover of Momentum and Signal from above +2)
📈 Bullish Swing (cross above +1 ATR) / 📉 Bearish Swing (cross below −1 ATR)
🔹DSL
💚 Bullish DSL Scalp / 💔 Bearish DSL Scalp
🔹Volume
🎯 Strong Up Volume (VPCI > 0 and VTTI up)
⏳ Strong Down Volume (VPCI < 0 and VTTI down)
🔹Divergences
🦅 Bullish, 🦆 Bearish, 🦅 Bullish Hidden, 🦆 Bearish Hidden
Management: Search Vanguard ETFs in your browser, look up full list of VOO holdings. Download it, or copy paste all the ticker symbols. Place that with a AI, just ask it to place , in between each ticker. NVDA, TSLA, AVGO, etc. Create a new watchlist, in the + add all tickers separated by commas. Place a watchlist alert ⚠️ only available for premium + subscribers.
Practical playbook
1) Classic Squeeze Break
Setup: 🔴(D)/🟡(2D)/🟣(3D) squeeze → wait for 🟢(1HR) Fired.
Confirm: Momentum > Signal and above +1 ATR (or DMI strong & rising).
Manage: add on pullbacks that hold +1 ATR; scale near +2 ATR or WVF AE/FE.
2) DSL Scalp in Trend
Setup: Clear trend (DMI strong) + DSL bull/bear trigger in the direction of trend.
Filter: avoid tight/very tight yellow/purple unless you want micro-scalps.
Exit: opposite DSL or ATR midline loss.
3) Mean-Reversion Fade
Setup: Momentum extended to ±3 ATR, WVF spike, and a regular divergence.
Entry: Counter signal only when mom crosses back through ±3 ATR toward mid. Exit early if squeeze ⚫/🔴, Momentum may extend to ±3/2 ATR in the same direction.
Risk: reduce size; this is a fade, not trend following.
4) Volume-Confirmed Breakout
Setup: Squeeze → 🟢 Fired + VPCI > 0 and VTTI up → trend continuation.
Manage: trail behind +1 ATR (long) or −1 ATR (short). 9 SMA works good.
Inputs at a glance (key ones)
Mode: Price or CVD momentum; Squeeze Sensitivity (σ); Momentum Length; Signal Length; ATR Smoothing.
🧮 Colors:
SQZMOM: per squeeze regime, momentum, ATR fills.
DSL: On/Off, Fast/Slow, Length.
ATR Zones: Bullish/Bearish levels (±1), ±2/±3 zone lines & fills.
DMI: Lengths, key & weak thresholds, label on/off.
WVF/VIX: Lookbacks, bands, AE/FE toggles, VIX proxy symbol.
VTTI/VPCI: Fast/slow/signal (VTTI), Short/Long (VPCI), and volume source (Tick/CVD/NVI/PVI/OBV/PVT/AccDist/VWAP).
Divergences: Regular/Hidden toggles, Sensitivity %, Lifetime, Live vs On-Close, Lines/Labels.
🔎 Suggested defaults (feel free to tweak)
Calibration: Size Momentum, so that when it's above zero the asset is trending up. For the signal, it can be kept the same or lower.
Intraday (60–240m): σ = 2.0, 18~20, 3~5, DSL Fast, DMI key 23, weak 17.
Daily/Weekly: keep σ = 2.0, consider DSL Slow, DMI key 25, weak 20, widen ATR filters; lean on VPCI/VTTI (4-HR).
CVD mode: use where tick/volume quality is high (index futures, liquid equities, crypto majors).
🪟 Tips & caveats
Swing Screener: Favor liquid underlyings (index futures/ETFs, large caps). Large-Cap, 2 M Vol, Mid-Cap, 500K Vol. Squeeze: BB( 20) upper < KC (20) upper, and BB (20) lower > KC (20) lower. Optional: Price above 9 SMA, 21 SMA, and 50 SMA, they are my SMA of choice. 200 SMA too, unless you are willing to fish in a bear market. Vice-versa for shorts. Optional: ADX 4 HR > 17, or 23 depending on what you are looking for.
Scalp Screener: Same as above, change the D 9 SMA to 5, and the BB/KC from D to 1 HR. Scalps may last 2~3 days.
Position Screener: Change all daily setting to W, aside from Volume. Optional: PEG < 1.5, FCF > 0, ROA > 8% or ROE > 6%.
Good with Moving averages (9/21/50) and low-volume zones.
Position size by IV, ATR, and account risk. Consider stop/hedge rules around ±2/±3 ATR.
Let alerts stage your watchlist; act only on combined squeeze + momentum signals.
Divergences in live mode can repaint (Real-Time); for algo or alerts, use on-close.
Tight/Very tight squeezes are great for scalps but choppy; combine with DMI rising + VPCI>0.
±3 ATR is exhaustion context, not an auto-fade—look for WVF/Div/DSL confirmation.
For alerts, pair “Fired Squeeze + Bullish Swing” (or bearish) to avoid false starts.
🎯 How to Trade Entry ~ Recap:
Tight/very tight squeeze → fires → momentum crosses up (or DSL bull).
Exit/Flip: Momentum crosses down into/after expansion or hits +2/+3 ATR with fade signs. Filter: Avoid fresh longs at +3 ATR; avoid fresh shorts at −3 ATR unless fading with confirmation.
📐 Options Integrations
✅ Risk Reversal/Modified Risk Reversal (Bullish: Short Put + Long Call)
Use when: Squeeze fires up from 🟡/🟣 and momentum crosses above signal (or zero/DSL).
Playbook Entry: On or just after the bullish fire and momentum upcross. DMI or Volume supports trend as well.
Structure: Sell a put at/just below the −2 ATR reference (or recent swing support). Buy a call at/above the breakout zone (prior high/mid-range +1 to +2 ATR).
A classic risk reversal is a long call plus a short put. That’s a very bullish structure—you gain if the price rallies (via the call), and you collect a premium by selling a put. But it has a naked downside risk. The modified risk reversal fixes that by adding a long lower put (making the short put into a defined put credit spread).
Management: If momentum stays above signal, ride toward +2 → +3 ATR. Sell the put near the current price → receive big premium. Buy the lower put → spend part of that premium (risk cap). Buy the call above the current price → spend more, but the short put premium mostly pays for it.
Exits/Adjust: Momentum downcross or squeeze flips back on (new compression) → reduce. If price retests −1/−2 ATR and holds, you can roll the short put down/out.
Breakout = Big Success; No Breakout = you keep the initial credit. Reversal = Max loss is capped by the long lower put.
✅ Iron Condor (Neutral: Short OTM Put Spread + Short OTM Call Spread)
Use when: Squeeze is active (🟡/🟣), momentum is flat near zero, and there is no directional edge. 🟢 lasts for around 5~8 bars typically. I measure the historical duration of it, and wait for a range period to occur.
Playbook Entry: During compression, set wings outside ±2 ATR (or recent range extremes). I prefer identifying boxes where the rectangle pattern occurs on the chart.
Management: Time decay works while price remains trapped in the coil. High-winrate ~80%, but 1 loser can wipe most of the gains.
Exits/Adjust: If a squeeze fires and momentum breaks hard one way, close the losing side, consider converting to a vertical or rotating to a directional spread aligned with momentum.
4HR-Bullish, closing one wing:
Tip: Align daily/weekly context with your intraday entries. 9 > 50 on Weekly, similar on Daily. Sell premium into compression; switch to directional spreads on expansion and momentum confirmation.
✅ Naked Call/Puts (Directional: 10~30 Delta Calls)
Stick to naked calls and puts when the squeezes are fired from either 🔴 or ⚫.
Look for Strikes slightly out of the money with an OI and Volume spread less than <10%.
If Strike Date is >45, manage 21 Days before expiration. Scalp: Expiration Strikes of 1/4 of the Squeeze period. Leap: Expiration Strikes of 1.75x of the Squeeze period.
📐 Futures Integrations
Playbook Entry:
Verify if the squeeze on the hourly is red or green, and enter on the 2- or 5-minute during a similar squeeze state.
Trend-Following: Traditional 2 Renko Block above 21 SMA and Momentum is bullish, or vice versa. (2~ES, 5~NQ)
Structure: Go long at/just below the ATR reference (or recent swing support). Exit below the breakout zone (prior high/mid-range +1 to +2 ATR).
Management: If momentum stays above +1 ATR ride toward +2 → +3 ATR, etc. House-money, should be kept.
Exits/Adjust: Momentum downcross or squeeze flips back on (new compression) → exit. On Renko Charts, lower the sensitivity to 0.7~1. If price retests 0/−1/−2 ATR and holds, you can enter when the 9 SMA flips. The 50 SMA is better for Daily and up; I wouldn't trade against it then.
📌 FOMO Trading Playbook
Credits & License
Credits: @JF10R (Multi-Timeframe Squeeze), @BigBeluga (DSL), @OskarGallard (Colored DMI base), @ChrisMoody (WVF ideas), @PineCodersTASC (VTTI/VPCI), @EliCobra (Divergence toolkit).
License: Mozilla Public License 2.0 (MPL-2.0).
Author: © GabrielAmadeusLau
Каналы_SMA_magistratura⚜️ Custom Indicator "ChannelsSMA_magistratura" — Seeing Trends as Zones, Not Lines
🔹 Why traditional trend lines aren't always practical?
— We draw trend lines, parallels, resistance levels — and the chart turns into a "geometry notebook",
— Every new impulse requires a new channel,
— Old lines get in the way and need to be deleted,
→ This is inefficient, unstructured, and not scalable.
🔹 Alternative — Moving Averages (SMA)
— SMAs show the trend direction without extra drawings,
— We already use SMA Magistratura to see all moving averages across timeframes,
— But we can go even further.
🔹 What are "ChannelsSMA_magistratura"?
— A custom indicator that turns SMA lines into zones,
— Each moving average is surrounded by a channel,
— Channel width is optimized based on backtesting,
→ Shows the normal price range around each SMA.
🔹 How the indicator works
— Visually:
SMA — central line,
Channel boundaries — on both sides,
The space between channels — filled in gray.
📌 Gray zones = deviations we call "gaps" (or "breakouts").
🔹 Normal vs. Anomaly
✅ Normal (Equilibrium):
— Price moves within the channel,
— Channels are nested:
→ Daily inside Weekly,
→ Weekly inside Monthly,
→ Monthly inside 3-Monthly.
→ Market is balanced — low volatility, stable trend.
⚠️ Anomaly (Gap):
— Price moves outside the channel,
— Channels diverge, forming a fan:
→ Monthly separates from 3-Monthly,
→ Weekly from Monthly,
→ Daily from Weekly.
→ This signals volatility, potential impulse, or correction.
🔹 Why are gaps the best entry points?
— A gap = anomalous deviation,
— Market is overbought or oversold,
— The wider the gap — the higher the chance of a strong reversal or new impulse.
📌 Example:
— Before a strong rally, price accumulates inside the channel,
— Then — a sharp breakout, piercing multiple levels.
🔹 Why is this indicator separate?
— Not always needed,
— Can be turned on/off for local analysis,
— Keeps the chart clean when using other tools.
🔹 Advantages over manual drawing
— No need to build channels manually,
— No guessing where support/resistance is,
— Everything is visible in advance, visually, and systematically.
"ChannelsSMA_magistratura" is not just an indicator.
It’s a way to see the trend not as a line — but as a zone.
It’s the ability to see not what has already happened,
but what is about to happen.
Multi-Timeframe SMA IndicatorOverview
The Multi-Timeframe Simple Moving Average (SMA) Indicator is a technical analysis tool designed for use in TradingView, written in Pine Script (version 6). It calculates and displays SMAs across multiple timeframes on a single chart, enabling traders to analyze price trends from various temporal perspectives simultaneously. This indicator is particularly useful for traders who employ multi-timeframe analysis to confirm trends, identify support and resistance levels, and make informed trading decisions.
Features
Multiple Timeframes: The indicator supports ten customizable timeframes: 1-minute, 2-minute, 3-minute, 5-minute, 15-minute, 30-minute, 1-hour, 4-hour, daily, and weekly. This allows traders to observe price trends across short-term, medium-term, and long-term perspectives.
Configurable SMA Lengths: Users can adjust the periods for three SMAs: 20-period, 50-period, and 200-period. These lengths are commonly used to represent short-term, medium-term, and long-term trends, respectively.
Customizable Colors: Each SMA (20, 50, and 200) can be assigned a unique color (default: blue for SMA 20, red for SMA 50, green for SMA 200) to distinguish them visually across different timeframes.
Overlay on Chart: The indicator is overlaid directly on the price chart, making it easy to compare SMAs with price action without cluttering the workspace.
Flexible Timeframe Selection: Users can modify the timeframes via input settings, allowing adaptability to different trading strategies (e.g., scalping, swing trading, or long-term investing).
Settings
The indicator provides the following configurable inputs:
Timeframe Inputs
1-Minute Timeframe: Default set to '1' (1-minute chart).
2-Minute Timeframe: Default set to '2'.
3-Minute Timeframe: Default set to '3'.
5-Minute Timeframe: Default set to '5'.
15-Minute Timeframe: Default set to '15'.
30-Minute Timeframe: Default set to '30'.
1-Hour Timeframe: Default set to '60'.
4-Hour Timeframe: Default set to '240'.
Daily Timeframe: Default set to 'D'.
Weekly Timeframe: Default set to 'W'.
SMA Length Inputs
SMA Length 20: Default set to 20 periods (short-term trend).
SMA Length 50: Default set to 50 periods (medium-term trend).
SMA Length 200: Default set to 200 periods (long-term trend).
Color Inputs
SMA 20 Color: Default is blue.
SMA 50 Color: Default is red.
SMA 200 Color: Default is green.
Plot Settings
Each SMA is plotted with a linewidth of 1 for most timeframes, except for the weekly timeframe, which uses a linewidth of 2 for better visibility.
Each SMA is labeled with its respective timeframe and period for easy identification in the chart’s legend.
Advantages
Comprehensive Trend Analysis: By displaying SMAs across multiple timeframes, the indicator allows traders to confirm trends across different scales. For example, a bullish signal on a 1-minute chart can be validated by checking alignment with higher timeframes like the 1-hour or daily charts.
Versatility: The indicator is adaptable to various trading styles, including day trading (using shorter timeframes like 1-minute or 5-minute) and swing trading (using 4-hour or daily timeframes).
Customizability: Traders can adjust SMA lengths and timeframes to suit their specific strategies, such as focusing on shorter periods for faster signals or longer periods for more reliable trends.
Visual Clarity: The use of distinct colors and clear labeling helps traders quickly differentiate between SMAs of different periods and timeframes, reducing confusion on complex charts.
Support and Resistance Identification: The 200-period SMA, in particular, often acts as a dynamic support or resistance level, especially on higher timeframes like daily or weekly, aiding in strategic entry and exit points.
Cross-Platform Compatibility: Built in Pine Script v6, the indicator is fully compatible with TradingView’s platform, ensuring seamless integration and performance.
Usage Tips
Trend Confirmation: Use the alignment of SMAs across timeframes to confirm trends. For instance, when the SMA 20 is above the SMA 50, and both are above the SMA 200 on multiple timeframes, it indicates a strong bullish trend.
Crossover Signals: Look for crossovers between the SMA 20 and SMA 50 for potential buy or sell signals. Confirm these with higher timeframe SMAs to filter out false signals.
Multi-Timeframe Strategy: Combine shorter timeframes (e.g., 1-minute, 5-minute) for entry points and longer timeframes (e.g., 4-hour, daily) for trend direction to enhance trade accuracy.
Adjust SMA Lengths: Experiment with different SMA lengths based on the asset’s volatility. For highly volatile assets, shorter SMAs (e.g., 10 or 15) may provide faster signals, while less volatile assets may benefit from longer SMAs.
Limitations
Lagging Indicator: As with all SMAs, this indicator is based on historical data, which may lag behind rapid price movements, potentially delaying signals in fast-moving markets.
Chart Clutter: Plotting SMAs for all ten timeframes can make the chart visually complex. Traders may need to toggle off certain timeframes to focus on relevant ones.
No Signal Generation: The indicator plots SMAs but does not include built-in alerts or signals for crossovers or trend changes, requiring manual interpretation.
Conclusion
The Multi-Timeframe SMA Indicator is a powerful and flexible tool for traders seeking to incorporate multi-timeframe analysis into their strategies. Its ability to display customizable SMAs across various timeframes provides a comprehensive view of market trends, making it suitable for traders of all experience levels. By leveraging its features, traders can enhance their decision-making process, improve trade timing, and better navigate dynamic market conditions.
Multi Timeframe Fair Value Gap Indicator ProMulti Timeframe Fair Value Gap Indicator Pro | MTF FVG Imbalance Zones | Institutional Supply Demand Levels
🎯 The Most Comprehensive Multi-Timeframe Fair Value Gap (FVG) Indicator on TradingView
Transform Your Trading with Institutional-Grade Multi-Timeframe FVG Analysis
Keywords: Multi Timeframe Indicator, MTF FVG, Fair Value Gap, Imbalance Zones, Supply and Demand, Institutional Trading, Order Flow Imbalance, Price Inefficiency, Smart Money Concepts, ICT Concepts, Volume Imbalance, Liquidity Voids, Multi Timeframe Analysis
📊 WHAT IS THIS INDICATOR?
The Multi Timeframe Fair Value Gap Indicator Pro is the most advanced FVG detection system on TradingView, designed to identify high-probability institutional supply and demand zones across multiple timeframes simultaneously. This professional-grade tool automatically detects Fair Value Gaps (FVGs), also known as imbalance zones, liquidity voids, or inefficiency gaps - the exact areas where institutional traders enter and exit positions.
🔍 What Are Fair Value Gaps (FVGs)?
Fair Value Gaps are three-candle price formations that create imbalances in the market structure. These gaps represent areas where buying or selling was so aggressive that price moved too quickly, leaving behind an inefficient zone that price often returns to "fill" or "mitigate." Professional traders use these zones as high-probability entry points.
Bullish FVG: When the low of candle 3 is higher than the high of candle 1
Bearish FVG: When the high of candle 3 is lower than the low of candle 1
⚡ KEY FEATURES
📈 Multi-Timeframe Analysis (MTF)
- 12 Timeframes Simultaneously: 1m, 3m, 5m, 15m, 30m, 45m, 1H, 2H, 3H, 4H, Daily, Weekly
- Real-Time Detection: Instantly identifies FVGs as they form across all selected timeframes
- Customizable Timeframe Selection: Choose which timeframes to display based on your trading style
- Higher Timeframe Confluence: See when multiple timeframes align for stronger signals
🎨 Three Professional Visual Themes
1. Dark Intergalactic: Futuristic neon colors with high contrast for dark mode traders
2. Light Minimal: Clean, professional appearance for traditional charting
3. Pro Modern: Low-saturation colors for extended screen time comfort
📊 Advanced FVG Dashboard
- Live FVG Counter: Real-time count of active bullish and bearish gaps
- Total Zone Tracking: Monitor all active imbalance zones at a glance
- Theme-Adaptive Display: Dashboard automatically adjusts to your selected visual theme
- Strategic Positioning: Optimally placed to not interfere with price action
🔧 Smart Zone Management
- Dynamic Zone Updates: FVG boxes automatically adjust when price touches them
- Mitigation Detection: Visual feedback when zones are tested or filled
- Color-Coded Status: Instantly see untested vs tested zones
- Extended Projection: Option to extend boxes to the right for future reference
- Timeframe Labels: Optional labels showing which timeframe each FVG originated from
💡 Intelligent Features
- Automatic Zone Cleanup: Removes fully mitigated FVGs to keep charts clean
- Touch-Based Level Adjustment: Zones adapt to partial fills
- Maximum Box Management: Optimized to handle 500 simultaneous FVG zones
- Performance Optimized: Efficient code ensures smooth operation even with multiple timeframes
🎯 TRADING APPLICATIONS
Day Trading & Scalping
- Use 1m, 3m, 5m FVGs for quick scalp entries
- Combine with higher timeframe FVGs for directional bias
- Perfect for futures (ES, NQ, MNQ), forex, and crypto scalping
Swing Trading
- Focus on 1H, 4H, and Daily FVGs for swing positions
- Identify major support/resistance zones
- Plan entries at untested higher timeframe gaps
Position Trading
- Utilize Daily and Weekly FVGs for long-term positions
- Identify institutional accumulation/distribution zones
- Major reversal points at significant imbalance areas
Multi-Timeframe Confluence Trading
- Stack multiple timeframe FVGs for high-probability zones
- Confirm entries when lower and higher timeframe FVGs align
- Professional edge through timeframe confluence
📚 HOW TO USE THIS INDICATOR
Step 1: Add to Your Chart
Click "Add to Favorites" and apply to any trading instrument - works on all markets including stocks, forex, crypto, futures, and indices.
Step 2: Configure Your Timeframes
In settings, select which timeframes you want to monitor. Day traders might focus on 1m-15m, while swing traders might use 1H-Weekly.
Step 3: Choose Your Visual Theme
Select from three professional themes based on your preference and trading environment.
Step 4: Identify Trading Opportunities
For Long Entries:
- Look for Bullish FVGs (green/cyan zones)
- Wait for price to return to untested zones
- Enter when price shows rejection from the FVG zone
- Higher timeframe FVGs provide stronger support
For Short Entries:
- Look for Bearish FVGs (red/pink zones)
- Wait for price to return to untested zones
- Enter when price shows rejection from the FVG zone
- Higher timeframe FVGs provide stronger resistance
Step 5: Manage Risk
- Place stops beyond the FVG zone
- Use partially filled FVGs as trailing stop levels
- Exit when opposite FVGs form (reversal signal)
🏆 WHY THIS IS THE BEST MTF FVG INDICATOR
✅ Most Comprehensive
- More timeframes than any other FVG indicator
- Advanced features not found elsewhere
- Professional-grade visual presentation
✅ Institutional-Grade
- Based on smart money concepts (SMC)
- ICT (Inner Circle Trader) methodology compatible
- Used by professional prop traders
✅ User-Friendly
- Clean, intuitive interface
- Detailed tooltips and descriptions
- Works out-of-the-box with optimal defaults
✅ Continuously Updated
- Regular improvements and optimizations
- Community feedback incorporated
- Professional development by PineProfits
🔥 PERFECT FOR
- Scalpers seeking quick FVG fills
- Day Traders using multi-timeframe analysis
- Swing Traders identifying major zones
- ICT/SMC Traders following smart money
- Prop Firm Traders needing reliable setups
- Algorithmic Traders building systematic strategies
- Technical Analysts studying market structure
- All Experience Levels from beginners to professionals
💎 ADVANCED TIPS
1. Confluence is Key: The strongest signals occur when multiple timeframe FVGs align at the same price level
2. Fresh vs Tested: Untested FVGs (original color) are stronger than tested ones (gray/muted color)
3. Time of Day: FVGs formed during high-volume sessions (London/NY) are more reliable
4. Trend Alignment: Trade FVGs in the direction of the higher timeframe trend for best results
5. Volume Confirmation: Combine with volume indicators for enhanced reliability
📈 INDICATOR SETTINGS
Visual Settings
- Visual Theme: Choose between Dark Intergalactic, Light Minimal, or Pro Modern
- Show Branding: Toggle PineProfits branding on/off
General Settings
- Move box levels with price touch: Dynamically adjust FVG zones
- Change box color with price touch: Visual feedback for tested zones
- Extend boxes to the right: Project zones into the future
- Plot Timeframe Label: Show origin timeframe on each FVG
- Show FVG Dashboard: Toggle the summary dashboard
Timeframe Selection
Select any combination of 12 available timeframes (1m to Weekly)
🚀 GET STARTED NOW
1. Click "Add to Favorites" to save this indicator
2. Apply to your chart - works on any instrument
3. Join thousands of traders already using this professional tool
4. Follow PineProfits for more institutional-grade indicators
⚖️ DISCLAIMER
This indicator is for educational and informational purposes only. It should not be considered financial advice. Always do your own research and practice proper risk management. Past performance does not guarantee future results. Trade responsibly.
© PineProfits - Professional Trading Tools for Modern Markets
If you find this indicator valuable, please leave a like and comment. Your support helps me create more professional-grade tools for the TradingView community!
TrendZonesTrendZones
This is an indicator which I use, have tested, tweaked and added features to for use in my trend following investing system. I got the idea for it when for some reason I was looking for a dynamic reference to measure the height of a channel or something. In search of this I made MA’s of the high and low borders of a Donchian channel which turned out to be two near parallel and stunningly smooth curves. This visual was so appealing that I immediately tried to turn it into a replacement for the KeltCOG which I previously used in my system. First I created a curve in the middle of the upper and lower curves, which I called COG (Center Of Gravity). Then I decided to enter only one lookback and let the script create a Donchian channel with half the lookback and use this to create the curves with an MA of whole lookback. For this reason the minimum lookback is set to 14, enough room for the Donchian Channel of 7 periods. This Donchian ChanneI has a special way of calculating the borders, involving a 5 period Median value. Thanks to this these borders are really a resistance and support level, which won’t change at a whim, e.g. when a ‘dead cat bounce’ occurs. I prevented the Donchian channel to show itself between the curves and only pop out from behind these. These pop outs now function as “strong trend zones”. I gave it colors (blue:-strong up, green: moderate up, orange: moderate down, red: strong down, near COG: gray, curves horizontal: gray) and it looked very appealing. I tested it in different time frames. In some weekend, when I was bored, I observed for a few hours the minute chart of bitcoin. It turned out that you can reliably tell that an uptrend ends when the candles go under the COG beginning a downtrend. Uptrend starts again once the candles go above COG. As Trends on minute charts only last around half an hour, this entertainment made the potential of this indicator very clear to me in just one afternoon.
Risk Management, Safe Level and Logical Stops.
In the inputs are settings for “Risk Tolerance”, and to activate “Show Logical Stop Level” (activated in example chart) and “Show Safe Level”. As a rule of thump a trade should not expose the invested capital to a risk of losing more than 2 percent. I divided my investment capital in ten equal parts which are allocated to ten different stocks or other instruments or kept liquid. This means that when a position is closed by triggering a Stop with a loss of 20 percent, the invested capital suffers only 2 percent (20% x 10% = 2%). This is why the value for “Risk Tolerance” has a default of 20. Because I put my Stops on the lower curve, a “Safe Level” can be calculated such that when you buy for a price below or at this level, the stop will protect the position sufficiently. Because I only buy when the instrument is in uptrend, the buying price should be between COG and Safe Level. Although I never do that, putting the stop at other curves is feasible and when you want to widen the stop (I never lower my stops btw) in a downtrend situation, even 1 ATR below the “Low Border”. I call these “Logical Stop Levels”, marked with dark green circles on the lower curve when safe buying by placing the Stoploss on this curve is possible, gray circles on the other curves, on the Upper Curve navy when price enters very profitable level. In a downtrend situation maroon circles appear.
Target lines
When I open a position I always set a Stoploss and a Target, for this purpose two types of Target values can be set and corresponding Target lines activated. These lines are drawn above the “High Border” at the set distance. If one expects some price to be used, differences will occur.
Other Features
Support Zone, this is 1 ATR below the “Low Border”, the maroon circles of the “Logal Stops” are placed on this “Support level”.
Stop distance and Channel Width. (activated in example chart) These are reported in a two cell table in the right lower corner of the main panel. I created this because I want to be able to check the volatility, whether the channel shows a situation in which safe buying in most levels of the channel is possible or what risk you take when you buy now and set the Stop at the nearest logical level (which is not always the “Lower curve”). This feature comes in handy for creating a setup I propose in the “Day Trading Fantasy” below.
Some General and User Settings. I never activate this, perhaps you will.
Use Of TrendZones In My System.
Create a list of stocks in uptrend. I define ‘stock in uptrend’ as in uptrend zone in all three monthly, weekly and daily charts, all three should at the same time be in uptrend. The advantage of TrendZones is that you can immediately see in which zone the candle moves.
Opening a position in a stock from the above list. I do this only when in both the daily and weekly the green dot on the lower curve indicates a buying opportunity. This is usually not the case in most of the items of the list, this feature thus provides a good timing for opening a position. Sometimes you need to wait a few weeks for this to happen.
Setting a target over a position. For this I use the Target percent line of the weekly chart with the default value of 10.
Updating the Stoploss and Target values. Every week or two weeks I set these to the new values of the “Lower Curve” and the Target line of the weekly. Attention: never shift down Stops, only up or let them stay the same when the curve moves down. I never use Stop levels on other curves.
I Check the charts whenever I like to do this. Close the position when the uptrend obviously shifts down. Otherwise I let the profits run until the Target triggers which closes the position with some profit.
For selecting stocks an checking charts for volume events, I also use a subpanel indicator called “TZanalyser”, which borrows the visual of my “Fibonacci Zone Oscillator”, is based on TrendZones and includes code from my REVE indicators. I intend to publish that as well.
Day Trading Fantasy.
Day trading is an attempt to earn a dime by opening a position in the morning and close it during the day again with a profit (or a loss). Before the market closes, you close all day trading positions.
In my fantasy the “Logical Stop Level” is repurposed for use as entry point and the ATR-based Target line is used to provide a target setting in an intraday chart, like e.g. 15 minute. To do this the “Safe Level” should be limited to between Channel width and COG. This can be done by showing “Safe Level” and “Channel Width” and then set “Risk Tolerance” to around the shown Channel Width. In this setting you can then wait for the green circle to show up for entering your trade and protect it with the stop.
I don’t know if this works fine or if it’s better than other day trade systems, because I don’t do day trading.
Take care and have fun.
OpenAI Signal Generator - Enhanced Accuracy# AI-Powered Trading Signal Generator Guide
## Overview
This is an advanced trading signal generator that combines multiple technical indicators using AI-enhanced logic to generate high-accuracy trading signals. The indicator uses a sophisticated combination of RSI, MACD, Bollinger Bands, EMAs, ADX, and volume analysis to provide reliable buy/sell signals with comprehensive market analysis.
## Key Features
### 1. Multi-Indicator Analysis
- **RSI (Relative Strength Index)**
- Length: 14 periods (default)
- Overbought: 70 (default)
- Oversold: 30 (default)
- Used for identifying overbought/oversold conditions
- **MACD (Moving Average Convergence Divergence)**
- Fast Length: 12 (default)
- Slow Length: 26 (default)
- Signal Length: 9 (default)
- Identifies trend direction and momentum
- **Bollinger Bands**
- Length: 20 periods (default)
- Multiplier: 2.0 (default)
- Measures volatility and potential reversal points
- **EMAs (Exponential Moving Averages)**
- Fast EMA: 9 periods (default)
- Slow EMA: 21 periods (default)
- Used for trend confirmation
- **ADX (Average Directional Index)**
- Length: 14 periods (default)
- Threshold: 25 (default)
- Measures trend strength
- **Volume Analysis**
- MA Length: 20 periods (default)
- Threshold: 1.5x average (default)
- Confirms signal strength
### 2. Advanced Features
- **Customizable Signal Frequency**
- Daily
- Weekly
- 4-Hour
- Hourly
- On Every Close
- **Enhanced Filtering**
- EMA crossover confirmation
- ADX trend strength filter
- Volume confirmation
- ATR-based volatility filter
- **Comprehensive Alert System**
- JSON-formatted alerts
- Detailed technical analysis
- Multiple timeframe analysis
- Customizable alert frequency
## How to Use
### 1. Initial Setup
1. Open TradingView and create a new chart
2. Select your preferred trading pair
3. Choose an appropriate timeframe
4. Apply the indicator to your chart
### 2. Configuration
#### Basic Settings
- **Signal Frequency**: Choose how often signals are generated
- Daily: Signals at the start of each day
- Weekly: Signals at the start of each week
- 4-Hour: Signals every 4 hours
- Hourly: Signals every hour
- On Every Close: Signals on every candle close
- **Enable Signals**: Toggle signal generation on/off
- **Include Volume**: Toggle volume analysis on/off
#### Technical Parameters
##### RSI Settings
- Adjust `rsi_length` (default: 14)
- Modify `rsi_overbought` (default: 70)
- Modify `rsi_oversold` (default: 30)
##### EMA Settings
- Fast EMA Length (default: 9)
- Slow EMA Length (default: 21)
##### MACD Settings
- Fast Length (default: 12)
- Slow Length (default: 26)
- Signal Length (default: 9)
##### Bollinger Bands
- Length (default: 20)
- Multiplier (default: 2.0)
##### Enhanced Filters
- ADX Length (default: 14)
- ADX Threshold (default: 25)
- Volume MA Length (default: 20)
- Volume Threshold (default: 1.5)
- ATR Length (default: 14)
- ATR Multiplier (default: 1.5)
### 3. Signal Interpretation
#### Buy Signal Requirements
1. RSI crosses above oversold level (30)
2. Price below lower Bollinger Band
3. MACD histogram increasing
4. Fast EMA above Slow EMA
5. ADX above threshold (25)
6. Volume above threshold (if enabled)
7. Market volatility check (if enabled)
#### Sell Signal Requirements
1. RSI crosses below overbought level (70)
2. Price above upper Bollinger Band
3. MACD histogram decreasing
4. Fast EMA below Slow EMA
5. ADX above threshold (25)
6. Volume above threshold (if enabled)
7. Market volatility check (if enabled)
### 4. Visual Indicators
#### Chart Elements
- **Moving Averages**
- SMA (Blue line)
- Fast EMA (Yellow line)
- Slow EMA (Purple line)
- **Bollinger Bands**
- Upper Band (Green line)
- Middle Band (Orange line)
- Lower Band (Green line)
- **Signal Markers**
- Buy Signals: Green triangles below bars
- Sell Signals: Red triangles above bars
- **Background Colors**
- Light green: Buy signal period
- Light red: Sell signal period
### 5. Alert System
#### Alert Types
1. **Signal Alerts**
- Generated when buy/sell conditions are met
- Includes comprehensive technical analysis
- JSON-formatted for easy integration
2. **Frequency-Based Alerts**
- Daily/Weekly/4-Hour/Hourly/Every Close
- Includes current market conditions
- Technical indicator values
#### Alert Message Format
```json
{
"symbol": "TICKER",
"side": "BUY/SELL/NONE",
"rsi": "value",
"macd": "value",
"signal": "value",
"adx": "value",
"bb_upper": "value",
"bb_middle": "value",
"bb_lower": "value",
"ema_fast": "value",
"ema_slow": "value",
"volume": "value",
"vol_ma": "value",
"atr": "value",
"leverage": 10,
"stop_loss_percent": 2,
"take_profit_percent": 5
}
```
## Best Practices
### 1. Signal Confirmation
- Wait for multiple confirmations
- Consider market conditions
- Check volume confirmation
- Verify trend strength with ADX
### 2. Risk Management
- Use appropriate position sizing
- Implement stop losses (default 2%)
- Set take profit levels (default 5%)
- Monitor market volatility
### 3. Optimization
- Adjust parameters based on:
- Trading pair volatility
- Market conditions
- Timeframe
- Trading style
### 4. Common Mistakes to Avoid
1. Trading without volume confirmation
2. Ignoring ADX trend strength
3. Trading against the trend
4. Not considering market volatility
5. Overtrading on weak signals
## Performance Monitoring
Regularly review:
1. Signal accuracy
2. Win rate
3. Average profit per trade
4. False signal frequency
5. Performance in different market conditions
## Disclaimer
This indicator is for educational purposes only. Past performance is not indicative of future results. Always use proper risk management and trade responsibly. Trading involves significant risk of loss and is not suitable for all investors.
Canuck Trading Projection IndicatorCanuck Trading Projection Indicator
Overview
The Canuck Trading Projection Indicator is a powerful PineScript v6 tool designed for TradingView to project potential bullish and bearish price trajectories based on historical price and volume movements. It provides traders with actionable insights by estimating future price targets and assigning confidence levels to each outlook, helping to identify probable market directions across any timeframe. Ideal for both short-term and long-term traders, this indicator combines momentum analysis, RSI filtering, support/resistance detection, and time-weighted trend analysis to deliver robust projections.
Features
Bullish and Bearish Projections: Forecasts price targets for upward (bullish) and downward (bearish) movements over a user-defined projection period (default 20 bars).
Confidence Levels: Assigns percentage confidence scores to each outlook, reflecting the likelihood of the projected price based on historical trends, volatility, and volume.
RSI Filter: Incorporates a 14-period Relative Strength Index (RSI) to validate trends, requiring RSI > 50 for bullish and RSI < 50 for bearish signals.
Support/Resistance Detection: Adjusts confidence levels when projections are near key swing highs/lows (within 2% of average price), boosting confidence by 5% for alignments.
Time-Based Weighting: Prioritizes recent price movements in trend analysis, giving more weight to newer bars for improved relevance.
Customizable Inputs: Allows users to tailor lookback period, projection bars, RSI period, confidence threshold, colors, and label positioning.
Forced Label Spacing: Prevents overlap of bullish and bearish text labels, even for tight projections, using fixed vertical slots when price differences are small (<2% of average price).
Timeframe Flexibility: Works seamlessly across all TradingView timeframes (e.g., 30-minute, hourly, daily, weekly, monthly), adapting projections to the chart’s resolution.
Clean Visualization: Displays projections as green (bullish) and red (bearish) dashed lines, with non-overlapping text labels at the projection endpoints showing price targets and confidence levels.
How It Works
The indicator analyzes historical price and volume data over a user-defined lookback period (default 50 bars) to calculate:
Momentum: Combines price changes and volume to assess trend strength, using a weighted moving average (WMA) for directional bias.
Trend Analysis: Counts bullish (price up, volume above average, RSI > 50) and bearish (price down, volume above average, RSI < 50) trends, weighting recent bars more heavily.
Projections:
Bullish Slope: Positive or flat when momentum is upward, scaled by price change and momentum intensity.
Bearish Slope: Negative or flat when momentum is downward, amplified by bearish confidence for stronger projections.
Projects prices forward by 20 bars (default) using current close plus slope times projection bars.
Confidence Levels:
Base confidence derived from the proportion of bullish/bearish trends, with a 5% minimum to avoid zero confidence.
Adjusted by volatility (lower volatility increases confidence), volume trends, and proximity to support/resistance levels.
Visualization:
Draws projection lines from the current close to the 20-bar future target.
Places text labels at line endpoints, showing price targets and confidence percentages, with forced spacing for readability.
Input Parameters
Lookback Period (default: 50): Number of bars for historical analysis (minimum 10).
Projection Bars (default: 20): Number of bars to project forward (minimum 5).
Confidence Threshold (default: 0.6): Minimum confidence for strong trend indication (0.1 to 1.0).
Bullish Projection Line Color (default: Green): Color for bullish projection line and label.
Bearish Projection Line Color (default: Red): Color for bearish projection line and label.
RSI Period (default: 14): Period for RSI momentum filter (minimum 5).
Label Vertical Offset (%) (default: 1.0): Base offset for labels as a percentage of price range (0.1% to 5.0%).
Minimum Label Spacing (%) (default: 2.0): Minimum vertical spacing between labels for tight projections (0.5% to 10.0%).
Usage Instructions
Add to Chart: Copy the script into TradingView’s Pine Editor, save, and add the indicator to your chart.
Select Timeframe: Apply to any timeframe (e.g., 30-minute, hourly, daily, weekly, monthly) to match your trading strategy.
Interpret Outputs:
Green Line/Label: Bullish price target and confidence (e.g., "Bullish: 414.37, Confidence: 35%").
Red Line/Label: Bearish price target and confidence (e.g., "Bearish: 279.08, Confidence: 41.3%").
Higher confidence indicates a stronger likelihood of the projected outcome.
Adjust Inputs:
Modify Lookback Period to focus on shorter/longer historical trends (e.g., 20 for short-term, 100 for long-term).
Change Projection Bars to adjust forecast horizon (e.g., 10 for shorter, 50 for longer).
Tweak RSI Period or Confidence Threshold for sensitivity to momentum or trend strength.
Customize Colors for visual preference.
Increase Minimum Label Spacing if labels overlap in volatile markets.
Combine with Analysis: Use alongside other indicators (e.g., moving averages, Bollinger Bands) or fundamental analysis to confirm signals, as projections are probabilistic.
Example: TSLA Across Timeframes
Using live TSLA data (close ~346.46 USD, May 31, 2025), the indicator produces:
30-Minute: Bullish 341.93 (13.3%), Bearish 327.96 (86.7%) – Strong bearish sentiment due to intraday volatility.
1-Hour: Bullish 342.00 (33.9%), Bearish 327.50 (62.3%) – Bearish but less intense, reflecting hourly swings.
4-Hour: Bullish 345.52 (73.4%), Bearish 344.44 (19.0%) – Flat outlook, indicating consolidation.
Daily: Bullish 391.26 (68.8%), Bearish 302.22 (31.2%) – Bullish bias from recent uptrend, bearish tempered by longer lookback.
Weekly: Bullish 414.37 (35.0%), Bearish 279.08 (41.3%) – Wide range, reflecting annual volatility.
Monthly: Bullish 396.70 (54.9%), Bearish 296.93 (10.2%) – Long-term bullish optimism.
These results align with market dynamics: short-term intervals capture volatility, while longer intervals smooth trends, providing balanced outlooks.
Notes
Accuracy: Projections are estimates based on historical data and should be used with other analysis tools. Confidence levels indicate likelihood, not certainty.
Timeframe Sensitivity: Short-term intervals (e.g., 30-minute) show larger price swings and higher confidence due to volatility, while longer intervals (e.g., monthly) are more stable.
Customization: Adjust inputs to match your trading style (e.g., shorter lookback for day trading, longer for swing trading).
Performance: Tested on volatile stocks like TSLA, NVIDIA, and others, ensuring robust performance across markets.
Limitations: May produce conservative bearish projections in strong uptrends due to momentum weighting. Adjust lookback or projection_bars for sensitivity.
Feedback
If you encounter issues (e.g., label overlap, projection mismatches), please share your timeframe, settings, or a screenshot. Suggestions for enhancements (e.g., additional filters, visual tweaks) are welcome!
Disclaimer
The Canuck Trading Projection Indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves significant risks, and past performance is not indicative of future results. Always perform your own due diligence and consult a qualified financial advisor before making trading decisions.
Advanced ORB IndicatorAdvanced ORB (Opening Range Breakout) Indicator
Overview
The Advanced ORB Indicator is a sophisticated trading tool designed to capture high-probability breakout opportunities across multiple markets. By identifying the opening range of a trading session and detecting meaningful breakouts, this indicator helps traders enter trending moves with strong momentum while filtering out false signals.
Core Concept
The Opening Range Breakout strategy is based on the principle that the initial trading range of a session often defines key support and resistance levels. When price breaks convincingly beyond this range with proper confirmation, it frequently indicates the beginning of a directional move that can persist throughout the session.
Key Features
### Intelligent Market Detection
- Automatically identifies market type (US Stocks, Forex, Crypto, EU/Asia Stocks)
- Applies optimal default timings based on market characteristics
- Configurable time zones (Exchange, UTC, Local) for precise session timing
Customizable Session Settings
- Adjustable opening range duration (15-240 minutes)
- Flexible reset periods (Daily, Weekly, Monthly, or Never)
- Custom session start times to match specific market opens or pre-market periods
Advanced Signal Filtering
- Multi-factor confirmation system requiring strong candle bodies, proper wick ratios, and minimum breakout percentages
- Smart cooldown periods preventing clustered signals
- Retracement detection that resets signals after meaningful pullbacks
Quality Control Mechanisms
- Volume threshold filter for stronger conviction entries
- RSI-based filters to avoid overbought/oversold conditions
- Trend alignment validation using EMA and directional analysis
- Consecutive candle confirmation for higher reliability
Visual Clarity
- Clear high/low boundary visualization
- Comprehensive status panel showing current levels, trend status, and filter conditions
- Clean, non-repainting signal triangles at breakout points
Trading Applications
Stocks & ETFs
Perfect for capturing morning momentum after market open, especially effective on US equities where the first 30-60 minutes often set the tone for the day. Excellent for gap fills, trend continuations, and reversal confirmations.
Forex & Futures
Ideal for session-based strategies around London/New York opens, capturing institutional order flow as major players enter the market. Can be configured for H4/H1 longer-term breakouts in 24-hour markets.
Cryptocurrency
Powerful for identifying key breakout levels in volatile crypto markets, with adjustable parameters to filter out noise while catching significant moves. Especially effective during high-volume periods following consolidation.
Strategic Implementation
The indicator excels when used as part of a complete trading system. Consider these approaches:
1. Pure Momentum Strategy: Enter on signal, exit at fixed R:R or end of session
2. Trend Continuation: Only take signals in the direction of the higher timeframe trend
3. Support/Resistance Validation: Combine with key S/R levels for higher probability entries
4. Volume Profile Confirmation: Use in conjunction with volume profile to verify breakout significance
Optimization Tips
- Adjust Opening Range Duration based on market volatility (shorter for choppy markets, longer for trending)
- Increase filter requirements during uncertain market conditions
- Loosen filters during strong trending environments
- Use longer durations (120+ minutes) for swing trading setups
- Consider Weekly/Monthly reset periods for positional trading approaches
Performance Notes
The Advanced ORB Indicator is designed to produce fewer, higher-quality signals rather than frequent low-conviction entries. The multiple confirmation requirements mean you'll catch fewer false breakouts at the expense of occasionally later entries.
For best results, combine with proper risk management, position sizing, and an understanding of the broader market context.
*This indicator works on all timeframes but performs optimally on 1-minute to 15-minute charts for intraday trading and 1-hour to 4-hour charts for swing trading opportunities.*
// @version=5
indicator("Advanced ORB Indicator", overlay=true)
// ===================================================================
// SIGNAL REQUIREMENTS DOCUMENTATION
// ===================================================================
//
// BULL SIGNAL REQUIREMENTS:
// - ORB period must be completed (not in the opening range duration anymore)
// - Price must close above the ORB high (if waitForClose is enabled)
// - Candle must have a strong body (body to range ratio >= minBodyToRangeRatio)
// - Valid upper wick (upper wick to body ratio <= wickThreshold)
// - Bullish candle (close > open)
// - Consecutive candle confirmation (if enabled, requires multiple candles meeting criteria)
// - Volume filter (if enabled, volume > average volume * threshold)
// - RSI filter (if enabled, RSI must not be overbought)
// - EMA filter (if enabled, price must be above short EMA)
// - Trend filter (if enabled, must be in an uptrend)
// - Cooldown period satisfied (minimum bars between signals)
// - Not already signaled a bull breakout for this ORB (unless reset by retracement)
//
// BEAR SIGNAL REQUIREMENTS:
// - ORB period must be completed (not in the opening range duration anymore)
// - Price must close below the ORB low (if waitForClose is enabled)
// - Candle must have a strong body (body to range ratio >= minBodyToRangeRatio)
// - Valid lower wick (lower wick to body ratio <= wickThreshold)
// - Bearish candle (close < open)
// - Consecutive candle confirmation (if enabled, requires multiple candles meeting criteria)
// - Volume filter (if enabled, volume > average volume * threshold)
// - RSI filter (if enabled, RSI must not be oversold)
// - EMA filter (if enabled, price must be below short EMA)
// - Trend filter (if enabled, must be in a downtrend)
// - Cooldown period satisfied (minimum bars between signals)
// - Not already signaled a bear breakout for this ORB (unless reset by retracement)
//
// SIGNAL RESET CONDITIONS (for both bull and bear):
// - A significant price retracement happens (determined by retracePercent)
// - Cooldown period expires (minimum bars between signals)
// ===================================================================
// ===================================================================
// SETTINGS GUIDE - DETAILED EXPLANATION
// ===================================================================
//
// MARKET SETTINGS
// ---------------------------------------------------------------------
// Market Type: Select your market or use auto-detection
// - US Stocks: NYSE, NASDAQ, etc. (9:30 AM default open)
// - Forex: Currency pairs (uses midnight or London open)
// - Crypto: Digital currencies (uses midnight UTC)
// - EU Stocks: European exchanges (9:00 AM default)
// - Asia Stocks: Asian exchanges (9:00 AM default)
// - Custom: Manually set your preferred session time
//
// Auto-Detect Market Type: Automatically identifies the market from symbol
// - Enable for convenience when switching between different markets
// - Disable to manually set your preferred market type
//
// Use Market Default Timing: Applies optimal session start times for selected market
// - Enable to use proven default timings for the market
// - Disable to set custom session start times
//
// Time Zone: Sets the reference time zone for session calculations
// - Exchange: Uses the exchange's native time zone (recommended)
// - UTC: Uses Coordinated Universal Time
// - Local: Uses your local computer's time zone
//
// TIME SETTINGS
// ---------------------------------------------------------------------
// Session Start Hour/Minute: Sets when the opening range begins
// - Only active when "Use Market Default Timing" is disabled
// - US Stocks typically use 9:30 AM
// - For pre-market analysis, try 4:00 AM (US) or 8:00 AM (EU)
//
// Opening Range Duration: How long to measure the initial range (minutes)
// - 30-60 mins: Standard for daily ORB strategies
// - 15 mins: More responsive, good for volatile markets
// - 120 mins: More stable, fewer false signals
//
// Reset Period: When to calculate a new opening range
// - Daily: Most common, resets each trading day
// - Weekly: Weekly opening range breakout strategy
// - Monthly: Long-term support/resistance levels
// - Never: Continuous tracking without resetting
//
// SIGNAL QUALITY SETTINGS
// ---------------------------------------------------------------------
// Minimum Bars Between Signals: Prevents clustering of multiple signals
// - Higher values (8-10): Fewer signals, better quality
// - Lower values (3-5): More signals, requires more filtering
//
// Required Retracement % Before New Signal: How far price must pull back
// - Higher values (50-60%): Only signals after significant pullbacks
// - Lower values (20-30%): More signals, may include false breakouts
//
// Minimum Breakout % Required: Strength needed for valid breakout
// - Higher values (0.5-1.0%): Stronger confirmation, fewer false breakouts
// - Lower values (0.1-0.3%): More sensitive, good for low-volatility
//
// Minimum Body to Range Ratio %: Requires strong candles for signals
// - Higher values (70-80%): Only strong momentum candles trigger signals
// - Lower values (40-50%): More signals, includes weaker breakouts
//
// BREAKOUT SETTINGS
// ---------------------------------------------------------------------
// Max Wick to Body Ratio: Controls acceptable candle shape
// - Lower values (0.2-0.3): Only clean breakout candles
// - Higher values (0.5-0.6): More signals, includes wicks
//
// Use Close Price: Uses close instead of High/Low for breakouts
// - Enable for more reliable but delayed confirmation
// - Disable for earlier signals using High/Low prices
//
// Wait for Candle Close: Only signals after candle completes
// - Enable to avoid false breakouts (recommended)
// - Disable for earlier entry but higher risk
//
// FILTER SETTINGS
// ---------------------------------------------------------------------
// Filter Signals Based on Trend: Aligns signals with the overall trend
// - Enable to filter out counter-trend signals (recommended)
// - Disable for range-bound markets or counter-trend strategies
//
// Trend Detection Period: Lookback period for trend calculation
// - Longer periods (50-100): Identifies major trends
// - Shorter periods (20-30): More responsive to recent price action
//
// Trend Strength Threshold: How strong trend must be
// - Higher values (0.7-0.8): Only strong trends generate signals
// - Lower values (0.5-0.6): More signals in choppy markets
//
// Use Volume Filter: Requires above-average volume for signals
// - Enable for stocks and futures (recommended)
// - May disable for some forex pairs with unreliable volume data
//
// Volume Threshold: How much above average volume is required
// - Higher values (2.0-3.0x): Only significant volume spikes
// - Lower values (1.2-1.5x): More signals, less volume confirmation
//
// Use RSI Filter: Prevents signals in overbought/oversold conditions
// - Enable to avoid exhausted moves
// - Disable for strong trend following
//
// Use EMA Alignment Filter: Ensures price is in the right direction
// - Enable for trend confirmation (recommended)
// - Disable for early reversal signals
//
// Require Consecutive Candle Confirmation: Needs multiple confirming candles
// - Enable for higher quality signals
// - Disable for faster but riskier entries
//
// DISPLAY SETTINGS
// ---------------------------------------------------------------------
// Show Label with Values: Displays current ORB levels and status
// Show Range Boundaries: Displays high/low lines on chart
// High/Low Boundary Color: Customize appearance
//
// ===================================================================
// RECOMMENDED SETTINGS BY MARKET TYPE
// ===================================================================
//
// US STOCKS - STANDARD
// ---------------------------------------------------------------------
// - Market Type: US Stocks
// - Opening Range Duration: 30 minutes
// - Reset Period: Daily
// - Wait for Candle Close: Enabled
// - Use Volume Filter: Enabled (Volume Threshold: 1.5-2.0x)
// - Use Trend Filter: Enabled
// - Minimum Breakout %: 0.3-0.5%
//
// US STOCKS - EARNINGS/HIGH VOLATILITY
// ---------------------------------------------------------------------
// - Opening Range Duration: 60 minutes (more stable)
// - Minimum Breakout %: 0.7-1.0% (stronger moves required)
// - Minimum Bars Between Signals: 8-10 (avoid whipsaws)
// - Required Retracement %: 40-50% (deeper pullbacks)
// - Volume Threshold: 2.5-3.0x (higher volume confirmation)
//
// CRYPTO
// ---------------------------------------------------------------------
// - Market Type: Crypto
// - Opening Range Duration: 120 minutes (crypto needs longer)
// - Reset Period: Daily
// - Minimum Breakout %: 1.0-1.5% (higher volatility needs stronger breakouts)
// - Volume Threshold: 2.0-2.5x
// - Consider disabling RSI Filter (trending crypto often stays overbought/oversold)
//
// FOREX - MAJOR PAIRS
// ---------------------------------------------------------------------
// - Market Type: Forex
// - Session Start: Consider 8:00 AM (London open) or 5:00 PM (Asian open)
// - Opening Range Duration: 60-120 minutes
// - Min Body to Range Ratio: 50-60% (forex can have smaller bodies)
// - Consider disabling Volume Filter (unreliable on some platforms)
// - Trend Strength Threshold: 0.6-0.7 (forex tends to trend well)
//
// EU STOCKS
// ---------------------------------------------------------------------
// - Market Type: EU Stocks
// - Opening Range Duration: 60 minutes
// - Reset Period: Daily
// - Use EMA Alignment: Enabled
// - Use Volume Filter: Enabled
//
// SMALL CAP/VOLATILE STOCKS
// ---------------------------------------------------------------------
// - Opening Range Duration: 15-30 minutes (captures early moves)
// - Minimum Breakout %: 1.0-2.0% (needs stronger breakouts)
// - Volume Threshold: 3.0x (needs significant volume)
// - Max Wick to Body Ratio: 0.3 (cleaner breakouts)
// - Use Consecutive Candle Confirmation: Enabled (2-3 candles)
//
// LOW VOLATILITY ENVIRONMENT
// ---------------------------------------------------------------------
// - Opening Range Duration: 30-60 minutes
// - Minimum Breakout %: 0.2-0.3% (lower threshold for tight ranges)
// - Required Retracement %: 20-30% (smaller pullbacks)
// - Consider disabling Consecutive Candle Confirmation
//
// HIGH VOLATILITY ENVIRONMENT
// ---------------------------------------------------------------------
// - Opening Range Duration: 60-120 minutes (more stable)
// - Minimum Breakout %: 0.8-1.5% (stronger confirmation)
// - Required Retracement %: 50-60% (deeper pullbacks)
// - Minimum Bars Between Signals: 8-10 (avoid choppy signals)
// - Use Consecutive Candle Confirmation: Enabled (2-3 candles)
// ===================================================================
TrendPredator ESThe TrendPredator Essential (ES)
Stacey Burke, a seasoned trader and mentor, developed his trading system over the years, drawing insights from influential figures such as George Douglas Taylor, Tony Crabel, Steve Mauro, and Robert Schabacker. His popular system integrates select concepts from these experts into a consistent framework. While powerful, it is highly discretionary, requiring significant real-time analysis, which can be challenging for novice traders.
The TrendPredator ES indicator supports this approach by automating the essential analysis required to trade the system effectively and incorporating a mechanical bias and multi-timeframe concept.
It provides value to traders by significantly reducing the time needed for session preparation and offering relevant chart analysis and signals for live trading through real-time updates and a unique consolidated table format.
The Stacey Burke Master Pattern
Inspired by Taylor’s 3-day cycle and Steve Mauro’s work with “Beat the Market Maker,” Burke’s system views markets as cyclical, driven by the manipulative patterns of market makers. These patterns often trap traders at the extremes of moves above or below significant levels with peak formations, then reverse to utilize their liquidity, initiating the next phase. Breakouts away from these traps often lead to range expansions, as described by Tony Crabel and Robert Schabacker. After multiple consecutive breakouts, especially after the psychological number three, overextension might develop. A break in structure may then lead to reversals or pullbacks. Burke’s system is designed to track these cycles on the daily timeframe and provides signals and trade setups to navigate along them.
Bias Logic and Multi-Timeframe Concept
The indicator covers the basic signals of his system:
- First Red Day (FRD): Bearish break in structure, signalling weak longs in the market.
- First Green Day (FGD): Bullish break in structure signalling weak shorts in the markt.
- Three Days of Longs (3DL): Overextension signalling potential weak longs in the market.
- Three Days of Shorts (3DS): Overextension signalling potential weak shorts in the market.
- Inside Day (ID): Contraction, signalling potential impulsive reversal or range expansion move.
It enhances the original system by introducing:
Structured Bias Logic:
Tracks bias by following how price trades concerning the last previous candle high or low that was hit. For example if the high was hit, we are bullish above and bearish below.
- Bullish state: Breakout (BO), Fakeout Low (FOL)
- Bearish state: Breakdown (BD), Fakeout High (FOH)
Multi-Timeframe Perspective:
- Tracks all signals across H4, H8, D, W, and M timeframes, to look for alignment and follow trends and momentum in a mechanical way.
The indicator monitors the bias and signals of the system across all relevant timeframes and automates the related graphical chart analysis to generate the information needed for the trader to identify key setups. Additional to the SB pattern, the system helps to identify the higher timeframe situation and follow the moves driven by other timeframe traders.
Example: Full Bullish Cycle on the Daily Timeframe with Signals
- The Trap/Peak Formation
The market breaks down from a previous day’s and maybe week’s low—potentially after multiple breakdowns—but fails to move lower and pulls back up to form a peak formation low and closes as a first green day.
Signal: Bullish daily and weekly fakeout low; three consecutive breakdown days (1W Curr FOL, 1D Curr FOL, BO 3S).
- Pullback and Consolidation
The next day pulls further up after first green day signal, potentially consolidates inside the previous day’s range.
Signal: Fakeout low and first green day closing as an inside day (1D Curr IS, Prev FOL, First G).
- Range Expansion/Trend
The following day breaks up through the previous day’s high, launching a range expansion away from the trap.
Signal: Bullish daily breakout of an inside day (1D Curr BO, Prev IS).
- Overextension
After multiple consecutive breakouts, the market reaches a state of overextension, signalling a possible reversal or pullback.
Signal: Three days of breakout longs (1D Curr BO, Prev BO, BO 3L).
Note: This is only one possible scenario; there are many variations and combinations.
Example Chart: Full Bullish Cycle with Correlated Signals
Note: The signals shown along the move are manually added illustrations. The indicator shows these in realtime in the table at the bottom right. This is only one possible scenario; there are many variations and combinations.
Due to the fractal nature of markets, this cycle can be observed across timeframes. The strongest setups show multi-timeframe alignment. For example, a peak formation and potential reversal on the daily timeframe has high probability and follow-through if it also aligns with bearish signals on higher timeframes (e.g., weekly/monthly BD/FOH) and confirmation on lower timeframes (H4/H8 FOH/BD). With this perspective the system enables the trader to follow the trend and momentum and identify rollover points in a very differentiated way.
Detailed Features and Options
1. Historic Highs and Lows
Displays historic highs and lows per timeframe for added context, enabling users to track sequences over time.
Timeframes: H4, H8, D, W, M
Options: Customize for timeframes shown, number of historic candles per timeframe, colors, formats, and labels.
2. Previous High and Low Extensions
Displays extended previous levels (high, low, and close) for each timeframe to assess how price trades relative to these levels.
H4: P4H, P4L, P4C
H8: P8H, P8L, P8C
Daily: PDH, PDL, PDC
Weekly: PWH, PWL, PWC
Monthly: PMH, PML, PMC
Options: Fully customizable for timeframes shown, colors, formats, and labels.
3. Breach Lines
Tracks live market reactions (e.g., breakouts or fakeouts) per timeframe for the last previous high or low that was hit, highlighting these levels originating at the breached candle to indicate bias (color-coded).
Red: Bearish below
Green: Bullish above
H4: 4FOL, 4FOH, 4BO, 4BD
H8: 8FOL, 8FOH, 8BO, 8BD
D: dFOL, dFOH, dBO, dBD
W: wFOL, wFOH, wBO, wBD
M: mFOL, mFOH, mBO, mBD
Options: Fully customizable for timeframes shown, colors, formats, and labels.
4. Multi-Timeframe Table
Provides a real-time view of system signals, including:
Current Timeframe (Curr): Bias states.
- Breakout (green BO): Bullish after breaking above the previous high.
- Fakeout High (red FOH): Bearish after breaking above the previous high but pulling back down.
- Breakdown (red BD): Bearish after breaking below the previous low.
- Fakeout Low (green FOL): Bullish after breaking below the previous low but pulling back up.
- Inside (IS): Price trading neutral inside the previous range, taking the previous bias (color indicates the previous bias).
Previous Timeframe (Prev): Tracks last candle bias state and transitions dynamically.
- Bias for last candle: BO, FOH, BD, FOL in respective colors.
- Inside bar (yellow IS): Indicated as standalone signal.
Note: Also previous timeframes get constantly updated in real time to track the bias state in relation to the level that was hit. This means a BO can still lose the level and become a FOH, and vice versa, and a BD can still become a FOL, and vice versa. This is critical to see for example if traders that are trapped in that timeframe with a FOH or FOL are released. An inside bar stays fixed, though, since no level was hit in that timeframe.
Breakouts (BO): Breakout count 3 longs and 3 shorts.
- 3 Longs (red 3L): Bearish after three breakouts without hitting a previous low.
- 3 Shorts (green 3S): Bullish after three breakdowns without hitting a previous high.
First Countertrend Close (First): Tracks First Red or Green Day.
- First Green (G): After two consecutive red closes.
- First Red (R): After two consecutive green closes.
Options: Customizable font size and label colors.
Overall Options:
Toggle single feature groups on/off.
Customize H8 open/close time as an offset to UTC to be provider independent.
Colour settings for dark or bright backgrounds.
Using the Indicator for Trading
The automated analysis provided by the indicator can be used for thesis generation in preparation for a session as well as for live trading, leveraging the real-time updates. It is recommended to customize the settings accordingly, such as hiding the lower timeframes for thesis generation to keep the charts clean.
1. Setup Identification:
Follow the bias of daily and H8 timeframes. A setup always requires alignment of these.
Setup Types:
- Trend Trade: Trade in alignment with the previous day’s trend.
Example: Price above the previous day’s high → Focus on long setups (dBO, H8 FOL) until overextension or reversal signs appear (H8 BO 3L, First R).
- Reversal Trade: Identify reversal setups when lower timeframes show rollovers after higher timeframe weakness.
Example: Price below the previous day’s high after FOH → Look for reversal signals at the current high of day (H8 FOH, BO 3L, First R).
2. Context Assessment:
Evaluate alignment of higher timeframes (e.g., Month/Week, Week/Day). More alignment → Stronger setups. Conflicting situations → Setups invalidated.
3. Entry Confirmation:
Confirm entries based on H8 and H4 alignment and candle closes (e.g., M15 or M5 close after entering setup zone as confirmation).
Example Chart for Reversal Trade:
1. Setup Identification: FOH continuation after BO 3L overextension, confirmed by H8 FOH, First R.
2. Context Assessment: Month in FOL with bearish First R; Week in BO but bearishly overextended with BO 3L.
3. Entry Confirmation: H4 BD, M5 close.
Further recommendations:
- Higher timeframe context: TPO or volume profile indicators can be used to gain an even better overview.
- Entry confirmation: Momentum indicators like VWAP, Supertrend, or EMA are helpful for increasing precision. Additionally, tracking lower timeframe fakeouts can provide powerful confluence.
- Late session trading: Entries later in the session, such as during the 3rd hour of the NY session, offer better analysis and follow-through on setups.
Limitations:
Data availability using TradingView has its limitations. The indicator leverages only the real-time data available for the specific timeframe being used. This means it cannot access data from timeframes lower than the one displayed on the chart. For example, if you are on a daily chart, it cannot use H8 data. Additionally, on very low timeframes, the historical availability of data might be limited, making higher timeframe signals unreliable.
To address this, the indicator automatically hides the affected columns in these specific situations, preventing false signals.
Disclaimer
This indicator is for educational purposes only and does not guarantee profits.
None of the information provided shall be considered financial advice.
The indicator does not provide final buy or sell signals but highlights zones for potential setups.
Users are fully responsible for their trading decisions and outcomes.
Dynamic Open Levels# Dynamic Open Levels Indicator v1.0
Release Date: November 5, 2024
Introducing the Dynamic Open Levels indicator on TradingView! This tool helps traders visualize and analyze key opening price levels across multiple timeframes, making your market analysis more effective.
---
### Key Features
- Multiple Timeframes : Yearly, Quarterly, Monthly, Weekly, Daily, 4H, and 1H levels available.
- Visibility Controls : Easily toggle visibility for each timeframe to suit your trading style.
- Line Customization : Set custom thickness and colors for lines, making charts easy to interpret.
- Monthly: Purple
- Weekly: Blue
- Daily: Green
- 4H: Red
- 1H: Orange
- Dynamic Coloring : Lines adjust color based on market conditions—teal for bullish (`rgb(34, 171, 148)`) and coral for bearish (`rgb(247, 82, 95)`).
### Labels & Customization
- Real-Time Labels : Each level is labeled for easy identification (e.g., Y for Yearly, Q for Quarterly).
- Label Settings : Customize opacity, text color, size, and position for clarity without cluttering your chart.
- Sizes : Choose from tiny, small, normal, large, to huge.
- Offset : Set labels from 1 to 10 to position them precisely.
- Color Management : Organize all colors under a dedicated Line Colors group for easy adjustments.
### Advanced Plotting & Performance
- Real-Time Updates : Levels are updated dynamically with the latest open prices.
- Extended Lines : Lines extend to the right, offering a consistent reference for future price movement.
- Optimized Performance : Handles up to 500 lines efficiently to maintain smooth performance.
---
### Installation Instructions
1. Add to Chart :
- Go to the Indicators section in TradingView.
- Search for Dynamic Open Levels and add it to your chart.
2. Customize Settings :
- Line Thickness : Adjust to suit your preference.
- Visibility : Toggle timeframes like Yearly, Monthly, Weekly, etc., as needed.
- Labels : Configure opacity, text color, size, and offset under the Label Settings group.
---
### Documentation & Support
For guidance on using the Dynamic Open Levels indicator, visit our Documentation (#). If you need assistance, check out our Support Channel (#).
---
Thank you for choosing Dynamic Open Levels . Stay tuned for future updates that will continue to improve your trading experience!
H A Z E D
AWR_WaveTrend Multitimeframe [adapted from LazyBear]I've adapted a script from Lazy Bear (WT trend oscillator)
WaveTrend Oscillator is a port of a famous TS/MT indicator.
When the oscillator (WT1 designed as a line) is above the overbought band (50 to 60) and crosses down the WT2 (dotted line), it is usually a good SELL signal. Similarly, when the oscillator crosses above the signal when below the Oversold band ( (-50 to -60)), it is a good BUY signal.
In this indicator, you can display at the same time, different time frames.
Choice possible are 1 mn, 15 mn, 30 mn, 60 mn, 120 mn, 240 mn, 1D, Week, Month.
Small time frames (1 to 30 mn) are represented by a blue lines (light to dark)
1H is in grey
2H & 4H are in purple (light to dark)
1D is in green
1W is in orange
1M is in black
You can choose which timeframes you want to display for the current period or for the last period closed.
In a few seconds, you perfectly see the selected timeframes trends.
There is also at the bottom right a table summing up all the different values of WT1, WT2 and difference between them.
Positive difference means an upside trend
Negative difference means a downside trend.
Another way of using this indicator is displaying only the difference between WT1 & WT2. It's giving the speed & the direction of all trends. Trends are our friends ...
You can observe the significent times frames and look if they are all positives or negatives or if the speed of lower timeframe cross a longer timeframe of if the speed is decreasing or increasing...
Difference values goes generaly from -20 to 20 (it can exceed a bit but really rare). 12 is already high level of speed.
Many uses possible.
In the exemple posted, I've selected WT1 and WT2 for timeframes 4H, Daily & Weekly.
Marker 1:
Orange lines (WT1) are far below - 50 (-67 here) and cross WT2 pointed lines : weekly buy signal
But this buy signal is balanced by 4H & Daily sell signal = it's marking start of hesitations of main trend !!!!
Marker 2 :
Next buy signal in 4H or daily would normaly confirm the start
Marker 3 :
Sell signal in 4H and daily but weekly has an upside trend ! Start of a counter trend in the trend. To find the perfect timing of that you have to look to lower time frames, because 4H and daily are giving many hesitations signals crossing down & crossing up many times in an overbought zone.
Marker 4 :
End of the counter trend. Most of the time, the countertrend don't go in the "over" zone. That's why if you trading in an counter trend, you have to keep it in mind.
Then a few days later you can see the sell signal. And what a sell signal ! 4H & daily are smashed down really fastly ! Trends change warning !
Marker 5
Long hesitation/change of the trend. Daily WT and 4H are below the weekly trends. Weekly start to go down.
Start of a counter trend inside the trend giving us the best selling signal at her end !
Marker 6 :
Long hesitation/change of the trend.
You have to look in lower time frames to identify the short trend. Difficult to find the best timing to get in. ....
I've add many alerts. When a time frame become positive or negative. When many time frames are positive or negative or above or below 47 level...
Please feel free to explore.
Hope it will help you.
Thanks to Lazybear ! Thousands thanks to Lazybear !
Exemple with difference
Dynamic Relative StrengthMainly this indicator is a Relative strength indicator which tells us about the strength of a scrip as compared to an index . That is it outperforming the index or underperforming . Outperformance signifies Strength and Under performance signifies Weakness .Inspired from Bharat trader's Relative Strength of a stock , but changing the period for all time frames is a hassle so i have set 10 period for Monthly and 52 period for Weekly. As for monthly we need around 10 months data or we can use 12 as 1 year has 12 months but 10 works best . used 52 period for Weekly time frame because there are 52 weeks in a year. These values are by default dynamically applied to the indicator when weekly or monthly timeframes are chosen . Daily Period can be chosen as per anyone's need . As can be seen in provided screenshot , that the stock has recently started gaining strength on weekly a compared to Small cap100 index . So we can conclude that it has more strength than the overall index it is representing so more chances of outperformance will be there.
Exhaustion ZonesOur Indicator “Exhaustion Zones” offers an insight into the expected Volatility of any given Instrument applied to. Understanding Volatility is essential for using this Indicator. If you are familiar with the concept, then you will most likely find this indicator useful in your trading. If you are unfamiliar with the concept and are interested in this topic, then continue reading a “Brief Concept of Volatility” at the end of the description, where we will provide some informational Links.
This description will provide a High Level description of how our Indicator identifies and visualizes Exhaustion Zones, followed by how to use the Indicator in your trading. At the end we would like to introduce our team and experience.
High Level Description of “Exhaustion Zones”:
Our indicator is predicting the expected market volatility for a predefined period based on recent historical Volatility, which will be referred to as “Period Volatility” from now on. Currently there are two Predefined Periods…
...a daily period, which starts from 21 UTC for the next 24 hours.
...a weekly period, which starts from Sunday 21 UTC for the next 7 days.
Our indicator calculates an expected volatility for the respective period and informes you, the trader, how large the price range could be. The indicator calculates “Exhaustion Zones” using the Period Volatility, which is a multiple (1x, 2x and 3x) of the Period Volatility, based on the Period High/Low. Basically, adding the multiple of the Period Volatility to the Period Low would equate to the “Upper Exhaustion Zones”, and subtracting the multiple of the Period Volatility from the Period High would equate to the “Lower Exhaustion Zones”.
Visualisation:
Our indicator needs to display 2 states …
…price range is SMALLER than predicted Period Volatility. An example would be, a daily period has just begun, and the Daily range is small, hence the Daily range is smaller than the predicted Period Volatility. This state will be referred to as “Moving Exhaustion Zones”
...price range is GREATER than predicted Period Volatility. An example would be, a market has experienced a shock leading to a huge price change and exceeding the Period Volatility. This state will be referred to as “Locked Exhaustion Zones”
What do the Boxes mean:
Our Indicator displays 2 different Periods - Daily and Weekly. The Daily period is displayed with a red accent color, whereas a weekly Period has a yellow accent.
The Boxes themself display the “Exhaustion Zones”. Each period displays upto 4 Exhaustion Zones - 2 Upper Exhaustion Zones, 2 Lower Exhaustion Zones, each having a “Zone 0” and “Zone 1”.
Moving Exhaustion Zones
Moving Exhaustion Zones displays 4 Exhaustion Zones. These Zones are based on the Period High/Low and are NOT locked, and can still be redrawn. As prices make new Period Highes and Lows, exhaustion Zones will be adjusted. This state is visualized by the Open Lock on the right side of the current Exhaustion Zone.
Locked Exhaustion Zones
Locked Exhaustion Zones display 2 Exhaustion Zones and a dashed Line. Price has made its move and has exceeded predicted Period Volatility. Exhaustion Zones are locked and will NOT be repainted from now on. This state is visualized by the closed Lock on the right side of the current Exhaustion Zone.
How to use it:
The Exhaustion Zones indicator is a mean reverting Indicator. That being said, when Price approaches/enters a Zone, the assumption is that Price will either slow down, or reverse.
The Exhaustion Zone is displayed as 2 Sub-Zones: Zone 0 and Zone 1. As Price continues through the Zones without a reversal, the potential for a reversal increases.
We recommend using this Indicator with a reversal Trading Strategy familiar to you.
Furthermore, this Indicator is well suited as a Target. You can use the Exhaustion Zones to define your Target or where you would like to remove partial Profits.
Important
Please note, that the indicator itself just presents price areas where there is a potential for a price reversal, and that these Zones should not be traded blindly
Time frame:
This indicator is programmed to be used on all Timeframes lower than Weekly Timeframes.
Instruments:
This indicator aims to visualize areas of where Market price has the potential to reverse, hence making this a mean reverting Indicator.
Taking this statement to account, it is recommended to apply this Indicator to Instruments with a mean reverting character.
Examples of mean reverting markets could be for example …
...all FOREX instruments, as FOREX is considered a mean reverting Market.
...an instrument that is in a consolidation, or which you are expecting to enter a period of consolidation.
Indicator settings and configuration:
The Indicator has no functional parameters, to reduce User error, and only has visual parameters. The color of the Zones can be tailored to your liking.
Furthermore you have decided what you would like to display on your chart:
… Display Weekly Zones
...Display Daily Zones
...Show History
...Show Zone States.
Brief concept of Volatility:
Volatility is a concept that has been around for a very long time. Following links are helpful to get a grasp of the concept:
en.wikipedia.org(finance)
www.investopedia.com
Our Team:
We are a team of 3 Traders with a co mbined experience of 40 years. We are using our experiences from the market to create Indicators to Visualize the most relevant Patterns to us in our trading today. Our goal is to reconstruct these patterns to match our understanding of the market and to simplify the process of creating reproducible trading Strategies.
Multi-timeframe Dashboard for RSI And Stochastic RSI Dashboard to check multi-timeframe RSI and Stochastic RSI on 4h, 8h, 12h, D and W
Great side tool to assist on the best time to buy and sell and asset.
Shows a green arrow on a good buy moment, and a red when to sell, for all timeframes. In case there are confluence on more than one, you have the info that you need.
Uses a formula with a weight of 5 for RSI and 2 for Stochastic RSI, resulting on a factor used to set up a color for each of the timeframes.
Legend per each timeframe:
- Blue: Excellent buy, RSI and Stoch RSI are low
- Green: Great buy, RSI and Stoch RSI with a quite positive entry point
- White: Good buy
- Yellow: A possible sell, depending on combination of timeframes. Not recommended for a buy
- Orange: Good sell, depending on combination of timeframes
- Red: If on more than one timeframe, especially higher ones, it is a good time to sell
For reference (But do your own research):
- Blue on Weekly: Might represent several weeks of growth. Lower timeframes will cycle from blue to red, while daily and Weekly gradually change
- Blue on Daily: Might represent 7-15 days of growth, depending on general resistance and how strongly is the weekly
PS: Check the RSI, Stochastic RSI and other indicators directly as well
Guru Dronacharya Pro Institutional Option Intelligence# Guru Dronacharya Pro – Institutional Option Intelligence
## 🎯 Professional Options Trading Indicator with Dynamic Intensity System
**Guru Dronacharya Pro** is an advanced institutional-grade indicator designed specifically for **NSE Options traders** (NIFTY, BANKNIFTY, FINNIFTY, MIDCPNIFTY). It combines intelligent option chain analysis, volatility detection, and a revolutionary **intensity-based visualization system** to help you identify high-probability option trades.
***
## ✨ KEY FEATURES
### 🔥 **Dynamic Intensity System** (Unique Feature)
- **Adaptive Brightness**: Candles automatically brighten when movement, volume, and volatility surge
- **Multi-Factor Analysis**: Combines Volume Surge + IV Expansion + Price Acceleration
- **Real-Time Intensity Score**: 0-100% intensity meter for both CE and PE
- **Visual Intelligence**: Instantly spot when options are heating up 🔥
### 🎯 **Intelligent Strike Selection**
- **Auto-Select Best Pair**: Scans ±5 strikes from ATM to find optimal CE/PE pairs
- **Compression Analysis**: Identifies strikes with minimal price difference (premium parity)
- **Liquidity Filter**: Ensures selected options have sufficient volume
- **Manual Override**: Take control with manual strike selection when needed
### 📈 **Advanced Signal Generation**
- **Buy Call Signals**: Triggered on CE breakout + volatility expansion + momentum
- **Buy Put Signals**: Triggered on PE breakout + volatility expansion + momentum
- **Multi-Filter Confirmation**: BBW expansion, EMA trend, delta momentum, dominance
- **No Repainting**: All signals confirmed on bar close
### 📊 **Professional Analytics Panel**
- **🔥 Intensity Metrics**: Real-time CE/PE activity levels
- **PCR (Put-Call Ratio)**: Volume-based market sentiment
- **Volume Delta**: CE vs PE volume comparison with trend
- **IV Percentile**: 1-year implied volatility ranking
- **BBW (Bollinger Band Width)**: Volatility expansion detector
- **Momentum Trackers**: Real-time CE/PE momentum analysis
- **Premium Ratio**: CE/PE price relationship analysis
### 🎨 **Customizable Visualization**
- **Dual Candle Display**: Side-by-side CE and PE premium tracking
- **Normalized View**: % change from open (easier comparison)
- **Absolute View**: Raw premium values
- **EMA Overlays**: Trend confirmation lines
- **Theme-Aware**: Auto-detects dark/light mode for optimal visibility
- **Adjustable Tables**: Position and size controls for metrics panel
***
## 💡 WHAT MAKES IT UNIQUE?
### **1. Intensity-Based Coloring** 🔥
Traditional indicators show static colors. **Guru Dronacharya Pro** uses dynamic brightness:
- **Dim Candles** = Low activity (avoid these setups)
- **Medium Brightness** = Building momentum (watch closely)
- **Bright Candles** = High activity (trade opportunities!) 🔥🔥
This helps you:
✅ Focus on liquid, moving options
✅ Avoid low-volume, dead zones
✅ Identify institutional money flow
✅ Time entries during volatility expansion
### **2. Smart Strike Selection**
No more guessing which strike to trade! The indicator:
- Scans multiple strikes simultaneously
- Finds pairs with balanced premiums
- Filters out illiquid options
- Highlights the best trading pair
### **3. Multi-Timeframe Compatible**
Works on any timeframe:
- **1-5 min**: Scalping and day trading
- **15-30 min**: Intraday swing trades
- **1H+**: Positional option strategies
***
## 📖 HOW TO USE
### **Step 1: Configure Your Symbol**
1. Set **Underlying** (NSE:NIFTY, NSE:BANKNIFTY, etc.)
2. Enter **Expiry Date** (Year, Month, Day)
3. Input **ATM Strike** (rounded to nearest strike interval)
4. Choose **Symbol Format** (NSE Standard, NSE Weekly, or Custom)
### **Step 2: Understand the Display**
**Chart Elements:**
- **Green/Lime Candles** = Call Option (CE)
- **Pink/Magenta Candles** = Put Option (PE)
- **Brightness** = Activity intensity (brighter = more action!)
- **Triangle Up** = Buy Call Signal ▲
- **Triangle Down** = Buy Put Signal ▼
**Metrics Panel (Bottom Right):**
- **🔥 CE/PE INT**: Intensity score (higher = better)
- **PCR**: Above 1.0 = Bullish, Below 1.0 = Bearish
- **VOL Δ**: Positive = CE volume dominance
- **IV%ile**: Above 70 = High IV (premium sellers advantage)
- **BBW**: Expansion indicator (⚡ = expanding)
- **Momentum**: Price acceleration tracker
### **Step 3: Trading Rules**
**For Buying Calls (Bullish):**
1. Wait for ▲ signal below CE candle
2. Check **CE INT > 40%** (moderate to high activity)
3. Confirm **CE BBW ⚡** (volatility expanding)
4. Verify **CE Mom** positive (momentum building)
5. **Entry**: Current CE premium
6. **Target**: Use Fibonacci levels or book on intensity drop
**For Buying Puts (Bearish):**
1. Wait for ▼ signal above PE candle
2. Check **PE INT > 40%** (moderate to high activity)
3. Confirm **PE BBW ⚡** (volatility expanding)
4. Verify **PE Mom** positive (momentum building)
5. **Entry**: Current PE premium
6. **Target**: Use Fibonacci levels or book on intensity drop
**Risk Management:**
- Avoid trades when intensity < 30% (low liquidity)
- Higher intensity = tighter stops (volatile moves)
- Watch for intensity divergence (price up, intensity down = weakness)
***
## ⚙️ SETTINGS GUIDE
### **Group 1: UNDERLYING & SYMBOL**
- **Underlying**: Main index/stock ticker
- **Option Root**: Symbol prefix (NIFTY, BANKNIFTY, etc.)
- **Strike Interval**: 50 for NIFTY, 100 for BANKNIFTY
- **Expiry Date**: Target expiry (Year/Month/Day)
- **Spot Source**: Auto (First 5m), Live Close, or Manual
### **Group 2: OPTION CHAIN SCANNER**
- **ATM Strike**: Center point for scanning (manually input)
- **Scan Range**: ±N strikes to scan (1-5)
- **Compression Threshold**: Max CE-PE difference % (8% default)
- **Min Volume**: Liquidity filter (100 default)
- **Auto-Select**: Enable for automatic best pair selection
### **Group 3: SIGNAL FILTERS**
- **BBW Length**: Volatility calculation period (20 default)
- **BBW Expansion Threshold**: Multiplier for expansion (1.30x)
- **Min BBW**: Minimum volatility % (2.0%)
- **EMA Filter**: Enable trend confirmation (21 EMA)
- **Delta Momentum**: Require CE > PE momentum for calls (vice versa)
### **Group 4: SIGNAL DISPLAY**
- **Show Buy Signals**: Toggle call/put signals
- Simple triangle markers (▲ for calls, ▼ for puts)
### **Group 5: VISUALIZATION**
- **Plot Candles**: Show CE/PE candlesticks
- **Normalize to % Change**: Compare premiums as % (recommended)
- **Show EMA**: Display trend lines
- **Show Metrics Panel**: Display analytics table
- **Table Position**: Move metrics panel (9 positions)
- **Table Size**: Adjust text size (Tiny to Huge)
### **Group 6: OPTION ANALYTICS**
- **Show PCR**: Put-Call Ratio display
- **Show Volume Analysis**: Volume delta tracking
- **Show IV Percentile**: 1-year IV ranking
### **Group 7: INTENSITY SYSTEM** 🔥
- **Enable Intensity Coloring**: Turn on dynamic brightness
- **Intensity Smoothing**: Higher = smoother (3 default)
- **Volume Weight**: Impact of volume surges (35%)
- **IV/BBW Weight**: Impact of volatility expansion (40%)
- **Movement Weight**: Impact of price acceleration (25%)
- **Min Brightness**: Dimmest state (70% transparency)
- **Max Brightness**: Brightest state (0% = fully opaque)
***
## 🎓 TRADING STRATEGIES
### **Strategy 1: Intensity Breakout**
- Wait for intensity to rise from <30% to >60%
- Enter on signal with bright candle
- Exit when intensity drops below 40%
### **Strategy 2: Volatility Expansion**
- Monitor BBW indicator
- Enter on ⚡ expansion + signal
- Target quick 20-30% premium gains
### **Strategy 3: PCR Contrarian**
- PCR > 1.3 = Oversold (look for call signals)
- PCR < 0.7 = Overbought (look for put signals)
- Combine with intensity confirmation
### **Strategy 4: Volume Delta Momentum**
- Strong positive VOL Δ = CE buying pressure
- Enter calls on dips with high CE intensity
- Vice versa for puts
***
## 📋 SUPPORTED EXCHANGES & SYMBOLS
**Exchanges:**
- NSE (National Stock Exchange of India)
**Supported Underlyings:**
- NIFTY 50
- BANKNIFTY
- FINNIFTY
- MIDCPNIFTY
- Individual stocks with liquid options
**Option Formats:**
- NSE Standard: `NSE:NIFTY251230C25900`
- NSE Weekly: `NSE:NIFTY25DEC25900CE`
- Custom/Broker-Specific formats
***
## ⚡ PERFORMANCE OPTIMIZATION
This indicator is optimized for speed:
- **Tuple-based security requests** (80% faster than standard)
- **Minimal repainting** (signals confirmed on bar close)
- **Efficient array operations**
- **Smart caching** of repeated calculations
- Works smoothly even on 1-minute charts
***
## 🚨 ALERTS
Built-in alert conditions:
- **Buy Call Signal**: Triggered on confirmed call entry
- **Buy Put Signal**: Triggered on confirmed put entry
**Setup:**
1. Click "Create Alert" on TradingView
2. Select "Guru Dronacharya Pro"
3. Choose "Buy Call Signal" or "Buy Put Signal"
4. Set notification method (popup/email/webhook)
***
## ⚠️ RISK DISCLAIMER
**IMPORTANT**: This indicator is for **educational purposes only**.
- Options trading carries substantial risk of loss
- Past performance does not guarantee future results
- Always use proper risk management (stop losses, position sizing)
- No indicator guarantees profitable trades
- Test thoroughly on paper/sim before live trading
- Consult a financial advisor before trading
**The creator is not responsible for any trading losses incurred using this indicator.**
***
## 🔄 VERSION HISTORY
**v1.0 (Current)**
- Initial release
- Dynamic intensity system
- Intelligent strike selection
- Multi-filter signal generation
- Professional analytics panel
- Theme-aware visualization
- Full customization support
***
## 💬 FEEDBACK & SUPPORT
Found this indicator helpful? Please:
- ⭐ Leave a rating
- 💬 Share your experience in comments
- 📊 Publish your chart ideas using this indicator
- 🔔 Follow for updates and new indicators
**Questions?** Drop a comment, and I'll help you optimize your settings!
***
## 🏆 WHO IS THIS FOR?
✅ **Intraday Option Traders** (scalping & day trading)
✅ **Swing Option Traders** (multi-day positions)
✅ **Premium Buyers** (directional option strategies)
✅ **Technical Analysts** (volatility & momentum-based)
✅ **NSE Options Specialists** (NIFTY/BANKNIFTY focused)
❌ **NOT suitable for:**
- Complete beginners (learn basics first)
- Premium sellers (different indicator needed)
- Set-and-forget strategies (requires active monitoring)
***
## 🙏 ACKNOWLEDGMENTS
Named after **Guru Dronacharya**, the legendary teacher from Mahabharata known for precision, discipline, and strategic mastery – qualities every successful trader needs.
**May your trades be profitable and your risk be managed! 🚀**
***
**Tags:** Options Trading, NSE Options, NIFTY Options, BANKNIFTY Options, Option Chain Analysis, Volatility Trading, Intensity System, Indian Stock Market, Intraday Trading, Premium Analysis, PCR Indicator, Options Signals
***
**Legal:** This indicator does not constitute financial advice. All trading decisions are your responsibility. Always trade with risk capital you can afford to lose.
COT Net Positions by thedatalayers.comCOT Net Positions by thedatalayers.com visualizes the net positioning of different trader groups based on the weekly Commitments of Traders (COT) reports published by the CFTC every Friday.
The indicator processes the raw COT data by calculating Long positions minus Short positions for each trader category. This results in the net position of every group per report.
The script then plots these net positions continuously over time, based on every available COT release. This creates a clear and easy-to-read visualization of how different market participants are positioned.
The indicator displays the three primary COT categories:
• Commercials
• Non-Commercials
• Non-Reportables
By observing how these trader groups shift their positioning, traders can better understand market sentiment and identify potential directional biases or changes in underlying market pressure.
This tool is designed to help traders incorporate positioning data into their analysis and to better interpret how institutional and speculative flows evolve over time.
This indicator is intended to be used exclusively on the weekly timeframe.
COT data is published once per week by the CFTC and therefore only updates weekly.
Using this script on lower timeframes may result in misleading visualization or irregular spacing between data points.
For correct interpretation, please apply it on 1W charts only.
End Of Week LineThis indicator will show a vertical line top to bottom on the last candle of the week.
Will show on all timeframes except daily.
Helps me visually with keeping this neat on the chart.
Hope this can help you out as well!
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.
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CLOSING STATEMENT
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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.
COT Net Positions by Luis TrompeterCOT Net Positions by Luis Trompeter visualizes the net positioning of different trader groups based on the weekly Commitments of Traders (COT) reports published by the CFTC every Friday.
The indicator processes the raw COT data by calculating Long positions minus Short positions for each trader category. This results in the net position of every group per report.
The script then plots these net positions continuously over time, based on every available COT release. This creates a clear and easy-to-read visualization of how different market participants are positioned.
The indicator displays the three primary COT categories:
• Commercials
• Non-Commercials
• Non-Reportables
By observing how these trader groups shift their positioning, traders can better understand market sentiment and identify potential directional biases or changes in underlying market pressure.
This tool is designed to help traders incorporate positioning data into their analysis and to better interpret how institutional and speculative flows evolve over time.
This indicator is intended to be used exclusively on the weekly timeframe.
COT data is published once per week by the CFTC and therefore only updates weekly.
Using this script on lower timeframes may result in misleading visualization or irregular spacing between data points.
For correct interpretation, please apply it on 1W charts only.
ICT HTF Volume Candles (Based on HTF Candles by Fadi)# ICT HTF Volume Candles - Multi-Timeframe Volume Analysis
## Overview
This indicator provides multi-timeframe volume visualization designed to complement price action analysis. It displays volume data from up to 6 higher timeframes simultaneously in a separate panel, allowing traders to identify volume spikes, divergences, and institutional activity without switching between timeframes.
**Original Concept Credits:** This indicator builds upon the HTF Candles framework by Fadi, adapting it specifically for volume analysis with enhanced features including gap-filling for extended hours, multiple scaling methods, and advanced synchronization.
## What Makes This Script Original
### Key Innovations:
1. **Three Volume Scaling Methods:**
- **Per-HTF Auto Scale:** Each timeframe scales independently for detailed comparison
- **Global Auto Scale:** All timeframes use unified scale for relative volume comparison
- **Manual Scale:** User-defined maximum for consistent analysis across sessions
2. **Bullish/Bearish Volume Differentiation:**
- Volume bars colored based on price movement (close vs open)
- Separate styling for bullish (green) and bearish (red) volume periods
- Helps identify whether volume supports price direction
3. **Advanced Time Synchronization:**
- Custom daily candle open times (Midnight, 8:30 AM, 9:30 AM ET)
- Timezone-aware calculations for New York trading hours
- Real-time countdown timers for each timeframe
- **Gap-filling technology** for continuous display during extended hours and weekends
4. **Flexible Display Options:**
- Configurable spacing and positioning
- Label placement (top, bottom, or both)
- Day-of-week or time interval labels on candles
- Works reliably in backtesting and live trading
## How It Works
### Volume Calculation
The indicator uses `request.security()` with optimized parameters to fetch volume data from higher timeframes:
- **Volume Open/High/Low/Close (OHLC):** Tracks volume changes within each HTF candle
- **Color Logic:** Compares HTF close vs open prices to determine bullish/bearish classification
- **Alignment:** All volume bars share a common baseline for easy visual comparison
- **Gap Handling:** Uses `gaps=barmerge.gaps_off` to maintain continuity during non-trading hours
### Technical Implementation
```
1. Monitors HTF timeframe changes using request.security() with lookahead
2. Creates new VolumeCandle object when HTF bar opens
3. Updates current candle's volume H/L/C on each chart bar
4. Applies selected scaling method to normalize display height
5. Repositions all candles and labels on each bar update
6. Fills gaps automatically during extended hours for consistent display
```
### Scaling Methods Explained
**Method 1 - Auto Scale per HTF:**
Each timeframe displays volume relative to its own maximum. Best for identifying patterns within each individual timeframe.
**Method 2 - Global Auto Scale:**
All timeframes share the same scale based on the highest volume across all HTFs. Best for comparing relative volume strength between timeframes.
**Method 3 - Manual Scale:**
User sets maximum volume value. Best for maintaining consistent scale across different trading sessions or instruments.
## How to Use This Indicator
### Setup
1. Add indicator to your chart (it appears in a separate panel below price)
2. Configure up to 6 higher timeframes (default: 5m, 15m, 1H, 4H, 1D, 1W)
3. Set number of candles to display for each timeframe
4. Choose volume scaling method based on your analysis needs
5. Enable "Fix gaps in non-trading hours" for extended hours trading (enabled by default)
### Interpretation
**Volume Spikes:**
- Sudden increase in volume height indicates institutional activity or strong conviction
- Compare volume between timeframes to identify where the real money is moving
- Look for volume spikes that appear across multiple timeframes simultaneously
**Bullish vs Bearish Volume:**
- **Green volume bars:** Price closed higher (buying pressure)
- **Red volume bars:** Price closed lower (selling pressure)
- High green volume during uptrend = confirmation of strength
- High red volume during downtrend = confirmation of weakness
- High volume opposite to trend = potential reversal warning
**Multi-Timeframe Context:**
- **5m/15m:** Scalping and day trading activity
- **1H/4H:** Swing trading and intraday institutional flows
- **Daily/Weekly:** Major position building and long-term trends
**Divergences:**
- Price making new highs but volume declining = weakening trend
- Volume increasing while price consolidates = potential breakout brewing
- Price breaks level but volume doesn't confirm = likely false breakout
### Practical Examples
**Example 1 - Institutional Confirmation:**
Price breaks above resistance. Check volume across timeframes:
- 5m shows spike = retail interest
- 15m + 1H + 4H all show spikes = institutional confirmation
- **Trade confidence: HIGH**
**Example 2 - False Breakout Detection:**
Price breaks resistance with:
- High volume on 5m only
- Normal/low volume on 1H and 4H
- **Interpretation:** Likely retail trap, institutions not participating
- **Action:** Wait for pullback or avoid
**Example 3 - Accumulation Phase:**
Price ranges sideways but:
- Daily volume gradually increasing
- Weekly volume above average
- **Interpretation:** Smart money accumulating
- **Action:** Prepare for breakout in direction of volume
**Example 4 - Volume Divergence:**
Price makes new high:
- Current high has lower volume than previous high across all timeframes
- **Interpretation:** Weakening momentum
- **Action:** Consider profit-taking or reversal trade
## Configuration Parameters
### Timeframe Settings
- **HTF 1-6:** Select timeframes (must be higher than chart timeframe)
- **Max Display:** Number of candles to show per timeframe (1-50)
- **Limit to Next HTFs:** Display only first N enabled timeframes (1-6)
### Styling
- **Bull/Bear Colors:** Separate colors for body, border, and wick
- **Padding from current candles:** Distance offset from live price action
- **Space between candles:** Gap between individual volume bars
- **Space between Higher Timeframes:** Gap between different timeframe groups
- **Candle Width:** Thickness of volume bars (1-4, multiplied by 2)
### Volume Settings
- **Volume Scale Method:** Choose 1, 2, or 3
- 1 = Auto Scale per HTF (each TF independent)
- 2 = Global Auto Scale (all TF unified)
- 3 = Manual Scale (user-defined max)
- **Auto Scale Volume:** Enable/disable automatic scaling
- **Manual Scale Max Volume:** Set maximum when using Method 3
### Label Settings
- **HTF Label:** Show/hide timeframe names with color and size options
- **Label Positions:** Display at Top, Bottom, or Both
- **Label Alignment:** Align centered or Follow Candles
- **Remaining Time:** Show countdown timer until next HTF candle
- **Interval Value:** Display day-of-week or time on each candle
### Custom Daily Candle
- **Enable Custom Daily:** Override default daily candle timing
- **Open Time Options:**
- **Midnight:** Standard 00:00 ET daily open
- **8:30 AM:** Align with economic data releases
- **9:30 AM:** Align with NYSE market open
- Useful for specific trading strategies or market alignment
### Advanced Settings
- **Fix gaps in non-trading hours:** Maintains alignment during extended hours and weekends (recommended: ON)
- Prevents visual gaps during forex weekend closures
- Ensures consistent display during crypto 24/7 trading
- Improves backtesting reliability
## Best Practices
1. **Pair with Price Action:** Use alongside HTF price candles indicator for complete picture
2. **Start Simple:** Enable 2-3 timeframes initially (e.g., 15m, 1H, 4H), add more as needed
3. **Match Settings:** Use same candle width/spacing as companion price indicator for visual alignment
4. **Scale Appropriately:**
- Use **Global scale** (Method 2) when comparing timeframes
- Use **Per-HTF scale** (Method 1) for pattern analysis within each timeframe
- Use **Manual scale** (Method 3) for consistent day-to-day comparison
5. **Watch for Volume Clusters:** High volume appearing simultaneously across multiple HTFs signals significant market events
6. **Confirm Breakouts:** Always check if volume supports the price movement across higher timeframes
7. **Extended Hours:** Keep "Fix gaps" enabled for 24/7 markets (Forex, Crypto) and weekend analysis
## Technical Notes
- **Timezone:** All calculations use America/New_York timezone for consistency
- **Real-time Updates:** Volume and timers update on each tick during market hours
- **Performance:** Optimized with max_bars_back=5000 for extensive historical analysis
- **Compatibility:** Works on all instruments with volume data (Stocks, Forex, Crypto, Futures)
- **Gap Handling:** Uses `barmerge.gaps_off` to fill data gaps during non-trading periods
- **Backtesting:** Uses `lookahead=barmerge.lookahead_on` for stable historical data without repainting
- **Data Continuity:** Automatically handles market closures, weekends, and extended hours
## Updates & Improvements
**Version 2.0 (Current):**
- ✅ Fixed alignment issues during extended hours and weekends
- ✅ Eliminated repainting in backtesting
- ✅ Added gap-filling technology for continuous display
- ✅ Improved data synchronization across all timeframes
- ✅ Enhanced NA value handling for data integrity
- ✅ Added advanced settings group for user control
## Support
For questions, suggestions, or feedback, please comment on the publication or message the author.
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**Disclaimer:** This indicator is for educational and informational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always perform your own analysis and implement proper risk management before making trading decisions.
