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Deviation Momentum For Loop | Lyro RSThe Deviation Momentum For Loop is a directional momentum tool that evaluates the persistence of price deviation from a moving average over a historical range using a custom loop-based scoring function. It quantifies relative momentum strength by comparing current deviation to previous values, assigning a net score used to assess trend bias, reversals, and volatility-adjusted strength. Key Features Deviation-Based Momentum: Calculates standardized deviation from a selected moving average by subtracting the MA from price and normalizing via standard deviation and a scaling constant. This standardization adjusts for volatility and creates a consistent scoring base across assets and timeframes. Historical For-Loop Scoring: Implements a user-defined for-loop function that compares current deviation to values from earlier bars (configurable range). Each comparison adds or subtracts from a cumulative score based on whether current deviation exceeds historical ones, producing a dynamic momentum read. Threshold-Based Signal Logic: Applies user-defined thresholds for long and short signals. If the loop score exceeds the long threshold, a bullish bias is inferred; if it drops below the short threshold, a bearish bias is indicated. These thresholds are plotted for visual clarity. Multi-Palette Visual System: Predefined Palettes – Choose from Classic, Mystic, Accented, or Royal schemes for bullish and bearish colors. Custom Colors – Toggle on custom color selection to manually define bullish (UpC) and bearish (DnC) tones. Dynamic Visuals – Oscillator line, threshold markers, background shading, bar and candle coloring are applied in real time based on trend state . Glow and Overlay Effects: Layered glow lines and optional bar/candle coloring reinforce signal strength and trend status directly on the chart. Built-In Alerts: Provides alert conditions when the for-loop score crosses above or below the defined thresholds, enabling automated monitoring of trend conditions. How It Works MA Calculation – Applies the selected moving average to the chosen price source (e.g., close, open, high). Offers 16 moving average types including SMA, EMA, WMA, TEMA, JMA, FRAMA, ZLSMA, KAMA, and others via the LyroRS/LMAs/1 library. Deviation Computation – Measures the deviation of price from its moving average, normalized by standard deviation and scaled by a fixed constant factor to account for volatility. Loop Function – Iterates from user-defined indices (From → To) and compares current deviation to past deviations. Increments score for each instance where current > past, decrements when current < past. The resulting value is plotted as the primary oscillator. Signal Logic – Long and short threshold values are configurable inputs. Crossing above the long threshold signals bullish bias; crossing below the short threshold signals bearish pressure. Color Coding & Visual Feedback Palette or custom colors reflect oscillator's directional state. Additional glow lines increase signal emphasis. Background shading highlights crossovers with mid-threshold for enhanced visibility. Optional bar and candle coloring aligns price visualization with indicator signals. Practical Use Momentum Confirmation – Use a score rising above the long threshold as a potential bullish signal; falling below the short threshold may indicate bearish strength. Volatility Normalization – Standardized deviation ensures consistency across assets of different volatility profiles. Multi-Timeframe Adaptation – The wide selection of moving averages and loop depth settings allows calibration for scalping, swing, or long-term analysis. Visual Clarity – Color-coordinated candles, bars, oscillator lines, and background simplify interpretation of signal context and trend structure. Customization Select from 16 moving average types to control sensitivity and smoothing. Adjust loop depth (From/To) to define how far back deviation is compared. Set custom threshold levels to reflect your preferred sensitivity to momentum shifts. Customize visuals with predefined palettes or manual color settings for full chart integration. ⚠️Disclaimer This indicator is a tool for technical analysis and does not provide guaranteed results. It should be used in conjunction with other analysis methods and proper risk management practices. The creators of this indicator are not responsible for any financial decisions made based on its signals.
Pine Script® indicator
by LyroRS
STAX# STAX - MapleStax Candle by Candle Automation ## Overview STAX is a trend-following indicator that automates the "MapleStax Candle by Candle (CBC)" methodology for futures and equity trading. This system uses a higher timeframe anchor trend combined with lower timeframe execution filters to identify high-probability pullback entries in the direction of the prevailing trend. ## How It Works ### 1. Anchor Trend Detection (10-Minute CBC Flip) The core of this system is the CBC (Candle by Candle) flip logic on the anchor timeframe (default: 10 minutes): - **Bullish Flip**: Occurs when a 10m candle closes ABOVE the high of the previous 10m candle - **Bearish Flip**: Occurs when a 10m candle closes BELOW the low of the previous 10m candle - Once a flip occurs, the trend remains in that direction until an opposite flip happens The anchor trend is calculated using `request.security()` with `lookahead=barmerge.lookahead_off` and indexed historical data ` ` to ensure non-repainting behavior. This means signals will not change or disappear after they appear. ### 2. Execution Filters (Current Timeframe) On your current chart timeframe (recommended: 3 minutes), the indicator applies two key filters: **EMA Confirmation**: - For LONG signals: 9-period EMA must be greater than 20-period EMA - For SHORT signals: 9-period EMA must be less than 20-period EMA **VWAP Filter** (Strict or Target mode): - **Strict Mode** (default): Only shows signals when price is on the correct side of VWAP - LONG signals only above VWAP - SHORT signals only below VWAP - **Target Mode**: Shows all valid signals but uses VWAP as the take profit target when price is on the "wrong" side ### 3. Entry Signal Logic The indicator looks for pullback entries: - **BUY Signal**: 10m trend is Bullish + EMA 9 > 20 + Current 3m candle is RED (close < open) - Logic: Wait for a red pullback candle in a bullish trend with bullish EMA alignment - **SELL Signal**: 10m trend is Bearish + EMA 9 < 20 + Current 3m candle is GREEN (close > open) - Logic: Wait for a green retracement candle in a bearish trend with bearish EMA alignment This pullback logic helps you enter after a brief counter-trend move, improving risk/reward compared to chasing breakouts. ### 4. Risk Management **Stop Loss**: Automatically set at the previous 10-minute candle's low (for longs) or high (for shorts). This represents the last swing point that would invalidate the trend structure. **Take Profit**: - When aligned with VWAP: Fixed tick-based target (default: 20 ticks, adjustable) - When counter to VWAP: Target is VWAP itself, providing a logical profit target The indicator displays TP and SL levels visually and alerts when they are hit. ### 5. Signal Management To prevent over-trading, the indicator includes a **cooldown period** (default: 10 bars minimum between signals). This stops signal spam in choppy conditions and forces you to wait for the market to develop before taking another trade. ### 6. Time Session Filters Two separate trading sessions can be configured with 12-hour clock inputs: - **Session 1**: Default 9:30 AM - 4:00 PM (New York regular hours) - **Session 2**: Optional second session for extended hours or different time zones Signals only appear during enabled sessions, helping you trade during liquid market hours. ## What Makes This Original This indicator automates a specific methodology (MapleStax CBC) that combines multiple proven concepts: 1. Higher timeframe trend structure (CBC flip logic) 2. Lower timeframe execution timing (EMA filters) 3. Pullback entry strategy (counter-colored candles) 4. Volume-based target selection (VWAP integration) 5. Swing-based stop placement (previous anchor swing points) The combination of these elements into an automated system with visual feedback and alert functionality is what provides value beyond using these indicators separately. ## How to Use 1. **Choose Your Timeframes**: - Anchor timeframe: 10 minutes (adjustable) for trend direction - Execution timeframe: 3-5 minutes recommended for entries 2. **Select VWAP Mode**: - **Strict Mode**: More conservative, only trades with VWAP bias - **Target Mode**: More aggressive, uses VWAP as profit target 3. **Configure Sessions**: Enable Session 1 and optionally Session 2 to match your trading hours 4. **Set Risk Parameters**: Adjust take profit ticks based on your instrument and risk tolerance 5. **Watch for Signals**: - Green "BUY" label below bars = Long entry - Red "SELL" label above bars = Short entry - Dashed red line = Stop loss level - Green "TP ✓" or Red "SL ✗" labels show exit points 6. **Monitor the Status Table**: The table in the top-right shows: - Current 10m trend direction - EMA alignment status - VWAP position - Active session status - Current signal state - Active trade information 7. **Set Alerts**: Use TradingView's alert system with the built-in alert conditions: - BUY Signal - SELL Signal - Take Profit Hit - Stop Loss Hit ## Best Practices - **Recommended Timeframes**: 3m execution chart with 10m anchor works well for active trading - **Instrument Selection**: Works best on liquid futures contracts (ES, NQ, CL, etc.) and major forex pairs - **Session Trading**: Enable Session 1 for New York hours; avoid low-volume periods - **Backtest First**: Always backtest the settings on your specific instrument before live trading - **Use Realistic Parameters**: Default 20-tick TP is conservative; adjust based on instrument volatility ## Limitations and Warnings **This indicator does NOT**: - Guarantee profitable trades (past performance does not indicate future results) - Account for slippage, commissions, or real-world execution challenges - Work equally well in all market conditions (performs poorly in low-volume, range-bound markets) - Replace proper risk management and position sizing - Provide financial advice **Repainting**: This indicator is designed to be non-repainting. Signals use indexed historical data from the anchor timeframe, meaning they will not change or disappear after they appear. However, the current bar's status will update in real-time until it closes. **Market Conditions**: This trend-following pullback system performs best in trending markets with clear directional bias. In choppy, range-bound conditions, expect more false signals despite the cooldown filter. **Stop Loss Execution**: The stop loss levels shown are theoretical. In fast-moving markets, actual fills may occur at worse prices due to slippage. ## Input Parameters **Anchor Settings**: - Anchor Timeframe: Higher timeframe for trend detection (default: 10 minutes) **EMA Settings**: - Fast EMA: Short-period EMA for execution bias (default: 9) - Slow EMA: Long-period EMA for execution bias (default: 20) **VWAP Settings**: - Strict VWAP Filter: Toggle between strict filtering and target mode **Signal Management**: - Min Bars Between Signals: Cooldown period to prevent spam (default: 10 bars) **Time Filters**: - Session 1 & 2: Configure up to two trading sessions with start/end times in 12-hour format **Risk Management**: - Take Profit (Ticks): Fixed tick target when aligned with VWAP (default: 20) **Visual Settings**: - Show Trend Background: Background color based on 10m trend - Show Stop Loss Lines: Display SL levels on chart - Show EMAs: Display 9/20 EMAs on chart - Show VWAP: Display daily VWAP on chart ## Technical Notes - Uses Pine Script v5 - Non-repainting implementation via `request.security()` with `lookahead_off` and indexed data - Suitable for alerts and automated trading integration - Maximum 50 labels and 50 lines to maintain performance - Status table updates on each bar close ## Credits This indicator automates the MapleStax Candle by Candle methodology. The CBC flip logic and pullback entry concept are part of the MapleStax trading education system. --- **Disclaimer**: This indicator is for educational and informational purposes only. It is not financial advice. Trading futures, forex, and equities carries substantial risk of loss. Past performance is not indicative of future results. Always trade with risk capital you can afford to lose and use proper position sizing.
Pine Script® indicator
by TradeManager_PRO
SMI Histogram State VisualizationStochastic Momentum Index (SMI) – State Histogram One issue I always had with the standard SMI was how difficult it can be to clearly distinguish EMA crosses, especially when the oscillator is compressed near overbought or oversold levels. Important information is there — it’s just not always easy to read quickly. This script is my solution to that problem. By transforming the SMI into a state-based histogram, momentum, exhaustion, and EMA crosses become visually obvious at a glance. How It Works The Stochastic Momentum Index (SMI) measures price position relative to the midpoint of its recent high–low range, which makes it: symmetric around zero smoother than a classic stochastic well suited for momentum analysis Instead of a traditional line oscillator, the SMI is displayed as a color-coded histogram, while an EMA of the SMI is used as a confirmation signal. Color-Coded Momentum States The histogram dynamically changes color based on momentum state and EMA position: 🔴 Red – Oversold & Below EMA Strong bearish momentum, downtrend still intact. 🟠 Orange – Oversold & Above EMA Bearish momentum weakening, early bullish reversal potential. 🟢 Green – Overbought & Above EMA Strong bullish momentum, trend continuation. 🟡 Yellow – Overbought & Below EMA Bullish momentum weakening, early bearish reversal potential. Faded colors represent neutral or transition phases near the zero line. Best Use Cases Quickly identifying EMA crosses inside overbought / oversold zones Spotting early reversals before price reacts Confirming momentum continuation Filtering noise in ranging markets This indicator is non-repainting and works on all timeframes. Inputs %K Length – Lookback window used to define the price range %D Length – Smoothing applied to the momentum calculation EMA Length – Confirmation smoothing applied to the SMI Notes This indicator is intended as a visual momentum and confirmation tool, not a standalone trading system. Always use it in confluence with price action, market structure, and proper risk management.
Pine Script® indicator
by sicktor
Bifurcation Zone - CAEBifurcation Zone — Cognitive Adversarial Engine (BZ-CAE) Bifurcation Zone — CAE (BZ-CAE) is a next-generation divergence detection system enhanced by a Cognitive Adversarial Engine that evaluates both sides of every potential trade before presenting signals. Unlike traditional divergence indicators that show every price-oscillator disagreement regardless of context, BZ-CAE applies comprehensive market-state intelligence to identify only the divergences that occur in favorable conditions with genuine probability edges. The system identifies structural bifurcation points — critical junctures where price and momentum disagree, signaling potential reversals or continuations — then validates these opportunities through five interconnected intelligence layers: Trend Conviction Scoring , Directional Momentum Alignment , Multi-Factor Exhaustion Modeling , Adversarial Validation , and Confidence Scoring . The result is a selective, context-aware signal system that filters noise and highlights high-probability setups. This is not a "buy the arrow" indicator. It's a decision support framework that teaches you how to read market state, evaluate divergence quality, and make informed trading decisions based on quantified intelligence rather than hope. What Sets BZ-CAE Apart: Technical Architecture The Problem With Traditional Divergence Indicators Most divergence indicators operate on a simple rule: if price makes a higher high and RSI makes a lower high, show a bearish signal. If price makes a lower low and RSI makes a higher low, show a bullish signal. This creates several critical problems: Context Blindness : They show counter-trend signals in powerful trends that rarely reverse, leading to repeated losses as you fade momentum. Signal Spam : Every minor price-oscillator disagreement generates an alert, overwhelming you with low-quality setups and creating analysis paralysis. No Quality Ranking : All signals are treated identically. A marginal divergence in choppy conditions receives the same visual treatment as a high-conviction setup at a major exhaustion point. Single-Sided Evaluation : They ask "Is this a good long?" without checking if the short case is overwhelmingly stronger, leading you into obvious bad trades. Static Configuration : You manually choose RSI 14 or Stochastic 14 and hope it works, with no systematic way to validate if that's optimal for your instrument. BZ-CAE's Solution: Cognitive Adversarial Intelligence BZ-CAE solves these problems through an integrated five-layer intelligence architecture: 1. Trend Conviction Score (TCS) — 0 to 1 Scale Most indicators check if ADX is above 25 to determine "trending" conditions. This binary approach misses nuance. TCS is a weighted composite metric: Formula : 0.35 × normalize(ADX, 10, 35) + 0.35 × structural_strength + 0.30 × htf_alignment Structural Strength : 10-bar SMA of consecutive directional bars. Captures persistence — are bulls or bears consistently winning? HTF Alignment : Multi-timeframe EMA stacking (20/50/100/200). When all EMAs align in the same direction, you're in institutional trend territory. Purpose : Quantifies how "locked in" the trend is. When TCS exceeds your threshold (default 0.80), the system knows to avoid counter-trend trades unless other factors override. Interpretation : TCS > 0.85: Very strong trend — counter-trading is extremely high risk TCS 0.70-0.85: Strong trend — favor continuation, require exhaustion for reversals TCS 0.50-0.70: Moderate trend — context matters, both directions viable TCS < 0.50: Weak/choppy — reversals more viable, range-bound conditions 2. Directional Momentum Alignment (DMA) — ATR-Normalized Formula : (EMA21 - EMA55) / ATR14 This isn't just "price above EMA" — it's a regime-aware momentum gauge. The same $100 price movement reads completely differently in high-volatility crypto versus low-volatility forex. By normalizing with ATR, DMA adapts its interpretation to current market conditions. Purpose : Quantifies the directional "force" behind current price action. Positive = bullish push, negative = bearish push. Magnitude = strength. Interpretation : DMA > 0.7: Strong bullish momentum — bearish divergences risky DMA 0.3 to 0.7: Moderate bullish bias DMA -0.3 to 0.3: Balanced/choppy conditions DMA -0.7 to -0.3: Moderate bearish bias DMA < -0.7: Strong bearish momentum — bullish divergences risky 3. Multi-Factor Exhaustion Modeling — 0 to 1 Probability Single-metric exhaustion detection (like "RSI > 80") misses complex market states. BZ-CAE aggregates five independent exhaustion signals: Volume Spikes : Current volume versus 50-bar average 2.5x average: 0.25 weight 2.0x average: 0.15 weight 1.5x average: 0.10 weight Divergence Present : The fact that a divergence exists contributes 0.30 weight — structural momentum disagreement is itself an exhaustion signal. RSI Extremes : Captures oscillator climax zones RSI > 80 or < 20: 0.25 weight RSI > 75 or < 25: 0.15 weight Pin Bar Detection : Identifies rejection candles (2:1 wick-to-body ratio, indicating failed breakout attempts): 0.15 weight Extended Runs : Consecutive bars above/below EMA20 without pullback 30+ bars: 0.15 weight (market hasn't paused to consolidate) Total exhaustion score is the sum of all applicable weights, capped at 1.0. Purpose : Detects when strong trends become vulnerable to reversal. High exhaustion can override trend filters, allowing counter-trend trades at genuine turning points that basic indicators would miss. Interpretation : Exhaustion > 0.75: High probability of climax — yellow background shading alerts you visually Exhaustion 0.50-0.75: Moderate overextension — watch for confirmation Exhaustion < 0.50: Fresh move — trend can continue, counter-trend trades higher risk 4. Adversarial Validation — Game Theory Applied to Trading This is BZ-CAE's signature innovation. Before approving any signal, the engine quantifies BOTH sides of the trade simultaneously: For Bullish Divergences , it calculates: Bull Case Score (0-1+) : Distance below EMA20 (pullback quality): up to 0.25 Bullish EMA alignment (close > EMA20 > EMA50): 0.25 Oversold RSI (< 40): 0.25 Volume confirmation (> 1.2x average): 0.25 Bear Case Score (0-1+) : Price below EMA50 (structural weakness): 0.30 Very oversold RSI (< 30, indicating knife-catching): 0.20 Differential = Bull Case - Bear Case If differential < -0.10 (default threshold), the bear case is dominating — signal is BLOCKED or ANNOTATED. For Bearish Divergences , the logic inverts (Bear Case vs Bull Case). Purpose : Prevents trades where you're fighting obvious strength in the opposite direction. This is institutional-grade risk management — don't just evaluate your trade, evaluate the counter-trade simultaneously. Why This Matters : You might see a bullish divergence at a local low, but if price is deeply below major support EMAs with strong bearish momentum, you're catching a falling knife. The adversarial check catches this and blocks the signal. 5. Confidence Scoring — 0 to 1 Quality Assessment Every signal that passes initial filters receives a comprehensive quality score: Formula : 0.30 × normalize(TCS) // Trend context + 0.25 × normalize(|DMA|) // Momentum magnitude + 0.20 × pullback_quality // Entry distance from EMA20 + 0.15 × state_quality // ADX + alignment + structure + 0.10 × divergence_strength // Slope separation magnitude + adversarial_bonus (0-0.30) // Your side's advantage Purpose : Ranks setup quality for filtering and position sizing decisions. You can set a minimum confidence threshold (default 0.35) to ensure only quality setups reach your chart. Interpretation : Confidence > 0.70: Premium setup — consider increased position size Confidence 0.50-0.70: Good quality — standard size Confidence 0.35-0.50: Acceptable — reduced size or skip if conservative Confidence < 0.35: Marginal — blocked in Filtering mode, annotated in Advisory mode CAE Operating Modes: Learning vs Enforcement Off : Disables all CAE logic. Raw divergence pipeline only. Use for baseline comparison. Advisory : Shows ALL signals regardless of CAE evaluation, but annotates signals that WOULD be blocked with specific warnings (e.g., "Bull: strong downtrend (TCS=0.87)" or "Adversarial bearish"). This is your learning mode — see CAE's decision logic in action without missing educational opportunities. Filtering : Actively blocks low-quality signals. Only setups that pass all enabled gates (Trend Filter, Adversarial Validation, Confidence Gating) reach your chart. This is your live trading mode — trust the system to enforce discipline. CAE Filter Gates: Three-Layer Protection When CAE is enabled, signals must pass through three independent gates (each can be toggled on/off): Gate 1: Strong Trend Filter If TCS ≥ tcs_threshold (default 0.80) And signal is counter-trend (bullish in downtrend or bearish in uptrend) And exhaustion < exhaustion_required (default 0.50) Then: BLOCK signal Logic: Don't fade strong trends unless the move is clearly overextended Gate 2: Adversarial Validation Calculate both bull case and bear case scores If opposing case dominates by more than adv_threshold (default 0.10) Then: BLOCK signal Logic: Avoid trades where you're fighting obvious strength in the opposite direction Gate 3: Confidence Gating Calculate composite confidence score (0-1) If confidence < min_confidence (default 0.35) Then: In Filtering mode, BLOCK signal; in Advisory mode, ANNOTATE with warning Logic: Only take setups with minimum quality threshold All three gates work together. A signal must pass ALL enabled gates to fire. Visual Intelligence System Bifurcation Zones (Supply/Demand Blocks) When a divergence signal fires, BZ-CAE draws a semi-transparent box extending 15 bars forward from the signal pivot: Demand Zones (Bullish) : Theme-colored box (cyan in Cyberpunk, blue in Professional, etc.) labeled "Demand" — marks where smart money likely placed buy orders as price diverged at the low. Supply Zones (Bearish) : Theme-colored box (magenta in Cyberpunk, orange in Professional) labeled "Supply" — marks where smart money likely placed sell orders as price diverged at the high. Theory : Divergences represent institutional disagreement with the crowd. The crowd pushed price to an extreme (new high or low), but momentum (oscillator) is waning, indicating smart money is taking the opposite side. These zones mark order placement areas that become future support/resistance. Use Cases : Exit targets: Take profit when price returns to opposite-side zone Re-entry levels: If price returns to your entry zone, consider adding Stop placement: Place stops just beyond your zone (below demand, above supply) Auto-Cleanup : System keeps the last 20 zones to prevent chart clutter. Adversarial Bar Coloring — Real-Time Market Debate Heatmap Each bar is colored based on the Bull Case vs Bear Case differential: Strong Bull Advantage (diff > 0.3): Full theme bull color (e.g., cyan) Moderate Bull Advantage (diff > 0.1): 50% transparency bull Neutral (diff -0.1 to 0.1): Gray/neutral theme Moderate Bear Advantage (diff < -0.1): 50% transparency bear Strong Bear Advantage (diff < -0.3): Full theme bear color (e.g., magenta) This creates a real-time visual heatmap showing which side is "winning" the market debate. When bars flip from cyan to magenta (or vice versa), you're witnessing a shift in adversarial advantage — a leading indicator of potential momentum changes. Exhaustion Shading When exhaustion score exceeds 0.75, the chart background displays a semi-transparent yellow highlight. This immediate visual warning alerts you that the current move is at high risk of reversal, even if trend indicators remain strong. Visual Themes — Six Aesthetic Options Cyberpunk : Cyan/Magenta/Yellow — High contrast, neon aesthetic, excellent for dark-themed trading environments Professional : Blue/Orange/Green — Corporate color palette, suitable for presentations and professional documentation Ocean : Teal/Red/Cyan — Aquatic palette, calming for extended monitoring sessions Fire : Orange/Red/Coral — Warm aggressive colors, high energy Matrix : Green/Red/Lime — Code aesthetic, homage to classic hacker visuals Monochrome : White/Gray — Minimal distraction, maximum focus on price action All visual elements (signal markers, zones, bar colors, dashboard) adapt to your selected theme. Divergence Engine — Core Detection System What Are Divergences? Divergences occur when price action and momentum indicators disagree, creating structural tension that often resolves in a change of direction: Regular Divergence (Reversal Signal) : Bearish Regular : Price makes higher high, oscillator makes lower high → Potential trend reversal down Bullish Regular : Price makes lower low, oscillator makes higher low → Potential trend reversal up Hidden Divergence (Continuation Signal) : Bearish Hidden : Price makes lower high, oscillator makes higher high → Downtrend continuation Bullish Hidden : Price makes higher low, oscillator makes lower low → Uptrend continuation Both types can be enabled/disabled independently in settings. Pivot Detection Methods BZ-CAE uses symmetric pivot detection with separate lookback and lookforward periods (default 5/5): Pivot High : Bar where high > all highs within lookback range AND high > all highs within lookforward range Pivot Low : Bar where low < all lows within lookback range AND low < all lows within lookforward range This ensures structural validity — the pivot must be a clear local extreme, not just a minor wiggle. Divergence Validation Requirements For a divergence to be confirmed, it must satisfy: Slope Disagreement : Price slope and oscillator slope must move in opposite directions (for regular divs) or same direction with inverted highs/lows (for hidden divs) Minimum Slope Change : |osc_slope| > min_slope_change / 100 (default 1.0) — filters weak, marginal divergences Maximum Lookback Range : Pivots must be within max_lookback bars (default 60) — prevents ancient, irrelevant divergences ATR-Normalized Strength : Divergence strength = min(|price_slope| × |osc_slope| × 10, 1.0) — quantifies the magnitude of disagreement in volatility context Regular divergences receive 1.0× weight; hidden divergences receive 0.8× weight (slightly less reliable historically). Oscillator Options — Five Professional Indicators RSI (Relative Strength Index) : Classic overbought/oversold momentum indicator. Best for: General purpose divergence detection across all instruments. Stochastic : Range-bound %K momentum comparing close to high-low range. Best for: Mean reversion strategies and range-bound markets. CCI (Commodity Channel Index) : Measures deviation from statistical mean, auto-normalized to 0-100 scale. Best for: Cyclical instruments and commodities. MFI (Money Flow Index) : Volume-weighted RSI incorporating money flow. Best for: Volume-driven markets like stocks and crypto. Williams %R : Inverse stochastic looking back over period, auto-adjusted to 0-100. Best for: Reversal detection at extremes. Each oscillator has adjustable length (2-200, default 14) and smoothing (1-20, default 1). You also set overbought (50-100, default 70) and oversold (0-50, default 30) thresholds. Signal Timing Modes — Understanding Repainting BZ-CAE offers two timing policies with complete transparency about repainting behavior: Realtime (1-bar, peak-anchored) How It Works : Detects peaks 1 bar ago using pattern: high > high AND high > high Signal prints on the NEXT bar after peak detection (bar_index) Visual marker anchors to the actual PEAK bar (bar_index - 1, offset -1) Signal locks in when bar CONFIRMS (closes) Repainting Behavior : On the FORMING bar (before close), the peak condition may change as new prices arrive Once bar CLOSES (barstate.isconfirmed), signal is locked permanently This is preview/early warning behavior by design Best For : Active monitoring and immediate alerts Learning the system (seeing signals develop in real-time) Responsive entry if you're watching the chart live Confirmed (lookforward) How It Works : Uses Pine Script's built-in ta.pivothigh() and ta.pivotlow() functions Requires full pivot validation period (lookback + lookforward bars) Signal prints pivot_lookforward bars after the actual peak (default 5-bar delay) Visual marker anchors to the actual peak bar (offset -pivot_lookforward) No Repainting Behavior Best For : Backtesting and historical analysis Conservative entries requiring full confirmation Automated trading systems Swing trading with larger timeframes Tradeoff : Delayed entry by pivot_lookforward bars (typically 5 bars) On a 5-minute chart, this is a 25-minute delay On a 4-hour chart, this is a 20-hour delay Recommendation : Use Confirmed for backtesting to verify system performance honestly. Use Realtime for live monitoring only if you're actively watching the chart and understand pre-confirmation repainting behavior. Signal Spacing System — Anti-Spam Architecture Even after CAE filtering, raw divergences can cluster. The spacing system enforces separation: Three Independent Filters 1. Min Bars Between ANY Signals (default 12): Prevents rapid-fire clustering across both directions If last signal (bull or bear) was within N bars, block new signal Ensures breathing room between all setups 2. Min Bars Between SAME-SIDE Signals (default 24, optional enforcement): Prevents bull-bull or bear-bear spam Separate tracking for bullish and bearish signal timelines Toggle enforcement on/off 3. Min ATR Distance From Last Signal (default 0, optional): Requires price to move N × ATR from last signal location Ensures meaningful price movement between setups 0 = disabled, 0.5-2.0 = typical range for enabled All three filters work independently. A signal must pass ALL enabled filters to proceed. Practical Guidance : Scalping (1-5m) : Any 6-10, Same-side 12-20, ATR 0-0.5 Day Trading (15m-1H) : Any 12, Same-side 24, ATR 0-1.0 Swing Trading (4H-D) : Any 20-30, Same-side 40-60, ATR 1.0-2.0 Dashboard — Real-Time Control Center The dashboard (toggleable, four corner positions, three sizes) provides comprehensive system intelligence: Oscillator Section Current oscillator type and value State: OVERBOUGHT / OVERSOLD / NEUTRAL (color-coded) Length parameter Cognitive Engine Section TCS (Trend Conviction Score) : Current value with emoji state indicator 🔥 = Strong trend (>0.75) 📊 = Moderate trend (0.50-0.75) 〰️ = Weak/choppy (<0.50) Color: Red if above threshold (trend filter active), yellow if moderate, green if weak DMA (Directional Momentum Alignment) : Current value with emoji direction indicator 🐂 = Bullish momentum (>0.5) ⚖️ = Balanced (-0.5 to 0.5) 🐻 = Bearish momentum (<-0.5) Color: Green if bullish, red if bearish Exhaustion : Current value with emoji warning indicator ⚠️ = High exhaustion (>0.75) 🟡 = Moderate (0.50-0.75) ✓ = Low (<0.50) Color: Red if high, yellow if moderate, green if low Pullback : Quality of current distance from EMA20 Values >0.6 are ideal entry zones (not too close, not too far) Bull Case / Bear Case (if Adversarial enabled): Current scores for both sides of the market debate Differential with emoji indicator: 📈 = Bull advantage (>0.2) ➡️ = Balanced (-0.2 to 0.2) 📉 = Bear advantage (<-0.2) Last Signal Metrics Section (New Feature) When a signal fires, this section captures and displays: Signal type (BULL or BEAR) Bars elapsed since signal Confidence % at time of signal TCS value at signal time DMA value at signal time Purpose : Provides a historical reference for learning. You can see what the market state looked like when the last signal fired, helping you correlate outcomes with conditions. Statistics Section Total Signals : Lifetime count across session Blocked Signals : Count and percentage (filter effectiveness metric) Bull Signals : Total bullish divergences Bear Signals : Total bearish divergences Purpose : System health monitoring. If blocked % is very high (>60%), filters may be too strict. If very low (<10%), filters may be too loose. Advisory Annotations When CAE Mode = Advisory, this section displays warnings for signals that would be blocked in Filtering mode: Examples: "Bull spacing: wait 8 bars" "Bear: strong uptrend (TCS=0.87)" "Adversarial bearish" "Low confidence 32%" Multiple warnings can stack, separated by " | ". This teaches you CAE's decision logic transparently. How to Use BZ-CAE — Complete Workflow Phase 1: Initial Setup (First Session) Apply BZ-CAE to your chart Select your preferred Visual Theme (Cyberpunk recommended for visibility) Set Signal Timing to "Confirmed (lookforward)" for learning Choose your Oscillator Type (RSI recommended for general use, length 14) Set Overbought/Oversold to 70/30 (standard) Enable both Regular Divergence and Hidden Divergence Set Pivot Lookback/Lookforward to 5/5 (balanced structure) Enable CAE Intelligence Set CAE Mode to "Advisory" (learning mode) Enable all three CAE filters: Strong Trend Filter , Adversarial Validation , Confidence Gating Enable Show Dashboard , position Top Right, size Normal Enable Draw Bifurcation Zones and Adversarial Bar Coloring Phase 2: Learning Period (Weeks 1-2) Goal : Understand how CAE evaluates market state and filters signals. Activities : Watch the dashboard during signals : Note TCS values when counter-trend signals fail — this teaches you the trend strength threshold for your instrument Observe exhaustion patterns at actual turning points — learn when overextension truly matters Study adversarial differential at signal times — see when opposing cases dominate Review blocked signals (orange X-crosses): In Advisory mode, you see everything — signals that would pass AND signals that would be blocked Check the advisory annotations to understand why CAE would block Track outcomes: Were the blocks correct? Did those signals fail? Use Last Signal Metrics : After each signal, check the dashboard capture of confidence, TCS, and DMA Journal these values alongside trade outcomes Identify patterns: Do confidence >0.70 signals work better? Does your instrument respect TCS >0.85? Understand your instrument's "personality" : Trending instruments (indices, major forex) may need TCS threshold 0.85-0.90 Choppy instruments (low-cap stocks, exotic pairs) may work best with TCS 0.70-0.75 High-volatility instruments (crypto) may need wider spacing Low-volatility instruments may need tighter spacing Phase 3: Calibration (Weeks 3-4) Goal : Optimize settings for your specific instrument, timeframe, and style. Calibration Checklist : Min Confidence Threshold : Review confidence distribution in your signal journal Identify the confidence level below which signals consistently fail Set min_confidence slightly above that level Day trading : 0.35-0.45 Swing trading : 0.40-0.55 Scalping : 0.30-0.40 TCS Threshold : Find the TCS level where counter-trend signals consistently get stopped out Set tcs_threshold at or slightly below that level Trending instruments : 0.85-0.90 Mixed instruments : 0.80-0.85 Choppy instruments : 0.75-0.80 Exhaustion Override Level : Identify exhaustion readings that marked genuine reversals Set exhaustion_required just below the average Typical range : 0.45-0.55 Adversarial Threshold : Default 0.10 works for most instruments If you find CAE is too conservative (blocking good trades), raise to 0.15-0.20 If signals are still getting caught in opposing momentum, lower to 0.07-0.09 Spacing Parameters : Count bars between quality signals in your journal Set min bars ANY to ~60% of that average Set min bars SAME-SIDE to ~120% of that average Scalping : Any 6-10, Same 12-20 Day trading : Any 12, Same 24 Swing : Any 20-30, Same 40-60 Oscillator Selection : Try different oscillators for 1-2 weeks each Track win rate and average winner/loser by oscillator type RSI : Best for general use, clear OB/OS Stochastic : Best for range-bound, mean reversion MFI : Best for volume-driven markets CCI : Best for cyclical instruments Williams %R : Best for reversal detection Phase 4: Live Deployment Goal : Disciplined execution with proven, calibrated system. Settings Changes : Switch CAE Mode from Advisory to Filtering System now actively blocks low-quality signals Only setups passing all gates reach your chart Keep Signal Timing on Confirmed for conservative entries OR switch to Realtime if you're actively monitoring and want faster entries (accept pre-confirmation repaint risk) Use your calibrated thresholds from Phase 3 Enable high-confidence alerts: "⭐ High Confidence Bullish/Bearish" (>0.70) Trading Discipline Rules : Respect Blocked Signals : If CAE blocks a trade you wanted to take, TRUST THE SYSTEM Don't manually override — if you consistently disagree, return to Phase 2/3 calibration The block exists because market state failed intelligence checks Confidence-Based Position Sizing : Confidence >0.70: Standard or increased size (e.g., 1.5-2.0% risk) Confidence 0.50-0.70: Standard size (e.g., 1.0% risk) Confidence 0.35-0.50: Reduced size (e.g., 0.5% risk) or skip if conservative TCS-Based Management : High TCS + counter-trend signal: Use tight stops, quick exits (you're fading momentum) Low TCS + reversal signal: Use wider stops, trail aggressively (genuine reversal potential) Exhaustion Awareness : Exhaustion >0.75 (yellow shading): Market is overextended, reversal risk is elevated — consider early exit or tighter trailing stops even on winning trades Exhaustion <0.30: Continuation bias — hold for larger move, wide trailing stops Adversarial Context : Strong differential against you (e.g., bullish signal with bear diff <-0.2): Use very tight stops, consider skipping Strong differential with you (e.g., bullish signal with bull diff >0.2): Trail aggressively, this is your tailwind Practical Settings by Timeframe & Style Scalping (1-5 Minute Charts) Objective : High frequency, tight stops, quick reversals in fast-moving markets. Oscillator : Type: RSI or Stochastic (fast response to quick moves) Length: 9-11 (more responsive than standard 14) Smoothing: 1 (no lag) OB/OS: 65/35 (looser thresholds ensure frequent crossings in fast conditions) Divergence : Pivot Lookback/Lookforward: 3/3 (tight structure, catch small swings) Max Lookback: 40-50 bars (recent structure only) Min Slope Change: 0.8-1.0 (don't be overly strict) CAE : Mode: Advisory first (learn), then Filtering Min Confidence: 0.30-0.35 (lower bar for speed, accept more signals) TCS Threshold: 0.70-0.75 (allow more counter-trend opportunities) Exhaustion Required: 0.45-0.50 (moderate override) Strong Trend Filter: ON (still respect major intraday trends) Adversarial: ON (critical for scalping protection — catches bad entries quickly) Spacing : Min Bars ANY: 6-10 (fast pace, many setups) Min Bars SAME-SIDE: 12-20 (prevent clustering) Min ATR Distance: 0 or 0.5 (loose) Timing : Realtime (speed over precision, but understand repaint risk) Visuals : Signal Size: Tiny (chart clarity in busy conditions) Show Zones: Optional (can clutter on low timeframes) Bar Coloring: ON (helps read momentum shifts quickly) Dashboard: Small size (corner reference, not main focus) Key Consideration : Scalping generates noise. Even with CAE, expect lower win rate (45-55%) but aim for favorable R:R (2:1 or better). Size conservatively. Day Trading (15-Minute to 1-Hour Charts) Objective : Balance quality and frequency. Standard divergence trading approach. Oscillator : Type: RSI or MFI (proven reliability, volume confirmation with MFI) Length: 14 (industry standard, well-studied) Smoothing: 1-2 OB/OS: 70/30 (classic levels) Divergence : Pivot Lookback/Lookforward: 5/5 (balanced structure) Max Lookback: 60 bars Min Slope Change: 1.0 (standard strictness) CAE : Mode: Filtering (enforce discipline from the start after brief Advisory learning) Min Confidence: 0.35-0.45 (quality filter without being too restrictive) TCS Threshold: 0.80-0.85 (respect strong trends) Exhaustion Required: 0.50 (balanced override threshold) Strong Trend Filter: ON Adversarial: ON Confidence Gating: ON (all three filters active) Spacing : Min Bars ANY: 12 (breathing room between all setups) Min Bars SAME-SIDE: 24 (prevent bull/bear clusters) Min ATR Distance: 0-1.0 (optional refinement, typically 0.5-1.0) Timing : Confirmed (1-bar delay for reliability, no repainting) Visuals : Signal Size: Tiny or Small Show Zones: ON (useful reference for exits/re-entries) Bar Coloring: ON (context awareness) Dashboard: Normal size (full visibility) Key Consideration : This is the "sweet spot" timeframe for BZ-CAE. Market structure is clear, CAE has sufficient data, and signal frequency is manageable. Expect 55-65% win rate with proper execution. Swing Trading (4-Hour to Daily Charts) Objective : Quality over quantity. High conviction only. Larger stops and targets. Oscillator : Type: RSI or CCI (robust on higher timeframes, smooth longer waves) Length: 14-21 (capture larger momentum swings) Smoothing: 1-3 OB/OS: 70/30 or 75/25 (strict extremes) Divergence : Pivot Lookback/Lookforward: 5/5 or 7/7 (structural purity, major swings only) Max Lookback: 80-100 bars (broader historical context) Min Slope Change: 1.2-1.5 (require strong, undeniable divergence) CAE : Mode: Filtering (strict enforcement, premium setups only) Min Confidence: 0.40-0.55 (high bar for entry) TCS Threshold: 0.85-0.95 (very strong trend protection — don't fade established HTF trends) Exhaustion Required: 0.50-0.60 (higher bar for override — only extreme exhaustion justifies counter-trend) Strong Trend Filter: ON (critical on HTF) Adversarial: ON (avoid obvious bad trades) Confidence Gating: ON (quality gate essential) Spacing : Min Bars ANY: 20-30 (substantial separation) Min Bars SAME-SIDE: 40-60 (significant breathing room) Min ATR Distance: 1.0-2.0 (require meaningful price movement) Timing : Confirmed (purity over speed, zero repaint for swing accuracy) Visuals : Signal Size: Small or Normal (clear markers on zoomed-out view) Show Zones: ON (important HTF levels) Bar Coloring: ON (long-term trend awareness) Dashboard: Normal or Large (comprehensive analysis) Key Consideration : Swing signals are rare but powerful. Expect 2-5 signals per month per instrument. Win rate should be 60-70%+ due to stringent filtering. Position size can be larger given confidence. Dashboard Interpretation Reference TCS (Trend Conviction Score) States 0.00-0.50: Weak/Choppy Emoji: 〰️ Color: Green/cyan Meaning: No established trend. Range-bound or consolidating. Both reversal and continuation signals viable. Action: Reversals (regular divs) are safer. Use wider profit targets (market has room to move). Consider mean reversion strategies. 0.50-0.75: Moderate Trend Emoji: 📊 Color: Yellow/neutral Meaning: Developing trend but not locked in. Context matters significantly. Action: Check DMA and exhaustion. If DMA confirms trend and exhaustion is low, favor continuation (hidden divs). If exhaustion is high, reversals are viable. 0.75-0.85: Strong Trend Emoji: 🔥 Color: Orange/warning Meaning: Well-established trend with persistence. Counter-trend is high risk. Action: Require exhaustion >0.50 for counter-trend entries. Favor continuation signals. Use tight stops on counter-trend attempts. 0.85-1.00: Very Strong Trend Emoji: 🔥🔥 Color: Red/danger (if counter-trading) Meaning: Locked-in institutional trend. Extremely high risk to fade. Action: Avoid counter-trend unless exhaustion >0.75 (yellow shading). Focus exclusively on continuation opportunities. Momentum is king here. DMA (Directional Momentum Alignment) Zones -2.0 to -1.0: Strong Bearish Momentum Emoji: 🐻🐻 Color: Dark red Meaning: Powerful downside force. Sellers are in control. Action: Bullish divergences are counter-momentum (high risk). Bearish divergences are with-momentum (lower risk). Size down on longs. -0.5 to 0.5: Neutral/Balanced Emoji: ⚖️ Color: Gray/neutral Meaning: No strong directional bias. Choppy or consolidating. Action: Both directions have similar probability. Focus on confidence score and adversarial differential for edge. 1.0 to 2.0: Strong Bullish Momentum Emoji: 🐂🐂 Color: Bright green/cyan Meaning: Powerful upside force. Buyers are in control. Action: Bearish divergences are counter-momentum (high risk). Bullish divergences are with-momentum (lower risk). Size down on shorts. Exhaustion States 0.00-0.50: Fresh Move Emoji: ✓ Color: Green Meaning: Trend is healthy, not overextended. Room to run. Action: Counter-trend trades are premature. Favor continuation. Hold winners for larger moves. Avoid early exits. 0.50-0.75: Mature Move Emoji: 🟡 Color: Yellow Meaning: Move is aging. Watch for signs of climax. Action: Tighten trailing stops on winning trades. Be ready for reversals. Don't add to positions aggressively. 0.75-0.85: High Exhaustion Emoji: ⚠️ Color: Orange Background: Yellow shading appears Meaning: Move is overextended. Reversal risk elevated significantly. Action: Counter-trend reversals are higher probability. Consider early exits on with-trend positions. Size up on reversal divergences (if CAE allows). 0.85-1.00: Critical Exhaustion Emoji: ⚠️⚠️ Color: Red Background: Yellow shading intensifies Meaning: Climax conditions. Reversal imminent or underway. Action: Aggressive reversal trades justified. Exit all with-trend positions. This is where major turns occur. Confidence Score Tiers 0.00-0.30: Low Quality Color: Red Status: Blocked in Filtering mode Action: Skip entirely. Setup lacks fundamental quality across multiple factors. 0.30-0.50: Moderate Quality Color: Yellow/orange Status: Marginal — passes in Filtering only if >min_confidence Action: Reduced position size (0.5-0.75% risk). Tight stops. Conservative profit targets. Skip if you're selective. 0.50-0.70: High Quality Color: Green/cyan Status: Good setup across most quality factors Action: Standard position size (1.0-1.5% risk). Normal stops and targets. This is your bread-and-butter trade. 0.70-1.00: Premium Quality Color: Bright green/gold Status: Exceptional setup — all factors aligned Visual: Double confidence ring appears Action: Consider increased position size (1.5-2.0% risk, maximum). Wider stops. Larger targets. High probability of success. These are rare — capitalize when they appear. Adversarial Differential Interpretation Bull Differential > 0.3 : Visual: Strong cyan/green bar colors Meaning: Bull case strongly dominates. Buyers have clear advantage. Action: Bullish divergences favored (with-advantage). Bearish divergences face headwind (reduce size or skip). Momentum is bullish. Bull Differential 0.1 to 0.3 : Visual: Moderate cyan/green transparency Meaning: Moderate bull advantage. Buyers have edge but not overwhelming. Action: Both directions viable. Slight bias toward longs. Differential -0.1 to 0.1 : Visual: Gray/neutral bars Meaning: Balanced debate. No clear advantage either side. Action: Rely on other factors (confidence, TCS, exhaustion) for direction. Adversarial is neutral. Bear Differential -0.3 to -0.1 : Visual: Moderate red/magenta transparency Meaning: Moderate bear advantage. Sellers have edge but not overwhelming. Action: Both directions viable. Slight bias toward shorts. Bear Differential < -0.3 : Visual: Strong red/magenta bar colors Meaning: Bear case strongly dominates. Sellers have clear advantage. Action: Bearish divergences favored (with-advantage). Bullish divergences face headwind (reduce size or skip). Momentum is bearish. Last Signal Metrics — Post-Trade Analysis After a signal fires, dashboard captures: Type : BULL or BEAR Bars Ago : How long since signal (updates every bar) Confidence : What was the quality score at signal time TCS : What was trend conviction at signal time DMA : What was momentum alignment at signal time Use Case : Post-trade journaling and learning. Example: "BULL signal 12 bars ago. Confidence: 68%, TCS: 0.42, DMA: -0.85" Analysis : This was a bullish reversal (regular div) with good confidence, weak trend (TCS), but strong bearish momentum (DMA). The bet was that momentum would reverse — a counter-momentum play requiring exhaustion confirmation. Check if exhaustion was high at that time to justify the entry. Track patterns: Do your best trades have confidence >0.65? Do low-TCS signals (<0.50) work better for you? Are you more successful with-momentum (DMA aligned with signal) or counter-momentum? Troubleshooting Guide Problem: No Signals Appearing Symptoms : Chart loads, dashboard shows metrics, but no divergence signals fire. Diagnosis Checklist : Check dashboard oscillator value : Is it crossing OB/OS levels (70/30)? If oscillator stays in 40-60 range constantly, it can't reach extremes needed for divergence detection. Are pivots forming? : Look for local swing highs/lows on your chart. If price is in tight consolidation, pivots may not meet lookback/lookforward requirements. Is spacing too tight? : Check "Last Signal" metrics — how many bars since last signal? If <12 and your min_bars_ANY is 12, spacing filter is blocking. Is CAE blocking everything? : Check dashboard Statistics section — what's the blocked signal count? High blocks indicate overly strict filters. Solutions : Loosen OB/OS Temporarily : Try 65/35 to verify divergence detection works If signals appear, the issue was threshold strictness Gradually tighten back to 67/33, then 70/30 as appropriate Lower Min Confidence : Try 0.25-0.30 (diagnostic level) If signals appear, filter was too strict Raise gradually to find sweet spot (0.35-0.45 typical) Disable Strong Trend Filter Temporarily : Turn off in CAE settings If signals appear, TCS threshold was blocking everything Re-enable and lower TCS_threshold to 0.70-0.75 Reduce Min Slope Change : Try 0.7-0.8 (from default 1.0) Allows weaker divergences through Helpful on low-volatility instruments Widen Spacing : Set min_bars_ANY to 6-8 Set min_bars_SAME_SIDE to 12-16 Reduces time between allowed signals Check Timing Mode : If using Confirmed, remember there's a pivot_lookforward delay (5+ bars) Switch to Realtime temporarily to verify system is working Realtime has no delay but repaints Verify Oscillator Settings : Length 14 is standard but might not fit all instruments Try length 9-11 for faster response Try length 18-21 for slower, smoother response Problem: Too Many Signals (Signal Spam) Symptoms : Dashboard shows 50+ signals in Statistics, confidence scores mostly <0.40, signals clustering close together. Solutions : Raise Min Confidence : Try 0.40-0.50 (quality filter) Blocks bottom-tier setups Targets top 50-60% of divergences only Tighten OB/OS : Use 70/30 or 75/25 Requires more extreme oscillator readings Reduces false divergences in mid-range Increase Min Slope Change : Try 1.2-1.5 (from default 1.0) Requires stronger, more obvious divergences Filters marginal slope disagreements Raise TCS Threshold : Try 0.85-0.90 (from default 0.80) Stricter trend filter blocks more counter-trend attempts Favors only strongest trend alignment Enable ALL CAE Gates : Turn on Trend Filter + Adversarial + Confidence Triple-layer protection Blocks aggressively — expect 20-40% reduction in signals Widen Spacing : min_bars_ANY: 15-20 (from 12) min_bars_SAME_SIDE: 30-40 (from 24) Creates substantial breathing room Switch to Confirmed Timing : Removes realtime preview noise Ensures full pivot validation 5-bar delay filters many false starts Problem: Signals in Strong Trends Get Stopped Out Symptoms : You take a bullish divergence in a downtrend (or bearish in uptrend), and it immediately fails. Dashboard showed high TCS at the time. Analysis : This is INTENDED behavior — CAE is protecting you from low-probability counter-trend trades. Understanding : Check Last Signal Metrics in dashboard — what was TCS when signal fired? If TCS was >0.85 and signal was counter-trend, CAE correctly identified it as high risk Strong trends rarely reverse cleanly without major exhaustion Your losses here are the system working as designed (blocking bad odds) If You Want to Override (Not Recommended) : Lower TCS_threshold to 0.70-0.75 (allows more counter-trend) Lower exhaustion_required to 0.40 (easier override) Disable Strong Trend Filter entirely (very risky) Better Approach : TRUST THE FILTER — it's preventing costly mistakes Wait for exhaustion >0.75 (yellow shading) before counter-trending strong TCS Focus on continuation signals (hidden divs) in high-TCS environments Use Advisory mode to see what CAE is blocking and learn from outcomes Problem: Adversarial Blocking Seems Wrong Symptoms : You see a divergence that "looks good" visually, but CAE blocks with "Adversarial bearish/bullish" warning. Diagnosis : Check dashboard Bull Case and Bear Case scores at that moment Look at Differential value Check adversarial bar colors — was there strong coloring against your intended direction? Understanding : Adversarial catches "obvious" opposing momentum that's easy to miss Example: Bullish divergence at a local low, BUT price is deeply below EMA50, bearish momentum is strong, and RSI shows knife-catching conditions Bull Case might be 0.20 while Bear Case is 0.55 Differential = -0.35, far beyond threshold Block is CORRECT — you'd be fighting overwhelming opposing flow If You Disagree Consistently Review blocked signals on chart — scroll back and check outcomes Did those blocked signals actually work, or did they fail as adversarial predicted? Raise adv_threshold to 0.15-0.20 (more permissive, allows closer battles) Disable Adversarial Validation temporarily (diagnostic) to isolate its effect Use Advisory mode to learn adversarial patterns over 50-100 signals Remember : Adversarial is conservative BY DESIGN. It prevents "obvious" bad trades where you're fighting strong strength the other way. Problem: Dashboard Not Showing or Incomplete Solutions : Toggle "Show Dashboard" to ON in settings Try different dashboard sizes (Small/Normal/Large) Try different positions (Top Left/Right, Bottom Left/Right) — might be off-screen Some sections require CAE Enable = ON (Cognitive Engine section won't appear if CAE is disabled) Statistics section requires at least 1 lifetime signal to populate Check that visual theme is set (dashboard colors adapt to theme) Problem: Performance Lag, Chart Freezing Symptoms : Chart loading is slow, indicator calculations cause delays, pinch-to-zoom lags. Diagnosis : Visual features are computationally expensive, especially adversarial bar coloring (recalculates every bar). Solutions (In Order of Impact) : Disable Adversarial Bar Coloring (MOST EXPENSIVE): Turn OFF "Adversarial Bar Coloring" in settings This is the single biggest performance drain Immediate improvement Reduce Vertical Lines : Lower "Keep last N vertical lines" to 20-30 Or set to 0 to disable entirely Moderate improvement Disable Bifurcation Zones : Turn OFF "Draw Bifurcation Zones" Reduces box drawing calculations Moderate improvement Set Dashboard Size to Small : Smaller dashboard = fewer cells = less rendering Minor improvement Use Shorter Max Lookback : Reduce max_lookback to 40-50 (from 60+) Fewer bars to scan for divergences Minor improvement Disable Exhaustion Shading : Turn OFF "Show Market State" Removes background coloring calculations Minor improvement Extreme Performance Mode : Disable ALL visual enhancements Keep only triangle markers Dashboard Small or OFF Use Minimal theme if available Problem: Realtime Signals Repainting Symptoms : You see a signal appear, but on next bar it disappears or moves. Explanation : Realtime mode detects peaks 1 bar ago: high > high AND high > high On the FORMING bar (before close), this condition can change as new prices arrive Example: At 10:05, high (10:04 bar) was 100, current high is 99 → peak detected At 10:05:30, new high of 101 arrives → peak condition breaks → signal disappears At 10:06 (bar close), final high is 101 → no peak at 10:04 anymore → signal gone permanently This is expected behavior for realtime responsiveness. You get preview/early warning, but it's not locked until bar confirms. Solutions : Use Confirmed Timing : Switch to "Confirmed (lookforward)" mode ZERO repainting — pivot must be fully validated 5-bar delay (pivot_lookforward) What you see in history is exactly what would have appeared live Accept Realtime Repaint as Tradeoff : Keep Realtime mode for speed and alerts Understand that pre-confirmation signals may vanish Only trade signals that CONFIRM at bar close (check barstate.isconfirmed) Use for live monitoring, NOT for backtesting Trade Only After Confirmation : In Realtime mode, wait 1 full bar after signal appears before entering If signal survives that bar close, it's locked This adds 1-bar delay but removes repaint risk Recommendation : Use Confirmed for backtesting and conservative trading. Use Realtime only for active monitoring with full understanding of preview behavior. Risk Management Integration BZ-CAE is a signal generation system, not a complete trading strategy. You must integrate proper risk management: Position Sizing by Confidence Confidence 0.70-1.00 (Premium) : Risk: 1.5-2.0% of account (MAXIMUM) Reasoning: High-quality setup across all factors Still cap at 2% — even premium setups can fail Confidence 0.50-0.70 (High Quality) : Risk: 1.0-1.5% of account Reasoning: Standard good setup Your bread-and-butter risk level Confidence 0.35-0.50 (Moderate Quality) : Risk: 0.5-1.0% of account Reasoning: Marginal setup, passes minimum threshold Reduce size or skip if you're selective Confidence <0.35 (Low Quality) : Risk: 0% (blocked in Filtering mode) Reasoning: Insufficient quality factors System protects you by not showing these Stop Placement Strategies For Reversal Signals (Regular Divergences) : Place stop beyond the divergence pivot plus buffer Bullish : Stop below the divergence low - 1.0-1.5 × ATR Bearish : Stop above the divergence high + 1.0-1.5 × ATR Reasoning: If price breaks the pivot, divergence structure is invalidated For Continuation Signals (Hidden Divergences) : Place stop beyond recent swing in opposite direction Bullish continuation : Stop below recent swing low (not the divergence pivot itself) Bearish continuation : Stop above recent swing high Reasoning: You're trading with trend, allow more breathing room ATR-Based Stops : 1.5-2.0 × ATR is standard Scale by timeframe: Scalping (1-5m): 1.0-1.5 × ATR (tight) Day trading (15m-1H): 1.5-2.0 × ATR (balanced) Swing (4H-D): 2.0-3.0 × ATR (wide) Never Use Fixed Dollar/Pip Stops : Markets have different volatility 50-pip stop on EUR/USD ≠ 50-pip stop on GBP/JPY Always normalize by ATR or pivot structure Profit Targets and Scaling Primary Target : 2-3 × ATR from entry (minimum 2:1 reward-risk) Example : Entry at 100, ATR = 2, stop at 97 (1.5 × ATR) → target at 106 (3 × ATR) = 2:1 R:R Scaling Out Strategy : Take 50% off at 1.5 × ATR (secure partial profit) Move stop to breakeven Trail remaining 50% with 1.0 × ATR trailing stop Let winners run if trend persists Targets by Confidence : High Confidence (>0.70) : Aggressive targets (3-4 × ATR), trail wider (1.5 × ATR) Standard Confidence (0.50-0.70) : Normal targets (2-3 × ATR), standard trail (1.0 × ATR) Low Confidence (0.35-0.50) : Conservative targets (1.5-2 × ATR), tight trail (0.75 × ATR) Use Bifurcation Zones : If opposite-side zone is visible on chart (from previous signal), use it as target Example : Bullish signal at 100, prior supply zone at 110 → use 110 as target Zones mark institutional resistance/support Exhaustion-Based Exits : If you're in a trade and exhaustion >0.75 develops (yellow shading), consider early exit Market is overextended — reversal risk is high Take profit even if target not reached Trade Management by TCS High TCS + Counter-Trend Trade (Risky) : Use very tight stops (1.0-1.5 × ATR) Conservative targets (1.5-2 × ATR) Quick exit if trade doesn't work immediately You're fading momentum — respect it Low TCS + Reversal Trade (Safer) : Use wider stops (2.0-2.5 × ATR) Aggressive targets (3-4 × ATR) Trail with patience Genuine reversal potential in weak trend High TCS + Continuation Trade (Safest) : Standard stops (1.5-2.0 × ATR) Very aggressive targets (4-5 × ATR) Trail wide (1.5-2.0 × ATR) You're with institutional momentum — let it run Educational Value — Learning Machine Intelligence BZ-CAE is designed as a learning platform, not just a tool: Advisory Mode as Teacher Most indicators are binary: signal or no signal. You don't learn WHY certain setups are better. BZ-CAE's Advisory mode shows you EVERY potential divergence, then annotates the ones that would be blocked in Filtering mode with specific reasons: "Bull: strong downtrend (TCS=0.87)" teaches you that TCS >0.85 makes counter-trend very risky "Adversarial bearish" teaches you that the opposing case was dominating "Low confidence 32%" teaches you that the setup lacked quality across multiple factors "Bull spacing: wait 8 bars" teaches you that signals need breathing room After 50-100 signals in Advisory mode, you internalize the CAE's decision logic. You start seeing these factors yourself BEFORE the indicator does. Dashboard Transparency Most "intelligent" indicators are black boxes — you don't know how they make decisions. BZ-CAE shows you ALL metrics in real-time: TCS tells you trend strength DMA tells you momentum alignment Exhaustion tells you overextension Adversarial shows both sides of the debate Confidence shows composite quality You learn to interpret market state holistically, a skill applicable to ANY trading system beyond this indicator. Divergence Quality Education Not all divergences are equal. BZ-CAE teaches you which conditions produce high-probability setups: Quality divergence : Regular bullish div at a low, TCS <0.50 (weak trend), exhaustion >0.75 (overextended), positive adversarial differential, confidence >0.70 Low-quality divergence : Regular bearish div at a high, TCS >0.85 (strong uptrend), exhaustion <0.30 (not overextended), negative adversarial differential, confidence <0.40 After using the system, you can evaluate divergences manually with similar intelligence. Risk Management Discipline Confidence-based position sizing teaches you to adjust risk based on setup quality, not emotions: Beginners often size all trades identically Or worse, size UP on marginal setups to "make up" for losses BZ-CAE forces systematic sizing: premium setups get larger size, marginal setups get smaller size This creates a probabilistic approach where your edge compounds over time. What This Indicator Is NOT Complete transparency about limitations and positioning: Not a Prediction System BZ-CAE does not predict future prices. It identifies structural divergences (price-momentum disagreements) and assesses current market state (trend, exhaustion, adversarial conditions). It tells you WHEN conditions favor a potential reversal or continuation, not WHAT WILL HAPPEN. Markets are probabilistic. Even premium-confidence setups fail ~30-40% of the time. The system improves your probability distribution over many trades — it doesn't eliminate risk. Not Fully Automated This is a decision support tool, not a trading robot. You must: Execute trades manually based on signals Manage positions (stops, targets, trailing) Apply discretionary judgment (news events, liquidity, context) Integrate with your broader strategy and risk rules The confidence scores guide position sizing, but YOU determine final risk allocation based on your account size, risk tolerance, and portfolio context. Not Beginner-Friendly BZ-CAE requires understanding of: Divergence trading concepts (regular vs hidden, reversal vs continuation) Market state interpretation (trend vs range, momentum, exhaustion) Basic technical analysis (pivots, support/resistance, EMAs) Risk management fundamentals (position sizing, stops, R:R) This is designed for intermediate to advanced traders willing to invest time learning the system. If you want "buy the arrow" simplicity, this isn't the tool. Not a Holy Grail There is no perfect indicator. BZ-CAE filters noise and improves signal quality significantly, but: Losing trades are inevitable (even at 70% win rate, 30% still fail) Market conditions change rapidly (yesterday's strong trend becomes today's chop) Black swan events occur (fundamentals override technicals) Execution matters (slippage, fees, emotional discipline) The system provides an EDGE, not a guarantee. Your job is to execute that edge consistently with proper risk management over hundreds of trades. Not Financial Advice BZ-CAE is an educational and analytical tool. All trading decisions are your responsibility. Past performance (backtested or live) does not guarantee future results. Only risk capital you can afford to lose. Consult a licensed financial advisor for investment advice specific to your situation. Ideal Market Conditions Best Performance Characteristics Liquid Instruments : Major forex pairs (EUR/USD, GBP/USD, USD/JPY) Large-cap stocks and index ETFs (SPY, QQQ, AAPL, MSFT) High-volume crypto (BTC, ETH) Major commodities (Gold, Oil, Natural Gas) Reasoning: Clean price structure, clear pivots, meaningful oscillator behavior Trending with Consolidations : Markets that trend for 20-40 bars, then consolidate 10-20 bars, repeat Creates divergences at consolidation boundaries (reversals) and within trends (continuations) Both regular and hidden divs find opportunities 5-Minute to Daily Timeframes : Below 5m: too much noise, false pivots, CAE metrics unstable Above daily: too few signals, edge diminishes (fundamentals dominate) Sweet spot: 15m to 4H for most traders Consistent Volume and Participation : Regular trading sessions (not holidays or thin markets) Predictable volatility patterns Avoid instruments with sudden gaps or circuit breakers Challenging Conditions Extremely Low Liquidity : Penny stocks, exotic forex pairs, low-volume crypto Erratic pivots, unreliable oscillator readings CAE metrics can't assess market state properly Very Low Timeframes (1-Minute or Below) : Dominated by market microstructure noise Divergences are everywhere but meaningless CAE filtering helps but still unreliable Extended Sideways Consolidation : 100+ bars of tight range with no clear pivots Oscillator hugs midpoint (45-55 range) No divergences to detect Fundamentally-Driven Gap Markets : Earnings releases, economic data, geopolitical events Price gaps over stops and targets Technical structure breaks down Recommendation: Disable trading around known events Calculation Methodology — Technical Depth For users who want to understand the math: Oscillator Computation Each oscillator type calculates differently, but all normalize to 0-100: RSI : ta.rsi(close, length) — Standard Relative Strength Index Stochastic : ta.stoch(high, low, close, length) — %K calculation CCI : (ta.cci(hlc3, length) + 100) / 2 — Normalized from -100/+100 to 0-100 MFI : ta.mfi(hlc3, length) — Volume-weighted RSI equivalent Williams %R : ta.wpr(length) + 100 — Inverted stochastic adjusted to 0-100 Smoothing: If smoothing > 1, apply ta.sma(oscillator, smoothing) Divergence Detection Algorithm Identify Pivots : Price high pivot: ta.pivothigh(high, lookback, lookforward) Price low pivot: ta.pivotlow(low, lookback, lookforward) Oscillator high pivot: ta.pivothigh(osc, lookback, lookforward) Oscillator low pivot: ta.pivotlow(osc, lookback, lookforward) Store Recent Pivots : Maintain arrays of last 10 pivots with bar indices When new pivot confirmed, unshift to array, pop oldest if >10 Scan for Slope Disagreements : Loop through last 5 pivots For each pair (current pivot, historical pivot): Check if within max_lookback bars Calculate slopes: (current - historical) / bars_between Regular bearish: price_slope > 0, osc_slope < 0, |osc_slope| > min_threshold Regular bullish: price_slope < 0, osc_slope > 0, |osc_slope| > min_threshold Hidden bearish: price_slope < 0, osc_slope > 0, osc_slope > min_threshold Hidden bullish: price_slope > 0, osc_slope < 0, |osc_slope| > min_threshold Important Disclaimers and Terms Performance Disclosure Past performance, whether backtested or live-traded, does not guarantee future results. Markets change. What works today may not work tomorrow. Hypothetical or simulated performance results have inherent limitations and do not represent actual trading. Risk of Loss Trading involves substantial risk of loss. Only trade with risk capital you can afford to lose entirely. The high degree of leverage often available in trading can work against you as well as for you. Leveraged trading may result in losses exceeding your initial deposit. Not Financial Advice BZ-CAE is an educational and analytical tool for technical analysis. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument. All trading decisions are your sole responsibility. Consult a licensed financial advisor for advice specific to your circumstances. Technical Indicator Limitations BZ-CAE is a technical analysis tool based on price and volume data. It does not account for: Fundamental analysis (earnings, economic data, financial health) Market sentiment and positioning Geopolitical events and news Liquidity conditions and market microstructure changes Regulatory changes or exchange rules Integrate with broader analysis and strategy. Do not rely solely on technical indicators for trading decisions. Repainting Acknowledgment As disclosed throughout this documentation: Realtime mode may repaint on forming bars before confirmation (by design for preview functionality) Confirmed mode has zero repainting (fully validated pivots only) Choose timing mode appropriate for your use case. Understand the tradeoffs. Testing Recommendation ALWAYS test on demo/paper accounts before committing real capital. Validate the indicator's behavior on your specific instruments and timeframes. Learn the system thoroughly in Advisory mode before using Filtering mode. Learning Resources : In-indicator tooltips (hover over setting names for detailed explanations) This comprehensive publishing statement (save for reference) User guide in script comments (top of code) Final Word — Philosophy of BZ-CAE BZ-CAE is not designed to replace your judgment — it's designed to enhance it. The indicator identifies structural inflection points (bifurcations) where price and momentum disagree. The Cognitive Engine evaluates market state to determine if this disagreement is meaningful or noise. The Adversarial model debates both sides of the trade to catch obvious bad setups. The Confidence system ranks quality so you can choose your risk appetite. But YOU still execute. YOU still manage risk. YOU still learn from outcomes. This is intelligence amplification, not intelligence replacement. Use Advisory mode to learn how expert traders evaluate market state. Use Filtering mode to enforce discipline when emotions run high. Use the dashboard to develop a systematic approach to reading markets. Use confidence scores to size positions probabilistically. The system provides an edge. Your job is to execute that edge with discipline, patience, and proper risk management over hundreds of trades. Markets are probabilistic. No system wins every trade. But a systematic edge + disciplined execution + proper risk management compounds over time. That's the path to consistent profitability. BZ-CAE gives you the edge. The discipline and risk management are on you. Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Pine Script® indicator
by DskyzInvestments
Trading IQ - ICT LibraryLibrary "ICTlibrary" Used to calculate various ICT related price levels and strategies. An ongoing project. Hello Coders! This library is meant for sourcing ICT related concepts. While some functions might generate more output than you require, you can specify "Lite Mode" as "true" in applicable functions to slim down necessary inputs. isLastBar(userTF)   Identifies the last bar on the chart before a timeframe change   Parameters:      userTF (simple int) : the timeframe you wish to calculate the last bar for, must be converted to integer using 'timeframe.in_seconds()'   Returns: bool true if bar on chart is last bar of higher TF, dalse if bar on chart is not last bar of higher TF necessaryData(atrTF)   returns necessaryData UDT for historical data access   Parameters:      atrTF (float) : user-selected timeframe ATR value.   Returns: logZ. log return Z score, used for calculating order blocks. method gradBoxes(gradientBoxes, idColor, timeStart, bottom, top, rightCoordinate)   creates neon like effect for box drawings   Namespace types: array   Parameters:      gradientBoxes (array) : an array.new() to store the gradient boxes      idColor (color)      timeStart (int) : left point of box      bottom (float) : bottom of box price point      top (float) : top of box price point      rightCoordinate (int) : right point of box   Returns: void checkIfTraded(tradeName)   checks if recent trade is of specific name   Parameters:      tradeName (string)   Returns: bool true if recent trade id matches target name, false otherwise checkIfClosed(tradeName)   checks if recent closed trade is of specific name   Parameters:      tradeName (string)   Returns: bool true if recent closed trade id matches target name, false otherwise IQZZ(atrMult, finalTF)   custom ZZ to quickly determine market direction.   Parameters:      atrMult (float) : an atr multiplier used to determine the required price move for a ZZ direction change      finalTF (string) : the timeframe used for the atr calcuation   Returns: dir market direction. Up => 1, down => -1 method drawBos(id, startPoint, getKeyPointTime, getKeyPointPrice, col, showBOS, isUp)   calculates and draws Break Of Structure   Namespace types: array   Parameters:      id (array)      startPoint (chart.point)      getKeyPointTime (int) : the actual time of startPoint, simplystartPoint.time      getKeyPointPrice (float) : the actual time of startPoint, simplystartPoint.price      col (color) : color of the BoS line / label      showBOS (bool) : whether to show label/line. This function still calculates internally for other ICT related concepts even if not drawn.      isUp (bool) : whether BoS happened during price increase or price decrease.   Returns: void method drawMSS(id, startPoint, getKeyPointTime, getKeyPointPrice, col, showMSS, isUp, upRejections, dnRejections, highArr, lowArr, timeArr, closeArr, openArr, atrTFarr, upRejectionsPrices, dnRejectionsPrices)   calculates and draws Market Structure Shift. This data is also used to calculate Rejection Blocks.   Namespace types: array   Parameters:      id (array)      startPoint (chart.point)      getKeyPointTime (int) : the actual time of startPoint, simplystartPoint.time      getKeyPointPrice (float) : the actual time of startPoint, simplystartPoint.price      col (color) : color of the MSS line / label      showMSS (bool) : whether to show label/line. This function still calculates internally for other ICT related concepts even if not drawn.      isUp (bool) : whether MSS happened during price increase or price decrease.      upRejections (array)      dnRejections (array)      highArr (array) : array containing historical highs, should be taken from the UDT "necessaryData" defined above      lowArr (array) : array containing historical lows, should be taken from the UDT "necessaryData" defined above      timeArr (array) : array containing historical times, should be taken from the UDT "necessaryData" defined above      closeArr (array) : array containing historical closes, should be taken from the UDT "necessaryData" defined above      openArr (array) : array containing historical opens, should be taken from the UDT "necessaryData" defined above      atrTFarr (array) : array containing historical atr values (of user-selected TF), should be taken from the UDT "necessaryData" defined above      upRejectionsPrices (array) : array containing up rejections prices. Is sorted and used to determine selective looping for invalidations.      dnRejectionsPrices (array) : array containing down rejections prices. Is sorted and used to determine selective looping for invalidations.   Returns: void method getTime(id, compare, timeArr)   gets time of inputted price (compare) in an array of data this is useful when the user-selected timeframe for ICT concepts is greater than the chart's timeframe   Namespace types: array   Parameters:      id (array) : the array of data to search through, to find which index has the same value as "compare"      compare (float) : the target data point to find in the array      timeArr (array) : array of historical times   Returns: the time that the data point in the array was recorded method OB(id, highArr, signArr, lowArr, timeArr, sign)   store bullish orderblock data   Namespace types: array   Parameters:      id (array)      highArr (array) : array of historical highs      signArr (array) : array of historical price direction "math.sign(close - open)"      lowArr (array) : array of historical lows      timeArr (array) : array of historical times      sign (int) : orderblock direction, -1 => bullish, 1 => bearish   Returns: void OTEstrat(OTEstart, future, closeArr, highArr, lowArr, timeArr, longOTEPT, longOTESL, longOTElevel, shortOTEPT, shortOTESL, shortOTElevel, structureDirection, oteLongs, atrTF, oteShorts)   executes the OTE strategy   Parameters:      OTEstart (chart.point)      future (int) : future time point for drawings      closeArr (array) : array of historical closes      highArr (array) : array of historical highs      lowArr (array) : array of historical lows      timeArr (array) : array of historical times      longOTEPT (string) : user-selected long OTE profit target, please create an input.string() for this using the example below      longOTESL (int) : user-selected long OTE stop loss, please create an input.string() for this using the example below      longOTElevel (float) : long entry price of selected retracement ratio for OTE      shortOTEPT (string) : user-selected short OTE profit target, please create an input.string() for this using the example below      shortOTESL (int) : user-selected short OTE stop loss, please create an input.string() for this using the example below      shortOTElevel (float) : short entry price of selected retracement ratio for OTE      structureDirection (string) : current market structure direction, this should be "Up" or "Down". This is used to cancel pending orders if market structure changes      oteLongs (bool) : input.bool() for whether OTE longs can be executed      atrTF (float) : atr of the user-seleceted TF      oteShorts (bool) : input.bool() for whether OTE shorts can be executed @exampleInputs oteLongs = input.bool(defval = false, title = "OTE Longs", group = "Optimal Trade Entry") longOTElevel = input.float(defval = 0.79, title = "Long Entry Retracement Level", options = , group = "Optimal Trade Entry") longOTEPT = input.string(defval = "-0.5", title = "Long TP", options = , group = "Optimal Trade Entry") longOTESL = input.int(defval = 0, title = "How Many Ticks Below Swing Low For Stop Loss", group = "Optimal Trade Entry") oteShorts = input.bool(defval = false, title = "OTE Shorts", group = "Optimal Trade Entry") shortOTElevel = input.float(defval = 0.79, title = "Short Entry Retracement Level", options = , group = "Optimal Trade Entry") shortOTEPT = input.string(defval = "-0.5", title = "Short TP", options = , group = "Optimal Trade Entry") shortOTESL = input.int(defval = 0, title = "How Many Ticks Above Swing Low For Stop Loss", group = "Optimal Trade Entry")   Returns: void (0) displacement(logZ, atrTFreg, highArr, timeArr, lowArr, upDispShow, dnDispShow, masterCoords, labelLevels, dispUpcol, rightCoordinate, dispDncol, noBorders)   calculates and draws dispacements   Parameters:      logZ (float) : log return of current price, used to determine a "significant price move" for a displacement      atrTFreg (float) : atr of user-seleceted timeframe      highArr (array) : array of historical highs      timeArr (array) : array of historical times      lowArr (array) : array of historical lows      upDispShow (int) : amount of historical upside displacements to show      dnDispShow (int) : amount of historical downside displacements to show      masterCoords (map) : a map to push the most recent displacement prices into, useful for having key levels in one data structure      labelLevels (string) : used to determine label placement for the displacement, can be inside box, outside box, or none, example below      dispUpcol (color) : upside displacement color      rightCoordinate (int) : future time for displacement drawing, best is "last_bar_time"      dispDncol (color) : downside displacement color      noBorders (bool) : input.bool() to remove box borders, example below @exampleInputs labelLevels = input.string(defval = "Inside" , title = "Box Label Placement", options = ) noBorders = input.bool(defval = false, title = "No Borders On Levels")   Returns: void method getStrongLow(id, startIndex, timeArr, lowArr, strongLowPoints)   unshift strong low data to array id   Namespace types: array   Parameters:      id (array)      startIndex (int) : the starting index for the timeArr array of the UDT "necessaryData". this point should start from at least 1 pivot prior to find the low before an upside BoS      timeArr (array) : array of historical times      lowArr (array) : array of historical lows      strongLowPoints (array) : array of strong low prices. Used to retrieve highest strong low price and see if need for removal of invalidated strong lows   Returns: void method getStrongHigh(id, startIndex, timeArr, highArr, strongHighPoints)   unshift strong high data to array id   Namespace types: array   Parameters:      id (array)      startIndex (int) : the starting index for the timeArr array of the UDT "necessaryData". this point should start from at least 1 pivot prior to find the high before a downside BoS      timeArr (array) : array of historical times      highArr (array) : array of historical highs      strongHighPoints (array)   Returns: void equalLevels(highArr, lowArr, timeArr, rightCoordinate, equalHighsCol, equalLowsCol, liteMode)   used to calculate recent equal highs or equal lows   Parameters:      highArr (array) : array of historical highs      lowArr (array) : array of historical lows      timeArr (array) : array of historical times      rightCoordinate (int) : a future time (right for boxes, x2 for lines)      equalHighsCol (color) : user-selected color for equal highs drawings      equalLowsCol (color) : user-selected color for equal lows drawings      liteMode (bool) : optional for a lite mode version of an ICT strategy. For more control over drawings leave as "True", "False" will apply neon effects   Returns: void quickTime(timeString)   used to quickly determine if a user-inputted time range is currently active in NYT time   Parameters:      timeString (string) : a time range   Returns: true if session is active, false if session is inactive macros(showMacros, noBorders)   used to calculate and draw session macros   Parameters:      showMacros (bool) : an input.bool() or simple bool to determine whether to activate the function      noBorders (bool) : an input.bool() to determine whether the box anchored to the session should have borders   Returns: void po3(tf, left, right, show)   use to calculate HTF po3 candle @tip only call this function on "barstate.islast"   Parameters:      tf (simple string)      left (int) : the left point of the candle, calculated as bar_index + left,      right (int) : :the right point of the candle, calculated as bar_index + right,      show (bool) : input.bool() whether to show the po3 candle or not   Returns: void silverBullet(silverBulletStratLong, silverBulletStratShort, future, userTF, H, L, H2, L2, noBorders, silverBulletLongTP, historicalPoints, historicalData, silverBulletLongSL, silverBulletShortTP, silverBulletShortSL)   used to execute the Silver Bullet Strategy   Parameters:      silverBulletStratLong (simple bool)      silverBulletStratShort (simple bool)      future (int) : a future time, used for drawings, example "last_bar_time"      userTF (simple int)      H (float) : the high price of the user-selected TF      L (float) : the low price of the user-selected TF      H2 (float) : the high price of the user-selected TF      L2 (float) : the low price of the user-selected TF      noBorders (bool) : an input.bool() used to remove the borders from box drawings      silverBulletLongTP (series silverBulletLevels)      historicalPoints (array)      historicalData (necessaryData)      silverBulletLongSL (series silverBulletLevels)      silverBulletShortTP (series silverBulletLevels)      silverBulletShortSL (series silverBulletLevels)   Returns: void method invalidFVGcheck(FVGarr, upFVGpricesSorted, dnFVGpricesSorted)   check if existing FVGs are still valid   Namespace types: array   Parameters:      FVGarr (array)      upFVGpricesSorted (array) : an array of bullish FVG prices, used to selective search through FVG array to remove invalidated levels      dnFVGpricesSorted (array) : an array of bearish FVG prices, used to selective search through FVG array to remove invalidated levels   Returns: void (0) method drawFVG(counter, FVGshow, FVGname, FVGcol, data, masterCoords, labelLevels, borderTransp, liteMode, rightCoordinate)   draws FVGs on last bar   Namespace types: map   Parameters:      counter (map) : a counter, as map, keeping count of the number of FVGs drawn, makes sure that there aren't more FVGs drawn than int FVGshow      FVGshow (int) : the number of FVGs to show. There should be a bullish FVG show and bearish FVG show. This function "drawFVG" is used separately for bearish FVG and bullish FVG.      FVGname (string) : the name of the FVG, "FVG Up" or "FVG Down"      FVGcol (color) : desired FVG color      data (FVG)      masterCoords (map) : a map containing the names and price points of key levels. Used to define price ranges.      labelLevels (string) : an input.string with options "Inside", "Outside", "Remove". Determines whether FVG labels should be inside box, outside, or na.      borderTransp (int)      liteMode (bool)      rightCoordinate (int) : the right coordinate of any drawings. Must be a time point.   Returns: void invalidBlockCheck(bullishOBbox, bearishOBbox, userTF)   check if existing order blocks are still valid   Parameters:      bullishOBbox (array) : an array declared using the UDT orderBlock that contains bullish order block related data      bearishOBbox (array) : an array declared using the UDT orderBlock that contains bearish order block related data      userTF (simple int)   Returns: void (0) method lastBarRejections(id, rejectionColor, idShow, rejectionString, labelLevels, borderTransp, liteMode, rightCoordinate, masterCoords)   draws rejectionBlocks on last bar   Namespace types: array   Parameters:      id (array) : the array, an array of rejection block data declared using the UDT rejection block      rejectionColor (color) : the desired color of the rejection box      idShow (int)      rejectionString (string) : the desired name of the rejection blocks      labelLevels (string) : an input.string() to determine if labels for the block should be inside the box, outside, or none.      borderTransp (int)      liteMode (bool) : an input.bool(). True = neon effect, false = no neon.      rightCoordinate (int) : atime for the right coordinate of the box      masterCoords (map) : a map that stores the price of key levels and assigns them a name, used to determine price ranges   Returns: void method OBdraw(id, OBshow, BBshow, OBcol, BBcol, bullishString, bearishString, isBullish, labelLevels, borderTransp, liteMode, rightCoordinate, masterCoords)   draws orderblocks and breaker blocks for data stored in UDT array()   Namespace types: array   Parameters:      id (array) : the array, an array of order block data declared using the UDT orderblock      OBshow (int) : the number of order blocks to show      BBshow (int) : the number of breaker blocks to show      OBcol (color) : color of order blocks      BBcol (color) : color of breaker blocks      bullishString (string) : the title of bullish blocks, which is a regular bullish orderblock or a bearish orderblock that's converted to breakerblock      bearishString (string) : the title of bearish blocks, which is a regular bearish orderblock or a bullish orderblock that's converted to breakerblock      isBullish (bool) : whether the array contains bullish orderblocks or bearish orderblocks. If bullish orderblocks, the array will naturally contain bearish BB, and if bearish OB, the array will naturally contain bullish BB      labelLevels (string) : an input.string() to determine if labels for the block should be inside the box, outside, or none.      borderTransp (int)      liteMode (bool) : an input.bool(). True = neon effect, false = no neon.      rightCoordinate (int) : atime for the right coordinate of the box      masterCoords (map) : a map that stores the price of key levels and assigns them a name, used to determine price ranges   Returns: void FVG   UDT for FVG calcualtions   Fields:      H (series float) : high price of user-selected timeframe      L (series float) : low price of user-selected timeframe      direction (series string) : FVG direction => "Up" or "Down"      T (series int) : => time of bar on user-selected timeframe where FVG was created      fvgLabel (series label) : optional label for FVG      fvgLineTop (series line) : optional line for top of FVG      fvgLineBot (series line) : optional line for bottom of FVG      fvgBox (series box) : optional box for FVG labelLine   quickly pair a line and label together as UDT   Fields:      lin (series line) : Line you wish to pair with label      lab (series label) : Label you wish to pair with line orderBlock   UDT for order block calculations   Fields:      orderBlockData (array) : array containing order block x and y points      orderBlockBox (series box) : optional order block box      vioCount (series int) : = 0 violation count of the order block. 0 = Order Block, 1 = Breaker Block      traded (series bool)      status (series string) : = "OB" status == "OB" => Level is order block. status == "BB" => Level is breaker block.      orderBlockLab (series label) : options label for the order block / breaker block. strongPoints   UDT for strong highs and strong lows   Fields:      price (series float) : price of the strong high or strong low      timeAtprice (series int) : time of the strong high or strong low      strongPointLabel (series label) : optional label for strong point      strongPointLine (series line) : optional line for strong point      overlayLine (series line) : optional lines for strong point to enhance visibility      overlayLine2 (series line) : optional lines for strong point to enhance visibility displacement   UDT for dispacements   Fields:      highPrice (series float) : high price of displacement      lowPrice (series float) : low price of displacement      timeAtPrice (series int) : time of bar where displacement occurred      displacementBox (series box) : optional box to draw displacement      displacementLab (series label) : optional label for displacement po3data   UDT for po3 calculations   Fields:      dHigh (series float) : higher timeframe high price      dLow (series float) : higher timeframe low price      dOpen (series float) : higher timeframe open price      dClose (series float) : higher timeframe close price      po3box (series box) : box to draw po3 candle body      po3line (array) : line array to draw po3 wicks      po3Labels (array) : label array to label price points of po3 candle macros   UDT for session macros   Fields:      sessions (array) : Array of sessions, you can populate this array using the "quickTime" function located above "export macros".      prices (matrix) : Matrix of session data -> open, high, low, close, time      sessionTimes (array) : Array of session names. Pairs with array sessions.      sessionLines (matrix) : Optional array for sesion drawings. OTEtimes   UDT for data storage and drawings associated with OTE strategy   Fields:      upTimes (array) : time of highest point before trade is taken      dnTimes (array) : time of lowest point before trade is taken      tpLineLong (series line) : line to mark tp level long      tpLabelLong (series label) : label to mark tp level long      slLineLong (series line) : line to mark sl level long      slLabelLong (series label) : label to mark sl level long      tpLineShort (series line) : line to mark tp level short      tpLabelShort (series label) : label to mark tp level short      slLineShort (series line) : line to mark sl level short      slLabelShort (series label) : label to mark sl level short sweeps   UDT for data storage and drawings associated with liquidity sweeps   Fields:      upSweeps (matrix) : matrix containing liquidity sweep price points and time points for up sweeps      dnSweeps (matrix) : matrix containing liquidity sweep price points and time points for down sweeps      upSweepDrawings (array) : optional up sweep box array. Pair the size of this array with the rows or columns,      dnSweepDrawings (array) : optional up sweep box array. Pair the size of this array with the rows or columns, raidExitDrawings   UDT for drawings associated with the Liquidity Raid Strategy   Fields:      tpLine (series line) : tp line for the liquidity raid entry      tpLabel (series label) : tp label for the liquidity raid entry      slLine (series line) : sl line for the liquidity raid entry      slLabel (series label) : sl label for the liquidity raid entry m2022   UDT for data storage and drawings associated with the Model 2022 Strategy   Fields:      mTime (series int) : time of the FVG where entry limit order is placed      mIndex (series int) : array index of FVG where entry limit order is placed. This requires an array of FVG data, which is defined above.      mEntryDistance (series float) : the distance of the FVG to the 50% range. M2022 looks for the fvg closest to 50% mark of range.      mEntry (series float) : the entry price for the most eligible fvg      fvgHigh (series float) : the high point of the eligible fvg      fvgLow (series float) : the low point of the eligible fvg      longFVGentryBox (series box) : long FVG box, used to draw the eligible FVG      shortFVGentryBox (series box) : short FVG box, used to draw the eligible FVG      line50P (series line) : line used to mark 50% of the range      line100P (series line) : line used to mark 100% (top) of the range      line0P (series line) : line used to mark 0% (bottom) of the range      label50P (series label) : label used to mark 50% of the range      label100P (series label) : label used to mark 100% (top) of the range      label0P (series label) : label used to mark 0% (bottom) of the range      sweepData (array) silverBullet   UDT for data storage and drawings associated with the Silver Bullet Strategy   Fields:      session (series bool)      sessionStr (series string) : name of the session for silver bullet      sessionBias (series string)      sessionHigh (series float) : = high high of session // use math.max(silverBullet.sessionHigh, high)      sessionLow (series float) : = low low of session // use math.min(silverBullet.sessionLow, low)      sessionFVG (series float) : if applicable, the FVG created during the session      sessionFVGdraw (series box) : if applicable, draw the FVG created during the session      traded (series bool)      tp (series float) : tp of trade entered at the session FVG      sl (series float) : sl of trade entered at the session FVG      sessionDraw (series box) : optional draw session with box      sessionDrawLabel (series label) : optional label session with label silverBulletDrawings   UDT for trade exit drawings associated with the Silver Bullet Strategy   Fields:      tpLine (series line) : tp line drawing for strategy      tpLabel (series label) : tp label drawing for strategy      slLine (series line) : sl line drawing for strategy      slLabel (series label) : sl label drawing for strategy unicornModel   UDT for data storage and drawings associated with the Unicorn Model Strategy   Fields:      hPoint (chart.point)      hPoint2 (chart.point)      hPoint3 (chart.point)      breakerBlock (series box) : used to draw the breaker block required for the Unicorn Model      FVG (series box) : used to draw the FVG required for the Unicorn model      topBlock (series float) : price of top of breaker block, can be used to detail trade entry      botBlock (series float) : price of bottom of breaker block, can be used to detail trade entry      startBlock (series int) : start time of the breaker block, used to set the "left = " param for the box      includes (array) : used to store the time of the breaker block, or FVG, or the chart point sequence that setup the Unicorn Model.      entry (series float) : // eligible entry price, for longs"math.max(topBlock, FVG.get_top())",      tpLine (series line) : optional line to mark PT      tpLabel (series label) : optional label to mark PT      slLine (series line) : optional line to mark SL      slLabel (series label) : optional label to mark SL rejectionBlocks   UDT for data storage and drawings associated with rejection blocks   Fields:      rejectionPoint (chart.point)      bodyPrice (series float) : candle body price closest to the rejection point, for "Up" rejections => math.max(open, close),      rejectionBox (series box) : optional box drawing of the rejection block      rejectionLabel (series label) : optional label for the rejection block equalLevelsDraw   UDT for data storage and drawings associated with equal highs / equal lows   Fields:      connector (series line) : single line placed at the first high or low, y = avgerage of distinguished equal highs/lows      connectorLab (series label) : optional label to be placed at the highs or lows      levels (array) : array containing the equal highs or lows prices      times (array) : array containing the equal highs or lows individual times      startTime (series int) : the time of the first high or low that forms a sequence of equal highs or lows      radiate (array) : options label to "radiate" the label in connector lab. Can be used for anything necessaryData   UDT for data storage of historical price points.   Fields:      highArr (array) : array containing historical high points      lowArr (array) : array containing historical low points      timeArr (array) : array containing historical time points      logArr (array) : array containing historical log returns      signArr (array) : array containing historical price directions      closeArr (array) : array containing historical close points      binaryTimeArr (array) : array containing historical time points, uses "push" instead of "unshift" to allow for binary search      binaryCloseArr (array) : array containing historical close points, uses "push" instead of "unshift" to allow the correct      binaryOpenArr (array) : array containing historical optn points, uses "push" instead of "unshift" to allow the correct      atrTFarr (array) : array containing historical user-selected TF atr points      openArr (array) : array containing historical open points
Pine Script® library
by Trading-IQ
1
Institutional Confluence Mapper [JOAT]Institutional Confluence Mapper (ICM) Introduction The Institutional Confluence Mapper is an open-source multi-factor analysis tool that combines five analytical modules into a unified confluence scoring system. It synthesizes institutional trading concepts including Relative Rotation analysis, Smart Money flow detection, Liquidity zone mapping, Session-based timing, and Volatility regime classification. Rather than relying on a single indicator, ICM evaluates market conditions through multiple lenses simultaneously, presenting a clear confluence score (0-100%) that reflects the alignment of various market factors. This script is fully open-source under the Mozilla Public License 2.0. Originality and Purpose This indicator is NOT a random mashup of existing indicators. It is an original implementation that creates a unified institutional analysis framework: Why Multiple Modules? Most retail traders struggle because they rely on single indicators that provide conflicting signals. Institutional traders evaluate markets through multiple frameworks simultaneously. ICM bridges this gap by providing a unified view of complementary analysis methods. The Confluence Scoring System: Each module contributes to a weighted confluence score (0-100%). Scores above 65% indicate bullish confluence; below 35% indicates bearish confluence. How Components Work Together: RRG (Relative Rotation) determines macro bias - is this asset outperforming or underperforming its benchmark? Institutional Flow confirms smart money activity - are institutions accumulating or distributing? Volatility Regime determines strategy selection - trend-follow or mean-revert? Liquidity Detection identifies key levels - where are the stop hunts happening? Session Analysis optimizes timing - when should you trade? The Five Core Modules 1. Relative Rotation Momentum Matrix (RRG) Compares the current symbol against a benchmark (default: SPY) using the JdK RS-Ratio methodology with double-smoothed EMA. Assets rotate through four quadrants: LEADING: Outperforming with positive momentum (strongest bullish) WEAKENING: Outperforming but losing momentum LAGGING: Underperforming with negative momentum (strongest bearish) IMPROVING: Underperforming but gaining momentum 2. Institutional Flow Analysis Analyzes volume patterns to detect smart money activity: Volume Z-Score measures how unusual current volume is Buy/Sell pressure estimation based on candle structure Unusual volume detection highlights institutional activity 3. Volatility Regime System Uses ATR percentile ranking to classify market conditions: COMPRESSION: Low volatility (ATR < 20th percentile) - potential breakout EXPANSION: High volatility (ATR > 80th percentile) - trending TRENDING_BULL/BEAR: Directional trends based on EMA alignment RANGING: Sideways consolidation 4. Liquidity Detection Identifies institutional liquidity targets using swing point analysis: Swing highs/lows are tracked and displayed as dashed lines Purple dashed lines mark resistance/sell-side liquidity Teal dashed lines mark support/buy-side liquidity Gold diamonds appear when liquidity sweeps are detected (potential reversals) 5. Session Momentum Profiler Tracks trading sessions based on your selected timezone: Asian Session: 7PM - 4AM EST London Session: 3AM - 12PM EST New York Session: 9:30AM - 4PM EST London/NY Overlap: 8AM - 12PM EST (peak liquidity) Visual Elements Main Dashboard (Top-Right): BIAS: Overall direction with confluence percentage RRG: Current quadrant and momentum FLOW: Smart money bias and volume status REGIME: Market condition and volatility percentile SESSION: Active trading session and current time LIQUIDITY: Active zones and grab signals SIGNAL: Actionable recommendation Chart Elements: Gold Diamond: Liquidity grab (potential reversal point) Teal Dashed Line: Support / Buy-side liquidity zone Purple Dashed Line: Resistance / Sell-side liquidity zone EMA 21/55/200: Trend structure with cloud fill Volatility Bands: ATR-based channels How to Use Step 1: Check the BIAS row for overall market direction Step 2: Check REGIME to understand market conditions Step 3: Identify key levels using liquidity zones and EMAs Step 4: Wait for confluence above 65% (bullish) or below 35% (bearish) Step 5: Look for gold diamond signals at key levels Best Setups Bullish: Confluence >65%, RRG in LEADING/IMPROVING, bullish flow, price near teal support zone. Bearish: Confluence <35%, RRG in LAGGING/WEAKENING, bearish flow, price near purple resistance zone. Reversal: Gold diamond appears after price sweeps a liquidity zone. Key Input Parameters Benchmark Symbol: Compare against (default: SPY) RS-Ratio/Momentum Lookback: RRG calculation periods Volume Analysis Period: Flow detection lookback Swing Length: Liquidity zone detection ATR Period/Rank Period: Regime classification Timezone: Session detection timezone Alerts Liquidity Grab Bull: Bullish sweep detected Liquidity Grab Bear: Bearish sweep detected High Confluence Bull: Confluence above 70% High Confluence Bear: Confluence below 30% Best Practices Use on 1H, 4H, or Daily timeframes for reliable signals Combine with price action for confirmation Respect the regime - don't fight strong trends Trade during London/NY overlap for best liquidity Wait for high confluence scores before entering Always use proper risk management Limitations Works best on liquid markets with sufficient volume Session features optimized for forex/crypto markets RRG requires a valid benchmark symbol No indicator predicts the future - use proper risk management Disclaimer This indicator is for educational and informational purposes only. It is not financial advice. Trading involves substantial risk of loss. Past performance does not guarantee future results. -Made with passion by officialjackofalltrades
Pine Script® indicator
by officialjackofalltrades
5
Adaptive Market Wave TheoryAdaptive Market Wave Theory 🌊 CORE INNOVATION: PROBABILISTIC PHASE DETECTION WITH MULTI-AGENT CONSENSUS Adaptive Market Wave Theory (AMWT) represents a fundamental paradigm shift in how traders approach market phase identification. Rather than counting waves subjectively or drawing static breakout levels, AMWT treats the market as a hidden state machine —using Hidden Markov Models, multi-agent consensus systems, and reinforcement learning algorithms to quantify what traditional methods leave to interpretation. The Wave Analysis Problem: Traditional wave counting methodologies (Elliott Wave, harmonic patterns, ABC corrections) share fatal weaknesses that AMWT directly addresses: 1. Non-Falsifiability : Invalid wave counts can always be "recounted" or "adjusted." If your Wave 3 fails, it becomes "Wave 3 of a larger degree" or "actually Wave C." There's no objective failure condition. 2. Observer Bias : Two expert wave analysts examining the same chart routinely reach different conclusions. This isn't a feature—it's a fundamental methodology flaw. 3. No Confidence Measure : Traditional analysis says "This IS Wave 3." But with what probability? 51%? 95%? The binary nature prevents proper position sizing and risk management. 4. Static Rules : Fixed Fibonacci ratios and wave guidelines cannot adapt to changing market regimes. What worked in 2019 may fail in 2024. 5. No Accountability : Wave methodologies rarely track their own performance. There's no feedback loop to improve. The AMWT Solution: AMWT addresses each limitation through rigorous mathematical frameworks borrowed from speech recognition, machine learning, and reinforcement learning: • Non-Falsifiability → Hard Invalidation : Wave hypotheses die permanently when price violates calculated invalidation levels. No recounting allowed. • Observer Bias → Multi-Agent Consensus : Three independent analytical agents must agree. Single-methodology bias is eliminated. • No Confidence → Probabilistic States : Every market state has a calculated probability from Hidden Markov Model inference. "72% probability of impulse state" replaces "This is Wave 3." • Static Rules → Adaptive Learning : Thompson Sampling multi-armed bandits learn which agents perform best in current conditions. The system adapts in real-time. • No Accountability → Performance Tracking : Comprehensive statistics track every signal's outcome. The system knows its own performance. The Core Insight: "Traditional wave analysis asks 'What count is this?' AMWT asks 'What is the probability we are in an impulsive state, with what confidence, confirmed by how many independent methodologies, and anchored to what liquidity event?'" 🔬 THEORETICAL FOUNDATION: HIDDEN MARKOV MODELS Why Hidden Markov Models? Markets exist in hidden states that we cannot directly observe—only their effects on price are visible. When the market is in an "impulse up" state, we see rising prices, expanding volume, and trending indicators. But we don't observe the state itself—we infer it from observables. This is precisely the problem Hidden Markov Models (HMMs) solve. Originally developed for speech recognition (inferring words from sound waves), HMMs excel at estimating hidden states from noisy observations. HMM Components: 1. Hidden States (S) : The unobservable market conditions 2. Observations (O) : What we can measure (price, volume, indicators) 3. Transition Matrix (A) : Probability of moving between states 4. Emission Matrix (B) : Probability of observations given each state 5. Initial Distribution (π) : Starting state probabilities AMWT's Six Market States: State 0: IMPULSE_UP • Definition: Strong bullish momentum with high participation • Observable Signatures: Rising prices, expanding volume, RSI >60, price above upper Bollinger Band, MACD histogram positive and rising • Typical Duration: 5-20 bars depending on timeframe • What It Means: Institutional buying pressure, trend acceleration phase State 1: IMPULSE_DN • Definition: Strong bearish momentum with high participation • Observable Signatures: Falling prices, expanding volume, RSI <40, price below lower Bollinger Band, MACD histogram negative and falling • Typical Duration: 5-20 bars (often shorter than bullish impulses—markets fall faster) • What It Means: Institutional selling pressure, panic or distribution acceleration State 2: CORRECTION • Definition: Counter-trend consolidation with declining momentum • Observable Signatures: Sideways or mild counter-trend movement, contracting volume, RSI returning toward 50, Bollinger Bands narrowing • Typical Duration: 8-30 bars • What It Means: Profit-taking, digestion of prior move, potential accumulation for next leg State 3: ACCUMULATION • Definition: Base-building near lows where informed participants absorb supply • Observable Signatures: Price near recent lows but not making new lows, volume spikes on up bars, RSI showing positive divergence, tight range • Typical Duration: 15-50 bars • What It Means: Smart money buying from weak hands, preparing for markup phase State 4: DISTRIBUTION • Definition: Top-forming near highs where informed participants distribute holdings • Observable Signatures: Price near recent highs but struggling to advance, volume spikes on down bars, RSI showing negative divergence, widening range • Typical Duration: 15-50 bars • What It Means: Smart money selling to late buyers, preparing for markdown phase State 5: TRANSITION • Definition: Regime change period with mixed signals and elevated uncertainty • Observable Signatures: Conflicting indicators, whipsaw price action, no clear momentum, high volatility without direction • Typical Duration: 5-15 bars • What It Means: Market deciding next direction, dangerous for directional trades The Transition Matrix: The transition matrix A captures the probability of moving from one state to another. AMWT initializes with empirically-derived values then updates online: From/To IMP_UP IMP_DN CORR ACCUM DIST TRANS IMP_UP 0.70 0.02 0.20 0.02 0.04 0.02 IMP_DN 0.02 0.70 0.20 0.04 0.02 0.02 CORR 0.15 0.15 0.50 0.10 0.10 0.00 ACCUM 0.30 0.05 0.15 0.40 0.05 0.05 DIST 0.05 0.30 0.15 0.05 0.40 0.05 TRANS 0.20 0.20 0.20 0.15 0.15 0.10 Key Insights from Transition Probabilities: • Impulse states are sticky (70% self-transition): Once trending, markets tend to continue • Corrections can transition to either impulse direction (15% each): The next move after correction is uncertain • Accumulation strongly favors IMP_UP transition (30%): Base-building leads to rallies • Distribution strongly favors IMP_DN transition (30%): Topping leads to declines The Viterbi Algorithm: Given a sequence of observations, how do we find the most likely state sequence? This is the Viterbi algorithm—dynamic programming to find the optimal path through the state space. Mathematical Formulation: δ_t(j) = max_i × B_j(O_t) Where: δ_t(j) = probability of most likely path ending in state j at time t A_ij = transition probability from state i to state j B_j(O_t) = emission probability of observation O_t given state j AMWT Implementation: AMWT runs Viterbi over a rolling window (default 50 bars), computing the most likely state sequence and extracting: • Current state estimate • State confidence (probability of current state vs alternatives) • State sequence for pattern detection Online Learning (Baum-Welch Adaptation): Unlike static HMMs, AMWT continuously updates its transition and emission matrices based on observed market behavior: f_onlineUpdateHMM(prev_state, curr_state, observation, decay) => // Update transition matrix A *= decay A += (1.0 - decay) // Renormalize row // Update emission matrix B *= decay B += (1.0 - decay) // Renormalize row The decay parameter (default 0.85) controls adaptation speed: • Higher decay (0.95): Slower adaptation, more stable, better for consistent markets • Lower decay (0.80): Faster adaptation, more reactive, better for regime changes Why This Matters for Trading: Traditional indicators give you a number (RSI = 72). AMWT gives you a probabilistic state assessment : "There is a 78% probability we are in IMPULSE_UP state, with 15% probability of CORRECTION and 7% distributed among other states. The transition matrix suggests 70% chance of remaining in IMPULSE_UP next bar, 20% chance of transitioning to CORRECTION." This enables: • Position sizing by confidence : 90% confidence = full size; 60% confidence = half size • Risk management by transition probability : High correction probability = tighten stops • Strategy selection by state : IMPULSE = trend-follow; CORRECTION = wait; ACCUMULATION = scale in 🎰 THE 3-BANDIT CONSENSUS SYSTEM The Multi-Agent Philosophy: No single analytical methodology works in all market conditions. Trend-following excels in trending markets but gets chopped in ranges. Mean-reversion excels in ranges but gets crushed in trends. Structure-based analysis works when structure is clear but fails in chaotic markets. AMWT's solution: employ three independent agents , each analyzing the market from a different perspective, then use Thompson Sampling to learn which agents perform best in current conditions. Agent 1: TREND AGENT Philosophy : Markets trend. Follow the trend until it ends. Analytical Components: • EMA Alignment: EMA8 > EMA21 > EMA50 (bullish) or inverse (bearish) • MACD Histogram: Direction and rate of change • Price Momentum: Close relative to ATR-normalized movement • VWAP Position: Price above/below volume-weighted average price Signal Generation: Strong Bull: EMA aligned bull AND MACD histogram > 0 AND momentum > 0.3 AND close > VWAP → Signal: +1 (Long), Confidence: 0.75 + |momentum| × 0.4 Moderate Bull: EMA stack bull AND MACD rising AND momentum > 0.1 → Signal: +1 (Long), Confidence: 0.65 + |momentum| × 0.3 Strong Bear: EMA aligned bear AND MACD histogram < 0 AND momentum < -0.3 AND close < VWAP → Signal: -1 (Short), Confidence: 0.75 + |momentum| × 0.4 Moderate Bear: EMA stack bear AND MACD falling AND momentum < -0.1 → Signal: -1 (Short), Confidence: 0.65 + |momentum| × 0.3 When Trend Agent Excels: • Trend days (IB extension >1.5x) • Post-breakout continuation • Institutional accumulation/distribution phases When Trend Agent Fails: • Range-bound markets (ADX <20) • Chop zones after volatility spikes • Reversal days at major levels Agent 2: REVERSION AGENT Philosophy: Markets revert to mean. Extreme readings reverse. Analytical Components: • Bollinger Band Position: Distance from bands, percent B • RSI Extremes: Overbought (>70) and oversold (<30) • Stochastic: %K/%D crossovers at extremes • Band Squeeze: Bollinger Band width contraction Signal Generation: Oversold Bounce: BB %B < 0.20 AND RSI < 35 AND Stochastic < 25 → Signal: +1 (Long), Confidence: 0.70 + (30 - RSI) × 0.01 Overbought Fade: BB %B > 0.80 AND RSI > 65 AND Stochastic > 75 → Signal: -1 (Short), Confidence: 0.70 + (RSI - 70) × 0.01 Squeeze Fire Bull: Band squeeze ending AND close > upper band → Signal: +1 (Long), Confidence: 0.65 Squeeze Fire Bear: Band squeeze ending AND close < lower band → Signal: -1 (Short), Confidence: 0.65 When Reversion Agent Excels: • Rotation days (price stays within IB) • Range-bound consolidation • After extended moves without pullback When Reversion Agent Fails: • Strong trend days (RSI can stay overbought for days) • Breakout moves • News-driven directional moves Agent 3: STRUCTURE AGENT Philosophy: Market structure reveals institutional intent. Follow the smart money. Analytical Components: • Break of Structure (BOS): Price breaks prior swing high/low • Change of Character (CHOCH): First break against prevailing trend • Higher Highs/Higher Lows: Bullish structure • Lower Highs/Lower Lows: Bearish structure • Liquidity Sweeps: Stop runs that reverse Signal Generation: BOS Bull: Price breaks above prior swing high with momentum → Signal: +1 (Long), Confidence: 0.70 + structure_strength × 0.2 CHOCH Bull: First higher low after downtrend, breaking structure → Signal: +1 (Long), Confidence: 0.75 BOS Bear: Price breaks below prior swing low with momentum → Signal: -1 (Short), Confidence: 0.70 + structure_strength × 0.2 CHOCH Bear: First lower high after uptrend, breaking structure → Signal: -1 (Short), Confidence: 0.75 Liquidity Sweep Long: Price sweeps below swing low then reverses strongly → Signal: +1 (Long), Confidence: 0.80 Liquidity Sweep Short: Price sweeps above swing high then reverses strongly → Signal: -1 (Short), Confidence: 0.80 When Structure Agent Excels: • After liquidity grabs (stop runs) • At major swing points • During institutional accumulation/distribution When Structure Agent Fails: • Choppy, structureless markets • During news events (structure becomes noise) • Very low timeframes (noise overwhelms structure) Thompson Sampling: The Bandit Algorithm With three agents giving potentially different signals, how do we decide which to trust? This is the multi-armed bandit problem —balancing exploitation (using what works) with exploration (testing alternatives). Thompson Sampling Solution: Each agent maintains a Beta distribution representing its success/failure history: Agent success rate modeled as Beta(α, β) Where: α = number of successful signals + 1 β = number of failed signals + 1 On Each Bar: 1. Sample from each agent's Beta distribution 2. Weight agent signals by sampled probabilities 3. Combine weighted signals into consensus 4. Update α/β based on trade outcomes Mathematical Implementation: // Beta sampling via Gamma ratio method f_beta_sample(alpha, beta) => g1 = f_gamma_sample(alpha) g2 = f_gamma_sample(beta) g1 / (g1 + g2) // Thompson Sampling selection for each agent: sampled_prob = f_beta_sample(agent.alpha, agent.beta) weight = sampled_prob / sum(all_sampled_probs) consensus += agent.signal × agent.confidence × weight Why Thompson Sampling? • Automatic Exploration : Agents with few samples get occasional chances (high variance in Beta distribution) • Bayesian Optimal : Mathematically proven optimal solution to exploration-exploitation tradeoff • Uncertainty-Aware : Small sample size = more exploration; large sample size = more exploitation • Self-Correcting : Poor performers naturally get lower weights over time Example Evolution: Day 1 (Initial): Trend Agent: Beta(1,1) → samples ~0.50 (high uncertainty) Reversion Agent: Beta(1,1) → samples ~0.50 (high uncertainty) Structure Agent: Beta(1,1) → samples ~0.50 (high uncertainty) After 50 Signals: Trend Agent: Beta(28,23) → samples ~0.55 (moderate confidence) Reversion Agent: Beta(18,33) → samples ~0.35 (underperforming) Structure Agent: Beta(32,19) → samples ~0.63 (outperforming) Result: Structure Agent now receives highest weight in consensus Consensus Requirements by Mode: Aggressive Mode: • Minimum 1/3 agents agreeing • Consensus threshold: 45% • Use case: More signals, higher risk tolerance Balanced Mode: • Minimum 2/3 agents agreeing • Consensus threshold: 55% • Use case: Standard trading Conservative Mode: • Minimum 2/3 agents agreeing • Consensus threshold: 65% • Use case: Higher quality, fewer signals Institutional Mode: • Minimum 2/3 agents agreeing • Consensus threshold: 75% • Additional: Session quality >0.65, mode adjustment +0.10 • Use case: Highest quality signals only 🌀 INTELLIGENT CHOP DETECTION ENGINE The Chop Problem: Most trading losses occur not from being wrong about direction, but from trading in conditions where direction doesn't exist . Choppy, range-bound markets generate false signals from every methodology—trend-following, mean-reversion, and structure-based alike. AMWT's chop detection engine identifies these low-probability environments before signals fire, preventing the most damaging trades. Five-Factor Chop Analysis: Factor 1: ADX Component (25% weight) ADX (Average Directional Index) measures trend strength regardless of direction. ADX < 15: Very weak trend (high chop score) ADX 15-20: Weak trend (moderate chop score) ADX 20-25: Developing trend (low chop score) ADX > 25: Strong trend (minimal chop score) adx_chop = (i_adxThreshold - adx_val) / i_adxThreshold × 100 Why ADX Works: ADX synthesizes +DI and -DI movements. Low ADX means price is moving but not directionally—the definition of chop. Factor 2: Choppiness Index (25% weight) The Choppiness Index measures price efficiency using the ratio of ATR sum to price range: CI = 100 × LOG10(SUM(ATR, n) / (Highest - Lowest)) / LOG10(n) CI > 61.8: Choppy (range-bound, inefficient movement) CI < 38.2: Trending (directional, efficient movement) CI 38.2-61.8: Transitional chop_idx_score = (ci_val - 38.2) / (61.8 - 38.2) × 100 Why Choppiness Index Works: In trending markets, price covers distance efficiently (low ATR sum relative to range). In choppy markets, price oscillates wildly but goes nowhere (high ATR sum relative to range). Factor 3: Range Compression (20% weight) Compares recent range to longer-term range, detecting volatility squeezes: recent_range = Highest(20) - Lowest(20) longer_range = Highest(50) - Lowest(50) compression = 1 - (recent_range / longer_range) compression > 0.5: Strong squeeze (potential breakout imminent) compression < 0.2: No compression (normal volatility) range_compression_score = compression × 100 Why Range Compression Matters: Compression precedes expansion. High compression = market coiling, preparing for move. Signals during compression often fail because the breakout hasn't occurred yet. Factor 4: Channel Position (15% weight) Tracks price position within the macro channel: channel_position = (close - channel_low) / (channel_high - channel_low) position 0.4-0.6: Center of channel (indecision zone) position <0.2 or >0.8: Near extremes (potential reversal or breakout) channel_chop = abs(0.5 - channel_position) < 0.15 ? high_score : low_score Why Channel Position Matters: Price in the middle of a range is in "no man's land"—equally likely to go either direction. Signals in the channel center have lower probability. Factor 5: Volume Quality (15% weight) Assesses volume relative to average: vol_ratio = volume / SMA(volume, 20) vol_ratio < 0.7: Low volume (lack of conviction) vol_ratio 0.7-1.3: Normal volume vol_ratio > 1.3: High volume (conviction present) volume_chop = vol_ratio < 0.8 ? (1 - vol_ratio) × 100 : 0 Why Volume Quality Matters: Low volume moves lack institutional participation. These moves are more likely to reverse or stall. Combined Chop Intensity: chopIntensity = (adx_chop × 0.25) + (chop_idx_score × 0.25) + (range_compression_score × 0.20) + (channel_chop × 0.15) + (volume_chop × i_volumeChopWeight × 0.15) Regime Classifications: Based on chop intensity and component analysis: • Strong Trend (0-20%): ADX >30, clear directional momentum, trade aggressively • Trending (20-35%): ADX >20, moderate directional bias, trade normally • Transitioning (35-50%): Mixed signals, regime change possible, reduce size • Mid-Range (50-60%): Price trapped in channel center, avoid new positions • Ranging (60-70%): Low ADX, price oscillating within bounds, fade extremes only • Compression (70-80%): Volatility squeeze, expansion imminent, wait for breakout • Strong Chop (80-100%): Multiple chop factors aligned, avoid trading entirely Signal Suppression: When chop intensity exceeds the configurable threshold (default 80%), signals are suppressed entirely. The dashboard displays "⚠️ CHOP ZONE" with the current regime classification. Chop Box Visualization: When chop is detected, AMWT draws a semi-transparent box on the chart showing the chop zone. This visual reminder helps traders avoid entering positions during unfavorable conditions. 💧 LIQUIDITY ANCHORING SYSTEM The Liquidity Concept: Markets move from liquidity pool to liquidity pool. Stop losses cluster at predictable locations—below swing lows (buy stops become sell orders when triggered) and above swing highs (sell stops become buy orders when triggered). Institutions know where these clusters are and often engineer moves to trigger them before reversing. AMWT identifies and tracks these liquidity events, using them as anchors for signal confidence. Liquidity Event Types: Type 1: Volume Spikes Definition: Volume > SMA(volume, 20) × i_volThreshold (default 2.8x) Interpretation: Sudden volume surge indicates institutional activity • Near swing low + reversal: Likely accumulation • Near swing high + reversal: Likely distribution • With continuation: Institutional conviction in direction Type 2: Stop Runs (Liquidity Sweeps) Definition: Price briefly exceeds swing high/low then reverses within N bars Detection: • Price breaks above recent swing high (triggering buy stops) • Then closes back below that high within 3 bars • Signal: Bullish stop run complete, reversal likely Or inverse for bearish: • Price breaks below recent swing low (triggering sell stops) • Then closes back above that low within 3 bars • Signal: Bearish stop run complete, reversal likely Type 3: Absorption Events Definition: High volume with small candle body Detection: • Volume > 2x average • Candle body < 30% of candle range • Interpretation: Large orders being filled without moving price • Implication: Accumulation (at lows) or distribution (at highs) Type 4: BSL/SSL Pools (Buy-Side/Sell-Side Liquidity) BSL (Buy-Side Liquidity): • Cluster of swing highs within ATR proximity • Stop losses from shorts sit above these highs • Breaking BSL triggers short covering (fuel for rally) SSL (Sell-Side Liquidity): • Cluster of swing lows within ATR proximity • Stop losses from longs sit below these lows • Breaking SSL triggers long liquidation (fuel for decline) Liquidity Pool Mapping: AMWT continuously scans for and maps liquidity pools: // Detect swing highs/lows using pivot function swing_high = ta.pivothigh(high, 5, 5) swing_low = ta.pivotlow(low, 5, 5) // Track recent swing points if not na(swing_high) bsl_levels.push(swing_high) if not na(swing_low) ssl_levels.push(swing_low) // Display on chart with labels Confluence Scoring Integration: When signals fire near identified liquidity events, confluence scoring increases: • Signal near volume spike: +10% confidence • Signal after liquidity sweep: +15% confidence • Signal at BSL/SSL pool: +10% confidence • Signal aligned with absorption zone: +10% confidence Why Liquidity Anchoring Matters: Signals "in a vacuum" have lower probability than signals anchored to institutional activity. A long signal after a liquidity sweep below swing lows has trapped shorts providing fuel. A long signal in the middle of nowhere has no such catalyst. 📊 SIGNAL GRADING SYSTEM The Quality Problem: Not all signals are created equal. A signal with 6/6 factors aligned is fundamentally different from a signal with 3/6 factors aligned. Traditional indicators treat them the same. AMWT grades every signal based on confluence. Confluence Components (100 points total): 1. Bandit Consensus Strength (25 points) consensus_str = weighted average of agent confidences score = consensus_str × 25 Example: Trend Agent: +1 signal, 0.80 confidence, 0.35 weight Reversion Agent: 0 signal, 0.50 confidence, 0.25 weight Structure Agent: +1 signal, 0.75 confidence, 0.40 weight Weighted consensus = (0.80×0.35 + 0×0.25 + 0.75×0.40) / (0.35 + 0.40) = 0.77 Score = 0.77 × 25 = 19.25 points 2. HMM State Confidence (15 points) score = hmm_confidence × 15 Example: HMM reports 82% probability of IMPULSE_UP Score = 0.82 × 15 = 12.3 points 3. Session Quality (15 points) Session quality varies by time: • London/NY Overlap: 1.0 (15 points) • New York Session: 0.95 (14.25 points) • London Session: 0.70 (10.5 points) • Asian Session: 0.40 (6 points) • Off-Hours: 0.30 (4.5 points) • Weekend: 0.10 (1.5 points) 4. Energy/Participation (10 points) energy = (realized_vol / avg_vol) × 0.4 + (range / ATR) × 0.35 + (volume / avg_volume) × 0.25 score = min(energy, 1.0) × 10 5. Volume Confirmation (10 points) if volume > SMA(volume, 20) × 1.5: score = 10 else if volume > SMA(volume, 20): score = 5 else: score = 0 6. Structure Alignment (10 points) For long signals: • Bullish structure (HH + HL): 10 points • Higher low only: 6 points • Neutral structure: 3 points • Bearish structure: 0 points Inverse for short signals 7. Trend Alignment (10 points) For long signals: • Price > EMA21 > EMA50: 10 points • Price > EMA21: 6 points • Neutral: 3 points • Against trend: 0 points 8. Entry Trigger Quality (5 points) • Strong trigger (multiple confirmations): 5 points • Moderate trigger (single confirmation): 3 points • Weak trigger (marginal): 1 point Grade Scale: Total Score → Grade 85-100 → A+ (Exceptional—all factors aligned) 70-84 → A (Strong—high probability) 55-69 → B (Acceptable—proceed with caution) Below 55 → C (Marginal—filtered by default) Grade-Based Signal Brightness: Signal arrows on the chart have transparency based on grade: • A+: Full brightness (alpha = 0) • A: Slight fade (alpha = 15) • B: Moderate fade (alpha = 35) • C: Significant fade (alpha = 55) This visual hierarchy helps traders instantly identify signal quality. Minimum Grade Filter: Configurable filter (default: C) sets the minimum grade for signal display: • Set to "A" for only highest-quality signals • Set to "B" for moderate selectivity • Set to "C" for all signals (maximum quantity) 🕐 SESSION INTELLIGENCE Why Sessions Matter: Markets behave differently at different times. The London open is fundamentally different from the Asian lunch hour. AMWT incorporates session-aware logic to optimize signal quality. Session Definitions: Asian Session (18:00-03:00 ET) • Characteristics: Lower volatility, range-bound tendency, fewer institutional participants • Quality Score: 0.40 (40% of peak quality) • Strategy Implications: Fade extremes, expect ranges, smaller position sizes • Best For: Mean-reversion setups, accumulation/distribution identification London Session (03:00-12:00 ET) • Characteristics: European institutional activity, volatility pickup, trend initiation • Quality Score: 0.70 (70% of peak quality) • Strategy Implications: Watch for trend development, breakouts more reliable • Best For: Initial trend identification, structure breaks New York Session (08:00-17:00 ET) • Characteristics: Highest liquidity, US institutional activity, major moves • Quality Score: 0.95 (95% of peak quality) • Strategy Implications: Best environment for directional trades • Best For: Trend continuation, momentum plays London/NY Overlap (08:00-12:00 ET) • Characteristics: Peak liquidity, both European and US participants active • Quality Score: 1.0 (100%—maximum quality) • Strategy Implications: Highest probability for successful breakouts and trends • Best For: All signal types—this is prime time Off-Hours • Characteristics: Thin liquidity, erratic price action, gaps possible • Quality Score: 0.30 (30% of peak quality) • Strategy Implications: Avoid new positions, wider stops if holding • Best For: Waiting Smart Weekend Detection: AMWT properly handles the Sunday evening futures open: // Traditional (broken): isWeekend = dayofweek == saturday OR dayofweek == sunday // AMWT (correct): anySessionActive = not na(asianTime) or not na(londonTime) or not na(nyTime) isWeekend = calendarWeekend AND NOT anySessionActive This ensures Sunday 6pm ET (when futures open) correctly shows "Asian Session" rather than "Weekend." Session Transition Boosts: Certain session transitions create trading opportunities: • Asian → London transition: +15% confidence boost (volatility expansion likely) • London → Overlap transition: +20% confidence boost (peak liquidity approaching) • Overlap → NY-only transition: -10% confidence adjustment (liquidity declining) • Any → Off-Hours transition: Signal suppression recommended 📈 TRADE MANAGEMENT SYSTEM The Signal Spam Problem: Many indicators generate signal after signal, creating confusion and overtrading. AMWT implements a complete trade lifecycle management system that prevents signal spam and tracks performance. Trade Lock Mechanism: Once a signal fires, the system enters a "trade lock" state: Trade Lock Duration: Configurable (default 30 bars) Early Exit Conditions: • TP3 hit (full target reached) • Stop Loss hit (trade failed) • Lock expiration (time-based exit) During lock: • No new signals of same type displayed • Opposite signals can override (reversal) • Trade status tracked in dashboard Target Levels: Each signal generates three profit targets based on ATR: TP1 (Conservative Target) • Default: 1.0 × ATR • Purpose: Quick partial profit, reduce risk • Action: Take 30-40% off position, move stop to breakeven TP2 (Standard Target) • Default: 2.5 × ATR • Purpose: Main profit target • Action: Take 40-50% off position, trail stop TP3 (Extended Target) • Default: 5.0 × ATR • Purpose: Runner target for trend days • Action: Close remaining position or continue trailing Stop Loss: • Default: 1.9 × ATR from entry • Purpose: Define maximum risk • Placement: Below recent swing low (longs) or above recent swing high (shorts) Invalidation Level: Beyond stop loss, AMWT calculates an "invalidation" level where the wave hypothesis dies: invalidation = entry - (ATR × INVALIDATION_MULT × 1.5) If price reaches invalidation, the current market interpretation is wrong—not just the trade. Visual Trade Management: During active trades, AMWT displays: • Entry arrow with grade label (▲A+, ▼B, etc.) • TP1, TP2, TP3 horizontal lines in green • Stop Loss line in red • Invalidation line in orange (dashed) • Progress indicator in dashboard Persistent Execution Markers: When targets or stops are hit, permanent markers appear: • TP hit: Green dot with "TP1"/"TP2"/"TP3" label • SL hit: Red dot with "SL" label These persist on the chart for review and statistics. 💰 PERFORMANCE TRACKING & STATISTICS Tracked Metrics: • Total Trades: Count of all signals that entered trade lock • Winning Trades: Signals where at least TP1 was reached before SL • Losing Trades: Signals where SL was hit before any TP • Win Rate: Winning / Total × 100% • Total R Profit: Sum of R-multiples from winning trades • Total R Loss: Sum of R-multiples from losing trades • Net R: Total R Profit - Total R Loss Currency Conversion System: AMWT can display P&L in multiple formats: R-Multiple (Default) • Shows risk-normalized returns • "Net P&L: +4.2R | 78 trades" means 4.2 times initial risk gained over 78 trades • Best for comparing across different position sizes Currency Conversion (USD/EUR/GBP/JPY/INR) • Converts R-multiples to currency based on: - Dollar Risk Per Trade (user input) - Tick Value (user input) - Selected currency Example Configuration: Dollar Risk Per Trade: $100 Display Currency: USD If Net R = +4.2R Display: Net P&L: +$420.00 | 78 trades Ticks • For futures traders who think in ticks • Converts based on tick value input Statistics Reset: Two reset methods: 1. Toggle Reset • Turn "Reset Statistics" toggle ON then OFF • Clears all statistics immediately 2. Date-Based Reset • Set "Reset After Date" (YYYY-MM-DD format) • Only trades after this date are counted • Useful for isolating recent performance 🎨 VISUAL FEATURES Macro Channel: Dynamic regression-based channel showing market boundaries: • Upper/lower bounds calculated from swing pivot linear regression • Adapts to current market structure • Shows overall trend direction and potential reversal zones Chop Boxes: Semi-transparent overlay during high-chop periods: • Purple/orange coloring indicates dangerous conditions • Visual reminder to avoid new positions Confluence Heat Zones: Background shading indicating setup quality: • Darker shading = higher confluence • Lighter shading = lower confluence • Helps identify optimal entry timing EMA Ribbon: Trend visualization via moving average fill: • EMA 8/21/50 with gradient fill between • Green fill when bullish aligned • Red fill when bearish aligned • Gray when neutral Absorption Zone Boxes: Marks potential accumulation/distribution areas: • High volume + small body = absorption • Boxes drawn at these levels • Often act as support/resistance Liquidity Pool Lines: BSL/SSL levels with labels: • Dashed lines at liquidity clusters • "BSL" label above swing high clusters • "SSL" label below swing low clusters Six Professional Themes: • Quantum: Deep purples and cyans (default) • Cyberpunk: Neon pinks and blues • Professional: Muted grays and greens • Ocean: Blues and teals • Matrix: Greens and blacks • Ember: Oranges and reds 🎓 PROFESSIONAL USAGE PROTOCOL Phase 1: Learning the System (Week 1) Goal: Understand AMWT concepts and dashboard interpretation Setup: • Signal Mode: Balanced • Display: All features enabled • Grade Filter: C (see all signals) Actions: • Paper trade ONLY—no real money • Observe HMM state transitions throughout the day • Note when agents agree vs disagree • Watch chop detection engage and disengage • Track which grades produce winners vs losers Key Learning Questions: • How often do A+ signals win vs B signals? (Should see clear difference) • Which agent tends to be right in current market? (Check dashboard) • When does chop detection save you from bad trades? • How do signals near liquidity events perform vs signals in vacuum? Phase 2: Parameter Optimization (Week 2) Goal: Tune system to your instrument and timeframe Signal Mode Testing: • Run 5 days on Aggressive mode (more signals) • Run 5 days on Conservative mode (fewer signals) • Compare: Which produces better risk-adjusted returns? Grade Filter Testing: • Track A+ only for 20 signals • Track A and above for 20 signals • Track B and above for 20 signals • Compare win rates and expectancy Chop Threshold Testing: • Default (80%): Standard filtering • Try 70%: More aggressive filtering • Try 90%: Less filtering • Which produces best results for your instrument? Phase 3: Strategy Development (Weeks 3-4) Goal: Develop personal trading rules based on system signals Position Sizing by Grade: • A+ grade: 100% position size • A grade: 75% position size • B grade: 50% position size • C grade: 25% position size (or skip) Session-Based Rules: • London/NY Overlap: Take all A/A+ signals • NY Session: Take all A+ signals, selective on A • Asian Session: Only A+ signals with extra confirmation • Off-Hours: No new positions Chop Zone Rules: • Chop >70%: Reduce position size 50% • Chop >80%: No new positions • Chop <50%: Full position size allowed Phase 4: Live Micro-Sizing (Month 2) Goal: Validate paper trading results with minimal risk Setup: • 10-20% of intended full position size • Take ONLY A+ signals initially • Follow trade management religiously Tracking: • Log every trade: Entry, Exit, Grade, HMM State, Chop Level, Agent Consensus • Calculate: Win rate by grade, by session, by chop level • Compare to paper trading (should be within 15%) Red Flags: • Win rate diverges significantly from paper trading: Execution issues • Consistent losses during certain sessions: Adjust session rules • Losses cluster when specific agent dominates: Review that agent's logic Phase 5: Scaling Up (Months 3-6) Goal: Gradually increase to full position size Progression: • Month 3: 25-40% size (if micro-sizing profitable) • Month 4: 40-60% size • Month 5: 60-80% size • Month 6: 80-100% size Scale-Up Requirements: • Minimum 30 trades at current size • Win rate ≥50% • Net R positive • No revenge trading incidents • Emotional control maintained 💡 DEVELOPMENT INSIGHTS Why HMM Over Simple Indicators: Early versions used standard indicators (RSI >70 = overbought, etc.). Win rates hovered at 52-55%. The problem: indicators don't capture state. RSI can stay "overbought" for weeks in a strong trend. The insight: markets exist in states, and state persistence matters more than indicator levels. Implementing HMM with state transition probabilities increased signal quality significantly. The system now knows not just "RSI is high" but "we're in IMPULSE_UP state with 70% probability of staying in IMPULSE_UP." The Multi-Agent Evolution: Original version used a single analytical methodology—trend-following. Performance was inconsistent: great in trends, destroyed in ranges. Added mean-reversion agent: now it was inconsistent the other way. The breakthrough: use multiple agents and let the system learn which works . Thompson Sampling wasn't the first attempt—tried simple averaging, voting, even hard-coded regime switching. Thompson Sampling won because it's mathematically optimal and automatically adapts without manual regime detection. Chop Detection Revelation: Chop detection was added almost as an afterthought. "Let's filter out obviously bad conditions." Testing revealed it was the most impactful single feature. Filtering chop zones reduced losing trades by 35% while only reducing total signals by 20%. The insight: avoiding bad trades matters more than finding good ones. Liquidity Anchoring Discovery: Watched hundreds of trades. Noticed pattern: signals that fired after liquidity events (stop runs, volume spikes) had significantly higher win rates than signals in quiet markets. Implemented liquidity detection and anchoring. Win rate on liquidity-anchored signals: 68% vs 52% on non-anchored signals. The Grade System Impact: Early system had binary signals (fire or don't fire). Adding grading transformed it. Traders could finally match position size to signal quality. A+ signals deserved full size; C signals deserved caution. Just implementing grade-based sizing improved portfolio Sharpe ratio by 0.3. 🚨 LIMITATIONS & CRITICAL ASSUMPTIONS What AMWT Is NOT: • NOT a Holy Grail : No system wins every trade. AMWT improves probability, not certainty. • NOT Fully Automated : AMWT provides signals and analysis; execution requires human judgment. • NOT News-Proof : Exogenous shocks (FOMC surprises, geopolitical events) invalidate all technical analysis. • NOT for Scalping : HMM state estimation needs time to develop. Sub-minute timeframes are not appropriate. Core Assumptions: 1. Markets Have States : Assumes markets transition between identifiable regimes. Violation: Random walk markets with no regime structure. 2. States Are Inferable : Assumes observable indicators reveal hidden states. Violation: Market manipulation creating false signals. 3. History Informs Future : Assumes past agent performance predicts future performance. Violation: Regime changes that invalidate historical patterns. 4. Liquidity Events Matter : Assumes institutional activity creates predictable patterns. Violation: Markets with no institutional participation. Performs Best On: • Liquid Futures : ES, NQ, MNQ, MES, CL, GC • Major Forex Pairs : EUR/USD, GBP/USD, USD/JPY • Large-Cap Stocks : AAPL, MSFT, TSLA, NVDA (>$5B market cap) • Liquid Crypto : BTC, ETH on major exchanges Performs Poorly On: • Illiquid Instruments : Low volume stocks, exotic pairs • Very Low Timeframes : Sub-5-minute charts (noise overwhelms signal) • Binary Event Days : Earnings, FDA approvals, court rulings • Manipulated Markets : Penny stocks, low-cap altcoins Known Weaknesses: • Warmup Period : HMM needs ~50 bars to initialize properly. Early signals may be unreliable. • Regime Change Lag : Thompson Sampling adapts over time, not instantly. Sudden regime changes may cause short-term underperformance. • Complexity : More parameters than simple indicators. Requires understanding to use effectively. ⚠️ RISK DISCLOSURE Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Adaptive Market Wave Theory, while based on rigorous mathematical frameworks including Hidden Markov Models and multi-armed bandit algorithms, does not guarantee profits and can result in significant losses. AMWT's methodologies—HMM state estimation, Thompson Sampling agent selection, and confluence-based grading—have theoretical foundations but past performance is not indicative of future results. Hidden Markov Model assumptions may not hold during: • Major news events disrupting normal market behavior • Flash crashes or circuit breaker events • Low liquidity periods with erratic price action • Algorithmic manipulation or spoofing Multi-agent consensus assumes independent analytical perspectives provide edge. Market conditions change. Edges that existed historically can diminish or disappear. Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively before risking capital. Start with micro position sizing. Never risk more than you can afford to lose completely. Use proper position sizing. Implement stop losses without exception. By using this indicator, you acknowledge these risks and accept full responsibility for all trading decisions and outcomes. "Elliott Wave was a first-order approximation of market phase behavior. AMWT is the second—probabilistic, adaptive, and accountable." Initial Public Release Core Engine: • True Hidden Markov Model with online Baum-Welch learning • Viterbi algorithm for optimal state sequence decoding • 6-state market regime classification Agent System: • 3-Bandit consensus (Trend, Reversion, Structure) • Thompson Sampling with true Beta distribution sampling • Adaptive weight learning based on performance Signal Generation: • Quality-based confluence grading (A+/A/B/C) • Four signal modes (Aggressive/Balanced/Conservative/Institutional) • Grade-based visual brightness Chop Detection: • 5-factor analysis (ADX, Choppiness Index, Range Compression, Channel Position, Volume) • 7 regime classifications • Configurable signal suppression threshold Liquidity: • Volume spike detection • Stop run (liquidity sweep) identification • BSL/SSL pool mapping • Absorption zone detection Trade Management: • Trade lock with configurable duration • TP1/TP2/TP3 targets • ATR-based stop loss • Persistent execution markers Session Intelligence: • Asian/London/NY/Overlap detection • Smart weekend handling (Sunday futures open) • Session quality scoring Performance: • Statistics tracking with reset functionality • 7 currency display modes • Win rate and Net R calculation Visuals: • Macro channel with linear regression • Chop boxes • EMA ribbon • Liquidity pool lines • 6 professional themes Dashboards: • Main Dashboard: Market State, Consensus, Trade Status, Statistics 📋 AMWT vs AMWT-PRO: This version includes all core AMWT functionality: ✓ Full Hidden Markov Model state estimation ✓ 3-Bandit Thompson Sampling consensus system ✓ Complete 5-factor chop detection engine ✓ All four signal modes ✓ Full trade management with TP/SL tracking ✓ Main dashboard with complete statistics ✓ All visual features (channels, zones, pools) ✓ Identical signal generation to PRO ✓ Six professional themes ✓ Full alert system The PRO version adds the AMWT Advisor panel—a secondary dashboard providing: • Real-time Market Pulse situation assessment • Agent Matrix visualization (individual agent votes) • Structure analysis breakdown • "Watch For" upcoming setups • Action Command coaching Both versions generate identical signals . The Advisor provides additional guidance for interpreting those signals. Taking you to school. - Dskyz, Trade with probability. Trade with consensus. Trade with AMWT.
Pine Script® indicator
by DskyzInvestments
3
Relative Strength Index_YJ//@version=5 indicator(title="MACD_YJ", shorttitle="MACD_YJ",format=format.price, precision=2) source = close useCurrentRes = input.bool(true, title="Use Current Chart Resolution?") resCustom = input.timeframe("60", title="Use Different Timeframe? Uncheck Box Above") smd = input.bool(true, title="Show MacD & Signal Line? Also Turn Off Dots Below") sd = input.bool(false, title="Show Dots When MacD Crosses Signal Line?") sh = input.bool(true, title="Show Histogram?") macd_colorChange = input.bool(true, title="Change MacD Line Color-Signal Line Cross?") hist_colorChange = input.bool(true, title="MacD Histogram 4 Colors?") // === Divergence inputs === grpDiv = "Divergence" calculateDivergence = input.bool(true, title="Calculate Divergence", group=grpDiv, tooltip="피벗 기반 정/역배 다이버전스 탐지 및 알람 사용") lookbackRight = input.int(5, "Lookback Right", group=grpDiv, minval=1) lookbackLeft = input.int(5, "Lookback Left", group=grpDiv, minval=1) rangeUpper = input.int(60, "Bars Range Upper", group=grpDiv, minval=1) rangeLower = input.int(5, "Bars Range Lower", group=grpDiv, minval=1) bullColor = input.color(color.new(#4CAF50, 0), "Bull Color", group=grpDiv) bearColor = input.color(color.new(#F23645, 0), "Bear Color", group=grpDiv) textColor = color.white noneColor = color.new(color.white, 100) res = useCurrentRes ? timeframe.period : resCustom fastLength = input.int(12, minval=1) slowLength = input.int(26, minval=1) signalLength= input.int(9, minval=1) fastMA = ta.ema(source, fastLength) slowMA = ta.ema(source, slowLength) macd = fastMA - slowMA signal = ta.sma(macd, signalLength) hist = macd - signal outMacD = request.security(syminfo.tickerid, res, macd) outSignal = request.security(syminfo.tickerid, res, signal) outHist = request.security(syminfo.tickerid, res, hist) // 가격도 같은 res로 hi_res = request.security(syminfo.tickerid, res, high) lo_res = request.security(syminfo.tickerid, res, low) // ── Histogram 색 histA_IsUp = outHist > outHist and outHist > 0 histA_IsDown = outHist < outHist and outHist > 0 histB_IsDown = outHist < outHist and outHist <= 0 histB_IsUp = outHist > outHist and outHist <= 0 macd_IsAbove = outMacD >= outSignal plot_color = hist_colorChange ? (histA_IsUp ? color.new(#00FF00, 0) : histA_IsDown ? color.new(#006900, 0) : histB_IsDown ? color.new(#FF0000, 0) : histB_IsUp ? color.new(#670000, 0) : color.yellow) : color.gray macd_color = macd_colorChange ? color.new(#00ffff, 0) : color.new(#00ffff, 0) signal_color = color.rgb(240, 232, 166) circleYPosition = outSignal // 골든/데드 크로스 (경고 해결: 먼저 계산) isBullCross = ta.crossover(outMacD, outSignal) isBearCross = ta.crossunder(outMacD, outSignal) cross_color = isBullCross ? color.new(#00FF00, 0) : isBearCross ? color.new(#FF0000, 0) : na // ── 플롯 plot(sh and outHist ? outHist : na, title="Histogram", color=plot_color, style=plot.style_histogram, linewidth=5) plot(smd and outMacD ? outMacD : na, title="MACD", color=macd_color, linewidth=1) plot(smd and outSignal? outSignal: na, title="Signal Line", color=signal_color, style=plot.style_line, linewidth=1) plot(sd and (isBullCross or isBearCross) ? circleYPosition : na, title="Cross", style=plot.style_circles, linewidth=3, color=cross_color) hline(0, "0 Line", linestyle=hline.style_dotted, color=color.white) // ===================== // Divergence (정배/역배) - 피벗 비교 // ===================== _inRange(cond) => bars = ta.barssince(cond) rangeLower <= bars and bars <= rangeUpper plFound = false phFound = false bullCond = false bearCond = false macdLBR = outMacD if calculateDivergence // 정배: 가격 LL, MACD HL plFound := not na(ta.pivotlow(outMacD, lookbackLeft, lookbackRight)) macdHL = macdLBR > ta.valuewhen(plFound, macdLBR, 1) and _inRange(plFound ) lowLBR = lo_res priceLL = lowLBR < ta.valuewhen(plFound, lowLBR, 1) bullCond := priceLL and macdHL and plFound // 역배: 가격 HH, MACD LH phFound := not na(ta.pivothigh(outMacD, lookbackLeft, lookbackRight)) macdLH = macdLBR < ta.valuewhen(phFound, macdLBR, 1) and _inRange(phFound ) highLBR = hi_res priceHH = highLBR > ta.valuewhen(phFound, highLBR, 1) bearCond := priceHH and macdLH and phFound // 시각화 (editable 파라미터 삭제) plot(plFound ? macdLBR : na, offset=-lookbackRight, title="Regular Bullish (MACD)", linewidth=2, color=(bullCond ? bullColor : noneColor), display=display.pane) plotshape(bullCond ? macdLBR : na, offset=-lookbackRight, title="Bullish Label", text=" Bull ", style=shape.labelup, location=location.absolute, color=bullColor, textcolor=textColor, display=display.pane) plot(phFound ? macdLBR : na, offset=-lookbackRight, title="Regular Bearish (MACD)", linewidth=2, color=(bearCond ? bearColor : noneColor), display=display.pane) plotshape(bearCond ? macdLBR : na, offset=-lookbackRight, title="Bearish Label", text=" Bear ", style=shape.labeldown, location=location.absolute, color=bearColor, textcolor=textColor, display=display.pane) // 알람 alertcondition(bullCond, title="MACD Regular Bullish Divergence", message="MACD 정배 다이버전스 발견: 현재 봉에서 lookbackRight 만큼 좌측.") alertcondition(bearCond, title="MACD Regular Bearish Divergence", message="MACD 역배 다이버전스 발견: 현재 봉에서 lookbackRight 만큼 좌측.")
Pine Script® indicator
by Donggrimon
Market Energy & Direction DashboardMarket Energy & Direction Dashboard - Daytrading Overview A comprehensive real-time market internals dashboard that combines NYSE TICK, NYSE Advance-Decline (ADD) momentum, VIX direction, and relative volume into a single visual traffic light system with intelligent signal synthesis. Designed for active daytraders who need instant confirmation of market direction and energy based on momentum alignment across all major internals. What It Does This indicator synthesizes multiple market internals using directional momentum analysis rather than static thresholds to provide clear, actionable signals: • Traffic Light System: Single glance confirmation of market state o Bright Green: Maximum bullish - all internals aligned (TICK + ADD rising + VIX falling + volume) o Bright Red: Maximum bearish - all internals aligned (TICK + ADD falling + VIX rising + volume) o Yellow: Exhaustion warning - TICK at extremes, potential reversal imminent o Moderate Colors: Partial alignment - some confirmation but not complete o Gray: Choppy, neutral, or conflicting signals • Real-Time Dashboard displays: o Current TICK value with exhaustion warnings o Current ADD with directional momentum indicator (↑ rising = breadth improving, ↓ falling = breadth deteriorating, ± compression) o VIX level with directional indicator (↓ declining = bullish, ↑ rising = bearish, ± compression = neutral) o Relative volume (current vs 20-period average) o Composite status message synthesizing all data into clear directional summary Key Features ✓ Momentum-based analysis - all indicators show direction/change, not just levels ✓ Intelligent signal hierarchy from "Maximum" to "Moderate" based on internal alignment ✓ ADD directional momentum - catches breadth shifts early, works in all market conditions ✓ VIX directional analysis - shows if fear is increasing, decreasing, or stagnant ✓ Color-coded traffic light for instant decision making ✓ Detects TICK/ADD divergences (conflicting signals = caution) ✓ Exhaustion warnings at extreme TICK levels (±1000+) ✓ Composite status messages - "Maximum Bull", "Strong Bull", "Moderate Bull", etc. ✓ Customizable thresholds for all parameters ✓ Moveable dashboard (9 position options) ✓ Built-in alerts for all signal strengths, exhaustion, and divergences How To Use Setup: 1. Add indicator to your main trading chart (SPY, ES, NQ, etc.) 2. Default settings work well for most traders, but you can customize: o TICK Extreme Level (default 1000) o ADD Compression Threshold (default 100 - detects when breadth is stagnant) o VIX Elevated Level (default 20) o VIX Compression Threshold (default 2% - detects low volatility) o Volume Threshold (default 1.5x average) 3. Position dashboard wherever convenient on your chart Reading The Signals: Signal Hierarchy (Strongest to Weakest): MAXIMUM SIGNALS ⭐ (Brightest colors - All 4 internals aligned) • "✓ MAXIMUM BULL": TICK bullish + ADD rising (↑) + VIX falling (↓) + Volume elevated o This is the holy grail setup - all momentum aligned, highest conviction longs • "✓ MAXIMUM BEAR": TICK bearish + ADD falling (↓) + VIX rising (↑) + Volume elevated o Perfect storm bearish - all momentum aligned, highest conviction shorts STRONG SIGNALS (Bright colors - Core internals aligned) • "✓ STRONG BULL": TICK bullish + ADD rising (↑) o Strong confirmation even without VIX/volume - breadth supporting the move • "✓ STRONG BEAR": TICK bearish + ADD falling (↓) o Strong confirmation - both momentum and breadth deteriorating MODERATE SIGNALS (Faded colors - Partial confirmation) • "MODERATE BULL": TICK bullish but ADD not confirming direction o Proceed with caution - momentum present but breadth questionable • "MODERATE BEAR": TICK bearish but ADD not confirming direction o Proceed with caution - selling but breadth not fully participating WARNING SIGNALS • "⚠ EXHAUSTION" (Yellow): TICK at ±1000+ extremes o Potential reversal zone - prepare to fade or take profits o Often marks blow-off tops or capitulation bottoms NEUTRAL/AVOID • "CHOPPY/NEUTRAL" (Gray): Conflicting signals or low conviction o Stay out or reduce size significantly Individual Indicator Interpretation: TICK: • Green: Bullish momentum (>+300) • Red: Bearish momentum (<-300) • Yellow: Exhaustion (±1000+) • Gray: Neutral ADD (Advance-Decline): • Green (↑): Breadth improving - more stocks participating in the move • Red (↓): Breadth deteriorating - fewer stocks participating • Gray (±): Breadth stagnant - no clear participation trend VIX: • Green (↓): Fear declining - healthy environment for rallies • Red (↑): Fear rising - risk-off mode, supports downward moves • Gray (±): Volatility compression - often precedes explosive moves Volume: • Green: High conviction (>1.5x average) • Gray: Low conviction Trading Strategy: 1. Wait for "MAXIMUM" or "STRONG" signals for highest probability entries o Maximum signals = go full size with confidence o Strong signals = good conviction, normal position sizing 2. Confirm directional alignment: o For longs: Want ADD ↑ (rising) and VIX ↓ (falling) o For shorts: Want ADD ↓ (falling) and VIX ↑ (rising) 3. Use exhaustion warnings (yellow) to: o Take profits on existing positions o Prepare counter-trend entries o Tighten stops 4. Avoid "MODERATE" signals unless you have strong conviction from other analysis o These work best as confirmation for existing setups o Not strong enough to initiate new positions alone 5. Never trade "CHOPPY/NEUTRAL" signals o Gray means stay out - preserve capital o Wait for clear alignment 6. Watch for divergences: o Price making new highs but ADD ↓ (falling) = distribution warning o Price making new lows but ADD ↑ (rising) = potential bottom o Divergence alert will notify you Best Practices: • Use on 1-5 minute charts for daytrading • Combine with your price action or technical setup (support/resistance, trendlines, patterns) • The dashboard confirms when to take your setup, not what setup to take • Most effective during regular market hours (9:30 AM - 4:00 PM ET) when volume is present • The strongest edge comes from "MAXIMUM" signals - wait for these for best risk/reward • Pay special attention to ADD direction - it's the most predictive breadth indicator • VIX compression (gray ±) often signals upcoming volatility expansion - prepare for bigger moves Customization Option All thresholds are adjustable in settings: • TICK Extreme: Higher = fewer exhaustion warnings (try 1200-1500 for less sensitivity) • ADD Compression Threshold: Change detection sensitivity o Default 100 = balanced o Lower (50) = more sensitive to small breadth changes o Higher (200-300) = only shows major breadth shifts • VIX Elevated: Adjust for current volatility regime (15-25 typical range) • VIX Compression Threshold: o Default 2% = balanced o Lower (0.5-1%) = catches subtle VIX changes o Higher (3-5%) = only shows significant VIX moves • Volume Threshold: Lower for quieter stocks/times, higher for more confirmation Alerts Available • Maximum Bullish: All 4 internals aligned bullish (TICK + ADD↑ + VIX↓ + Volume) • Maximum Bearish: All 4 internals aligned bearish (TICK + ADD↓ + VIX↑ + Volume) • Strong Bullish: TICK bullish + ADD rising • Strong Bearish: TICK bearish + ADD falling • Exhaustion Warning: TICK at extreme levels • Divergence Warning: TICK and ADD directions conflicting Understanding the Signal Synthesis The indicator uses intelligent logic to combine all internals: "MAXIMUM" Signals require: • TICK direction (bullish/bearish) • ADD momentum (rising/falling) in same direction • VIX direction (falling for bulls, rising for bears) • Volume elevated (>1.5x average) "STRONG" Signals require: • TICK direction (bullish/bearish) • ADD momentum (rising/falling) in same direction • (VIX and volume are bonuses but not required) "MODERATE" Signals: • TICK showing direction • But ADD not confirming or contradicting • Weakest actionable signal This hierarchy ensures you know exactly how much conviction the market has behind any move. Technical Details • Pulls real-time data from NYSE TICK (USI:TICK), NYSE ADD (USI:ADD), and CBOE VIX • ADD direction calculated using bar-to-bar change with compression detection • VIX direction calculated using bar-to-bar percentage change • Volume calculation uses 20-period simple moving average • Dashboard updates every bar • No repainting - all calculations based on closed bar data Who This Is For • Active daytraders of stocks, futures (ES/NQ), and options • Scalpers needing quick directional confirmation with multiple internal alignment • Swing traders looking to time intraday entries with maximum confluence • Volatility traders who monitor VIX behavior • Market makers and professionals who trade based on breadth and internals • Anyone who monitors market internals but wants intelligent synthesis vs raw data Tips For Success Trading Philosophy: • Quality over quantity - wait for "MAXIMUM" signals for best results • One "MAXIMUM" signal trade is worth five "MODERATE" signal trades • Gray/neutral is not a sign of missing opportunity - it's protecting your capital Signal Confidence Levels: 1. MAXIMUM (95%+ confidence) - Trade these aggressively with full size 2. STRONG (80-85% confidence) - Trade these with normal position sizing 3. MODERATE (60-70% confidence) - Only if confirmed by strong technical setup 4. CHOPPY/NEUTRAL - Do not trade, wait for clarity Advanced Techniques: • Breadth divergences: Watch for price making new highs while ADD shows ↓ (falling) = major warning • VIX/Price divergences: Rallies with rising VIX (↑) are usually false moves • Volume confirmation: "MAXIMUM" signals with 2x+ volume are the absolute best • Compression zones: When both ADD and VIX show compression (±), expect explosive breakout soon • Sequential signals: Back-to-back "MAXIMUM" signals in same direction = strong trending day Common Patterns: • Opening surge with "MAXIMUM BULL" that shifts to "EXHAUSTION" (yellow) = fade the high • Selloff with "MAXIMUM BEAR" followed by ADD ↑ (rising) divergence = potential reversal • Choppy morning followed by "MAXIMUM" signal afternoon = best trending opportunity Example Scenarios Perfect Bull Entry: • Bright green signal box • TICK: +650 • ADD: +1200 (↑) • VIX: 18.30 (↓) • Volume: 2.3x • Status: "✓ MAXIMUM BULL" → ALL SYSTEMS GO - Take aggressive long positions Strong Bull (Good Confidence): • Green signal box (slightly less bright) • TICK: +500 • ADD: +800 (↑) • VIX: 19.50 (±) • Volume: 1.2x • Status: "✓ STRONG BULL" → Good long setup - breadth confirming even without VIX/volume Caution Bull (Moderate): • Faded green signal box • TICK: +400 • ADD: +900 (↓) • VIX: 20.10 (↑) • Volume: 0.9x • Status: "MODERATE BULL" → CAUTION - TICK bullish but breadth deteriorating and VIX rising = weak rally Exhaustion Warning: • Yellow signal box • TICK: +1350 ⚠ • ADD: +2100 (↑) • VIX: 17.20 (↓) • Volume: 1.8x • Status: "⚠ EXHAUSTION" → Take profits or prepare to fade - TICK overextended despite good internals Divergence Setup (Potential Reversal): • Faded green signal • TICK: +300 • ADD: +1800 (↓) • VIX: 21.50 (↑) • Volume: 1.6x • Status: "MODERATE BULL" → WARNING - Price rallying but breadth collapsing and fear rising = distribution Perfect Bear Entry: • Bright red signal box • TICK: -780 • ADD: -1600 (↓) • VIX: 24.80 (↑) • Volume: 2.5x • Status: "✓ MAXIMUM BEAR" → Perfect short setup - all momentum bearish with conviction Compression (Wait Mode): • Gray signal box • TICK: +50 • ADD: -200 (±) • VIX: 16.40 (±) • Volume: 0.7x • Status: "CHOPPY/NEUTRAL" → STAY OUT - Volatility compression, no conviction, await breakout Performance Optimization Best Market Conditions: • Works excellent in trending markets (up or down) • Particularly powerful during high-volume sessions (first/last hours) • "MAXIMUM" signals most reliable during 9:45-11:00 AM and 2:00-3:30 PM ET Less Effective During: • Lunch period (11:30 AM - 1:30 PM) - lower volume reduces signal quality • Low-volatility environments - compression signals dominate • Major news events in first 5 minutes - wait for internals to stabilize Recommended Use Cases: • Scalping: Trade only "MAXIMUM" signals for quick 5-15 minute moves • Daytrading: Use "MAXIMUM" and "STRONG" signals for position entries • Swing entries: Use "MAXIMUM" signals for optimal intraday entry timing • Exit timing: Use "EXHAUSTION" (yellow) warnings to take profits ________________________________________ Pro Tip: Create a dedicated workspace with this indicator on SPY/ES/NQ charts. Set alerts for "MAXIMUM BULL", "MAXIMUM BEAR", and "EXHAUSTION" signals. Most professional traders only trade the "MAXIMUM" setups and ignore everything else - this alone can dramatically improve win rates.
Pine Script® indicator
by BlouToro
RSI Overbought/Oversold + Divergence Indicator (new)//@version=5 indicator('CryptoSignalScanner - RSI Overbought/Oversold + Divergence Indicator (new)', //--------------------------------------------------------------------------------------------------------------------------------- //--- Define Colors --------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------- vWhite = #FFFFFF vViolet = #C77DF3 vIndigo = #8A2BE2 vBlue = #009CDF vGreen = #5EBD3E vYellow = #FFB900 vRed = #E23838 longColor = color.green shortColor = color.red textColor = color.white bullishColor = color.rgb(38,166,154,0) //Used in the display table bearishColor = color.rgb(239,83,79,0) //Used in the display table nomatchColor = color.silver //Used in the display table //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Functions-------------------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- TF2txt(TF) => switch TF "S" => "RSI 1s:" "5S" => "RSI 5s:" "10S" => "RSI 10s:" "15S" => "RSI 15s:" "30S" => "RSI 30s" "1" => "RSI 1m:" "3" => "RSI 3m:" "5" => "RSI 5m:" "15" => "RSI 15m:" "30" => "RSI 30m" "45" => "RSI 45m" "60" => "RSI 1h:" "120" => "RSI 2h:" "180" => "RSI 3h:" "240" => "RSI 4h:" "480" => "RSI 8h:" "D" => "RSI 1D:" "1D" => "RSI 1D:" "2D" => "RSI 2D:" "3D" => "RSI 2D:" "3D" => "RSI 3W:" "W" => "RSI 1W:" "1W" => "RSI 1W:" "M" => "RSI 1M:" "1M" => "RSI 1M:" "3M" => "RSI 3M:" "6M" => "RSI 6M:" "12M" => "RSI 12M:" //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Show/Hide Settings ---------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- rsiShowInput = input(true, title='Show RSI', group='Show/Hide Settings') maShowInput = input(false, title='Show MA', group='Show/Hide Settings') showRSIMAInput = input(true, title='Show RSIMA Cloud', group='Show/Hide Settings') rsiBandShowInput = input(true, title='Show Oversold/Overbought Lines', group='Show/Hide Settings') rsiBandExtShowInput = input(true, title='Show Oversold/Overbought Extended Lines', group='Show/Hide Settings') rsiHighlightShowInput = input(true, title='Show Oversold/Overbought Highlight Lines', group='Show/Hide Settings') DivergenceShowInput = input(true, title='Show RSI Divergence Labels', group='Show/Hide Settings') //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Table Settings -------------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- rsiShowTable = input(true, title='Show RSI Table Information box', group="RSI Table Settings") rsiTablePosition = input.string(title='Location', defval='middle_right', options= , group="RSI Table Settings", inline='1') rsiTextSize = input.string(title=' Size', defval='small', options= , group="RSI Table Settings", inline='1') rsiShowTF1 = input(true, title='Show TimeFrame1', group="RSI Table Settings", inline='tf1') rsiTF1 = input.timeframe("15", title=" Time", group="RSI Table Settings", inline='tf1') rsiShowTF2 = input(true, title='Show TimeFrame2', group="RSI Table Settings", inline='tf2') rsiTF2 = input.timeframe("60", title=" Time", group="RSI Table Settings", inline='tf2') rsiShowTF3 = input(true, title='Show TimeFrame3', group="RSI Table Settings", inline='tf3') rsiTF3 = input.timeframe("240", title=" Time", group="RSI Table Settings", inline='tf3') rsiShowTF4 = input(true, title='Show TimeFrame4', group="RSI Table Settings", inline='tf4') rsiTF4 = input.timeframe("D", title=" Time", group="RSI Table Settings", inline='tf4') rsiShowHist = input(true, title='Show RSI Historical Columns', group="RSI Table Settings", tooltip='Show the information of the 2 previous closed candles') //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- RSI Input Settings ---------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- rsiSourceInput = input.source(close, 'Source', group='RSI Settings') rsiLengthInput = input.int(14, minval=1, title='RSI Length', group='RSI Settings', tooltip='Here we set the RSI lenght') rsiColorInput = input.color(#26a69a, title="RSI Color", group='RSI Settings') rsimaColorInput = input.color(#ef534f, title="RSIMA Color", group='RSI Settings') rsiBandColorInput = input.color(#787B86, title="RSI Band Color", group='RSI Settings') rsiUpperBandExtInput = input.int(title='RSI Overbought Extended Line', defval=80, minval=50, maxval=100, group='RSI Settings') rsiUpperBandInput = input.int(title='RSI Overbought Line', defval=70, minval=50, maxval=100, group='RSI Settings') rsiLowerBandInput = input.int(title='RSI Oversold Line', defval=30, minval=0, maxval=50, group='RSI Settings') rsiLowerBandExtInput = input.int(title='RSI Oversold Extended Line', defval=20, minval=0, maxval=50, group='RSI Settings') //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- MA Input Settings ----------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- maTypeInput = input.string("EMA", title="MA Type", options= , group="MA Settings") maLengthInput = input.int(14, title="MA Length", group="MA Settings") maColorInput = input.color(color.yellow, title="MA Color", group='MA Settings') //#7E57C2 //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Divergence Input Settings --------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- lbrInput = input(title="Pivot Lookback Right", defval=2, group='RSI Divergence Settings') lblInput = input(title="Pivot Lookback Left", defval=2, group='RSI Divergence Settings') lbRangeMaxInput = input(title="Max of Lookback Range", defval=10, group='RSI Divergence Settings') lbRangeMinInput = input(title="Min of Lookback Range", defval=2, group='RSI Divergence Settings') plotBullInput = input(title="Plot Bullish", defval=true, group='RSI Divergence Settings') plotHiddenBullInput = input(title="Plot Hidden Bullish", defval=true, group='RSI Divergence Settings') plotBearInput = input(title="Plot Bearish", defval=true, group='RSI Divergence Settings') plotHiddenBearInput = input(title="Plot Hidden Bearish", defval=true, group='RSI Divergence Settings') //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- RSI Calculation ------------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- rsi = ta.rsi(rsiSourceInput, rsiLengthInput) rsiprevious = rsi = request.security(syminfo.tickerid, rsiTF1, [rsi, rsi , rsi ], lookahead=barmerge.lookahead_on) = request.security(syminfo.tickerid, rsiTF2, [rsi, rsi , rsi ], lookahead=barmerge.lookahead_on) = request.security(syminfo.tickerid, rsiTF3, [rsi, rsi , rsi ], lookahead=barmerge.lookahead_on) = request.security(syminfo.tickerid, rsiTF4, [rsi, rsi , rsi ], lookahead=barmerge.lookahead_on) //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- MA Calculation ------------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- ma(source, length, type) => switch type "SMA" => ta.sma(source, length) "Bollinger Bands" => ta.sma(source, length) "EMA" => ta.ema(source, length) "SMMA (RMA)" => ta.rma(source, length) "WMA" => ta.wma(source, length) "VWMA" => ta.vwma(source, length) rsiMA = ma(rsi, maLengthInput, maTypeInput) rsiMAPrevious = rsiMA //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Stoch RSI Settings + Calculation -------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- showStochRSI = input(false, title="Show Stochastic RSI", group='Stochastic RSI Settings') smoothK = input.int(title="Stochastic K", defval=3, minval=1, maxval=10, group='Stochastic RSI Settings') smoothD = input.int(title="Stochastic D", defval=4, minval=1, maxval=10, group='Stochastic RSI Settings') lengthRSI = input.int(title="Stochastic RSI Lenght", defval=14, minval=1, group='Stochastic RSI Settings') lengthStoch = input.int(title="Stochastic Lenght", defval=14, minval=1, group='Stochastic RSI Settings') colorK = input.color(color.rgb(41,98,255,0), title="K Color", group='Stochastic RSI Settings', inline="1") colorD = input.color(color.rgb(205,109,0,0), title="D Color", group='Stochastic RSI Settings', inline="1") StochRSI = ta.rsi(rsiSourceInput, lengthRSI) k = ta.sma(ta.stoch(StochRSI, StochRSI, StochRSI, lengthStoch), smoothK) //Blue Line d = ta.sma(k, smoothD) //Red Line //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Divergence Settings ------------------------------------------------------------------------------------------------------------------------------------------ //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- bearColor = color.red bullColor = color.green hiddenBullColor = color.new(color.green, 50) hiddenBearColor = color.new(color.red, 50) //textColor = color.white noneColor = color.new(color.white, 100) osc = rsi plFound = na(ta.pivotlow(osc, lblInput, lbrInput)) ? false : true phFound = na(ta.pivothigh(osc, lblInput, lbrInput)) ? false : true _inRange(cond) => bars = ta.barssince(cond == true) lbRangeMinInput <= bars and bars <= lbRangeMaxInput //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Define Plot & Line Colors --------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- rsiColor = rsi >= rsiMA ? rsiColorInput : rsimaColorInput //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Plot Lines ------------------------------------------------------------------------------------------------------------------------------------------------------ //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- // Create a horizontal line at a specific price level myLine = line.new(bar_index , 75, bar_index, 75, color = color.rgb(187, 14, 14), width = 2) bottom = line.new(bar_index , 50, bar_index, 50, color = color.rgb(223, 226, 28), width = 2) mymainLine = line.new(bar_index , 60, bar_index, 60, color = color.rgb(13, 154, 10), width = 3) hline(50, title='RSI Baseline', color=color.new(rsiBandColorInput, 50), linestyle=hline.style_solid, editable=false) hline(rsiBandExtShowInput ? rsiUpperBandExtInput : na, title='RSI Upper Band', color=color.new(rsiBandColorInput, 10), linestyle=hline.style_dashed, editable=false) hline(rsiBandShowInput ? rsiUpperBandInput : na, title='RSI Upper Band', color=color.new(rsiBandColorInput, 10), linestyle=hline.style_dashed, editable=false) hline(rsiBandShowInput ? rsiLowerBandInput : na, title='RSI Upper Band', color=color.new(rsiBandColorInput, 10), linestyle=hline.style_dashed, editable=false) hline(rsiBandExtShowInput ? rsiLowerBandExtInput : na, title='RSI Upper Band', color=color.new(rsiBandColorInput, 10), linestyle=hline.style_dashed, editable=false) bgcolor(rsiHighlightShowInput ? rsi >= rsiUpperBandExtInput ? color.new(rsiColorInput, 70) : na : na, title="Show Extended Oversold Highlight", editable=false) bgcolor(rsiHighlightShowInput ? rsi >= rsiUpperBandInput ? rsi < rsiUpperBandExtInput ? color.new(#64ffda, 90) : na : na: na, title="Show Overbought Highlight", editable=false) bgcolor(rsiHighlightShowInput ? rsi <= rsiLowerBandInput ? rsi > rsiLowerBandExtInput ? color.new(#F43E32, 90) : na : na : na, title="Show Extended Oversold Highlight", editable=false) bgcolor(rsiHighlightShowInput ? rsi <= rsiLowerBandInput ? color.new(rsimaColorInput, 70) : na : na, title="Show Oversold Highlight", editable=false) maPlot = plot(maShowInput ? rsiMA : na, title='MA', color=color.new(maColorInput,0), linewidth=1) rsiMAPlot = plot(showRSIMAInput ? rsiMA : na, title="RSI EMA", color=color.new(rsimaColorInput,0), editable=false, display=display.none) rsiPlot = plot(rsiShowInput ? rsi : na, title='RSI', color=color.new(rsiColor,0), linewidth=1) fill(rsiPlot, rsiMAPlot, color=color.new(rsiColor, 60), title="RSIMA Cloud") plot(showStochRSI ? k : na, title='Stochastic K', color=colorK, linewidth=1) plot(showStochRSI ? d : na, title='Stochastic D', color=colorD, linewidth=1) //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Plot Divergence ------------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- // Regular Bullish // Osc: Higher Low oscHL = osc > ta.valuewhen(plFound, osc , 1) and _inRange(plFound ) // Price: Lower Low priceLL = low < ta.valuewhen(plFound, low , 1) bullCond = plotBullInput and priceLL and oscHL and plFound plot( plFound ? osc : na, offset=-lbrInput, title="Regular Bullish", linewidth=2, color=(bullCond ? bullColor : noneColor) ) plotshape( DivergenceShowInput ? bullCond ? osc : na : na, offset=-lbrInput, title="Regular Bullish Label", text=" Bull ", style=shape.labelup, location=location.absolute, color=bullColor, textcolor=textColor ) //------------------------------------------------------------------------------ // Hidden Bullish // Osc: Lower Low oscLL = osc < ta.valuewhen(plFound, osc , 1) and _inRange(plFound ) // Price: Higher Low priceHL = low > ta.valuewhen(plFound, low , 1) hiddenBullCond = plotHiddenBullInput and priceHL and oscLL and plFound plot( plFound ? osc : na, offset=-lbrInput, title="Hidden Bullish", linewidth=2, color=(hiddenBullCond ? hiddenBullColor : noneColor) ) plotshape( DivergenceShowInput ? hiddenBullCond ? osc : na : na, offset=-lbrInput, title="Hidden Bullish Label", text=" H Bull ", style=shape.labelup, location=location.absolute, color=bullColor, textcolor=textColor ) //------------------------------------------------------------------------------ // Regular Bearish // Osc: Lower High oscLH = osc < ta.valuewhen(phFound, osc , 1) and _inRange(phFound ) // Price: Higher High priceHH = high > ta.valuewhen(phFound, high , 1) bearCond = plotBearInput and priceHH and oscLH and phFound plot( phFound ? osc : na, offset=-lbrInput, title="Regular Bearish", linewidth=2, color=(bearCond ? bearColor : noneColor) ) plotshape( DivergenceShowInput ? bearCond ? osc : na : na, offset=-lbrInput, title="Regular Bearish Label", text=" Bear ", style=shape.labeldown, location=location.absolute, color=bearColor, textcolor=textColor ) //------------------------------------------------------------------------------ // Hidden Bearish // Osc: Higher High oscHH = osc > ta.valuewhen(phFound, osc , 1) and _inRange(phFound ) // Price: Lower High priceLH = high < ta.valuewhen(phFound, high , 1) hiddenBearCond = plotHiddenBearInput and priceLH and oscHH and phFound plot( phFound ? osc : na, offset=-lbrInput, title="Hidden Bearish", linewidth=2, color=(hiddenBearCond ? hiddenBearColor : noneColor) ) plotshape( DivergenceShowInput ? hiddenBearCond ? osc : na : na, offset=-lbrInput, title="Hidden Bearish Label", text=" H Bear ", style=shape.labeldown, location=location.absolute, color=bearColor, textcolor=textColor ) //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Check RSI Lineup ------------------------------------------------------------------------------------------------------------------------------------------------ //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- bullTF = rsi > rsi and rsi > rsi bearTF = rsi < rsi and rsi < rsi bullTF1 = rsi1 > rsi1_1 and rsi1_1 > rsi1_2 bearTF1 = rsi1 < rsi1_1 and rsi1_1 < rsi1_2 bullTF2 = rsi2 > rsi2_1 and rsi2_1 > rsi2_2 bearTF2 = rsi2 < rsi2_1 and rsi2_1 < rsi2_2 bullTF3 = rsi3 > rsi3_1 and rsi3_1 > rsi3_2 bearTF3 = rsi3 < rsi3_1 and rsi3_1 < rsi3_2 bullTF4 = rsi4 > rsi4_1 and rsi4_1 > rsi4_2 bearTF4 = rsi4 < rsi4_1 and rsi4_1 < rsi4_2 bbTxt(bull,bear) => bull ? "BULLISH" : bear ? "BEARISCH" : 'NO LINEUP' bbColor(bull,bear) => bull ? bullishColor : bear ? bearishColor : nomatchColor newTC(tBox, col, row, txt, width, txtColor, bgColor, txtHA, txtSize) => table.cell(table_id=tBox,column=col, row=row, text=txt, width=width,text_color=txtColor,bgcolor=bgColor, text_halign=txtHA, text_size=txtSize) //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Define RSI Table Setting ---------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- width_c0 = 0 width_c1 = 0 if rsiShowTable var tBox = table.new(position=rsiTablePosition, columns=5, rows=6, bgcolor=color.rgb(18,22,33,50), frame_color=color.black, frame_width=1, border_color=color.black, border_width=1) newTC(tBox, 0,1,"RSI Current",width_c0,color.orange,color.rgb(0,0,0,100),'right',rsiTextSize) newTC(tBox, 1,1,str.format(" {0,number,#.##} ", rsi),width_c0,vWhite,rsi < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 4,1,bbTxt(bullTF, bearTF),width_c0,vWhite,bbColor(bullTF, bearTF),'center',rsiTextSize) if rsiShowHist newTC(tBox, 2,1,str.format(" {0,number,#.##} ", rsi ),width_c0,vWhite,rsi < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 3,1,str.format(" {0,number,#.##} ", rsi ),width_c0,vWhite,rsi < 50 ? bearishColor:bullishColor,'left',rsiTextSize) if rsiShowTF1 newTC(tBox, 0,2,TF2txt(rsiTF1),width_c0,vWhite,color.rgb(0,0,0,100),'right',rsiTextSize) newTC(tBox, 1,2,str.format(" {0,number,#.##} ", rsi1),width_c0,vWhite,rsi1 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 4,2,bbTxt(bullTF1, bearTF1),width_c0,vWhite,bbColor(bullTF1,bearTF1),'center',rsiTextSize) if rsiShowHist newTC(tBox, 2,2,str.format(" {0,number,#.##} ", rsi1_1),width_c0,vWhite,rsi1_1 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 3,2,str.format(" {0,number,#.##} ", rsi1_2),width_c0,vWhite,rsi1_2 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) if rsiShowTF2 newTC(tBox, 0,3,TF2txt(rsiTF2),width_c0,vWhite,color.rgb(0,0,0,100),'right',rsiTextSize) newTC(tBox, 1,3,str.format(" {0,number,#.##} ", rsi2),width_c0,vWhite,rsi2 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 4,3,bbTxt(bullTF2, bearTF2),width_c0,vWhite,bbColor(bullTF2,bearTF2),'center',rsiTextSize) if rsiShowHist newTC(tBox, 2,3,str.format(" {0,number,#.##} ", rsi2_1),width_c0,vWhite,rsi2_1 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 3,3,str.format(" {0,number,#.##} ", rsi2_2),width_c0,vWhite,rsi2_2 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) if rsiShowTF3 newTC(tBox, 0,4,TF2txt(rsiTF3),width_c0,vWhite,color.rgb(0,0,0,100),'right',rsiTextSize) newTC(tBox, 1,4,str.format(" {0,number,#.##} ", rsi3),width_c0,vWhite,rsi3 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 4,4,bbTxt(bullTF3, bearTF3),width_c0,vWhite,bbColor(bullTF3,bearTF3),'center',rsiTextSize) if rsiShowHist newTC(tBox, 2,4,str.format(" {0,number,#.##} ", rsi3_1),width_c0,vWhite,rsi3_1 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 3,4,str.format(" {0,number,#.##} ", rsi3_2),width_c0,vWhite,rsi3_2 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) if rsiShowTF4 newTC(tBox, 0,5,TF2txt(rsiTF4),width_c0,vWhite,color.rgb(0,0,0,100),'right',rsiTextSize) newTC(tBox, 1,5,str.format(" {0,number,#.##} ", rsi4),width_c0,vWhite,rsi4 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 4,5,bbTxt(bullTF4, bearTF4),width_c0,vWhite,bbColor(bullTF4,bearTF4),'center',rsiTextSize) if rsiShowHist newTC(tBox, 2,5,str.format(" {0,number,#.##} ", rsi4_1),width_c0,vWhite,rsi4_1 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 3,5,str.format(" {0,number,#.##} ", rsi4_2),width_c0,vWhite,rsi4_2 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) //------------------------------------------------------ //--- Alerts ------------------------------------------- //------------------------------------------------------
Pine Script® indicator
by RandyDahlin
COT IndexTHE HIDDEN INTELLIGENCE IN FUTURES MARKETS What if you could see what the smartest players in the futures markets are doing before the crowd catches on? While retail traders chase momentum indicators and moving averages, obsess over Japanese candlestick patterns, and debate whether the RSI should be set to fourteen or twenty-one periods, institutional players leave footprints in the sand through their mandatory reporting to the Commodity Futures Trading Commission. These footprints, published weekly in the Commitment of Traders reports, have been hiding in plain sight for decades, available to anyone with an internet connection, yet remarkably few traders understand how to interpret them correctly. The COT Index indicator transforms this raw institutional positioning data into actionable trading signals, bringing Wall Street intelligence to your trading screen without requiring expensive Bloomberg terminals or insider connections. The uncomfortable truth is this: Most retail traders operate in a binary world. Long or short. Buy or sell. They apply technical analysis to individual positions, constrained by limited capital that forces them to concentrate risk in single directional bets. Meanwhile, institutional traders operate in an entirely different dimension. They manage portfolios dynamically weighted across multiple markets, adjusting exposure based on evolving market conditions, correlation shifts, and risk assessments that retail traders never see. A hedge fund might be simultaneously long gold, short oil, neutral on copper, and overweight agricultural commodities, with position sizes calibrated to volatility and portfolio Greeks. When they increase gold exposure from five percent to eight percent of portfolio allocation, this rebalancing decision reflects sophisticated analysis of opportunity cost, risk parity, and cross-market dynamics that no individual chart pattern can capture. This portfolio reweighting activity, multiplied across hundreds of institutional participants, manifests in the aggregate positioning data published weekly by the CFTC. The Commitment of Traders report does not show individual trades or strategies. It shows the collective footprint of how actual commercial hedgers and large speculators have allocated their capital across different markets. When mining companies collectively increase forward gold sales to hedge thirty percent more production than last quarter, they are not reacting to a moving average crossover. They are making strategic allocation decisions based on production forecasts, cost structures, and price expectations derived from operational realities invisible to outside observers. This is portfolio management in action, revealed through positioning data rather than price charts. If you want to understand how institutional capital actually flows, how sophisticated traders genuinely position themselves across market cycles, the COT report provides a rare window into that hidden world. But understand what you are getting into. This is not a tool for scalpers seeking confirmation of the next five-minute move. This is not an oscillator that flashes oversold at market bottoms with convenient precision. COT analysis operates on a timescale measured in weeks and months, revealing positioning shifts that precede major market turns but offer no precision timing. The data arrives three days stale, published only once per week, capturing strategic positioning rather than tactical entries. If you need instant gratification, if you trade intraday moves, if you demand mechanical signals with ninety percent accuracy, close this document now. COT analysis rewards patience, position sizing discipline, and tolerance for being early. It punishes impatience, overleveraging, and the expectation that any single indicator can substitute for market understanding. The premise is deceptively simple. Every Tuesday, large traders in futures markets must report their positions to the CFTC. By Friday afternoon, this data becomes public. Academic research spanning three decades has consistently shown that not all market participants are created equal. Some traders consistently profit while others consistently lose. Some anticipate major turning points while others chase trends into exhaustion. Bessembinder and Chan (1992) demonstrated in their seminal study that commercial hedgers, those with actual exposure to the underlying commodity or financial instrument, possess superior forecasting ability compared to speculators. Their research, published in the Journal of Finance, found statistically significant predictive power in commercial positioning, particularly at extreme levels. This finding challenged the efficient market hypothesis and opened the door to a new approach to market analysis based on positioning rather than price alone. Think about what this means. Every week, the government publishes a report showing you exactly how the most informed market participants are positioned. Not their opinions. Not their predictions. Their actual money at risk. When agricultural producers collectively hold their largest short hedge in five years, they are not making idle speculation. They are locking in prices for crops they will harvest, informed by private knowledge of weather conditions, soil quality, inventory levels, and demand expectations invisible to outside observers. When energy companies aggressively hedge forward production at current prices, they reveal information about expected supply that no analyst report can capture. This is not technical analysis based on past prices. This is not fundamental analysis based on publicly available data. This is behavioral analysis based on how the smartest money is actually positioned, how institutions allocate capital across portfolios, and how those allocation decisions shift as market conditions evolve. WHY SOME TRADERS KNOW MORE THAN OTHERS Building on this foundation, Sanders, Boris and Manfredo (2004) conducted extensive research examining the behaviour patterns of different trader categories. Their work, which analyzed over a decade of COT data across multiple commodity markets, revealed a fascinating dynamic that challenges much of what retail traders are taught. Commercial hedgers consistently positioned themselves against market extremes, buying when speculators were most bearish and selling when speculators reached peak bullishness. The contrarian positioning of commercials was not random noise but rather reflected their superior information about supply and demand fundamentals. Meanwhile, large speculators, primarily hedge funds and commodity trading advisors, exhibited strong trend-following behaviour that often amplified market moves beyond fundamental values. Small traders, the retail participants, consistently entered positions late in trends, frequently near turning points, making them reliable contrary indicators. Wang (2003) extended this research by demonstrating that the predictive power of commercial positioning varies significantly across different commodity sectors. His analysis of agricultural commodities showed particularly strong forecasting ability, with commercial net positions explaining up to fifteen percent of return variance in subsequent weeks. This finding suggests that the informational advantages of hedgers are most pronounced in markets where physical supply and demand fundamentals dominate, as opposed to purely financial markets where information asymmetries are smaller. When a corn farmer hedges six months of expected harvest, that decision incorporates private observations about rainfall patterns, crop health, pest pressure, and local storage capacity that no distant analyst can match. When an oil refinery hedges crude oil purchases and gasoline sales simultaneously, the spread relationships reveal expectations about refining margins that reflect operational realities invisible in public data. The theoretical mechanism underlying these empirical patterns relates to information asymmetry and different participant motivations. Commercial hedgers engage in futures markets not for speculative profit but to manage business risks. An agricultural producer selling forward six months of expected harvest is not making a bet on price direction but rather locking in revenue to facilitate financial planning and ensure business viability. However, this hedging activity necessarily incorporates private information about expected supply, inventory levels, weather conditions, and demand trends that the hedger observes through their commercial operations (Irwin and Sanders, 2012). When aggregated across many participants, this private information manifests in collective positioning. Consider a gold mining company deciding how much forward production to hedge. Management must estimate ore grades, recovery rates, production costs, equipment reliability, labor availability, and dozens of other operational variables that determine whether locking in prices at current levels makes business sense. If the industry collectively hedges more aggressively than usual, it suggests either exceptional production expectations or concern about sustaining current price levels or combination of both. Either way, this positioning reveals information unavailable to speculators analyzing price charts and economic data. The hedger sees the physical reality behind the financial abstraction. Large speculators operate under entirely different incentives and constraints. Commodity Trading Advisors managing billions in assets typically employ systematic, trend-following strategies that respond to price momentum rather than fundamental supply and demand. When crude oil rallies from sixty dollars to seventy dollars per barrel, these systems generate buy signals. As the rally continues to eighty dollars, position sizes increase. The strategy works brilliantly during sustained trends but becomes a liability at reversals. By the time oil reaches ninety dollars, trend-following funds are maximally long, having accumulated positions progressively throughout the rally. At this point, they represent not smart money anticipating further gains but rather crowded money vulnerable to reversal. Sanders, Boris and Manfredo (2004) documented this pattern across multiple energy markets, showing that extreme speculator positioning typically marked late-stage trend exhaustion rather than early-stage trend development. Small traders, the retail participants who fall below reporting thresholds, display the weakest forecasting ability. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns, meaning their aggregate positioning served as a reliable contrary indicator. The explanation combines several factors. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, entering trends after mainstream media coverage when institutional participants are preparing to exit. Perhaps most importantly, they trade with emotion, buying into euphoria and selling into panic at precisely the wrong times. At major turning points, the three groups often position opposite each other with commercials extremely bearish, large speculators extremely bullish, and small traders piling into longs at the last moment. These high-divergence environments frequently precede increased volatility and trend reversals. The insiders with business exposure quietly exit as the momentum traders hit maximum capacity and retail enthusiasm peaks. Within weeks, the reversal begins, and positions unwind in the opposite sequence. FROM RAW DATA TO ACTIONABLE SIGNALS The COT Index indicator operationalizes these academic findings into a practical trading tool accessible through TradingView. At its core, the indicator normalizes net positioning data onto a zero to one hundred scale, creating what we call the COT Index. This normalization is critical because absolute position sizes vary dramatically across different futures contracts and over time. A commercial trader holding fifty thousand contracts net long in crude oil might be extremely bullish by historical standards, or it might be quite neutral depending on the context of total market size and historical ranges. Raw position numbers mean nothing without context. The COT Index solves this problem by calculating where current positioning stands relative to its range over a specified lookback period, typically two hundred fifty-two weeks or approximately five years of weekly data. The mathematical transformation follows the methodology originally popularized by legendary trader Larry Williams, though the underlying concept appears in statistical normalization techniques across many fields. For any given trader category, we calculate the highest and lowest net position values over the lookback period, establishing the historical range for that specific market and trader group. Current positioning is then expressed as a percentage of this range, where zero represents the most bearish positioning ever seen in the lookback window and one hundred represents the most bullish extreme. A reading of fifty indicates positioning exactly in the middle of the historical range, suggesting neither extreme optimism nor pessimism relative to recent history (Williams and Noseworthy, 2009). This index-based approach allows for meaningful comparison across different markets and time periods, overcoming the scaling problems inherent in analyzing raw position data. A commercial index reading of eighty-five in gold carries the same interpretive meaning as an eighty-five reading in wheat or crude oil, even though the absolute position sizes differ by orders of magnitude. This standardization enables systematic analysis across entire futures portfolios rather than requiring market-specific expertise for each contract. The lookback period selection involves a fundamental tradeoff between responsiveness and stability. Shorter lookback periods, perhaps one hundred twenty-six weeks or approximately two and a half years, make the index more sensitive to recent positioning changes. However, it also increases noise and produces more false signals. Longer lookback periods, perhaps five hundred weeks or approximately ten years, create smoother readings that filter short-term noise but become slower to recognize regime changes. The indicator settings allow users to adjust this parameter based on their trading timeframe, risk tolerance, and market characteristics. UNDERSTANDING CFTC DATA STRUCTURES The indicator supports both Legacy and Disaggregated COT report formats, reflecting the evolution of CFTC reporting standards over decades of market development. Legacy reports categorize market participants into three broad groups: commercial traders (hedgers with underlying business exposure), non-commercial traders (large speculators seeking profit without commercial interest), and non-reportable traders (small speculators below reporting thresholds). Each category brings distinct motivations and information advantages to the market (CFTC, 2020). The Disaggregated reports, introduced in September 2009 for physical commodity markets, provide finer granularity by splitting participants into five categories (CFTC, 2009). Producer and merchant positions capture those actually producing, processing, or merchandising the physical commodity. Swap dealers represent financial intermediaries facilitating derivative transactions for clients. Managed money includes commodity trading advisors and hedge funds executing systematic or discretionary strategies. Other reportables encompasses diverse participants not fitting the main categories. Small traders remain as the fifth group, representing retail participation. This enhanced categorization reveals nuances invisible in Legacy reports, particularly distinguishing between different types of institutional capital and their distinct behavioural patterns. The indicator automatically detects which report type is appropriate for each futures contract and adjusts the display accordingly. Importantly, Disaggregated reports exist only for physical commodity futures. Agricultural commodities like corn, wheat, and soybeans have Disaggregated reports because clear producer, merchant, and swap dealer categories exist. Energy commodities like crude oil and natural gas similarly have well-defined commercial hedger categories. Metals including gold, silver, and copper also receive Disaggregated treatment (CFTC, 2009). However, financial futures such as equity index futures, Treasury bond futures, and currency futures remain available only in Legacy format. The CFTC has indicated no plans to extend Disaggregated reporting to financial futures due to different market structures and participant categories in these instruments (CFTC, 2020). THE BEHAVIORAL FOUNDATION Understanding which trader perspective to follow requires appreciation of their distinct trading styles, success rates, and psychological profiles. Commercial hedgers exhibit anticyclical behaviour rooted in their fundamental knowledge and business imperatives. When agricultural producers hedge forward sales during harvest season, they are not speculating on price direction but rather locking in revenue for crops they will harvest. Their business requires converting volatile commodity exposure into predictable cash flows to facilitate planning and ensure survival through difficult periods. Yet their aggregate positioning reveals valuable information because these hedging decisions incorporate private information about supply conditions, inventory levels, weather observations, and demand expectations that hedgers observe through their commercial operations (Bessembinder and Chan, 1992). Consider a practical example from energy markets. Major oil companies continuously hedge portions of forward production based on price levels, operational costs, and financial planning needs. When crude oil trades at ninety dollars per barrel, they might aggressively hedge the next twelve months of production, locking in prices that provide comfortable profit margins above their extraction costs. This hedging appears as short positioning in COT reports. If oil rallies further to one hundred dollars, they hedge even more aggressively, viewing these prices as exceptional opportunities to secure revenue. Their short positioning grows increasingly extreme. To an outside observer watching only price charts, the rally suggests bullishness. But the commercial positioning reveals that the actual producers of oil find these prices attractive enough to lock in years of sales, suggesting skepticism about sustaining even higher levels. When the eventual reversal occurs and oil declines back to eighty dollars, the commercials who hedged at ninety and one hundred dollars profit while speculators who chased the rally suffer losses. Large speculators or managed money traders operate under entirely different incentives and constraints. Their systematic, momentum-driven strategies mean they amplify existing trends rather than anticipate reversals. Trend-following systems, the most common approach among large speculators, by definition require confirmation of trend through price momentum before entering positions (Sanders, Boris and Manfredo, 2004). When crude oil rallies from sixty dollars to eighty dollars per barrel over several months, trend-following algorithms generate buy signals based on moving average crossovers, breakouts, and other momentum indicators. As the rally continues, position sizes increase according to the systematic rules. However, this approach becomes a liability at turning points. By the time oil reaches ninety dollars after a sustained rally, trend-following funds are maximally long, having accumulated positions progressively throughout the move. At this point, their positioning does not predict continued strength. Rather, it often marks late-stage trend exhaustion. The psychological and mechanical explanation is straightforward. Trend followers by definition chase price momentum, entering positions after trends establish rather than anticipating them. Eventually, they become fully invested just as the trend nears completion, leaving no incremental buying power to sustain the rally. When the first signs of reversal appear, systematic stops trigger, creating a cascade of selling that accelerates the downturn. Small traders consistently display the weakest track record across academic studies. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns in his analysis across multiple commodity markets. This result means that whatever small traders collectively do, the opposite typically proves profitable. The explanation for small trader underperformance combines several factors documented in behavioral finance literature. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, learning about commodity trends through mainstream media coverage that arrives after institutional participants have already positioned. Perhaps most importantly, retail traders are more susceptible to emotional decision-making, buying into euphoria and selling into panic at precisely the wrong times (Tharp, 2008). SETTINGS, THRESHOLDS, AND SIGNAL GENERATION The practical implementation of the COT Index requires understanding several key features and settings that users can adjust to match their trading style, timeframe, and risk tolerance. The lookback period determines the time window for calculating historical ranges. The default setting of two hundred fifty-two bars represents approximately one year on daily charts or five years on weekly charts, balancing responsiveness with stability. Conservative traders seeking only the most extreme, highest-probability signals might extend the lookback to five hundred bars or more. Aggressive traders seeking earlier entry and willing to accept more false positives might reduce it to one hundred twenty-six bars or even less for shorter-term applications. The bullish and bearish thresholds define signal generation levels. Default settings of eighty and twenty respectively reflect academic research suggesting meaningful information content at these extremes. Readings above eighty indicate positioning in the top quintile of the historical range, representing genuine extremes rather than temporary fluctuations. Conversely, readings below twenty occupy the bottom quintile, indicating unusually bearish positioning (Briese, 2008). However, traders must recognize that appropriate thresholds vary by market, trader category, and personal risk tolerance. Some futures markets exhibit wider positioning swings than others due to seasonal patterns, volatility characteristics, or participant behavior. Conservative traders seeking high-probability setups with fewer signals might raise thresholds to eighty-five and fifteen. Aggressive traders willing to accept more false positives for earlier entry could lower them to seventy-five and twenty-five. The key is maintaining meaningful differentiation between bullish, neutral, and bearish zones. The default settings of eighty and twenty create a clear three-zone structure. Readings from zero to twenty represent bearish territory where the selected trader group holds unusually bearish positions. Readings from twenty to eighty represent neutral territory where positioning falls within normal historical ranges. Readings from eighty to one hundred represent bullish territory where the selected trader group holds unusually bullish positions. The trading perspective selection determines which participant group the indicator follows, fundamentally shaping interpretation and signal meaning. For counter-trend traders seeking reversal opportunities, monitoring commercial positioning makes intuitive sense based on the academic research discussed earlier. When commercials reach extreme bearish readings below twenty, indicating unprecedented short positioning relative to recent history, they are effectively betting against the crowd. Given their informational advantages demonstrated by Bessembinder and Chan (1992), this contrarian stance often precedes major bottoms. Trend followers might instead monitor large speculator positioning, but with inverted logic compared to commercials. When managed money reaches extreme bullish readings above eighty, the trend may be exhausting rather than accelerating. This seeming paradox reflects their late-cycle participation documented by Sanders, Boris and Manfredo (2004). Sophisticated traders thus use speculator extremes as fade signals, entering positions opposite to speculator consensus. Small trader monitoring serves primarily as a contrary indicator for all trading styles. Extreme small trader bullishness above seventy-five or eighty typically warns of retail FOMO at market tops. Extreme small trader bearishness below twenty or twenty-five often marks capitulation bottoms where the last weak hands have sold. VISUALIZATION AND USER INTERFACE The visual design incorporates multiple elements working together to facilitate decision-making and maintain situational awareness during active trading. The primary COT Index line plots in bold with adjustable line width, defaulting to two pixels for clear visibility against busy price charts. An optional glow effect, controlled by a simple toggle, adds additional visual prominence through multiple plot layers with progressively increasing transparency and width. A twenty-one period exponential moving average overlays the index line, providing trend context for positioning changes. When the index crosses above its moving average, it signals accelerating bullish sentiment among the selected trader group regardless of whether absolute positioning is extreme. Conversely, when the index crosses below its moving average, it signals deteriorating sentiment and potentially the beginning of a reversal in positioning trends. The EMA provides a dynamic reference line for assessing positioning momentum. When the index trades far above its EMA, positioning is not only extreme in absolute terms but also building with momentum. When the index trades far below its EMA, positioning is contracting or reversing, which may indicate weakening conviction even if absolute levels remain elevated. The data table positioned at the top right of the chart displays eleven metrics for each trader category, transforming the indicator from a simple index calculation into an analytical dashboard providing multidimensional market intelligence. Beyond the COT Index itself, users can monitor positioning extremity, which measures how unusual current levels are compared to historical norms using statistical techniques. The extremity metric clarifies whether a reading represents the ninety-fifth or ninety-ninth percentile, with values above two standard deviations indicating genuinely exceptional positioning. Market power quantifies each group's influence on total open interest. This metric expresses each trader category's net position as a percentage of total market open interest. A commercial entity holding forty percent of total open interest commands significantly more influence than one holding five percent, making their positioning signals more meaningful. Momentum and rate of change metrics reveal whether positions are building or contracting, providing early warning of potential regime shifts. Position velocity measures the rate of change in positioning changes, effectively a second derivative providing even earlier insight into inflection points. Sentiment divergence highlights disagreements between commercial and speculative positioning. This metric calculates the absolute difference between normalized commercial and large speculator index values. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The table also displays concentration metrics when available, showing how positioning is distributed among the largest handful of traders in each category. High concentration indicates a few dominant players controlling most of the positioning, while low concentration suggests broad-based participation across many traders. THE ALERT SYSTEM AND MONITORING The alert system, comprising five distinct alert conditions, enables systematic monitoring of dozens of futures markets without constant screen watching. The bullish and bearish COT signal alerts trigger when the index crosses user-defined thresholds, indicating the selected trader group has reached extreme positioning worthy of attention. These alerts fire in real-time as new weekly COT data publishes, typically Friday afternoon following the Tuesday measurement date. Extreme positioning alerts fire at ninety and ten index levels, representing the top and bottom ten percent of the historical range, warning of particularly stretched readings that historically precede reversals with high probability. When commercials reach a COT Index reading below ten, they are expressing their most bearish stance in the entire lookback period. The data staleness alert notifies users when COT reports have not updated for more than ten days, preventing reliance on outdated information for trading decisions. Government shutdowns or federal holidays can interrupt the normal Friday publication schedule. Using stale signals while believing them current creates dangerous false confidence. The indicator's watermark information display positioned in the bottom right corner provides essential context at a glance. This persistent display shows the symbol and timeframe, the COT report date timestamp, days since last update, and the current signal state. A trader analyzing a potential short entry in crude oil can glance at the watermark to instantly confirm positioning context without interrupting analysis flow. LIMITATIONS AND REALISTIC EXPECTATIONS Practical application requires understanding both the indicator's considerable strengths and inherent limitations. COT data inherently lags price action by three days, as Tuesday positions are not published until Friday afternoon. This delay means the indicator cannot catch rapid intraday reversals or respond to surprise news events. Traders using the COT Index for timing entries must accept this latency and focus on swing trading and position trading timeframes where three-day lags matter less than in day trading or scalping. The weekly publication schedule similarly makes the indicator unsuitable for short-term trading strategies requiring immediate feedback. The COT Index works best for traders operating on weekly or longer timeframes, where positioning shifts measured in weeks and months align with trading horizon. Extreme COT readings can persist far longer than typical technical indicators suggest, testing the patience and capital reserves of traders attempting to fade them. When crude oil enters a sustained bull market driven by genuine supply disruptions, commercial hedgers may maintain bearish positioning for many months as prices grind higher. A commercial COT Index reading of fifteen indicating extreme bearishness might persist for three months while prices continue rallying before finally reversing. Traders without sufficient capital and risk tolerance to weather such drawdowns will exit prematurely, precisely when the signal is about to work (Irwin and Sanders, 2012). Position sizing discipline becomes paramount when implementing COT-based strategies. Rather than risking large percentages of capital on individual signals, successful COT traders typically allocate modest position sizes across multiple signals, allowing some to take time to mature while others work more quickly. The indicator also cannot overcome fundamental regime changes that alter the structural drivers of markets. If gold enters a true secular bull market driven by monetary debasement, commercial hedgers may remain persistently bearish as mining companies sell forward years of production at what they perceive as favorable prices. Their positioning indicates valuation concerns from a production cost perspective, but cannot stop prices from rising if investment demand overwhelms physical supply-demand balance. Similarly, structural changes in market participation can alter the meaning of positioning extremes. The growth of commodity index investing in the two thousands brought massive passive long-only capital into futures markets, fundamentally changing typical positioning ranges. Traders relying on COT signals without recognizing this regime change would have generated numerous false bearish signals during the commodity supercycle from 2003 to 2008. The research foundation supporting COT analysis derives primarily from commodity markets where the commercial hedger information advantage is most pronounced. Studies specifically examining financial futures like equity indices and bonds show weaker but still present effects. Traders should calibrate expectations accordingly, recognizing that COT analysis likely works better for crude oil, natural gas, corn, and wheat than for the S&P 500, Treasury bonds, or currency futures. Another important limitation involves the reporting threshold structure. Not all market participants appear in COT data, only those holding positions above specified minimums. In markets dominated by a few large players, concentration metrics become critical for proper interpretation. A single large trader accounting for thirty percent of commercial positioning might skew the entire category if their individual circumstances are idiosyncratic rather than representative. GOLD FUTURES DURING A HYPOTHETICAL MARKET CYCLE Consider a practical example using gold futures during a hypothetical but realistic market scenario that illustrates how the COT Index indicator guides trading decisions through a complete market cycle. Suppose gold has rallied from fifteen hundred to nineteen hundred dollars per ounce over six months, driven by inflation concerns following aggressive monetary expansion, geopolitical uncertainty, and sustained buying by Asian central banks for reserve diversification. Large speculators, operating primarily trend-following strategies, have accumulated increasingly bullish positions throughout this rally. Their COT Index has climbed progressively from forty-five to eighty-five. The table display shows that large speculators now hold net long positions representing thirty-two percent of total open interest, their highest in four years. Momentum indicators show positive readings, indicating positions are still building though at a decelerating rate. Position velocity has turned negative, suggesting the pace of position building is slowing. Meanwhile, commercial hedgers have responded to the rally by aggressively selling forward production and inventory. Their COT Index has moved inversely to price, declining from fifty-five to twenty. This bearish commercial positioning represents mining companies locking in forward sales at prices they view as attractive relative to production costs. The table shows commercials now hold net short positions representing twenty-nine percent of total open interest, their most bearish stance in five years. Concentration metrics indicate this positioning is broadly distributed across many commercial entities, suggesting the bearish stance reflects collective industry view rather than idiosyncratic positioning by a single firm. Small traders, attracted by mainstream financial media coverage of gold's impressive rally, have recently piled into long positions. Their COT Index has jumped from forty-five to seventy-eight as retail investors chase the trend. Television financial networks feature frequent segments on gold with bullish guests. Internet forums and social media show surging retail interest. This retail enthusiasm historically marks late-stage trend development rather than early opportunity. The COT Index indicator, configured to monitor commercial positioning from a contrarian perspective, displays a clear bearish signal given the extreme commercial short positioning. The table displays multiple confirming metrics: positioning extremity shows commercials at the ninety-sixth percentile of bearishness, market power indicates they control twenty-nine percent of open interest, and sentiment divergence registers sixty-five, indicating massive disagreement between commercial hedgers and large speculators. This divergence, the highest in three years, places the market in the historically high-risk category for reversals. The interpretation requires nuance and consideration of context beyond just COT data. Commercials are not necessarily predicting an imminent crash. Rather, they are hedging business operations at what they collectively view as favorable price levels. However, the data reveals they have sold unusually large quantities of forward production, suggesting either exceptional production expectations for the year ahead or concern about sustaining current price levels or combination of both. Combined with extreme speculator positioning indicating a crowded long trade, and small trader enthusiasm confirming retail FOMO, the confluence suggests elevated reversal risk even if the precise timing remains uncertain. A prudent trader analyzing this situation might take several actions based on COT Index signals. Existing long positions could be tightened with closer stop losses. Profit-taking on a portion of long exposure could lock in gains while maintaining some participation. Some traders might initiate modest short positions as portfolio hedges, sizing them appropriately for the inherent uncertainty in timing reversals. Others might simply move to the sidelines, avoiding new long entries until positioning normalizes. The key lesson from case study analysis is that COT signals provide probabilistic edges rather than deterministic predictions. They work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five percent win rate with proper risk management produces substantial profits over time, yet still means forty-five percent of signals will be premature or wrong. Traders must embrace this probabilistic reality rather than seeking the impossible goal of perfect accuracy. INTEGRATION WITH TRADING SYSTEMS Integration with existing trading systems represents a natural and powerful use case for COT analysis, adding a positioning dimension to price-based technical approaches or fundamental analytical frameworks. Few traders rely exclusively on a single indicator or methodology. Rather, they build systems that synthesize multiple information sources, with each component addressing different aspects of market behavior. Trend followers might use COT extremes as regime filters, modifying position sizing or avoiding new trend entries when positioning reaches levels historically associated with reversals. Consider a classic trend-following system based on moving average crossovers and momentum breakouts. Integration of COT analysis adds nuance. When large speculator positioning exceeds ninety or commercial positioning falls below ten, the regime filter recognizes elevated reversal risk. The system might reduce position sizing by fifty percent for new signals during these high-risk periods (Kaufman, 2013). Mean reversion traders might require COT signal confluence before fading extended moves. When crude oil becomes technically overbought and large speculators show extreme long positioning above eighty-five, both signals confirm. If only technical indicators show extremes while positioning remains neutral, the potential short signal is rejected, avoiding fades of trends with underlying institutional support (Kaufman, 2013). Discretionary traders can monitor the indicator as a continuous awareness tool, informing bias and position sizing without dictating mechanical entries and exits. A discretionary trader might notice commercial positioning shifting from neutral to progressively more bullish over several months. This trend informs growing positive bias even without triggering mechanical signals. Multi-timeframe analysis represents another powerful integration approach. A trader might use daily charts for trade execution and timing while monitoring weekly COT positioning for strategic context. When both timeframes align, highest-probability opportunities emerge. Portfolio construction for futures traders can incorporate COT signals as an additional selection criterion. Markets showing strong technical setups AND favorable COT positioning receive highest allocations. Markets with strong technicals but neutral or unfavorable positioning receive reduced allocations. ADVANCED METRICS AND INTERPRETATION The metrics table transforms simple positioning data into multidimensional market intelligence. Position extremity, calculated as the absolute deviation from the historical mean normalized by standard deviation, helps identify truly unusual readings versus routine fluctuations. A reading above two standard deviations indicates ninety-fifth percentile or higher extremity. Above three standard deviations indicates ninety-ninth percentile or higher, genuinely rare positioning that historically precedes major events with high probability. Market power, expressed as a percentage of total open interest, reveals whose positioning matters most from a mechanical market impact perspective. Consider two scenarios in gold futures. In scenario one, commercials show a COT Index reading of fifteen while their market power metric shows they hold net shorts representing thirty-five percent of open interest. This is a high-confidence bearish signal. In scenario two, commercials also show a reading of fifteen, but market power shows only eight percent. While positioning is extreme relative to this category's normal range, their limited market share means less mechanical influence on price. The rate of change and momentum metrics highlight whether positions are accelerating or decelerating, often providing earlier warnings than absolute levels alone. A COT Index reading of seventy-five with rapidly building momentum suggests continued movement toward extremes. Conversely, a reading of eighty-five with decelerating or negative momentum indicates the positioning trend is exhausting. Position velocity measures the rate of change in positioning changes, effectively a second derivative. When velocity shifts from positive to negative, it indicates that while positioning may still be growing, the pace of growth is slowing. This deceleration often precedes actual reversal in positioning direction by several weeks. Sentiment divergence calculates the absolute difference between normalized commercial and large speculator index values. When commercials show extreme bearish positioning at twenty while large speculators show extreme bullish positioning at eighty, the divergence reaches sixty, representing near-maximum disagreement. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The mechanism is intuitive. Extreme divergence indicates the informed hedgers and momentum-following speculators have positioned opposite each other with conviction. One group will prove correct and profit while the other proves incorrect and suffers losses. The resolution of this disagreement through price movement often involves volatility. The table also displays concentration metrics when available. High concentration indicates a few dominant players controlling most of the positioning within a category, while low concentration suggests broad-based participation. Broad-based positioning more reliably reflects collective market intelligence and industry consensus. If mining companies globally all independently decide to hedge aggressively at similar price levels, it suggests genuine industry-wide view about price valuations rather than circumstances specific to one firm. DATA QUALITY AND RELIABILITY The CFTC has maintained COT reporting in various forms since the nineteen twenties, providing nearly a century of positioning data across multiple market cycles. However, data quality and reporting standards have evolved substantially over this long period. Modern electronic reporting implemented in the late nineteen nineties and early two thousands significantly improved accuracy and timeliness compared to earlier paper-based systems. Traders should understand that COT reports capture positions as of Tuesday's close each week. Markets remain open three additional days before publication on Friday afternoon, meaning the reported data is three days stale when received. During periods of rapid market movement or major news events, this lag can be significant. The indicator addresses this limitation by including timestamp information and staleness warnings. The three-day lag creates particular challenges during extreme volatility episodes. Flash crashes, surprise central bank interventions, geopolitical shocks, and other high-impact events can completely transform market positioning within hours. Traders must exercise judgment about whether reported positioning remains relevant given intervening events. Reporting thresholds also mean that not all market participants appear in disaggregated COT data. Traders holding positions below specified minimums aggregate into the non-reportable or small trader category. This aggregation affects different markets differently. In highly liquid contracts like crude oil with thousands of participants, reportable traders might represent seventy to eighty percent of open interest. In thinly traded contracts with only dozens of active participants, a few large reportable positions might represent ninety-five percent of open interest. Another data quality consideration involves trader classification into categories. The CFTC assigns traders to commercial or non-commercial categories based on reported business purpose and activities. However, this process is not perfect. Some entities engage in both commercial and speculative activities, creating ambiguity about proper classification. The transition to Disaggregated reports attempted to address some of these ambiguities by creating more granular categories. COMPARISON WITH ALTERNATIVE APPROACHES Several alternative approaches to COT analysis exist in the trading community beyond the normalization methodology employed by this indicator. Some analysts focus on absolute position changes week-over-week rather than index-based normalization. This approach calculates the change in net positioning from one week to the next. The emphasis falls on momentum in positioning changes rather than absolute levels relative to history. This method potentially identifies regime shifts earlier but sacrifices cross-market comparability (Briese, 2008). Other practitioners employ more complex statistical transformations including percentile rankings, z-score standardization, and machine learning classification algorithms. Ruan and Zhang (2018) demonstrated that machine learning models applied to COT data could achieve modest improvements in forecasting accuracy compared to simple threshold-based approaches. However, these gains came at the cost of interpretability and implementation complexity. The COT Index indicator intentionally employs a relatively straightforward normalization methodology for several important reasons. First, transparency enhances user understanding and trust. Traders can verify calculations manually and develop intuitive feel for what different readings mean. Second, academic research suggests that most of the predictive power in COT data comes from extreme positioning levels rather than subtle patterns requiring complex statistical methods to detect. Third, robust methods that work consistently across many markets and time periods tend to be simpler rather than more complex, reducing the risk of overfitting to historical data. Fourth, the complexity costs of implementation matter for retail traders without programming teams or computational infrastructure. PSYCHOLOGICAL ASPECTS OF COT TRADING Trading based on COT data requires psychological fortitude that differs from momentum-based approaches. Contrarian positioning signals inherently mean betting against prevailing market sentiment and recent price action. When commercials reach extreme bearish positioning, prices have typically been rising, sometimes for extended periods. The price chart looks bullish, momentum indicators confirm strength, moving averages align positively. The COT signal says bet against all of this. This psychological difficulty explains why COT analysis remains underutilized relative to trend-following methods. Human psychology strongly predisposes us toward extrapolation and recency bias. When prices rally for months, our pattern-matching brains naturally expect continued rally. The recent price action dominates our perception, overwhelming rational analysis about positioning extremes and historical probabilities. The COT signal asking us to sell requires overriding these powerful psychological impulses. The indicator design attempts to support the required psychological discipline through several features. Clear threshold markers and signal states reduce ambiguity about when signals trigger. When the commercial index crosses below twenty, the signal is explicit and unambiguous. The background shifts to red, the signal label displays bearish, and alerts fire. This explicitness helps traders act on signals rather than waiting for additional confirmation that may never arrive. The metrics table provides analytical justification for contrarian positions, helping traders maintain conviction during inevitable periods of adverse price movement. When a trader enters short positions based on extreme commercial bearish positioning but prices continue rallying for several weeks, doubt naturally emerges. The table display provides reassurance. Commercial positioning remains extremely bearish. Divergence remains high. The positioning thesis remains intact even though price action has not yet confirmed. Alert functionality ensures traders do not miss signals due to inattention while also not requiring constant monitoring that can lead to emotional decision-making. Setting alerts for COT extremes enables a healthier relationship with markets. When meaningful signals occur, alerts notify them. They can then calmly assess the situation and execute planned responses. However, no indicator design can completely overcome the psychological difficulty of contrarian trading. Some traders simply cannot maintain short positions while prices rally. For these traders, COT analysis might be better employed as an exit signal for long positions rather than an entry signal for shorts. Ultimately, successful COT trading requires developing comfort with probabilistic thinking rather than certainty-seeking. The signals work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five or sixty percent win rate with proper risk management produces substantial profits over years, yet still means forty to forty-five percent of signals will be premature or wrong. COT analysis provides genuine edge, but edge means probability advantage, not elimination of losing trades. EDUCATIONAL RESOURCES AND CONTINUOUS LEARNING The indicator provides extensive built-in educational resources through its documentation, detailed tooltips, and transparent calculations. However, mastering COT analysis requires study beyond any single tool or resource. Several excellent resources provide valuable extensions of the concepts covered in this guide. Books and practitioner-focused monographs offer accessible entry points. Stephen Briese published The Commitments of Traders Bible in two thousand eight, offering detailed breakdowns of how different markets and trader categories behave (Briese, 2008). Briese's work stands out for its empirical focus and market-specific insights. Jack Schwager includes discussion of COT analysis within the broader context of market behavior in his book Market Sense and Nonsense (Schwager, 2012). Perry Kaufman's Trading Systems and Methods represents perhaps the most rigorous practitioner-focused text on systematic trading approaches including COT analysis (Kaufman, 2013). Academic journal articles provide the rigorous statistical foundation underlying COT analysis. The Journal of Futures Markets regularly publishes research on positioning data and its predictive properties. Bessembinder and Chan's earlier work on systematic risk, hedging pressure, and risk premiums in futures markets provides theoretical foundation (Bessembinder, 1992). Chang's examination of speculator returns provides historical context (Chang, 1985). Irwin and Sanders provide essential skeptical perspective in their two thousand twelve article (Irwin and Sanders, 2012). Wang's two thousand three article provides one of the most empirical analyses of COT data across multiple commodity markets (Wang, 2003). Online resources extend beyond academic and book-length treatments. The CFTC website provides free access to current and historical COT reports in multiple formats. The explanatory materials section offers detailed documentation of report construction, category definitions, and historical methodology changes. Traders serious about COT analysis should read these official CFTC documents to understand exactly what they are analyzing. Commercial COT data services such as Barchart provide enhanced visualization and analysis tools beyond raw CFTC data. TradingView's educational materials, published scripts library, and user community provide additional resources for exploring different approaches to COT analysis. The key to mastering COT analysis lies not in finding a single definitive source but rather in building understanding through multiple perspectives and information sources. Academic research provides rigorous empirical foundation. Practitioner-focused books offer practical implementation insights. Direct engagement with data through systematic backtesting develops intuition about how positioning dynamics manifest across different market conditions. SYNTHESIZING KNOWLEDGE INTO PRACTICE The COT Index indicator represents the synthesis of academic research, trading experience, and software engineering into a practical tool accessible to retail traders equipped with nothing more than a TradingView account and willingness to learn. What once required expensive data subscriptions, custom programming capabilities, statistical software, and institutional resources now appears as a straightforward indicator requiring only basic parameter selection and modest study to understand. This democratization of institutional-grade analysis tools represents a broader trend in financial markets over recent decades. Yet technology and data access alone provide no edge without understanding and discipline. Markets remain relentlessly efficient at eliminating edges that become too widely known and mechanically exploited. The COT Index indicator succeeds only when users invest time learning the underlying concepts, understand the limitations and probability distributions involved, and integrate signals thoughtfully into trading plans rather than applying them mechanically. The academic research demonstrates conclusively that institutional positioning contains genuine information about future price movements, particularly at extremes where commercial hedgers are maximally bearish or bullish relative to historical norms. This informational content is neither perfect nor deterministic but rather probabilistic, providing edge over many observations through identification of higher-probability configurations. Bessembinder and Chan's finding that commercial positioning explained modest but significant variance in future returns illustrates this probabilistic nature perfectly (Bessembinder and Chan, 1992). The effect is real and statistically significant, yet it explains perhaps ten to fifteen percent of return variance rather than most variance. Much of price movement remains unpredictable even with positioning intelligence. The practical implication is that COT analysis works best as one component of a trading system rather than a standalone oracle. It provides the positioning dimension, revealing where the smart money has positioned and where the crowd has followed, but price action analysis provides the timing dimension. Fundamental analysis provides the catalyst dimension. Risk management provides the survival dimension. These components work together synergistically. The indicator's design philosophy prioritizes transparency and education over black-box complexity, empowering traders to understand exactly what they are analyzing and why. Every calculation is documented and user-adjustable. The threshold markers, background coloring, tables, and clear signal states provide multiple reinforcing channels for conveying the same information. This educational approach reflects a conviction that sustainable trading success comes from genuine understanding rather than mechanical system-following. Traders who understand why commercial positioning matters, how different trader categories behave, what positioning extremes signify, and where signals fit within probability distributions can adapt when market conditions change. Traders mechanically following black-box signals without comprehension abandon systems after normal losing streaks. The research foundation supporting COT analysis comes primarily from commodity markets where commercial hedger informational advantages are most pronounced. Agricultural producers hedging crops know more about supply conditions than distant speculators. Energy companies hedging production know more about operating costs than financial traders. Metals miners hedging output know more about ore grades than index funds. Financial futures markets show weaker but still present effects. The journey from reading this documentation to profitable trading based on COT analysis involves several stages that cannot be rushed. Initial reading and basic understanding represents the first stage. Historical study represents the second stage, reviewing past market cycles to observe how positioning extremes preceded major turning points. Paper trading or small-size real trading represents the third stage to experience the psychological challenges. Refinement based on results and personal psychology represents the fourth stage. Markets will continue evolving. New participant categories will emerge. Regulatory structures will change. Technology will advance. Yet the fundamental dynamics driving COT analysis, that different market participants have different information, different motivations, and different forecasting abilities that manifest in their positioning, will persist as long as futures markets exist. While specific thresholds or optimal parameters may shift over time, the core logic remains sound and adaptable. The trader equipped with this indicator, understanding of the theory and evidence behind COT analysis, realistic expectations about probability rather than certainty, discipline to maintain positions through adverse volatility, and patience to allow signals time to develop possesses genuine edge in markets. The edge is not enormous, markets cannot allow large persistent inefficiencies without arbitraging them away, but it is real, measurable, and exploitable by those willing to invest in learning and disciplined application. REFERENCES Bessembinder, H. (1992) Systematic risk, hedging pressure, and risk premiums in futures markets, Review of Financial Studies, 5(4), pp. 637-667. Bessembinder, H. and Chan, K. (1992) The profitability of technical trading rules in the Asian stock markets, Pacific-Basin Finance Journal, 3(2-3), pp. 257-284. Briese, S. (2008) The Commitments of Traders Bible: How to Profit from Insider Market Intelligence. Hoboken: John Wiley & Sons. Chang, E.C. (1985) Returns to speculators and the theory of normal backwardation, Journal of Finance, 40(1), pp. 193-208. Commodity Futures Trading Commission (CFTC) (2009) Explanatory Notes: Disaggregated Commitments of Traders Report. Available at: www.cftc.gov (Accessed: 15 January 2025). Commodity Futures Trading Commission (CFTC) (2020) Commitments of Traders: About the Report. Available at: www.cftc.gov (Accessed: 15 January 2025). Irwin, S.H. and Sanders, D.R. (2012) Testing the Masters Hypothesis in commodity futures markets, Energy Economics, 34(1), pp. 256-269. Kaufman, P.J. (2013) Trading Systems and Methods. 5th edn. Hoboken: John Wiley & Sons. Ruan, Y. and Zhang, Y. (2018) Forecasting commodity futures prices using machine learning: Evidence from the Chinese commodity futures market, Applied Economics Letters, 25(12), pp. 845-849. Sanders, D.R., Boris, K. and Manfredo, M. (2004) Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports, Energy Economics, 26(3), pp. 425-445. Schwager, J.D. (2012) Market Sense and Nonsense: How the Markets Really Work and How They Don't. Hoboken: John Wiley & Sons. Tharp, V.K. (2008) Super Trader: Make Consistent Profits in Good and Bad Markets. New York: McGraw-Hill. Wang, C. (2003) The behavior and performance of major types of futures traders, Journal of Futures Markets, 23(1), pp. 1-31. Williams, L.R. and Noseworthy, M. (2009) The Right Stock at the Right Time: Prospering in the Coming Good Years. Hoboken: John Wiley & Sons. FURTHER READING For traders seeking to deepen their understanding of COT analysis and futures market positioning beyond this documentation, the following resources provide valuable extensions: Academic Journal Articles: Fishe, R.P.H. and Smith, A. (2012) Do speculators drive commodity prices away from supply and demand fundamentals?, Journal of Commodity Markets, 1(1), pp. 1-16. Haigh, M.S., Hranaiova, J. and Overdahl, J.A. (2007) Hedge funds, volatility, and liquidity provision in energy futures markets, Journal of Alternative Investments, 9(4), pp. 10-38. Kocagil, A.E. (1997) Does futures speculation stabilize spot prices? Evidence from metals markets, Applied Financial Economics, 7(1), pp. 115-125. Sanders, D.R. and Irwin, S.H. (2011) The impact of index funds in commodity futures markets: A systems approach, Journal of Alternative Investments, 14(1), pp. 40-49. Books and Practitioner Resources: Murphy, J.J. (1999) Technical Analysis of the Financial Markets: A Guide to Trading Methods and Applications. New York: New York Institute of Finance. Pring, M.J. (2002) Technical Analysis Explained: The Investor's Guide to Spotting Investment Trends and Turning Points. 4th edn. New York: McGraw-Hill. Federal Reserve and Research Institution Publications: Federal Reserve Banks regularly publish working papers examining commodity markets, futures positioning, and price discovery mechanisms. The Federal Reserve Bank of San Francisco and Federal Reserve Bank of Kansas City maintain active research programs in this area. Online Resources: The CFTC website provides free access to current and historical COT reports, explanatory materials, and regulatory documentation. Barchart offers enhanced COT data visualization and screening tools. TradingView's community library contains numerous published scripts and educational materials exploring different approaches to positioning analysis.
Pine Script® indicator
by EdgeTools
14
Relative Strength Index Remastered [CHE]Relative Strength Index Remastered — Enhanced RSI with robust divergence detection using price-based pivots and line-of-sight validation to reduce false signals compared to the standard RSI indicator. Summary RSI Remastered builds on the classic Relative Strength Index by adding a more reliable divergence detection system that relies on price pivots rather than RSI pivots alone, incorporating a line-of-sight check to ensure the RSI path between points remains clear. This approach filters out many false divergences that occur in the original RSI indicator due to its volatile pivot detection on the RSI line itself. Users benefit from clearer reversal and continuation signals, especially in noisy markets, with optional hidden divergence support for trend confirmation. The core RSI calculation and smoothing options remain familiar, but the divergence logic provides materially fewer alerts while maintaining sensitivity. Motivation: Why this design? The standard RSI indicator often generates misleading divergence signals because it detects pivots directly on the RSI values, which can fluctuate erratically in volatile conditions, leading to frequent false positives that confuse traders during ranging or choppy price action. RSI Remastered addresses this by shifting pivot detection to the underlying price highs and lows, which are more stable, and adding a validation step that confirms the RSI line does not cross the direct path between pivot points. This design targets the real problem of over-signaling in the original, promoting more actionable insights without altering the RSI's core momentum measurement. What’s different vs. standard approaches? - Reference baseline: The classical TradingView RSI indicator, which uses simple RSI-based pivot detection for divergences. - Architecture differences: - Pivot identification on price extremes (highs and lows) instead of RSI values, extracting RSI levels at those points for comparison. - Addition of a line-of-sight validation that checks the RSI path bar by bar between pivots to prevent signals where the line is interrupted. - Inclusion of hidden divergence types alongside regular ones, using the same robust framework. - Configurable drawing of connecting lines between validated pivot RSI points for visual clarity. - Practical effect: Charts show fewer but higher-quality divergence markers and lines, reducing clutter from the original's frequent RSI pivot triggers; this matters for avoiding whipsaws in intraday trading, where the standard version might flag dozens of invalid setups per session. Key Comparison Aspects Aspect: Title/Shorttitle Original RSI: "Relative Strength Index" / "RSI" Robust Variant: "Relative Strength Index Remastered " / "RSI RM" Aspect: Max. Lines/Labels Original RSI: No specification (Standard: 50/50) Robust Variant: max_lines_count=200, max_labels_count=200 (for more lines/markers in divergences) Aspect: RSI Calculation & Plots Original RSI: Identical: RSI with RMA, Plots (line, bands, gradient fills) Robust Variant: Identical: RSI with RMA, Plots (line, bands, gradient fills) Aspect: Smoothing (MA) Original RSI: Identical: Inputs for MA types (SMA, EMA etc.), Bollinger Bands optional Robust Variant: Identical: Inputs for MA types (SMA, EMA etc.), Bollinger Bands optional Aspect: Divergence Activation Original RSI: input.bool(false, "Calculate Divergence") (disabled by default) Robust Variant: input.bool(true, "Calculate Divergence") (enabled by default, with tooltip) Aspect: Pivot Calculation Original RSI: Pivots on RSI (ta.pivotlow/high on RSI values) Robust Variant: Pivots on price (ta.pivotlow/high on low/high), RSI values then extracted Aspect: Lookback Values Original RSI: Fixed: lookbackLeft=5, lookbackRight=5 Robust Variant: Input: L=5 (Pivot Left), R=5 (Pivot Right), adjustable (min=1, max=50) Aspect: Range Between Pivots Original RSI: Fixed: rangeUpper=60, rangeLower=5 (via _inRange function) Robust Variant: Input: rangeUpper=60 (Max Bars), rangeLower=5 (Min Bars), adjustable (min=1–6, max=100–300) Aspect: Divergence Types Original RSI: Only Regular Bullish/Bearish: - Bull: Price LL + RSI HL - Bear: Price HH + RSI LH Robust Variant: Regular + Hidden (optional via showHidden=true): - Regular Bull: Price LL + RSI HL - Regular Bear: Price HH + RSI LH - Hidden Bull: Price HL + RSI LL - Hidden Bear: Price LH + RSI HH Aspect: Validation Original RSI: No additional check (only pivot + range check) Robust Variant: Line-of-Sight Check: RSI line must not cross the connecting line between pivots (line_clear function with slope calculation and loop for each bar in between) Aspect: Signals (Plots/Shapes) Original RSI: - Plot of pivot points (if divergence) - Shapes: "Bull"/"Bear" at RSI value, offset=-5 Robust Variant: - No pivot plots, instead shapes at RSI , offset=-R (adjustable) - Shapes: "Bull"/"Bear" (Regular), "HBull"/"HBear" (Hidden) - Colors: Lime/Red (Regular), Teal/Orange (Hidden) Aspect: Line Drawing Original RSI: No lines Robust Variant: Optional (showLines=true): Lines between RSI pivots (thick for regular, dashed/thin for hidden), extend=none Aspect: Alerts Original RSI: Only Regular Bullish/Bearish (with pivot lookback reference) Robust Variant: Regular Bullish/Bearish + Hidden Bullish/Bearish (specific "at latest pivot low/high") Aspect: Robustness Original RSI: Simple, prone to false signals (RSI pivots can be volatile) Robust Variant: Higher: Price pivots are more stable, line-of-sight filters "broken" divergences, hidden support for trend continuations Aspect: Code Length/Structure Original RSI: ~100 lines, simple if-blocks for bull/bear Robust Variant: ~150 lines, extended helper functions (e.g., inRange, line_clear), var group for inputs How it works (technical) The indicator first computes the core RSI value based on recent price changes, separating upward and downward movements over the specified length and smoothing them to derive a momentum reading scaled between zero and one hundred. This value is then plotted in a separate pane with fixed upper and lower reference lines at seventy and thirty, along with optional gradient fills to highlight overbought and oversold zones. For smoothing, a moving average type is applied to the RSI if enabled, with an option to add bands around it based on the variability of recent RSI values scaled by a multiplier. Divergence detection activates on confirmed price pivots: lows for bullish checks and highs for bearish. At each new pivot, the system retrieves the bar index and values (price and RSI) for the current and prior pivot, ensuring they fall within a configurable bar range to avoid unrelated points. Comparisons then assess whether the price has made a lower low (or higher high) while the RSI at those points moves in the opposite direction—higher for bullish regular, lower for bearish regular. For hidden types, the directions reverse to capture trend strength. The line-of-sight check calculates the straight path between the two RSI points and verifies that the actual RSI values in between stay entirely above (for bullish) or below (for bearish) that path, breaking the signal if any bar violates it. Valid signals trigger shapes at the RSI level of the new pivot and optional lines connecting the points. Initialization uses built-in functions to track prior occurrences, with states persisting across bars for accurate historical comparisons. No higher timeframe data is used, so confirmation occurs after the right pivot bars close, minimizing live-bar repaints. Parameter Guide Length — Controls the period for measuring price momentum changes — Default: 14 — Trade-offs/Tips: Shorter values increase responsiveness but add noise and more false signals; longer smooths trends but delays entries in fast markets. Source — Selects the price input for RSI calculation — Default: Close — Trade-offs/Tips: Use high or low for volatility focus, but close works best for most assets; mismatches can skew overbought/oversold reads. Calculate Divergence — Enables the enhanced divergence logic — Default: True — Trade-offs/Tips: Disable for pure RSI view to save computation; essential for signal reliability over the standard method. Type (Smoothing) — Chooses the moving average applied to RSI — Default: SMA — Trade-offs/Tips: None for raw RSI; EMA for quicker adaptation, but SMA reduces whipsaws; Bollinger Bands option adds volatility context at cost of added lines. Length (Smoothing) — Period for the smoothing average — Default: 14 — Trade-offs/Tips: Match RSI length for consistency; shorter boosts signal speed but amplifies noise in the smoothed line. BB StdDev — Multiplier for band width around smoothed RSI — Default: 2.0 — Trade-offs/Tips: Lower narrows bands for tighter signals, risking more touches; higher widens for fewer but stronger breakouts. Pivot Left — Bars to the left for confirming price pivots — Default: 5 — Trade-offs/Tips: Increase for stricter pivots in noisy data, reducing signals; too high delays confirmation excessively. Pivot Right — Bars to the right for confirming price pivots — Default: 5 — Trade-offs/Tips: Balances with left for symmetry; longer right ensures maturity but shifts signals backward. Max Bars Between Pivots — Upper limit on distance for valid pivot pairs — Default: 60 — Trade-offs/Tips: Tighten for short-term trades to focus recent action; widen for swing setups but risks unrelated comparisons. Min Bars Between Pivots — Lower limit to avoid clustered pivots — Default: 5 — Trade-offs/Tips: Raise to filter micro-moves; too low invites overlapping signals like the original RSI. Detect Hidden — Includes trend-continuation hidden types — Default: True — Trade-offs/Tips: Enable for full trend analysis; disable simplifies to reversals only, akin to basic RSI. Draw Lines — Shows connecting lines between valid pivots — Default: True — Trade-offs/Tips: Turn off for cleaner charts; helps visually confirm line-of-sight in backtests. Reading & Interpretation The main RSI line oscillates between zero and one hundred, crossing above fifty suggesting building momentum and below indicating weakness; touches near seventy or thirty flag potential extremes. The optional smoothed line and bands provide a filtered view—price above the upper band on the RSI pane hints at overextension. Divergence shapes appear as upward labels for bullish (lime for regular, teal for hidden) and downward for bearish (red regular, orange hidden) at the pivot's RSI level, signaling a mismatch only after validation. Connecting lines, if drawn, slope between points without RSI interference, their color matching the shape type; a dashed style denotes hidden. Fewer shapes overall compared to the standard RSI mean higher conviction, but always confirm with price structure. Practical Workflows & Combinations - Trend following: Enter longs on regular bullish shapes near support with higher highs in price; filter hidden bullish for pullback buys in uptrends, pairing with a rising smoothed RSI above fifty. - Exits/Stops: Use bearish regular as reversal warnings to tighten stops; hidden bearish in downtrends confirms continuation—exit if lines show RSI crossing the path. - Multi-asset/Multi-TF: Defaults suit forex and stocks on one-hour charts; for crypto volatility, widen pivot ranges to ten; scale min/max bars proportionally on daily for swings, avoiding the original's intraday spam. Behavior, Constraints & Performance Signals confirm only after the right pivot bars close, so live bars may show tentative pivots that vanish on close, unlike the standard RSI's immediate RSI-pivot triggers—plan for this delay in automation. No higher timeframe calls, so no security-related repaints. Resources include up to two hundred lines and labels for dense charts, with a loop in validation scanning up to three hundred bars between pivots, which is efficient but could slow on very long histories. Known limits: Slight lag at pivot confirmation in trending markets; volatile RSI might rarely miss fine path violations; not ideal for gap-heavy assets where pivots skip. Sensible Defaults & Quick Tuning Start with defaults for balanced momentum and divergence on most timeframes. For too many signals (like the original), raise pivot left/right to eight and min bars to ten to filter noise. If sluggish in trends, shorten RSI length to nine and enable EMA smoothing for faster adaptation. In high-volatility assets, widen max bars to one hundred but disable hidden to focus essentials. For clean reversal hunts, set smoothing to none and lines on. What this indicator is—and isn’t RSI Remastered serves as a refined momentum and divergence visualization tool, enhancing the standard RSI for better signal quality in technical analysis setups. It is not a standalone trading system, nor does it predict price moves—pair it with volume, structure breaks, and risk rules for decisions. Use alongside position sizing and broader context, not in isolation. Disclaimer The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context. Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved. By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk. Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts. Best regards and happy trading Chervolino
Pine Script® indicator
by chervolino
Trend Fib Zone Bounce (TFZB) [KedArc Quant]Description: Trend Fib Zone Bounce (TFZB) trades with the latest confirmed Supply/Demand zone using a single, configurable Fib pullback (0.3/0.5/0.6). Trade only in the direction of the most recent zone and use a single, configurable fib level for pullback entries. • Detects market structure via confirmed swing highs/lows using a rolling window. • Draws Supply/Demand zones (bearish/bullish rectangles) from the latest MSS (CHOCH or BOS) event. • Computes intra zone Fib guide rails and keeps them extended in real time. • Triggers BUY only inside bullish zones and SELL only inside bearish zones when price touches the selected fib and closes back beyond it (bounce confirmation). • Optional labels print BULL/BEAR + fib next to the triangle markers. What it does Finds structure using confirmed swing highs/lows (you choose the confirmation length). Builds the latest zone (bullish = demand, bearish = supply) after a CHOCH/BOS event. Draws intra-zone “guide rails” (Fib lines) and extends them live. Signals only with the trend of that zone: BUY inside a bullish zone when price tags the selected Fib and closes back above it. SELL inside a bearish zone when price tags the selected Fib and closes back below it. Optional labels print BULL/BEAR + Fib next to triangles for quick context Why this is different Most “zone + fib + signal” tools bolt together several indicators, or fire counter-trend signals because they don’t fully respect structure. TFZB is intentionally minimal: Single bias source: the latest confirmed zone defines direction; nothing else overrides it. Single entry rule: one Fib bounce (0.3/0.5/0.6 selectable) inside that zone—no counter-trend trades by design. Clean visuals: you can show only the most recent zone, clamp overlap, and keep just the rails that matter. Deterministic & transparent: every plot/label comes from the code you see—no external series or hidden smoothing How it helps traders Cuts decision noise: you always know the bias and the only entry that matters right now. Forces discipline: if price isn’t inside the active zone, you don’t trade. Adapts to volatility: pick 0.3 in strong trends, 0.5 as the default, 0.6 in chop. Non-repainting zones: swings are confirmed after Structure Length bars, then used to build zones that extend forward (they don’t “teleport” later) How it works (details) *Structure confirmation A swing high/low is only confirmed after Structure Length bars have elapsed; the dot is plotted back on the original bar using offset. Expect a confirmation delay of about Structure Length × timeframe. *Zone creation After a CHOCH/BOS (momentum shift / break of prior swing), TFZB draws the new Supply/Demand zone from the swing anchors and sets it active. *Fib guide rails Inside the active zone TFZB projects up to five Fib lines (defaults: 0.3 / 0.5 / 0.7) and extends them as time passes. *Entry logic (with-trend only) BUY: bar’s low ≤ fib and close > fib inside a bullish zone. SELL: bar’s high ≥ fib and close < fib inside a bearish zone. *Optionally restrict to one signal per zone to avoid over-trading. (Optional) Aggressive confirm-bar entry When do the swing dots print? * The code confirms a swing only after `structureLen` bars have elapsed since that candidate high/low. * On a 5-min chart with `structureLen = 10`, that’s about 50 minutes later. * When the swing confirms, the script plots the dot back on the original bar (via `offset = -structureLen`). So you *see* the dot on the old bar, but it only appears on the chart once the confirming bar arrives. > Practical takeaway: expect swing markers to appear roughly `structureLen × timeframe` later. Zones and signals are built from those confirmed swings. Best timeframe for this Indicator Use the timeframe that matches your holding period and the noise level of the instrument: * Intraday : * 5m or 15m are the sweet spots. * Suggested `structureLen`: * 5m: 10–14 (confirmation delay \~50–70 min) * 15m: 8–10 (confirmation delay \~2–2.5 hours) * Keep Entry Fib at 0.5 to start; try 0.3 in strong trends, 0.6 in chop. * Tip: avoid the first 10–15 minutes after the open; let the initial volatility set the early structure. * Swing/overnight: * 1h or 4h. * `structureLen`: * 1h: 6–10 (6–10 hours confirmation) * 4h: 5–8 (20–32 hours confirmation) * 1m scalping: not recommended here—the confirmation lag relative to the noise makes zones less reliable. Inputs (all groups) Structure • Show Swing Points (structureTog) o Plots small dots on the bar where a swing point is confirmed (offset back by Structure Length). • Structure Length (structureLen) o Lookback used to confirm swing highs/lows and determine local structure. Higher = fewer, stronger swings; lower = more reactive. Zones • Show Last (zoneDispNum) o Maximum number of zones kept on the chart when Display All Zones is off. • Display All Zones (dispAll) o If on, ignores Show Last and keeps all zones/levels. • Zone Display (zoneFilter): Bullish Only / Bearish Only / Both o Filters which zone types are drawn and eligible for signals. • Clean Up Level Overlap (noOverlap) o Prevents fib lines from overlapping when a new zone starts near the previous one (clamps line start/end times for readability). Fib Levels Each row controls whether a fib is drawn and how it looks: • Toggle (f1Tog…f5Tog): Show/hide a given fib line. • Level (f1Lvl…f5Lvl): Numeric ratio in . Defaults active: 0.3, 0.5, 0.7 (0 and 1 off by default). • Line Style (f1Style…f5Style): Solid / Dashed / Dotted. • Bull/Bear Colors (f#BullColor, f#BearColor): Per-fib color in bullish vs bearish zones. Style • Structure Color: Dot color for confirmed swing points. • Bullish Zone Color / Bearish Zone Color: Rectangle fills (transparent by default). Signals • Entry Fib for Signals (entryFibSel): Choose 0.3, 0.5 (default), or 0.6 as the trigger line. • Show Buy/Sell Signals (showSignals): Toggles triangle markers on/off. • One Signal Per Zone (oneSignalPerZone): If on, suppresses additional entries within the same zone after the first trigger. • Show Signal Text Labels (Bull/Bear + Fib) (showSignalLabels): Adds a small label next to each triangle showing zone bias and the fib used (e.g., BULL 0.5 or BEAR 0.3). How TFZB decides signals With trend only: • BUY 1. Latest active zone is bullish. 2. Current bar’s close is inside the zone (between top and bottom). 3. The bar’s low ≤ selected fib and it closes > selected fib (bounce). • SELL 1. Latest active zone is bearish. 2. Current bar’s close is inside the zone. 3. The bar’s high ≥ selected fib and it closes < selected fib. Markers & labels • BUY: triangle up below the bar; optional label “BULL 0.x” above it. • SELL: triangle down above the bar; optional label “BEAR 0.x” below it. Right-Panel Swing Log (Table) What it is A compact, auto-updating log of the most recent Swing High/Low events, printed in the top-right of the chart. It helps you see when a pivot formed, when it was confirmed, and at what price—so you know the earliest bar a zone-based signal could have appeared. Columns Type – Swing High or Swing Low. Date – Calendar date of the swing bar (follows the chart’s timezone). Swing @ – Time of the original swing bar (where the dot is drawn). Confirm @ – Time of the bar that confirmed that swing (≈ Structure Length × timeframe after the swing). This is also the earliest moment a new zone/entry can be considered. Price – The swing price (high for SH, low for SL). Why it’s useful Clarity on repaint/confirmation: shows the natural delay between a swing forming and being usable—no guessing. Planning & journaling: quick reference of today’s pivots and prices for notes/backtesting. Scanning intraday: glance to see if you already have a confirmed zone (and therefore valid fib-bounce entries), or if you’re still waiting. Context for signals: if a fib-bounce triangle appears before the time listed in Confirm @, it’s not a valid trade (you were too early). Settings (Inputs → Logging) Log swing times / Show table – turn the table on/off. Rows to keep – how many recent entries to display. Show labels on swing bar – optional tags on the chart (“Swing High 11:45”, “Confirm SH 14:15”) that match the table. Recommended defaults • Structure Length: 10–20 for intraday; 20–40 for swing. • Entry Fib for Signals: 0.5 to start; try 0.3 in stronger trends and 0.6 in choppier markets. • One Signal Per Zone: ON (prevents over trading). • Zone Display: Both. • Fib Lines: Keep 0.3/0.5/0.7 on; turn on 0 and 1 only if you need anchors. Alerts Two alert conditions are available: • BUY signal – fires when a with trend bullish bounce at the selected fib occurs inside a bullish zone. • SELL signal – fires when a with trend bearish bounce at the selected fib occurs inside a bearish zone. Create alerts from the chart’s Alerts panel and select the desired condition. Use Once Per Bar Close to avoid intrabar flicker. Notes & tips • Swing dots are confirmed only after Structure Length bars, so they plot back in time; zones built from these confirmed swings do not repaint (though they extend as new bars form). • If you don’t see a BUY where you expect one, check: (1) Is the active zone bullish? (2) Did the candle’s low actually pierce the selected fib and close above it? (3) Is One Signal Per Zone suppressing a second entry? • You can hide visual clutter by reducing Show Last to 1–3 while keeping Display All Zones off. Glossary • CHOCH (Change of Character): A shift where price breaks beyond the last opposite swing while local momentum flips. • BOS (Break of Structure): A cleaner break beyond the prior swing level in the current momentum direction. • MSS: Either CHOCH or BOS – any event that spawns a new zone. Extension ideas (optional) • Add fib extensions (1.272 / 1.618) for target lines. • Zone quality score using ATR normalization to filter weak impulses. • HTF filter to only accept zones aligned with a higher timeframe trend. ⚠️ Disclaimer This script is provided for educational purposes only. Past performance does not guarantee future results. Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
Pine Script® indicator
by kedarcquant
4
Market Zone Analyzer[BullByte]Understanding the Market Zone Analyzer --- 1. Purpose of the Indicator The Market Zone Analyzer is a Pine Script™ (version 6) indicator designed to streamline market analysis on TradingView. Rather than scanning multiple separate tools, it unifies four core dimensions—trend strength, momentum, price action, and market activity—into a single, consolidated view. By doing so, it helps traders: • Save time by avoiding manual cross-referencing of disparate signals. • Reduce decision-making errors that can arise from juggling multiple indicators. • Gain a clear, reliable read on whether the market is in a bullish, bearish, or sideways phase, so they can more confidently decide to enter, exit, or hold a position. --- 2. Why a Trader Should Use It • Unified View: Combines all essential market dimensions into one easy-to-read score and dashboard, eliminating the need to piece together signals manually. • Adaptability: Automatically adjusts its internal weighting for trend, momentum, and price action based on current volatility. Whether markets are choppy or calm, the indicator remains relevant. • Ease of Interpretation: Outputs a simple “BULLISH,” “BEARISH,” or “SIDEWAYS” label, supplemented by an intuitive on-chart dashboard and an oscillator plot that visually highlights market direction. • Reliability Features: Built-in smoothing of the net score and hysteresis logic (requiring consecutive confirmations before flips) minimize false signals during noisy or range-bound phases. --- 3. Why These Specific Indicators? This script relies on a curated set of well-established technical tools, each chosen for its particular strength in measuring one of the four core dimensions: 1. Trend Strength: • ADX/DMI (Average Directional Index / Directional Movement Index): Measures how strong a trend is, and whether the +DI line is above the –DI line (bullish) or vice versa (bearish). • Moving Average Slope (Fast MA vs. Slow MA): Compares a shorter-period SMA to a longer-period SMA; if the fast MA sits above the slow MA, it confirms an uptrend, and vice versa for a downtrend. • Ichimoku Cloud Differential (Senkou A vs. Senkou B): Provides a forward-looking view of trend direction; Senkou A above Senkou B signals bullishness, and the opposite signals bearishness. 2. Momentum: • Relative Strength Index (RSI): Identifies overbought (above its dynamically calculated upper bound) or oversold (below its lower bound) conditions; changes in RSI often precede price reversals. • Stochastic %K: Highlights shifts in short-term momentum by comparing closing price to the recent high/low range; values above its upper band signal bullish momentum, below its lower band signal bearish momentum. • MACD Histogram: Measures the difference between the MACD line and its signal line; a positive histogram indicates upward momentum, a negative histogram indicates downward momentum. 3. Price Action: • Highest High / Lowest Low (HH/LL) Range: Over a defined lookback period, this captures breakout or breakdown levels. A closing price near the recent highs (with a positive MA slope) yields a bullish score, and near the lows (with a negative MA slope) yields a bearish score. • Heikin-Ashi Doji Detection: Uses Heikin-Ashi candles to identify indecision or continuation patterns. A small Heikin-Ashi body (doji) relative to recent volatility is scored as neutral; a larger body in the direction of the MA slope is scored bullish or bearish. • Candle Range Measurement: Compares each candle’s high-low range against its own dynamic band (average range ± standard deviation). Large candles aligning with the prevailing trend score bullish or bearish accordingly; unusually small candles can indicate exhaustion or consolidation. 4. Market Activity: • Bollinger Bands Width (BBW): Measures the distance between BB upper and lower bands; wide bands indicate high volatility, narrow bands indicate low volatility. • Average True Range (ATR): Quantifies average price movement (volatility). A sudden spike in ATR suggests a volatile environment, while a contraction suggests calm. • Keltner Channels Width (KCW): Similar to BBW but uses ATR around an EMA. Provides a second layer of volatility context, confirming or contrasting BBW readings. • Volume (with Moving Average): Compares current volume to its moving average ± standard deviation. High volume validates strong moves; low volume signals potential lack of conviction. By combining these tools, the indicator captures trend direction, momentum strength, price-action nuances, and overall market energy, yielding a more balanced and comprehensive assessment than any single tool alone. --- 4. What Makes This Indicator Stand Out • Multi-Dimensional Analysis: Rather than relying on a lone oscillator or moving average crossover, it simultaneously evaluates trend, momentum, price action, and activity. • Dynamic Weighting: The relative importance of trend, momentum, and price action adjusts automatically based on real-time volatility (Market Activity State). For example, in highly volatile conditions, trend and momentum signals carry more weight; in calm markets, price action signals are prioritized. • Stability Mechanisms: • Smoothing: The net score is passed through a short moving average, filtering out noise, especially on lower timeframes. • Hysteresis: Both Market Activity State and the final bullish/bearish/sideways zone require two consecutive confirmations before flipping, reducing whipsaw. • Visual Interpretation: A fully customizable on-chart dashboard displays each sub-indicator’s value, regime, score, and comment, all color-coded. The oscillator plot changes color to reflect the current market zone (green for bullish, red for bearish, gray for sideways) and shows horizontal threshold lines at +2, 0, and –2. --- 5. Recommended Timeframes • Short-Term (5 min, 15 min): Day traders and scalpers can benefit from rapid signals, but should enable smoothing (and possibly disable hysteresis) to reduce false whipsaws. • Medium-Term (1 h, 4 h): Swing traders find a balance between responsiveness and reliability. Less smoothing is required here, and the default parameters (e.g., ADX length = 14, RSI length = 14) perform well. • Long-Term (Daily, Weekly): Position traders tracking major trends can disable smoothing for immediate raw readings, since higher-timeframe noise is minimal. Adjust lookback lengths (e.g., increase adxLength, rsiLength) if desired for slower signals. Tip: If you keep smoothing off, stick to timeframes of 1 h or higher to avoid excessive signal “chatter.” --- 6. How Scoring Works A. Individual Indicator Scores Each sub-indicator is assigned one of three discrete scores: • +1 if it indicates a bullish condition (e.g., RSI above its dynamically calculated upper bound). • 0 if it is neutral (e.g., RSI between upper and lower bounds). • –1 if it indicates a bearish condition (e.g., RSI below its dynamically calculated lower bound). Examples of individual score assignments: • ADX/DMI: • +1 if ADX ≥ adxThreshold and +DI > –DI (strong bullish trend) • –1 if ADX ≥ adxThreshold and –DI > +DI (strong bearish trend) • 0 if ADX < adxThreshold (trend strength below threshold) • RSI: • +1 if RSI > RSI_upperBound • –1 if RSI < RSI_lowerBound • 0 otherwise • ATR (as part of Market Activity): • +1 if ATR > (ATR_MA + stdev(ATR)) • –1 if ATR < (ATR_MA – stdev(ATR)) • 0 otherwise Each of the four main categories shares this same +1/0/–1 logic across their sub-components. B. Category Scores Once each sub-indicator reports +1, 0, or –1, these are summed within their categories as follows: • Trend Score = (ADX score) + (MA slope score) + (Ichimoku differential score) • Momentum Score = (RSI score) + (Stochastic %K score) + (MACD histogram score) • Price Action Score = (Highest-High/Lowest-Low score) + (Heikin-Ashi doji score) + (Candle range score) • Market Activity Raw Score = (BBW score) + (ATR score) + (KC width score) + (Volume score) Each category’s summed value can range between –3 and +3 (for Trend, Momentum, and Price Action), and between –4 and +4 for Market Activity raw. C. Market Activity State and Dynamic Weight Adjustments Rather than contributing directly to the netScore like the other three categories, Market Activity determines how much weight to assign to Trend, Momentum, and Price Action: 1. Compute Market Activity Raw Score by summing BBW, ATR, KCW, and Volume individual scores (each +1/0/–1). 2. Bucket into High, Medium, or Low Activity: • High if raw Score ≥ 2 (volatile market). • Low if raw Score ≤ –2 (calm market). • Medium otherwise. 3. Apply Hysteresis (if enabled): The state only flips after two consecutive bars register the same high/low/medium label. 4. Set Category Weights: • High Activity: Trend = 50 %, Momentum = 35 %, Price Action = 15 %. • Low Activity: Trend = 25 %, Momentum = 20 %, Price Action = 55 %. • Medium Activity: Use the trader’s base weight inputs (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 % by default). D. Calculating the Net Score 5. Normalize Base Weights (so that the sum of Trend + Momentum + Price Action always equals 100 %). 6. Determine Current Weights based on the Market Activity State (High/Medium/Low). 7. Compute Each Category’s Contribution: Multiply (categoryScore) × (currentWeight). 8. Sum Contributions to get the raw netScore (a floating-point value that can exceed ±3 when scores are strong). 9. Smooth the netScore over two bars (if smoothing is enabled) to reduce noise. 10. Apply Hysteresis to the Final Zone: • If the smoothed netScore ≥ +2, the bar is classified as “Bullish.” • If the smoothed netScore ≤ –2, the bar is classified as “Bearish.” • Otherwise, it is “Sideways.” • To prevent rapid flips, the script requires two consecutive bars in the new zone before officially changing the displayed zone (if hysteresis is on). E. Thresholds for Zone Classification • BULLISH: netScore ≥ +2 • BEARISH: netScore ≤ –2 • SIDEWAYS: –2 < netScore < +2 --- 7. Role of Volatility (Market Activity State) in Scoring Volatility acts as a dynamic switch that shifts which category carries the most influence: 1. High Activity (Volatile): • Detected when at least two sub-scores out of BBW, ATR, KCW, and Volume equal +1. • The script sets Trend weight = 50 % and Momentum weight = 35 %. Price Action weight is minimized at 15 %. • Rationale: In volatile markets, strong trending moves and momentum surges dominate, so those signals are more reliable than nuanced candle patterns. 2. Low Activity (Calm): • Detected when at least two sub-scores out of BBW, ATR, KCW, and Volume equal –1. • The script sets Price Action weight = 55 %, Trend = 25 %, and Momentum = 20 %. • Rationale: In quiet, sideways markets, subtle price-action signals (breakouts, doji patterns, small-range candles) are often the best early indicators of a new move. 3. Medium Activity (Balanced): • Raw Score between –1 and +1 from the four volatility metrics. • Uses whatever base weights the trader has specified (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 %). Because volatility can fluctuate rapidly, the script employs hysteresis on Market Activity State: a new High or Low state must occur on two consecutive bars before weights actually shift. This avoids constant back-and-forth weight changes and provides more stability. --- 8. Scoring Example (Hypothetical Scenario) • Symbol: Bitcoin on a 1-hour chart. • Market Activity: Raw volatility sub-scores show BBW (+1), ATR (+1), KCW (0), Volume (+1) → Total raw Score = +3 → High Activity. • Weights Selected: Trend = 50 %, Momentum = 35 %, Price Action = 15 %. • Trend Signals: • ADX strong and +DI > –DI → +1 • Fast MA above Slow MA → +1 • Ichimoku Senkou A > Senkou B → +1 → Trend Score = +3 • Momentum Signals: • RSI above upper bound → +1 • MACD histogram positive → +1 • Stochastic %K within neutral zone → 0 → Momentum Score = +2 • Price Action Signals: • Highest High/Lowest Low check yields 0 (close not near extremes) • Heikin-Ashi doji reading is neutral → 0 • Candle range slightly above upper bound but trend is strong, so → +1 → Price Action Score = +1 • Compute Net Score (before smoothing): • Trend contribution = 3 × 0.50 = 1.50 • Momentum contribution = 2 × 0.35 = 0.70 • Price Action contribution = 1 × 0.15 = 0.15 • Raw netScore = 1.50 + 0.70 + 0.15 = 2.35 • Since 2.35 ≥ +2 and hysteresis is met, the final zone is “Bullish.” Although the netScore lands at 2.35 (Bullish), smoothing might bring it slightly below 2.00 on the first bar (e.g., 1.90), in which case the script would wait for a second consecutive reading above +2 before officially classifying the zone as Bullish (if hysteresis is enabled). --- 9. Correlation Between Categories The four categories—Trend Strength, Momentum, Price Action, and Market Activity—often reinforce or offset one another. The script takes advantage of these natural correlations: • Bullish Alignment: If ADX is strong and pointed upward, fast MA is above slow MA, and Ichimoku is positive, that usually coincides with RSI climbing above its upper bound and the MACD histogram turning positive. In such cases, both Trend and Momentum categories generate +1 or +2. Because the Market Activity State is likely High (given the accompanying volatility), Trend and Momentum weights are at their peak, so the netScore quickly crosses into Bullish territory. • Sideways/Consolidation: During a low-volatility, sideways phase, ADX may fall below its threshold, MAs may flatten, and RSI might hover in the neutral band. However, subtle price-action signals (like a small breakout candle or a Heikin-Ashi candle with a slight bias) can still produce a +1 in the Price Action category. If Market Activity is Low, Price Action’s weight (55 %) can carry enough influence—even if Trend and Momentum are neutral—to push the netScore out of “Sideways” into a mild bullish or bearish bias. • Opposing Signals: When Trend is bullish but Momentum turns negative (for example, price continues up but RSI rolls over), the two scores can partially cancel. Market Activity may remain Medium, in which case the netScore lingers near zero (Sideways). The trader can then wait for either a clearer momentum shift or a fresh price-action breakout before committing. By dynamically recognizing these correlations and adjusting weights, the indicator ensures that: • When Trend and Momentum align (and volatility supports it), the netScore leaps strongly into Bullish or Bearish. • When Trend is neutral but Price Action shows an early move in a low-volatility environment, Price Action’s extra weight in the Low Activity State can still produce actionable signals. --- 10. Market Activity State & Its Role (Detailed) The Market Activity State is not a direct category score—it is an overarching context setter for how heavily to trust Trend, Momentum, or Price Action. Here’s how it is derived and applied: 1. Calculate Four Volatility Sub-Scores: • BBW: Compare the current band width to its own moving average ± standard deviation. If BBW > (BBW_MA + stdev), assign +1 (high volatility); if BBW < (BBW_MA × 0.5), assign –1 (low volatility); else 0. • ATR: Compare ATR to its moving average ± standard deviation. A spike above the upper threshold is +1; a contraction below the lower threshold is –1; otherwise 0. • KCW: Same logic as ATR but around the KCW mean. • Volume: Compare current volume to its volume MA ± standard deviation. Above the upper threshold is +1; below the lower threshold is –1; else 0. 2. Sum Sub-Scores → Raw Market Activity Score: Range between –4 and +4. 3. Assign Market Activity State: • High Activity: Raw Score ≥ +2 (at least two volatility metrics are strongly spiking). • Low Activity: Raw Score ≤ –2 (at least two metrics signal unusually low volatility or thin volume). • Medium Activity: Raw Score is between –1 and +1 inclusive. 4. Hysteresis for Stability: • If hysteresis is enabled, a new state only takes hold after two consecutive bars confirm the same High, Medium, or Low label. • This prevents the Market Activity State from bouncing around when volatility is on the fence. 5. Set Category Weights Based on Activity State: • High Activity: Trend = 50 %, Momentum = 35 %, Price Action = 15 %. • Low Activity: Trend = 25 %, Momentum = 20 %, Price Action = 55 %. • Medium Activity: Use trader’s base weights (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 %). 6. Impact on netScore: Because category scores (–3 to +3) multiply by these weights, High Activity amplifies the effect of strong Trend and Momentum scores; Low Activity amplifies the effect of Price Action. 7. Market Context Tooltip: The dashboard includes a tooltip summarizing the current state—e.g., “High activity, trend and momentum prioritized,” “Low activity, price action prioritized,” or “Balanced market, all categories considered.” --- 11. Category Weights: Base vs. Dynamic Traders begin by specifying base weights for Trend Strength, Momentum, and Price Action that sum to 100 %. These apply only when volatility is in the Medium band. Once volatility shifts: • High Volatility Overrides: • Trend jumps from its base (e.g., 40 %) to 50 %. • Momentum jumps from its base (e.g., 30 %) to 35 %. • Price Action is reduced to 15 %. Example: If base weights were Trend = 40 %, Momentum = 30 %, Price Action = 30 %, then in High Activity they become 50/35/15. A Trend score of +3 now contributes 3 × 0.50 = +1.50 to netScore; a Momentum +2 contributes 2 × 0.35 = +0.70. In total, Trend + Momentum can easily push netScore above the +2 threshold on its own. • Low Volatility Overrides: • Price Action leaps from its base (30 %) to 55 %. • Trend falls to 25 %, Momentum falls to 20 %. Why? When markets are quiet, subtle candle breakouts, doji patterns, and small-range expansions tend to foreshadow the next swing more effectively than raw trend readings. A Price Action score of +3 in this state contributes 3 × 0.55 = +1.65, which can carry the netScore toward +2—even if Trend and Momentum are neutral or only mildly positive. Because these weight shifts happen only after two consecutive bars confirm a High or Low state (if hysteresis is on), the indicator avoids constantly flipping its emphasis during borderline volatility phases. --- 12. Dominant Category Explained Within the dashboard, a label such as “Trend Dominant,” “Momentum Dominant,” or “Price Action Dominant” appears when one category’s absolute weighted contribution to netScore is the largest. Concretely: • Compute each category’s weighted contribution = (raw category score) × (current weight). • Compare the absolute values of those three contributions. • The category with the highest absolute value is flagged as Dominant for that bar. Why It Matters: • Momentum Dominant: Indicates that the combined force of RSI, Stochastic, and MACD (after weighting) is pushing netScore farther than either Trend or Price Action. In practice, it means that short-term sentiment and speed of change are the primary drivers right now, so traders should watch for continued momentum signals before committing to a trade. • Trend Dominant: Means ADX, MA slope, and Ichimoku (once weighted) outweigh the other categories. This suggests a strong directional move is in place; trend-following entries or confirming pullbacks are likely to succeed. • Price Action Dominant: Occurs when breakout/breakdown patterns, Heikin-Ashi candle readings, and range expansions (after weighting) are the most influential. This often happens in calmer markets, where subtle shifts in candle structure can foreshadow bigger moves. By explicitly calling out which category is carrying the most weight at any moment, the dashboard gives traders immediate insight into why the netScore is tilting toward bullish, bearish, or sideways. --- 13. Oscillator Plot: How to Read It The “Net Score” oscillator sits below the dashboard and visually displays the smoothed netScore as a line graph. Key features: 1. Value Range: In normal conditions it oscillates roughly between –3 and +3, but extreme confluences can push it outside that range. 2. Horizontal Threshold Lines: • +2 Line (Bullish threshold) • 0 Line (Neutral midline) • –2 Line (Bearish threshold) 3. Zone Coloring: • Green Background (Bullish Zone): When netScore ≥ +2. • Red Background (Bearish Zone): When netScore ≤ –2. • Gray Background (Sideways Zone): When –2 < netScore < +2. 4. Dynamic Line Color: • The plotted netScore line itself is colored green in a Bullish Zone, red in a Bearish Zone, or gray in a Sideways Zone, creating an immediate visual cue. Interpretation Tips: • Crossing Above +2: Signals a strong enough combined trend/momentum/price-action reading to classify as Bullish. Many traders wait for a clear crossing plus a confirmation candle before entering a long position. • Crossing Below –2: Indicates a strong Bearish signal. Traders may consider short or exit strategies. • Rising Slope, Even Below +2: If netScore climbs steadily from neutral toward +2, it demonstrates building bullish momentum. • Divergence: If price makes a higher high but the oscillator fails to reach a new high, it can warn of weakening momentum and a potential reversal. --- 14. Comments and Their Necessity Every sub-indicator (ADX, MA slope, Ichimoku, RSI, Stochastic, MACD, HH/LL, Heikin-Ashi, Candle Range, BBW, ATR, KCW, Volume) generates a short comment that appears in the detailed dashboard. Examples: • “Strong bullish trend” or “Strong bearish trend” for ADX/DMI • “Fast MA above slow MA” or “Fast MA below slow MA” for MA slope • “RSI above dynamic threshold” or “RSI below dynamic threshold” for RSI • “MACD histogram positive” or “MACD histogram negative” for MACD Hist • “Price near highs” or “Price near lows” for HH/LL checks • “Bullish Heikin Ashi” or “Bearish Heikin Ashi” for HA Doji scoring • “Large range, trend confirmed” or “Small range, trend contradicted” for Candle Range Additionally, the top-row comment for each category is: • Trend: “Highly Bullish,” “Highly Bearish,” or “Neutral Trend.” • Momentum: “Strong Momentum,” “Weak Momentum,” or “Neutral Momentum.” • Price Action: “Bullish Action,” “Bearish Action,” or “Neutral Action.” • Market Activity: “Volatile Market,” “Calm Market,” or “Stable Market.” Reasons for These Comments: • Transparency: Shows exactly how each sub-indicator contributed to its category score. • Education: Helps traders learn why a category is labeled bullish, bearish, or neutral, building intuition over time. • Customization: If, for example, the RSI comment says “RSI neutral” despite an impending trend shift, a trader might choose to adjust RSI length or thresholds. In the detailed dashboard, hovering over each comment cell also reveals a tooltip with additional context (e.g., “Fast MA above slow MA” or “Senkou A above Senkou B”), helping traders understand the precise rule behind that +1, 0, or –1 assignment. --- 15. Real-Life Example (Consolidated) • Instrument & Timeframe: Bitcoin (BTCUSD), 1-hour chart. • Current Market Activity: BBW and ATR both spike (+1 each), KCW is moderately high (+1), but volume is only neutral (0) → Raw Market Activity Score = +2 → State = High Activity (after two bars, if hysteresis is on). • Category Weights Applied: Trend = 50 %, Momentum = 35 %, Price Action = 15 %. • Trend Sub-Scores: 1. ADX = 25 (above threshold 20) with +DI > –DI → +1. 2. Fast MA (20-period) sits above Slow MA (50-period) → +1. 3. Ichimoku: Senkou A > Senkou B → +1. → Trend Score = +3. • Momentum Sub-Scores: 4. RSI = 75 (above its moving average +1 stdev) → +1. 5. MACD histogram = +0.15 → +1. 6. Stochastic %K = 50 (mid-range) → 0. → Momentum Score = +2. • Price Action Sub-Scores: 7. Price is not within 1 % of the 20-period high/low and slope = positive → 0. 8. Heikin-Ashi body is slightly larger than stdev over last 5 bars with haClose > haOpen → +1. 9. Candle range is just above its dynamic upper bound but trend is already captured, so → +1. → Price Action Score = +2. • Calculate netScore (before smoothing): • Trend contribution = 3 × 0.50 = 1.50 • Momentum contribution = 2 × 0.35 = 0.70 • Price Action contribution = 2 × 0.15 = 0.30 • Raw netScore = 1.50 + 0.70 + 0.30 = 2.50 → Immediately classified as Bullish. • Oscillator & Dashboard Output: • The oscillator line crosses above +2 and turns green. • Dashboard displays: • Trend Regime “BULLISH,” Trend Score = 3, Comment = “Highly Bullish.” • Momentum Regime “BULLISH,” Momentum Score = 2, Comment = “Strong Momentum.” • Price Action Regime “BULLISH,” Price Action Score = 2, Comment = “Bullish Action.” • Market Activity State “High,” Comment = “Volatile Market.” • Weights: Trend 50 %, Momentum 35 %, Price Action 15 %. • Dominant Category: Trend (because 1.50 > 0.70 > 0.30). • Overall Score: 2.50, posCount = (three +1s in Trend) + (two +1s in Momentum) + (two +1s in Price Action) = 7 bullish signals, negCount = 0. • Final Zone = “BULLISH.” • The trader sees that both Trend and Momentum are reinforcing each other under high volatility. They might wait one more candle for confirmation but already have strong evidence to consider a long. --- • . --- Disclaimer This indicator is strictly a technical analysis tool and does not constitute financial advice. All trading involves risk, including potential loss of capital. Past performance is not indicative of future results. Traders should: • Always backtest the “Market Zone Analyzer ” on their chosen symbols and timeframes before committing real capital. • Combine this tool with sound risk management, position sizing, and, if possible, fundamental analysis. • Understand that no indicator is foolproof; always be prepared for unexpected market moves. Goodluck -BullByte! ---
Pine Script® indicator
by BullByte
2
MTF_DrawingsLibrary 'MTF_Drawings' This library helps with drawing indicators and candle charts on all timeframes. FEATURES CHART DRAWING : Library provides functions for drawing High Time Frame (HTF) and Low Time Frame (LTF) candles. INDICATOR DRAWING : Library provides functions for drawing various types of HTF and LTF indicators. CUSTOM COLOR DRAWING : Library allows to color candles and indicators based on specific conditions. LINEFILLS : Library provides functions for drawing linefills. CATEGORIES The functions are named in a way that indicates they purpose: {Ind} : Function is meant only for indicators. {Hist} : Function is meant only for histograms. {Candle} : Function is meant only for candles. {Draw} : Function draws indicators, histograms and candle charts. {Populate} : Function generates necessary arrays required by drawing functions. {LTF} : Function is meant only for lower timeframes. {HTF} : Function is meant only for higher timeframes. {D} : Function draws indicators that are composed of two lines. {CC} : Function draws custom colored indicators. USAGE Import the library into your script. Before using any {Draw} function it is necessary to use a {Populate} function. Choose the appropriate one based on the category, provide the necessary arguments, and then use the {Draw} function, forwarding the arrays generated by the {Populate} function. This doesn't apply to {Draw_Lines}, {LineFill}, or {Barcolor} functions. EXAMPLE import Spacex_trader/MTF_Drawings/1 as tf //Request lower timeframe data. Security(simple string Ticker, simple string New_LTF, float Ind) => float Value = request.security_lower_tf(Ticker, New_LTF, Ind) Value Timeframe = input.timeframe('1', 'Timeframe: ') tf.Draw_Ind(tf.Populate_LTF_Ind(Security(syminfo.tickerid, Timeframe, ta.rsi(close, 14)), 498, color.purple), 1, true) FUNCTION LIST HTF_Candle(BarsBack, BodyBear, BodyBull, BordersBear, BordersBull, WickBear, WickBull, LineStyle, BoxStyle, LineWidth, HTF_Open, HTF_High, HTF_Low, HTF_Close, HTF_Bar_Index)   Populates two arrays with drawing data of the HTF candles.   Parameters:      BarsBack (int) : Bars number to display.      BodyBear (color) : Candle body bear color.      BodyBull (color) : Candle body bull color.      BordersBear (color) : Candle border bear color.      BordersBull (color) : Candle border bull color.      WickBear (color) : Candle wick bear color.      WickBull (color) : Candle wick bull color.      LineStyle (string) : Wick style (Solid-Dotted-Dashed).      BoxStyle (string) : Border style (Solid-Dotted-Dashed).      LineWidth (int) : Wick width.      HTF_Open (float) : HTF open price.      HTF_High (float) : HTF high price.      HTF_Low (float) : HTF low price.      HTF_Close (float) : HTF close price.      HTF_Bar_Index (int) : HTF bar_index.   Returns: Two arrays with drawing data of the HTF candles. LTF_Candle(BarsBack, BodyBear, BodyBull, BordersBear, BordersBull, WickBear, WickBull, LineStyle, BoxStyle, LineWidth, LTF_Open, LTF_High, LTF_Low, LTF_Close)   Populates two arrays with drawing data of the LTF candles.   Parameters:      BarsBack (int) : Bars number to display.      BodyBear (color) : Candle body bear color.      BodyBull (color) : Candle body bull color.      BordersBear (color) : Candle border bear color.      BordersBull (color) : Candle border bull color.      WickBear (color) : Candle wick bear color.      WickBull (color) : Candle wick bull color.      LineStyle (string) : Wick style (Solid-Dotted-Dashed).      BoxStyle (string) : Border style (Solid-Dotted-Dashed).      LineWidth (int) : Wick width.      LTF_Open (float ) : LTF open price.      LTF_High (float ) : LTF high price.      LTF_Low (float ) : LTF low price.      LTF_Close (float ) : LTF close price.   Returns: Two arrays with drawing data of the LTF candles. Draw_Candle(Box, Line, Offset)   Draws HTF or LTF candles.   Parameters:      Box (box ) : Box array with drawing data.      Line (line ) : Line array with drawing data.      Offset (int) : Offset of the candles.   Returns: Drawing of the candles. Populate_HTF_Ind(IndValue, BarsBack, IndColor, HTF_Bar_Index)   Populates one array with drawing data of the HTF indicator.   Parameters:      IndValue (float) : Indicator value.      BarsBack (int) : Indicator lines to display.      IndColor (color) : Indicator color.      HTF_Bar_Index (int) : HTF bar_index.   Returns: An array with drawing data of the HTF indicator. Populate_LTF_Ind(IndValue, BarsBack, IndColor)   Populates one array with drawing data of the LTF indicator.   Parameters:      IndValue (float ) : Indicator value.      BarsBack (int) : Indicator lines to display.      IndColor (color) : Indicator color.   Returns: An array with drawing data of the LTF indicator. Draw_Ind(Line, Mult, Exe)   Draws one HTF or LTF indicator.   Parameters:      Line (line ) : Line array with drawing data.      Mult (int) : Coordinates multiplier.      Exe (bool) : Display the indicator.   Returns: Drawing of the indicator. Populate_HTF_Ind_D(IndValue_1, IndValue_2, BarsBack, IndColor_1, IndColor_2, HTF_Bar_Index)   Populates two arrays with drawing data of the HTF indicators.   Parameters:      IndValue_1 (float) : First indicator value.      IndValue_2 (float) : Second indicator value.      BarsBack (int) : Indicator lines to display.      IndColor_1 (color) : First indicator color.      IndColor_2 (color) : Second indicator color.      HTF_Bar_Index (int) : HTF bar_index.   Returns: Two arrays with drawing data of the HTF indicators. Populate_LTF_Ind_D(IndValue_1, IndValue_2, BarsBack, IndColor_1, IndColor_2)   Populates two arrays with drawing data of the LTF indicators.   Parameters:      IndValue_1 (float ) : First indicator value.      IndValue_2 (float ) : Second indicator value.      BarsBack (int) : Indicator lines to display.      IndColor_1 (color) : First indicator color.      IndColor_2 (color) : Second indicator color.   Returns: Two arrays with drawing data of the LTF indicators. Draw_Ind_D(Line_1, Line_2, Mult, Exe_1, Exe_2)   Draws two LTF or HTF indicators.   Parameters:      Line_1 (line ) : First line array with drawing data.      Line_2 (line ) : Second line array with drawing data.      Mult (int) : Coordinates multiplier.      Exe_1 (bool) : Display the first indicator.      Exe_2 (bool) : Display the second indicator.   Returns: Drawings of the indicators. Barcolor(Box, Line, BarColor)   Colors the candles based on indicators output.   Parameters:      Box (box ) : Candle box array.      Line (line ) : Candle line array.      BarColor (color ) : Indicator color array.   Returns: Colored candles. Populate_HTF_Ind_D_CC(IndValue_1, IndValue_2, BarsBack, BullColor, BearColor, IndColor_1, HTF_Bar_Index)   Populates two array with drawing data of the HTF indicators with color based on: IndValue_1 >= IndValue_2 ? BullColor : BearColor.   Parameters:      IndValue_1 (float) : First indicator value.      IndValue_2 (float) : Second indicator value.      BarsBack (int) : Indicator lines to display.      BullColor (color) : Bull color.      BearColor (color) : Bear color.      IndColor_1 (color) : First indicator color.      HTF_Bar_Index (int) : HTF bar_index.   Returns: Three arrays with drawing and color data of the HTF indicators. Populate_LTF_Ind_D_CC(IndValue_1, IndValue_2, BarsBack, BullColor, BearColor, IndColor_1)   Populates two arrays with drawing data of the LTF indicators with color based on: IndValue_1 >= IndValue_2 ? BullColor : BearColor.   Parameters:      IndValue_1 (float ) : First indicator value.      IndValue_2 (float ) : Second indicator value.      BarsBack (int) : Indicator lines to display.      BullColor (color) : Bull color.      BearColor (color) : Bearcolor.      IndColor_1 (color) : First indicator color.   Returns: Three arrays with drawing and color data of the LTF indicators. Populate_HTF_Hist_CC(HistValue, IndValue_1, IndValue_2, BarsBack, BullColor, BearColor, HTF_Bar_Index)   Populates one array with drawing data of the HTF histogram with color based on: IndValue_1 >= IndValue_2 ? BullColor : BearColor.   Parameters:      HistValue (float) : Indicator value.      IndValue_1 (float) : First indicator value.      IndValue_2 (float) : Second indicator value.      BarsBack (int) : Indicator lines to display.      BullColor (color) : Bull color.      BearColor (color) : Bearcolor.      HTF_Bar_Index (int) : HTF bar_index   Returns: Two arrays with drawing and color data of the HTF histogram. Populate_LTF_Hist_CC(HistValue, IndValue_1, IndValue_2, BarsBack, BullColor, BearColor)   Populates one array with drawing data of the LTF histogram with color based on: IndValue_1 >= IndValue_2 ? BullColor : BearColor.   Parameters:      HistValue (float ) : Indicator value.      IndValue_1 (float ) : First indicator value.      IndValue_2 (float ) : Second indicator value.      BarsBack (int) : Indicator lines to display.      BullColor (color) : Bull color.      BearColor (color) : Bearcolor.   Returns: Two array with drawing and color data of the LTF histogram. Populate_LTF_Hist_CC_VA(HistValue, Value, BarsBack, BullColor, BearColor)   Populates one array with drawing data of the LTF histogram with color based on: HistValue >= Value ? BullColor : BearColor.   Parameters:      HistValue (float ) : Indicator value.      Value (float) : First indicator value.      BarsBack (int) : Indicator lines to display.      BullColor (color) : Bull color.      BearColor (color) : Bearcolor.   Returns: Two array with drawing and color data of the LTF histogram. Populate_HTF_Ind_CC(IndValue, IndValue_1, BarsBack, BullColor, BearColor, HTF_Bar_Index)   Populates one array with drawing data of the HTF indicator with color based on: IndValue >= IndValue_1 ? BullColor : BearColor.   Parameters:      IndValue (float) : Indicator value.      IndValue_1 (float) : Second indicator value.      BarsBack (int) : Indicator lines to display.      BullColor (color) : Bull color.      BearColor (color) : Bearcolor.      HTF_Bar_Index (int) : HTF bar_index   Returns: Two arrays with drawing and color data of the HTF indicator. Populate_LTF_Ind_CC(IndValue, IndValue_1, BarsBack, BullColor, BearColor)   Populates one array with drawing data of the LTF indicator with color based on: IndValue >= IndValue_1 ? BullColor : BearColor.   Parameters:      IndValue (float ) : Indicator value.      IndValue_1 (float ) : Second indicator value.      BarsBack (int) : Indicator lines to display.      BullColor (color) : Bull color.      BearColor (color) : Bearcolor.   Returns: Two arrays with drawing and color data of the LTF indicator. Draw_Lines(BarsBack, y1, y2, LineType, Fill)   Draws price lines on indicators.   Parameters:      BarsBack (int) : Indicator lines to display.      y1 (float) : Coordinates of the first line.      y2 (float) : Coordinates of the second line.      LineType (string) : Line type.      Fill (color) : Fill color.   Returns: Drawing of the lines. LineFill(Upper, Lower, BarsBack, FillColor)   Fills two lines with linefill HTF or LTF.   Parameters:      Upper (line ) : Upper line.      Lower (line ) : Lower line.      BarsBack (int) : Indicator lines to display.      FillColor (color) : Fill color.   Returns: Linefill of the lines. Populate_LTF_Hist(HistValue, BarsBack, HistColor)   Populates one array with drawing data of the LTF histogram.   Parameters:      HistValue (float ) : Indicator value.      BarsBack (int) : Indicator lines to display.      HistColor (color) : Indicator color.   Returns: One array with drawing data of the LTF histogram. Populate_HTF_Hist(HistValue, BarsBack, HistColor, HTF_Bar_Index)   Populates one array with drawing data of the HTF histogram.   Parameters:      HistValue (float) : Indicator value.      BarsBack (int) : Indicator lines to display.      HistColor (color) : Indicator color.      HTF_Bar_Index (int) : HTF bar_index.   Returns: One array with drawing data of the HTF histogram. Draw_Hist(Box, Mult, Exe)   Draws HTF or LTF histogram.   Parameters:      Box (box ) : Box Array.      Mult (int) : Coordinates multiplier.      Exe (bool) : Display the histogram.   Returns: Drawing of the histogram.
Pine Script® library
by Spacex_trader
TQ Gold / DXY (Weekly Macro)What this indicator does This indicator tracks the relative performance of gold versus the U.S. dollar using the Gold/DXY ratio. It helps determine whether gold’s strength is real (monetary) or merely nominal. Why Gold/DXY matters Gold rising with a rising dollar is not a strong signal. Gold rising against a weakening dollar signals monetary outperformance. This ratio filters out dollar noise and focuses on true purchasing-power strength. How it works The indicator calculates Gold ÷ DXY using weekly data. A 30-week SMA is applied to the ratio. Regimes are defined as: Bull: Ratio above a rising 30-week SMA (gold beating the dollar) Bear: Ratio below a falling 30-week SMA Neutral: Transition or range-bound periods A clear on-chart label shows the current regime. How to use it Use after confirming Gold Trend is Bull. When Gold/DXY is Bull, gold has a true monetary tailwind. When Gold/DXY is Bear, gold rallies are often fragile or dollar-driven. Neutral readings signal consolidation or regime change. Best timeframe Designed for weekly charts and macro analysis. Not intended for short-term trading signals.
Pine Script® indicator
by tradingqueen18
MTF Confluence Reporter - Trend & Momentum AlignmentThis indicator is a multi-timeframe confluence dashboard designed to answer one question clearly: “Across my key timeframes, is the market leaning Bullish, Bearish, or Mixed—and how strong is that lean?” It combines two separate “votes” per timeframe: 4MA Direction (trend alignment / slope bias) StochRSI State (momentum bias) Those votes are then blended into a single Confluence result, shown as a clean readout with a 0–100 Strength score, plus hysteresis to reduce flicker near the decision boundary. What you see in the table 1) 4MA This is the trend component. It summarizes whether the selected timeframes are generally Bull or Bear based on the moving-average direction logic (your 4MA engine). 2) Stoch This is the momentum component. It summarizes whether StochRSI across the selected timeframes is leaning Bull or Bear. 3) Qualified (YES/NO) A safety gate. “Qualified = YES” means the internal conditions required for a valid confluence read are met (i.e., enough alignment/consistency to treat the output as actionable). If it’s NO, treat the market as mixed / transitional and tighten risk. 4) Strength (0–100) Your blended score (trend + momentum). Higher = stronger agreement across timeframes. A simple way to interpret it: 80–100: Strong alignment (clean regime) 60–79: Moderate alignment (tradable, but expect chop) 50–59: Weak / transitioning (be cautious) < 50: Bearish side of the regime logic (or mixed turning down) 5) Strength Bar A visual “battery meter” for the Strength score. This is meant to be read at a glance during fast decision-making. 6) Confluence (BULL/BEAR) The actual regime output. This is the “final answer” based on the Strength score and hysteresis rules. 7) Hysteresis (Enter / Exit thresholds) This is the anti-flicker system. Example shown on the chart: Enter > 60 Exit < 50 Meaning: The script only “flips ON” a Bull regime when strength becomes convincingly Bullish (above 60). It won’t “flip OFF” until strength meaningfully weakens (below 50). This reduces rapid flipping during 50/50 conditions. How to use it (practical workflow) Step 1 — Use Confluence as your “market mode” BULL: Favor longs, trend-following entries, buying pullbacks. BEAR: Favor defense, shorts/hedges (if you trade them), or wait for reset. Qualified = NO: Reduce size, tighten stops, or wait—conditions are not clean. Step 2 — Use Strength to time aggressiveness Strength rising: Momentum is joining trend → entries tend to have better follow-through. Strength falling: Alignment is fading → take profit quicker or tighten risk. Step 3 — Use hysteresis as your “noise filter” If you’re a swing trader, hysteresis is your friend: Don’t overreact to a single bar change. Let the regime confirm and stay confirmed. Best use-cases Swing trading / position bias (daily/weekly context) Hedge decisions (when alignment flips and stays flipped) Filtering entries from other tools (only take signals that match the regime) Settings notes: This script is designed to be flexible: You can choose which timeframes matter most to you (commonly 1H / 4H / 1D / 1W / 1M). If your version includes weighting, you can tune weights to match your trading style (short-term vs swing). Thresholds (Enter/Exit) can be tightened for faster flips or widened for smoother regimes. Important notes / disclaimer (TradingView-safe) This tool is an informational confluence dashboard, not financial advice. No indicator can predict the future. Always confirm with market structure, risk management, and your own plan. Past behavior on a chart does not guarantee future results. How I Use This Indicator (Example Workflow) I use this tool primarily as a market-bias and risk-filter, not as a standalone entry signal. Establish the regime first I start by checking the Confluence row: BULL: I focus on long-side ideas and bullish continuation setups. BEAR: I become defensive, avoid counter-trend trades, or look for short/hedge opportunities where applicable. Qualified = NO: I treat the market as transitional and reduce risk. Use Strength to adjust aggressiveness When Strength is elevated and rising, I am more comfortable holding positions and allowing trades more room to develop. When Strength is declining, I tighten stops, reduce position size, or manage trades more actively. Let hysteresis do the work I do not react to every minor fluctuation near the midpoint. The built-in hysteresis thresholds help me stay aligned with the prevailing regime instead of over-trading during indecision. Entries come from other tools Actual entries are taken using price structure, support/resistance, or other indicators. This dashboard simply tells me whether the broader environment supports that idea or not. In short, I treat this indicator as a context and confirmation layer—it helps answer when to be aggressive, cautious, or patient.
Pine Script® indicator
by Dinjin
Market Regime | NY Session Killzones Indicator [ApexLegion]Market Regime | NY Session Killzones Indicator Introduction and Theoretical Background The Market Regime | NY Session Killzones indicator is designed exclusively for New York market hours (07:00-16:00 ET). Unlike universal indicators that attempt to function across disparate global sessions, this tool employs session-specific calibration to target the distinct liquidity characteristics of the NY trading day: Pre-Market structural formation (08:00-09:30), the Morning breakout window (09:30-12:00), and the Afternoon Killzone (13:30-16:00)—periods when institutional order flow exhibits the highest concentration and most definable technical structure. By restricting its operational scope to these statistically significant time windows, the indicator focuses on signal relevance while filtering the noise inherent in lower-liquidity overnight or extended-hours trading environments. I. TECHNICAL RATIONALE: THE PRINCIPLE OF CONTEXTUAL FUSION 1. The Limitation of Acontextual Indicators Traditional technical indicators often fail because they treat every bar and every market session equally, applying static thresholds (e.g., RSI > 70) without regard for the underlying market structure or liquidity environment. However, institutional volume and market volatility are highly dependent on the time of day (session) and the prevailing long-term risk environment. This indicator was developed to address this "contextual deficit" by fusing three distinct yet interdependent analytical layers: • Time and Structure (Macro): Identifying high-probability trading windows (Killzones) and critical structural levels (Pre-Market Range, PDH/PDL). • Volatility and Scoring (Engine): Normalizing intraday momentum against annual volatility data to create an objective, statistically grounded AI Score. • Risk Management (Execution): Implementing dynamic, volatility-adjusted Stop Loss (SL) and Take Profit (TP) parameters based on the Average True Range (ATR). 2. The Mandate for 252-Day Normalization (Z-Score) What makes this tool unique is its 252-day Z-Score normalization engine that transforms raw momentum readings into statistically grounded probability scores, allowing the same indicator to deliver consistent, context-aware signals across any timeframe—from 1-minute scalping to 1-hour swing trades—without manual recalibration. THE PROBLEM OF SCALE INVARIANCE A high Relative Strength Index (RSI) reading on a 1-minute chart has a completely different market implication than a high RSI reading on a Daily chart. Simple percentage-based thresholds (like 70 or 30) do not provide true contextual significance. A sudden spike in momentum may look extreme on a 5-minute chart, but if it is statistically insignificant compared to the overall volatility of the last year, it may be a poor signal. THE SOLUTION: CROSS-TIMEFRAME Z-SCORE NORMALIZATION This indicator utilizes the Pine Script function request.security to reference the Daily timeframe for calculating the mean (μ) and standard deviation (σ) of a momentum oscillator (RSI) over the past 252 trading days (one year). The indicator then calculates the Z-Score (Z) for the current bar's raw momentum (x): Z = (x - μ) / σ Core Implementation: float raw_rsi = ta.rsi(close, 14) // x = request.security(syminfo.tickerid, "D", , // σ (252 days) lookahead=barmerge.lookahead_on) float cur_rsi_norm = d_rsi_std != 0 ? (raw_rsi - d_rsi_mean) / d_rsi_std : 0.0 // Z This score provides an objective measurement of current intraday momentum significance by evaluating its statistical extremity against the yearly baseline of daily momentum. This standardized approach provides the scoring engine with consistent, global contextual information, independent of the chart's current viewing timeframe. II. CORE COMPONENTS AND TECHNICAL ANALYSIS BREAKDOWN 1. TIME AND SESSION ANALYSIS (KILLZONES AND BIAS) The indicator visually segments the trading day based on New York (NY) trading sessions, aligning the analysis with periods of high institutional liquidity events. Pre-Market (PRE) • Function: Defines the range before the core market opens. This range establishes structural support and resistance levels (PMH/PML). • Technical Implementation: Uses a dedicated Session input (ny_pre_sess). The High and Low values (pm_h_val/pm_l_val) within this session are stored and plotted for structural reference. • Smart Extension Logic: PMH/PML lines are automatically extended until the next Pre-Market session begins, providing continuous support/resistance references overnight. NY Killzones (AM/PM) • Function: Highlights high-probability volatility windows where institutional liquidity is expected to be highest (e.g., NY open, lunch, NY close). • Technical Implementation: Separate session inputs (kz_ny_am, kz_ny_pm) are utilized to draw translucent background fills, providing a clear visual cue for timing. Market Regime Bias • Function: Determines the initial directional premise for the trading day. The bias is confirmed when the price breaks either the Pre-Market High (PMH) or the Pre-Market Low (PML). • Technical Implementation: Involves the comparison of the close price against the predefined structural levels (check_h for PMH, check_l for PML). The variable active_bias is set to Bullish or Bearish upon confirmed breakout. Trend Bar Coloring • Function: Applies a visual cue to the bars based on the established regime (Bullish=Cyan, Bearish=Red). This visual filter helps mitigate noise from counter-trend candles. • Technical Implementation: The Pine Script barcolor() function is tied directly to the value of the determined active_bias. 2. VOLATILITY NORMALIZED SCORING ENGINE The internal scoring mechanism accumulates points from multiple market factors to determine the strength and validity of a signal. The purpose is to apply a robust filtering mechanism before generating an entry. The score accumulation logic is based on the following factors: • Market Bias Alignment (+3 Points): Points are awarded for conformance with the determined active_bias (Bullish/Bearish). • VWAP Alignment (+2 Points): Assesses the position of the current price relative to the Volume-Weighted Average Price (VWAP). Alignment suggests conformity with the average institutional transaction price. • Volume Anomaly (+2 Points): Detects a price move accompanied by an abnormally high relative volume (odd_vol_spike). This suggests potential institutional participation or significant order flow. • VIX Integration (+2 Points): A score derived from the CBOE VIX index, assessing overall market stability and stress. Stable VIX levels add points, while high VIX levels (stress regimes) remove points or prevent signal generation entirely. • ML Probability Score (+3 Points): This is the core predictive engine. It utilizes a Log-Manhattan Distance Kernel to compare the current market state against historical volatility patterns. The script implements a Log-linear distance formula (log(1 + |Δ|) ). This approach mathematically dampens the impact of extreme volatility spikes (outliers), ensuring that the similarity score reflects true structural alignment rather than transient market noise. Core Technical Logic (Z-Score Normalization) float cur_rsi_norm = d_rsi_std != 0 ? (raw_rsi - d_rsi_mean) / d_rsi_std : 0.0 • Technical Purpose: This line calculates the Z-Score (cur_rsi_norm) of the current momentum oscillator reading (raw_rsi) by normalizing it against the mean (d_rsi_mean) and standard deviation (d_rsi_std) derived from 252 days of Daily momentum data. If the standard deviation is zero (market is perfectly flat), it safely returns 0.0 to prevent division by zero runtime errors. This allows the AI's probability score to be based on the current signal's significance within the context of the entire trading year. 3. EXECUTION AND RISK MANAGEMENT (ATR MODEL) The indicator utilizes the Average True Range (ATR) volatility model. This helps risk management scale dynamically with market volatility by allowing users to define TP/SL distances independently based on the current ATR. Stop Loss Multiplier (sl_mult) • Function: Sets the Stop Loss (SL) distance as a configurable multiple of the current ATR (e.g., 1.5 × ATR). • Technical Logic: The price level is calculated as: last_sl_price := close - (atr_val * sl_mult). The mathematical sign is reversed for short trades. Take Profit Multiplier (tp_mult) • Function: Sets the Take Profit (TP) distance as a configurable multiple of the current ATR (e.g., 3.0 × ATR). • Technical Logic: The price level is calculated as: last_tp_price := close + (atr_val * tp_mult). The mathematical sign is reversed for short trades. Structural SL Option • Function: Provides an override to the ATR-based SL calculation. When enabled, it forces the Stop Loss to the Pre-Market High/Low (PMH/PML) level, aligning the stop with a key institutional structural boundary. • Technical Logic: The indicator checks the use_struct_sl input. If true, the calculated last_sl_price is overridden with either pm_h_val or pm_l_val, dependent on the specific trade direction. Trend Continuation Logic • Function: Enables signal generation in established, strong trends (typically in the Afternoon session) based on follow-through momentum (a new high/low of the previous bar) combined with a high Signal Score, rather than exclusively relying on the initial PMH/PML breakout. • Technical Logic: For a long signal, the is_cont_long logic specifically requires checks like active_bias == s_bull AND close > high , confirming follow-through momentum within the established regime. Smart Snapping & Cleanup (16:00 Market Close) • Function: To maintain chart cleanliness, all trade boxes (TP/SL), AI Prediction zones, Killzone overlays (NY AM/PM), and Liquidity lines (PDH/PDL) are automatically "snapped" and cut off precisely at 16:00 NY Time (Market Close). • Technical Logic: When is_market_close condition is met (hour == 16 and minute == 0), the script executes cleanup logic that: ◦ Closes active trades and evaluates final P&L ◦ Snaps all TP/SL box widths to current bar ◦ Truncates AI Prediction ghost boxes at market close ◦ Cuts off NY AM/PM Killzone background fills ◦ Terminates PDH/PDL line extensions ◦ Prevents visual clutter from extending into post-market sessions 4. LIQUIDITY AND STRUCTURAL ANALYSIS The indicator plots key structural levels that serve as high-probability magnet zones or areas of potential liquidity absorption. • Pre-Market High/Low (PMH/PML): These are the high and low established during the configured pre-market session (ny_pre_sess). They define the primary structural breakout level for the day, often serving as the initial market inflection point or the key entry level for the morning session. • PDH (Previous Day High): The high of the calendar day immediately preceding the current bar. This represents a key Liquidity Pool; large orders are often placed above this level, making it a frequent target for stop hunts or liquidity absorption by market makers. • PDL (Previous Day Low): The low of the calendar day immediately preceding the current bar. This also represents a key Liquidity Pool and a high-probability reversal or accumulation point, particularly during the Killzones. FIFO Array Management The indicator uses FIFO (First-In-First-Out) array structures to manage liquidity lines and labels, automatically deleting the oldest objects when the count exceeds 500 to comply with drawing object limits. 5. AI PREDICTION BOX (PREDICTIVE MODEL) Function: Analyzes AI scores and volatility to project predicted killzone ranges and duration with asymmetric directional bias. A. DIRECTIONAL BIAS (ASYMMETRIC EXPANSION) The prediction model calculates directional probability using the ML kernel's 252-day Normalized RSI (Z-Score) and Relative Volume (RVOL). The prediction box dynamically adjusts its range based on this probability to provide immediate visual feedback on high-probability direction. Bullish Scenario (ml_prob > 1.0): • Upper Range: Expands significantly (1.5x multiplier) to show the aggressive upside target • Lower Range: Tightens (0.5x multiplier) to show the invalidation level • Visual Intent: The box is visibly skewed upward, immediately communicating bullish bias without requiring numerical analysis. Bearish Scenario (ml_prob < -1.0): • Upper Range: Tightens (0.5x multiplier) to show the invalidation level • Lower Range: Expands significantly (1.5x multiplier) to show the aggressive downside target • Visual Intent: The box is visibly skewed downward, immediately communicating bearish bias. Neutral Scenario (-1.0 < ml_prob < 1.0): Both ranges use balanced multipliers, creating a symmetrical box that indicates uncertainty. B. DYNAMIC VOLATILITY BOOSTER (SESSION-BASED ADAPTATION) The prediction box adjusts its volatility multiplier based on the current session and market conditions to account for intraday volatility patterns. AM Session (Morning: 07:00-12:00): • Base Multiplier: 1.0x (Neutral Base) • Logic: Morning sessions often contain false breakouts and noise. The base multiplier starts neutral to avoid over-projecting during consolidation. • Trend Booster: Multiplier jumps to 1.5x when: Price > London Session Open AND AI is Bullish (ml_prob > 0), OR Price < London Session Open AND AI is Bearish (ml_prob < 0) • Logic: When the London trend (typically 03:00-08:00 NY time) aligns with the AI model's directional conviction, the indicator aggressively targets higher volatility expansion. This filters for "institutional follow-through" rather than random morning chop. PM Session (Afternoon: 13:00-16:00): • Fixed Multiplier: 1.8x • Logic: The PM session, particularly the 13:30-16:00 ICT Silver Bullet window, often contains the "True Move" of the day. A higher baseline multiplier is applied to emphasize this session's significance over morning noise. Safety Floor: A minimum range of 0.2% of the current price is enforced regardless of volatility conditions. • Purpose: Maintains the prediction box visibility during extreme low-volatility consolidation periods where ATR might collapse to near-zero values. Volatility Clamp Protection: Maximum volatility is capped at three times the current ATR value. During flash crashes, circuit breaker halts, or large overnight gaps, raw volatility calculations can spike to extreme levels. This clamp prevents prediction boxes from expanding to unrealistic widths. Technical Implementation: f_get_ai_multipliers(float _prob) => float _abs_prob = math.abs(_prob) float _range_mult = 1.0 float _dur_mult = 1.0 if _abs_prob > 30 _range_mult := 1.8 else if _abs_prob > 10 _range_mult := 1.2 else _range_mult := 0.7 C. PRACTICAL INTERPRETATION • Wide Upper Range + Tight Lower Range: Strong bullish conviction. The model expects significant upside with limited downside risk. • Tight Upper Range + Wide Lower Range: Strong bearish conviction. The model expects significant downside with limited upside. • Symmetrical Range: Neutral/uncertain market. Wait for directional confirmation before entry. • Large Box (Extended Duration): High-confidence prediction expecting sustained movement. • Small Box (Short Duration): Low-confidence or choppy conditions. Expect quick resolution. III. PRACTICAL USAGE GUIDE: METHODOLOGY AND EXECUTION A. ESTABLISHING TRADING CONTEXT (THE THREE CHECKS) The primary goal of the dashboard is to filter out low-probability trade setups before they occur. • Timeframe Selection: Although the core AI is normalized to the Daily context, the indicator performs optimally on intraday timeframes (e.g., 5m, 15m) where session-based volatility is most pronounced. • PHASE Check (Timing): Always confirm the current phase. The highest probability signals typically occur within the visually highlighted NY AM/PM Killzones because this is when institutional liquidity and volume are at their peak. Signals outside these zones should be treated with skepticism. • MARKET REGIME Check (Bias): Ensure the signal (BUY/SELL arrow) aligns with the established MARKET REGIME bias (BULLISH/BEARISH). Counter-bias signals are technically allowed if the score is high, but they represent a higher risk trade. • VIX REGIME Check (Risk): Review the VIX REGIME for overall market stress. Periods marked DANGER (high VIX) indicate elevated volatility and market uncertainty. During DANGER regimes, reducing position size or choosing a wider SL Multiplier is advisable. B. DASHBOARD INTERPRETATION (THE REAL-TIME STATUS DISPLAY) The indicator features a non-intrusive dashboard that provides real-time, context-aware information based on the core analytical engines. PHASE: (PRE-MARKET, NY-AM, LUNCH, NY-PM) • Meaning: Indicates the current institutional session time. This is derived from the customizable session inputs. • Interpretation: Signals generated during NY-AM or NY-PM (Killzones) are generally considered higher-probability due to increased institutional participation and liquidity. MARKET REGIME: (BULLISH, BEARISH, NEUTRAL) • Meaning: The established directional bias for the trading day, confirmed by the price breaking above the Pre-Market High (PMH) or below the Pre-Market Low (PML). • Interpretation: Trading with the established regime (e.g., taking a BUY signal when the regime is BULLISH) is the primary method. NEUTRAL indicates that the PMH/PML boundary has not yet been broken, suggesting market ambiguity. VIX REGIME: (STABLE, DANGER) • Meaning: A measure of overall market stress and stability, based on the CBOE VIX index integration. The thresholds (20.0 and 35.0 default) are customizable by the user. • Interpretation: STABLE indicates stable volatility, favoring momentum trades. DANGER (VIX > 35.0) indicates extreme stress; signals generated in this environment require caution and often necessitate smaller position sizing. SIGNAL SCORE: (0 to 10+ Points) • Meaning: The accumulated score derived from the VOLATILITY NORMALIZED AI SCORING ENGINE, factoring in bias, VWAP alignment, volume, and the Z-Score probability. • Interpretation: The indicator generates a signal when this score meets or exceeds the Minimum Entry Score (default 3). A higher score (e.g., 7+) indicates greater statistical confluence and a stronger potential entry. AI PROBABILITY: (Bull/Bear %) • Meaning: Directional probability derived from the ML kernel, expressed as a percentage with Bull/Bear label. • Interpretation: Higher absolute values (>20%) indicate stronger directional conviction from the ML model. LIVE METRICS SECTION: • STATUS: Shows current trade state (LONG, SHORT, or INACTIVE) • ENTRY: Displays the entry price for active trades • TARGET: Shows the calculated Take Profit level • ROI | KILL ZONE: ◦ For Active Trades: Displays real-time P&L percentage during NY session hours. ◦ At Market Close (16:00 NY): Since this is a NY session-specific indicator, any active position is automatically evaluated and closed at 16:00. The final result (VALIDATED or INVALIDATED) is determined based on whether the trade reached profit or loss at market close. ◦ Result Persistence: The killzone result (VALIDATED/INVALIDATED) remains displayed on the dashboard until the next NY AM KILLZONE session begins, providing a clear performance reference for the previous trading day. Note: If a trade is still trending at 16:00, it will be force-closed and evaluated at that moment, as the indicator operates strictly within NY trading hours. C. SIGNAL GENERATION AND ENTRY LOGIC The indicator generates signals based on two distinct technical setups, both of which require the accumulated SIGNAL SCORE to be above the configured Minimum Entry Score. Breakout Entry • Trigger Condition: Price closes beyond the Pre-Market High (PMH) or Low (PML). • Rationale: This setup targets the initial directional movement for the day. A breakout confirms the institutional bias by decisively breaking the first major structural boundary, making the signal high-probability. Continuation Entry • Trigger Condition: The market is already in an established regime (e.g., BULLISH), and the price closes above the high (or below the low) of the previous bar, while the SIGNAL SCORE remains high. Requires the Allow Trend Continuation parameter to be active. • Rationale: This setup targets follow-through trades, typically in the afternoon session, capturing momentum after the morning's direction has been confirmed. This filters for sustainability in the established trend. Execution: Execute the trade immediately upon the close of the bar that prints the BUY or SELL signal arrow. D. MANAGING RISK AND EXITS 1. RISK PARAMETER SELECTION The indicator immediately draws the dynamic TP/SL zones upon entry. • Volatility-Based (Recommended Default): By setting the SL Multiplier (e.g., 1.5) and the TP Multiplier (e.g., 3.0), the indicator enforces a constant, dynamically sized risk-to-reward ratio (e.g., 1:2 in this example). This helps that risk management scales proportionally with the current market volatility (ATR). • Structural Override: Selecting the Use Structural SL parameter fixes the stop-loss not to the ATR calculation, but to the more significant structural level of the PMH or PML. This is utilized by traders who favor institutional entry rules where the stop is placed behind the liquidity boundary. 2. EXIT METHODS • Hard Exit: Price hits the visual TP or SL box boundary. • Soft Exit (Momentum Decay Filter): If the trade is active and the SIGNAL SCORE drops below the Exit Score Threshold (default 3), it indicates that the momentum supporting the trade has significantly collapsed. This serves as a momentum decay filter, prompting the user to consider a manual early exit even if the SL/TP levels have not been hit, thereby preserving capital during low-momentum consolidation. • Market Close Auto-Exit: At 16:00 NY time, any active trade is automatically closed and classified as VALIDATED (profit) or INVALIDATED (loss) based on current price vs. entry price. IV. PARAMETER REFERENCE AND CONFIGURATION A. GLOBAL SETTINGS • Language (String, Default: English): Selects the language for the dashboard and notification text. Options: English, Korean, Chinese, Spanish, Portuguese, Russian, Ukrainian, Vietnamese. B. SESSION TIMES (3 BOX SYSTEM) • PRE-MARKET (Session, Default: 0800-0930): Defines the session range used for Pre-Market High/Low (PMH/PML) structural calculation. • REGULAR (Morning) (Session, Default: 0930-1200): Defines the core Morning trading session. • AFTERNOON (PM) (Session, Default: 1300-1600): Defines the main Afternoon trading session. • Timezone (String, Default: America/New_York): Sets the timezone for all session and time-based calculations. C. NY KILLZONES (OVERLAYS) • Show NY Killzones (Bool, Default: True): Toggles the translucent background fills that highlight high-probability trading times (Killzones). • NY AM Killzone (Session, Default: 0700-1000): Defines the specific time window for the first key liquidity surge (Open overlap). • NY PM Killzone (Session, Default: 1330-1600): Defines the afternoon liquidity window, aligned with the ICT Silver Bullet and PM Trend entry timing. • Allow Entry in Killzones (Bool, Default: True): Enables or disables signal generation specifically during the defined Killzone hours. • Activate AI Prediction Box (Bool, Default: True): Toggles the drawing of the predicted target range boxes on the chart. D. CORE SCORING ENGINE • Minimum Entry Score (Int, Default: 3): The lowest accumulated score required for a Buy/Sell signal to be generated and plotted. • Allow Trend Continuation (Bool, Default: True): Enables the secondary entry logic that fires signals based on momentum in an established trend. • Force Ignore Volume (Bool, Default: False): Overrides the volume checks in the scoring engine. Useful for markets where volume data is unreliable or nonexistent. • Force Show Signals (Ignore Score) (Bool, Default: False): Debug mode that displays all signals regardless of score threshold. • Integrate CBOE:VIX (Bool, Default: True): Enables the connection to the VIX index for market stress assessment. • Stable VIX (<) (Float, Default: 20.0): VIX level below which market stress is considered low (increases score). • Stress VIX (>) (Float, Default: 35.0): VIX level above which market stress is considered high (decreases score/flags DANGER). • Use ML Probability (Bool, Default: True): Activates the volatility-normalized AI Z-Score kernel. Disabling this removes the cross-timeframe normalization filter. • Max Learning History (Int, Default: 2000): Maximum number of bars stored in the ML training arrays. • Normalization Lookback (252 Days) (Int, Default: 252): The number of DAILY bars used to calculate the Z-Score mean and standard deviation (representing approximately 1 year of data). E. RISK MANAGEMENT (ATR MODEL) • Use Structural SL (Bool, Default: False): Overrides the ATR-based Stop Loss distance to use the Pre-Market High/Low as the fixed stop level. • Stop Loss Multiplier (x ATR) (Float, Default: 1.5): Defines the Stop Loss distance in multiples of the current Average True Range (ATR). • Take Profit Multiplier (x ATR) (Float, Default: 3.0): Defines the Take Profit distance in multiples of the current Average True Range (ATR). • Exit Score Threshold (<) (Int, Default: 3): The minimum score below which an active trade is flagged for a Soft Exit due to momentum collapse. F. VISUAL SETTINGS • Show Dashboard (Bool, Default: True): Toggles the real-time data panel. • Show NY Killzones (Bool, Default: True): Toggles killzone background fills. • Show TP/SL Zones (Bool, Default: True): Toggles the drawing of Take Profit and Stop Loss boxes. • Show Pre-Market Extensions (Bool, Default: True): Extends PM High/Low lines across the entire chart for support/resistance reference. • Activate AI Prediction Box (Bool, Default: True): Enable or disable the predictive range projection. • Light Mode Optimization (Bool, Default: True): Toggles dashboard and plot colors for optimal visibility on white (light) chart backgrounds. • Enforce Trend Coloring (Bool, Default: True): Forces candle colors based on Market Regime (Bullish=Cyan, Bearish=Pink) to emphasize trend direction. • Label Size (String, Default: Normal): Options: Tiny, Small, Normal. G. LIQUIDITY POOLS (PDH/PDL) • Show Liquidity Lines (Bool, Default: True): Toggles the display of the Previous Day High (PDH) and Low (PDL) lines. • Liquidity High Color (Color, Default: Green): Color setting for the PDH line. • Liquidity Low Color (Color, Default: Red): Color setting for the PDL line. 🔔 ALERT CONFIGURATION GUIDE The indicator is equipped with specific alert conditions. How to Set Up an Alert: Click the "Alert" (Clock icon) in the top TradingView toolbar. Select "Market Regime NY Session " from the Condition dropdown menu. Choose one of the specific trigger conditions below depending on your strategy: 🚀 Available Alert Conditions 1. BUY (Long Entry) Trigger: Fires immediately when a confirmed Bullish Setup is detected. Conditions: Market Bias is Bullish (or valid Continuation) + Signal Score ≥ Minimum Entry Score. Usage: Use this alert to open new Long positions or close existing Short positions. 2. SELL (Short Entry) Trigger: Fires immediately when a confirmed Bearish Setup is detected. Conditions: Market Bias is Bearish (or valid Continuation) + Signal Score ≥ Minimum Entry Score. Usage: Use this alert to open new Short positions or close existing Long positions. V. IMPORTANT TECHNICAL LIMITATIONS ⚠️ Intraday Only (Timeframe Compatibility) This indicator is strictly designed for Intraday Timeframes (1m to 4h). Daily/Weekly Charts: The session logic (e.g., "09:30-16:00") cannot function on Daily bars because a single bar encompasses the entire session. Session boxes, TP/SL zones, and AI prediction boxes will NOT draw on the Daily timeframe. Only the PDH/PDL liquidity lines remain visible on Daily charts. This is expected behavior, not a limitation. Maximum Supported Timeframe: All visual components (session boxes, killzone overlays, TP/SL zones, AI prediction boxes) are displayed up to the 4-hour timeframe. Above this timeframe, only PDH/PDL lines and the dashboard remain functional. ⚠️ Drawing Object Limit (Max 500) A single script can display a maximum of 500 drawing objects (boxes/lines) simultaneously. On lower timeframes (e.g., 1-minute), where many signals and session boxes are generated, older history (typically beyond 10-14 days) will automatically disappear to make room for new real-time data. For deeper historical backtesting visualization, switch to higher timeframes (e.g., 15m, 1h). The indicator implements FIFO array management to comply with this limit while maintaining the most recent and relevant visual data. VI. PRACTICAL TRADING TIPS AND BEST PRACTICES • Killzone Confirmation: The highest statistical validity is observed when a high-score signal occurs directly within a visible NY AM/PM Killzone. Use the Killzones as a strict time filter. • Liquidity Awareness (PDH/PDL): Treat the Previous Day High (PDH) and Low (PDL) lines as magnets. If your dynamic Take Profit (TP) is placed just above PDH, consider adjusting your target slightly below PDH or utilizing the Soft Exit, as liquidity absorption at these levels often results in sudden, sharp reversals that stop out a trade just before the target is reached. • VIX as a Position Sizer: During DANGER VIX regimes, the resulting high volatility means the ATR value will be large. It is prudent to either reduce the SL Multiplier or, more commonly, reduce the overall position size to maintain a constant currency risk exposure per trade. • Continuation Filter Timing: Trend Continuation signals are most effective during the Afternoon (PM) session when the morning's directional breakout has had time to establish a strong, clear, and sustainable trend. Avoid using them in the initial AM session when the direction is still being contested. • 16:00 Market Close Rule: All trades, boxes, and lines are automatically cleaned up at 16:00 NY time. This prevents overnight chart clutter and maintains visual clarity. VII. DISCLAIMER & RISK WARNINGS • Educational Purpose Only This indicator, including all associated code, documentation, and visual outputs, is provided strictly for educational and informational purposes. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. • No Guarantee of Performance Past performance is not indicative of future results. All metrics displayed on the dashboard (including "ROI" and trade results) are theoretical calculations based on historical data. These figures do not account for real-world trading factors such as slippage, liquidity gaps, spread costs, or broker commissions. • High-Risk Warning Trading cryptocurrencies, futures, and leveraged financial products involves a substantial risk of loss. The use of leverage can amplify both gains and losses. Users acknowledge that they are solely responsible for their trading decisions and should conduct independent due diligence before executing any trades. • Software Limitations The software is provided "as is" without warranty. Users should be aware that market data feeds on analysis platforms may experience latency or outages, which can affect signal generation accuracy.
Pine Script® indicator
by ApexLegion
ORB Fusion🎯 CORE INNOVATION: INSTITUTIONAL ORB FRAMEWORK WITH FAILED BREAKOUT INTELLIGENCE ORB Fusion represents a complete institutional-grade Opening Range Breakout system combining classic Market Profile concepts (Initial Balance, day type classification) with modern algorithmic breakout detection, failed breakout reversal logic, and comprehensive statistical tracking. Rather than simply drawing lines at opening range extremes, this system implements the full trading methodology used by professional floor traders and market makers—including the critical concept that failed breakouts are often higher-probability setups than successful breakouts . The Opening Range Hypothesis: The first 30-60 minutes of trading establishes the day's value area —the price range where the majority of participants agree on fair value. This range is formed during peak information flow (overnight news digestion, gap reactions, early institutional positioning). Breakouts from this range signal directional conviction; failures to hold breakouts signal trapped participants and create exploitable reversals. Why Opening Range Matters: 1. Information Aggregation : Opening range reflects overnight news, pre-market sentiment, and early institutional orders. It's the market's initial "consensus" on value. 2. Liquidity Concentration : Stop losses cluster just outside opening range. Breakouts trigger these stops, creating momentum. Failed breakouts trap traders, forcing reversals. 3. Statistical Persistence : Markets exhibit range expansion tendency —when price accepts above/below opening range with volume, it often extends 1.0-2.0x the opening range size before mean reversion. 4. Institutional Behavior : Large players (market makers, institutions) use opening range as reference for the day's trading plan. They fade extremes in rotation days and follow breakouts in trend days. Historical Context: Opening Range Breakout methodology originated in commodity futures pits (1970s-80s) where floor traders noticed consistent patterns: the first 30-60 minutes established a "fair value zone," and directional moves occurred when this zone was violated with conviction. J. Peter Steidlmayer formalized this observation in Market Profile theory, introducing the "Initial Balance" concept—the first hour (two 30-minute periods) defining market structure. 📊 OPENING RANGE CONSTRUCTION Four ORB Timeframe Options: 1. 5-Minute ORB (0930-0935 ET): Captures immediate market direction during "opening drive"—the explosive first few minutes when overnight orders hit the tape. Use Case: • Scalping strategies • High-frequency breakout trading • Extremely liquid instruments (ES, NQ, SPY) Characteristics: • Very tight range (often 0.2-0.5% of price) • Early breakouts common (7 of 10 days break within first hour) • Higher false breakout rate (50-60%) • Requires sub-minute chart monitoring Psychology: Captures panic buyers/sellers reacting to overnight news. Range is small because sample size is minimal—only 5 minutes of price discovery. Early breakouts often fail because they're driven by retail FOMO rather than institutional conviction. 2. 15-Minute ORB (0930-0945 ET): Balances responsiveness with statistical validity. Captures opening drive plus initial reaction to that drive. Use Case: • Day trading strategies • Balanced scalping/swing hybrid • Most liquid instruments Characteristics: • Moderate range (0.4-0.8% of price typically) • Breakout rate ~60% of days • False breakout rate ~40-45% • Good balance of opportunity and reliability Psychology: Includes opening panic AND the first retest/consolidation. Sophisticated traders (institutions, algos) start expressing directional bias. This is the "Goldilocks" timeframe—not too reactive, not too slow. 3. 30-Minute ORB (0930-1000 ET): Classic ORB timeframe. Default for most professional implementations. Use Case: • Standard intraday trading • Position sizing for full-day trades • All liquid instruments (equities, indices, futures) Characteristics: • Substantial range (0.6-1.2% of price) • Breakout rate ~55% of days • False breakout rate ~35-40% • Statistical sweet spot for extensions Psychology: Full opening auction + first institutional repositioning complete. By 10:00 AM ET, headlines are digested, early stops are hit, and "real" directional players reveal themselves. This is when institutional programs typically finish their opening positioning. Statistical Advantage: 30-minute ORB shows highest correlation with daily range. When price breaks and holds outside 30m ORB, probability of reaching 1.0x extension (doubling the opening range) exceeds 60% historically. 4. 60-Minute ORB (0930-1030 ET) - Initial Balance: Steidlmayer's "Initial Balance"—the foundation of Market Profile theory. Use Case: • Swing trading entries • Day type classification • Low-frequency institutional setups Characteristics: • Wide range (0.8-1.5% of price) • Breakout rate ~45% of days • False breakout rate ~25-30% (lowest) • Best for trend day identification Psychology: Full first hour captures A-period (0930-1000) and B-period (1000-1030). By 10:30 AM ET, all early positioning is complete. Market has "voted" on value. Subsequent price action confirms (trend day) or rejects (rotation day) this value assessment. Initial Balance Theory: IB represents the market's accepted value area . When price extends significantly beyond IB (>1.5x IB range), it signals a Trend Day —strong directional conviction. When price remains within 1.0x IB, it signals a Rotation Day —mean reversion environment. This classification completely changes trading strategy. 🔬 LTF PRECISION TECHNOLOGY The Chart Timeframe Problem: Traditional ORB indicators calculate range using the chart's current timeframe. This creates critical inaccuracies: Example: • You're on a 5-minute chart • ORB period is 30 minutes (0930-1000 ET) • Indicator sees only 6 bars (30min ÷ 5min/bar = 6 bars) • If any 5-minute bar has extreme wick, entire ORB is distorted The Problem Amplifies: • On 15-minute chart with 30-minute ORB: Only 2 bars sampled • On 30-minute chart with 30-minute ORB: Only 1 bar sampled • Opening spike or single large wick defines entire range (invalid) Solution: Lower Timeframe (LTF) Precision: ORB Fusion uses `request.security_lower_tf()` to sample 1-minute bars regardless of chart timeframe: ``` For 30-minute ORB on 15-minute chart: - Traditional method: Uses 2 bars (15min × 2 = 30min) - LTF Precision: Requests thirty 1-minute bars, calculates true high/low ``` Why This Matters: Scenario: ES futures, 15-minute chart, 30-minute ORB • Traditional ORB: High = 5850.00, Low = 5842.00 (range = 8 points) • LTF Precision ORB: High = 5848.50, Low = 5843.25 (range = 5.25 points) Difference: 2.75 points distortion from single 15-minute wick hitting 5850.00 at 9:31 AM then immediately reversing. LTF precision filters this out by seeing it was a fleeting wick, not a sustained high. Impact on Extensions: With inflated range (8 points vs 5.25 points): • 1.5x extension projects +12 points instead of +7.875 points • Difference: 4.125 points (nearly $200 per ES contract) • Breakout signals trigger late; extension targets unreachable Implementation: ```pinescript getLtfHighLow() => float ha = request.security_lower_tf(syminfo.tickerid, "1", high) float la = request.security_lower_tf(syminfo.tickerid, "1", low) ``` Function returns arrays of 1-minute high/low values, then finds true maximum and minimum across all samples. When LTF Precision Activates: Only when chart timeframe exceeds ORB session window: • 5-minute chart + 30-minute ORB: LTF used (chart TF > session bars needed) • 1-minute chart + 30-minute ORB: LTF not needed (direct sampling sufficient) Recommendation: Always enable LTF Precision unless you're on 1-minute charts. The computational overhead is negligible, and accuracy improvement is substantial. ⚖️ INITIAL BALANCE (IB) FRAMEWORK Steidlmayer's Market Profile Innovation: J. Peter Steidlmayer developed Market Profile in the 1980s for the Chicago Board of Trade. His key insight: market structure is best understood through time-at-price (value area) rather than just price-over-time (traditional charts). Initial Balance Definition: IB is the price range established during the first hour of trading, subdivided into: • A-Period : First 30 minutes (0930-1000 ET for US equities) • B-Period : Second 30 minutes (1000-1030 ET) A-Period vs B-Period Comparison: The relationship between A and B periods forecasts the day: B-Period Expansion (Bullish): • B-period high > A-period high • B-period low ≥ A-period low • Interpretation: Buyers stepping in after opening assessed • Implication: Bullish continuation likely • Strategy: Buy pullbacks to A-period high (now support) B-Period Expansion (Bearish): • B-period low < A-period low • B-period high ≤ A-period high • Interpretation: Sellers stepping in after opening assessed • Implication: Bearish continuation likely • Strategy: Sell rallies to A-period low (now resistance) B-Period Contraction: • B-period stays within A-period range • Interpretation: Market indecisive, digesting A-period information • Implication: Rotation day likely, stay range-bound • Strategy: Fade extremes, sell high/buy low within IB IB Extensions: Professional traders use IB as a ruler to project price targets: Extension Levels: • 0.5x IB : Initial probe outside value (minor target) • 1.0x IB : Full extension (major target for normal days) • 1.5x IB : Trend day threshold (classifies as trending) • 2.0x IB : Strong trend day (rare, ~10-15% of days) Calculation: ``` IB Range = IB High - IB Low Bull Extension 1.0x = IB High + (IB Range × 1.0) Bear Extension 1.0x = IB Low - (IB Range × 1.0) ``` Example: ES futures: • IB High: 5850.00 • IB Low: 5842.00 • IB Range: 8.00 points Extensions: • 1.0x Bull Target: 5850 + 8 = 5858.00 • 1.5x Bull Target: 5850 + 12 = 5862.00 • 2.0x Bull Target: 5850 + 16 = 5866.00 If price reaches 5862.00 (1.5x), day is classified as Trend Day —strategy shifts from mean reversion to trend following. 📈 DAY TYPE CLASSIFICATION SYSTEM Four Day Types (Market Profile Framework): 1. TREND DAY: Definition: Price extends ≥1.5x IB range in one direction and stays there. Characteristics: • Opens and never returns to IB • Persistent directional movement • Volume increases as day progresses (conviction building) • News-driven or strong institutional flow Frequency: ~20-25% of trading days Trading Strategy: • DO: Follow the trend, trail stops, let winners run • DON'T: Fade extremes, take early profits • Key: Add to position on pullbacks to previous extension level • Risk: Getting chopped in false trend (see Failed Breakout section) Example: FOMC decision, payroll report, earnings surprise—anything creating one-sided conviction. 2. NORMAL DAY: Definition: Price extends 0.5-1.5x IB, tests both sides, returns to IB. Characteristics: • Two-sided trading • Extensions occur but don't persist • Volume balanced throughout day • Most common day type Frequency: ~45-50% of trading days Trading Strategy: • DO: Take profits at extension levels, expect reversals • DON'T: Hold for massive moves • Key: Treat each extension as a profit-taking opportunity • Risk: Holding too long when momentum shifts Example: Typical day with no major catalysts—market balancing supply and demand. 3. ROTATION DAY: Definition: Price stays within IB all day, rotating between high and low. Characteristics: • Never accepts outside IB • Multiple tests of IB high/low • Decreasing volume (no conviction) • Classic range-bound action Frequency: ~25-30% of trading days Trading Strategy: • DO: Fade extremes (sell IB high, buy IB low) • DON'T: Chase breakouts • Key: Enter at extremes with tight stops just outside IB • Risk: Breakout finally occurs after multiple failures Example: [/b> Pre-holiday trading, summer doldrums, consolidation after big move. 4. DEVELOPING: Definition: Day type not yet determined (early in session). Usage: Classification before 12:00 PM ET when IB extension pattern unclear. ORB Fusion's Classification Algorithm: ```pinescript if close > ibHigh: ibExtension = (close - ibHigh) / ibRange direction = "BULLISH" else if close < ibLow: ibExtension = (ibLow - close) / ibRange direction = "BEARISH" if ibExtension >= 1.5: dayType = "TREND DAY" else if ibExtension >= 0.5: dayType = "NORMAL DAY" else if close within IB: dayType = "ROTATION DAY" ``` Why Classification Matters: Same setup (bullish ORB breakout) has opposite implications: • Trend Day : Hold for 2.0x extension, trail stops aggressively • Normal Day : Take profits at 1.0x extension, watch for reversal • Rotation Day : Fade the breakout immediately (likely false) Knowing day type prevents catastrophic errors like fading a trend day or holding through rotation. 🚀 BREAKOUT DETECTION & CONFIRMATION Three Confirmation Methods: 1. Close Beyond Level (Recommended): Logic: Candle must close above ORB high (bull) or below ORB low (bear). Why: • Filters out wicks (temporary liquidity grabs) • Ensures sustained acceptance above/below range • Reduces false breakout rate by ~20-30% Example: • ORB High: 5850.00 • Bar high touches 5850.50 (wick above) • Bar closes at 5848.00 (inside range) • Result: NO breakout signal vs. • Bar high touches 5850.50 • Bar closes at 5851.00 (outside range) • Result: BREAKOUT signal confirmed Trade-off: Slightly delayed entry (wait for close) but much higher reliability. 2. Wick Beyond Level: Logic: [/b> Any touch of ORB high/low triggers breakout. Why: • Earliest possible entry • Captures aggressive momentum moves Risk: • High false breakout rate (60-70%) • Stop runs trigger signals • Requires very tight stops (difficult to manage) Use Case: Scalping with 1-2 point profit targets where any penetration = trade. 3. Body Beyond Level: Logic: [/b> Candle body (close vs open) must be entirely outside range. Why: • Strictest confirmation • Ensures directional conviction (not just momentum) • Lowest false breakout rate Example: Trade-off: [/b> Very conservative—misses some valid breakouts but rarely triggers on false ones. Volume Confirmation Layer: All confirmation methods can require volume validation: Volume Multiplier Logic: Rationale: [/b> True breakouts are driven by institutional activity (large size). Volume spike confirms real conviction vs. stop-run manipulation. Statistical Impact: [/b> • Breakouts with volume confirmation: ~65% success rate • Breakouts without volume: ~45% success rate • Difference: 20 percentage points edge Implementation Note: [/b> Volume confirmation adds complexity—you'll miss breakouts that work but lack volume. However, when targeting 1.5x+ extensions (ambitious goals), volume confirmation becomes critical because those moves require sustained institutional participation. Recommended Settings by Strategy: [/b> Scalping (1-2 point targets): [/b> • Method: Close • Volume: OFF • Rationale: Quick in/out doesn't need perfection Intraday Swing (5-10 point targets): [/b> • Method: Close • Volume: ON (1.5x multiplier) • Rationale: Balance reliability and opportunity Position Trading (full-day holds): [/b> • Method: Body • Volume: ON (2.0x multiplier) • Rationale: Must be certain—large stops require high win rate 🔥 FAILED BREAKOUT SYSTEM The Core Insight: [/b> Failed breakouts are often more profitable [/b> than successful breakouts because they create trapped traders with predictable behavior. Failed Breakout Definition: [/b> A breakout that: 1. Initially penetrates ORB level with confirmation 2. Attracts participants (volume spike, momentum) 3. Fails to extend (stalls or immediately reverses) 4. Returns inside ORB range within N bars Psychology of Failure: [/b> When breakout fails: • Breakout buyers are trapped [/b>: Bought at ORB high, now underwater • Early longs reduce: Take profit, fearful of reversal • Shorts smell blood: See failed breakout as reversal signal • Result: Cascade of selling as trapped bulls exit + new shorts enter Mirror image for failed bearish breakouts (trapped shorts cover + new longs enter). Failure Detection Parameters: [/b> 1. Failure Confirmation Bars (default: 3): [/b> How many bars after breakout to confirm failure? Logic: Settings: [/b> • 2 bars: Aggressive failure detection (more signals, more false failures) • 3 bars Balanced (default) • 5-10 bars: Conservative (wait for clear reversal) Why This Matters: Too few bars: You call "failed breakout" when price is just consolidating before next leg. Too many bars: You miss the reversal entry (price already back in range). 2. Failure Buffer (default: 0.1 ATR): [/b> How far inside ORB must price return to confirm failure? Formula: Why Buffer Matters: clear rejection [/b> (not just hovering at level). Settings: [/b> • 0.0 ATR: No buffer, immediate failure signal • 0.1 ATR: Small buffer (default) - filters noise • [b>0.2-0.3 ATR: Large buffer - only dramatic failures count Example: Reversal Entry System: [/b> When failure confirmed, system generates complete reversal trade: For Failed Bull Breakout (Short Reversal): [/b> Entry: [/b> Current close when failure confirmed Stop Loss: [/b> Extreme high since breakout + 0.10 ATR padding Target 1: [/b> ORB High - (ORB Range × 0.5) Target 2: Target 3: [/b> ORB High - (ORB Range × 1.5) Example: • ORB High: 5850, ORB Low: 5842, Range: 8 points • Breakout to 5853, fails, reverses to 5848 (entry) • Stop: 5853 + 1 = 5854 (6 point risk) • T1: 5850 - 4 = 5846 (-2 points, 1:3 R:R) • T2: 5850 - 8 = 5842 (-6 points, 1:1 R:R) • T3: 5850 - 12 = 5838 (-10 points, 1.67:1 R:R) [b>Why These Targets? [/b> • T1 (0.5x ORB below high): Trapped bulls start panic • T2 (1.0x ORB = ORB Mid): Major retracement, momentum fully reversed • T3 (1.5x ORB): Reversal extended, now targeting opposite side Historical Performance: [/b> Failed breakout reversals in ORB Fusion's tracking system show: • Win Rate: 65-75% (significantly higher than initial breakouts) • Average Winner: 1.2x ORB range • Average Loser: 0.5x ORB range (protected by stop at extreme) • Expectancy: Strongly positive even with <70% win rate Why Failed Breakouts Outperform: [/b> 1. Information Advantage: You now know what price did (failed to extend). Initial breakout trades are speculative; reversal trades are reactive to confirmed failure. 2. Trapped Participant Pressure: Every trapped bull becomes a seller. This creates sustained pressure. 3. Stop Loss Clarity: Extreme high is obvious stop (just beyond recent high). Breakout trades have ambiguous stops (ORB mid? Recent low? Too wide or too tight). 4. Mean Reversion Edge: Failed breakouts return to value (ORB mid). Initial breakouts try to escape value (harder to sustain). Critical Insight: [/b> "The best trade is often the one that trapped everyone else." Failed breakouts create asymmetric opportunity because you're trading against [/b> trapped participants rather than with [/b> them. When you see a failed breakout signal, you're seeing real-time evidence that the market rejected directional conviction—that's exploitable. 📐 FIBONACCI EXTENSION SYSTEM Six Extension Levels: [/b> Extensions project how far price will travel after ORB breakout. Based on Fibonacci ratios + empirical market behavior. 1. 1.272x (27.2% Extension): [/b> Formula: [/b> ORB High/Low + (ORB Range × 0.272) Psychology: [/b> Initial probe beyond ORB. Early momentum + trapped shorts (on bull side) covering. Probability of Reach: [/b> ~75-80% after confirmed breakout Trading: [/b> • First resistance/support after breakout • Partial profit target (take 30-50% off) • Watch for rejection here (could signal failure in progress) Why 1.272? [/b> Related to harmonic patterns (1.272 is √1.618). Empirically, markets often stall at 25-30% extension before deciding whether to continue or fail. 2. 1.5x (50% Extension): Formula: [/b> ORB High/Low + (ORB Range × 0.5) Psychology: [/b> Breakout gaining conviction. Requires sustained buying/selling (not just momentum spike). Probability of Reach: [/b> ~60-65% after confirmed breakout Trading: [/b> • Major partial profit (take 50-70% off) • Move stops to breakeven • Trail remaining position Why 1.5x? [/b> Classic halfway point to 2.0x. Markets often consolidate here before final push. If day type is "Normal," this is likely the high/low for the day. 3. 1.618x (Golden Ratio Extension): [/b> Formula: [/b> ORB High/Low + (ORB Range × 0.618) Psychology: [/b> Strong directional day. Institutional conviction + retail FOMO. Probability of Reach: [/b> ~45-50% after confirmed breakout Trading: [/b> • Final partial profit (close 80-90%) • Trail remainder with wide stop (allow breathing room) Why 1.618? [/b> Fibonacci golden ratio. Appears consistently in market geometry. When price reaches 1.618x extension, move is "mature" and reversal risk increases. 4. 2.0x (100% Extension): [/b> Formula: ORB High/Low + (ORB Range × 1.0) Psychology: [/b> Trend day confirmed. Opening range completely duplicated. Probability of Reach: [/b> ~30-35% after confirmed breakout Trading: Why 2.0x? [/b> Psychological level—range doubled. Also corresponds to typical daily ATR in many instruments (opening range ~ 0.5 ATR, daily range ~ 1.0 ATR). 5. 2.618x (Super Extension): Formula: [/b> ORB High/Low + (ORB Range × 1.618) Psychology: [/b> Parabolic move. News-driven or squeeze. Probability of Reach: [/b> ~10-15% after confirmed breakout [b>Trading: Why 2.618? [/b> Fibonacci ratio (1.618²). Rare to reach—when it does, move is extreme. Often precedes multi-day consolidation or reversal. 6. 3.0x (Extreme Extension): [/b> Formula: [/b> ORB High/Low + (ORB Range × 2.0) Psychology: [/b> Market melt-up/crash. Only in extreme events. [b>Probability of Reach: [/b> <5% after confirmed breakout Trading: [/b> • Close immediately if reached • These are outlier events (black swans, flash crashes, squeeze-outs) • Holding for more is greed—take windfall profit Why 3.0x? [/b> Triple opening range. So rare it's statistical noise. When it happens, it's headline news. Visual Example: ES futures, ORB 5842-5850 (8 point range), Bullish breakout: • ORB High : 5850.00 (entry zone) • 1.272x : 5850 + 2.18 = 5852.18 (first resistance) • 1.5x : 5850 + 4.00 = 5854.00 (major target) • 1.618x : 5850 + 4.94 = 5854.94 (strong target) • 2.0x : 5850 + 8.00 = 5858.00 (trend day) • 2.618x : 5850 + 12.94 = 5862.94 (extreme) • 3.0x : 5850 + 16.00 = 5866.00 (parabolic) Profit-Taking Strategy: Optimal scaling out at extensions: • Breakout entry at 5850.50 • 30% off at 1.272x (5852.18) → +1.68 points • 40% off at 1.5x (5854.00) → +3.50 points • 20% off at 1.618x (5854.94) → +4.44 points • 10% off at 2.0x (5858.00) → +7.50 points [b>Average Exit: Conclusion: [/b> Scaling out at extensions produces 40% higher expectancy than holding for home runs. 📊 GAP ANALYSIS & FILL PSYCHOLOGY [b>Gap Definition: [/b> Price discontinuity between previous close and current open: • Gap Up : Open > Previous Close + noise threshold (0.1 ATR) • Gap Down : Open < Previous Close - noise threshold Why Gaps Matter: [/b> Gaps represent unfilled orders [/b>. When market gaps up, all limit buy orders between yesterday's close and today's open are never filled. Those buyers are "left behind." Psychology: they wait for price to return ("fill the gap") so they can enter. This creates magnetic pull [/b> toward gap level. Gap Fill Statistics (Empirical): [/b> • Gaps <0.5% [/b>: 85-90% fill within same day • Gaps 0.5-1.0% [/b>: 70-75% fill within same day, 90%+ within week • Gaps >1.0% [/b>: 50-60% fill within same day (major news often prevents fill) Gap Fill Strategy: [/b> Setup 1: Gap-and-Go Gap opens, extends away from gap (doesn't fill). • ORB confirms direction away from gap • Trade WITH ORB breakout direction • Expectation: Gap won't fill today (momentum too strong) Setup 2: Gap-Fill Fade Gap opens, but fails to extend. Price drifts back toward gap. • ORB breakout TOWARD gap (not away) • Trade toward gap fill level • Target: Previous close (gap fill complete) Setup 3: Gap-Fill Rejection Gap fills (touches previous close) then rejects. • ORB breakout AWAY from gap after fill • Trade away from gap direction • Thesis: Gap filled (orders executed), now resume original direction [b>Example: Scenario A (Gap-and-Go): • ORB breaks upward to $454 (away from gap) • Trade: LONG breakout, expect continued rally • Gap becomes support ($452) Scenario B (Gap-Fill): • ORB breaks downward through $452.50 (toward gap) • Trade: SHORT toward gap fill at $450.00 • Target: $450.00 (gap filled), close position Scenario C (Gap-Fill Rejection): • Price drifts to $450.00 (gap filled) early in session • ORB establishes $450-$451 after gap fill • ORB breaks upward to $451.50 • Trade: LONG breakout (gap is filled, now resume rally) ORB Fusion Integration: [/b> Dashboard shows: • Gap type (Up/Down/None) • Gap size (percentage) • Gap fill status (Filled ✓ / Open) This informs setup confidence: • ORB breakout AWAY from unfilled gap: +10% confidence (gap becomes support/resistance) • ORB breakout TOWARD unfilled gap: -10% confidence (gap fill may override ORB) [b>📈 VWAP & INSTITUTIONAL BIAS [/b> [b>Volume-Weighted Average Price (VWAP): [/b> Average price weighted by volume at each price level. Represents true "average" cost for the day. [b>Calculation: Institutional Benchmark [/b>: Institutions (mutual funds, pension funds) use VWAP as performance benchmark. If they buy above VWAP, they underperformed; below VWAP, they outperformed. 2. [b>Algorithmic Target [/b>: Many algos are programmed to buy below VWAP and sell above VWAP to achieve "fair" execution. 3. [b>Support/Resistance [/b>: VWAP acts as dynamic support (price above) or resistance (price below). [b>VWAP Bands (Standard Deviations): [/b> • [b>1σ Band [/b>: VWAP ± 1 standard deviation - Contains ~68% of volume - Normal trading range - Bounces common • [b>2σ Band [/b>: VWAP ± 2 standard deviations - Contains ~95% of volume - Extreme extension - Mean reversion likely ORB + VWAP Confluence: [/b> Highest-probability setups occur when ORB and VWAP align: Bullish Confluence: [/b> • ORB breakout upward (bullish signal) • Price above VWAP (institutional buying) • Confidence boost: +15% Bearish Confluence: [/b> • ORB breakout downward (bearish signal) • Price below VWAP (institutional selling) • Confidence boost: +15% [b>Divergence Warning: • ORB breakout upward BUT price below VWAP • Conflict: Breakout says "buy," VWAP says "sell" • Confidence penalty: -10% • Interpretation: Retail buying but institutions not participating (lower quality breakout) 📊 MOMENTUM CONTEXT SYSTEM [b>Innovation: Candle Coloring by Position Rather than fixed support/resistance lines, ORB Fusion colors candles based on their [b>relationship to ORB : [b>Three Zones: [/b> 1. Inside ORB (Blue Boxes): [/b> [b>Calculation: • Darker blue: Near extremes of ORB (potential breakout imminent) • Lighter blue: Near ORB mid (consolidation) [b>Trading: [/b> Coiled spring—await breakout. [b>2. Above ORB (Green Boxes): [b>Calculation: 3. Below ORB (Red Boxes): Mirror of above ORB logic. [b>Special Contexts: [/b> [b>Breakout Bar (Darkest Green/Red): [/b> The specific bar where breakout occurs gets maximum color intensity regardless of distance. This highlights the pivotal moment. [b>Failed Breakout Bar (Orange/Warning): [/b> When failed breakout is confirmed, that bar gets orange/warning color. Visual alert: "reversal opportunity here." [b>Near Extension (Cyan/Magenta Tint): [/b> When price is within 0.5 ATR of an extension level, candle gets tinted cyan (bull) or magenta (bear). Indicates "target approaching—prepare to take profit." [b>Why Visual Context? [/b> Traditional indicators show lines. ORB Fusion shows [b>context-aware momentum [/b>. Glance at chart: • Lots of blue? Consolidation day (fade extremes). • Progressive green? Trend day (follow). • Green then orange? Failed breakout (reversal setup). This visual language communicates market state instantly—no interpretation needed. 🎯 TRADE SETUP GENERATION & GRADING [/b> [b>Algorithmic Setup Detection: [/b> ORB Fusion continuously evaluates market state and generates current best trade setup with: • Action (LONG / SHORT / FADE HIGH / FADE LOW / WAIT) • Entry price • Stop loss • Three targets • Risk:Reward ratio • Confidence score (0-100) • Grade (A+ to D) [b>Setup Types: [/b> [b>1. ORB LONG (Bullish Breakout): [/b> [b>Trigger: [/b> • Bullish ORB breakout confirmed • Not failed [b>Parameters: • Entry: Current close • Stop: ORB mid (protects against failure) • T1: ORB High + 0.5x range (1.5x extension) • T2: ORB High + 1.0x range (2.0x extension) • T3: ORB High + 1.618x range (2.618x extension) [b>Confidence Scoring: [b>Trigger: [/b> • Bearish breakout occurred • Failed (returned inside ORB) [b>Parameters: [/b> • Entry: Close when failure confirmed • Stop: Extreme low since breakout + 0.10 ATR • T1: ORB Low + 0.5x range • T2: ORB Low + 1.0x range (ORB mid) • T3: ORB Low + 1.5x range [b>Confidence Scoring: [b>Trigger: • Inside ORB • Close > ORB mid (near high) [b>Parameters: [/b> • Entry: ORB High (limit order) • Stop: ORB High + 0.2x range • T1: ORB Mid • T2: ORB Low [b>Confidence Scoring: [/b> Base: 40 points (lower base—range fading is lower probability than breakout/reversal) [b>Use Case: [/b> Rotation days. Not recommended on normal/trend days. [b>6. FADE LOW (Range Trade): Mirror of FADE HIGH. [b>7. WAIT: [b>Trigger: [/b> • ORB not complete yet OR • No clear setup (price in no-man's-land) [b>Action: [/b> Observe, don't trade. [b>Confidence: [/b> 0 points [b>Grading System: ``` Confidence → Grade 85-100 → A+ 75-84 → A 65-74 → B+ 55-64 → B 45-54 → C 0-44 → D ``` [b>Grade Interpretation: [/b> • [b>A+ / A: High probability setup. Take these trades. • [b>B+ / B [/b>: Decent setup. Trade if fits system rules. • [b>C [/b>: Marginal setup. Only if very experienced. • [b>D [/b>: Poor setup or no setup. Don't trade. [b>Example Scenario: [/b> ES futures: • ORB: 5842-5850 (8 point range) • Bullish breakout to 5851 confirmed • Volume: 2.0x average (confirmed) • VWAP: 5845 (price above VWAP ✓) • Day type: Developing (too early, no bonus) • Gap: None [b>Setup: [/b> • Action: LONG • Entry: 5851 • Stop: 5846 (ORB mid, -5 point risk) • T1: 5854 (+3 points, 1:0.6 R:R) • T2: 5858 (+7 points, 1:1.4 R:R) • T3: 5862.94 (+11.94 points, 1:2.4 R:R) [b>Confidence: LONG with 55% confidence. Interpretation: Solid setup, not perfect. Trade it if your system allows B-grade signals. [b>📊 STATISTICS TRACKING & PERFORMANCE ANALYSIS [/b> [b>Real-Time Performance Metrics: [/b> ORB Fusion tracks comprehensive statistics over user-defined lookback (default 50 days): [b>Breakout Performance: [/b> • [b>Bull Breakouts: [/b> Total count, wins, losses, win rate • [b>Bear Breakouts: [/b> Total count, wins, losses, win rate [b>Win Definition: [/b> Breakout reaches ≥1.0x extension (doubles the opening range) before end of day. [b>Example: [/b> • ORB: 5842-5850 (8 points) • Bull breakout at 5851 • Reaches 5858 (1.0x extension) by close • Result: WIN [b>Failed Breakout Performance: [/b> • [b>Total Failed Breakouts [/b>: Count of breakouts that failed • [b>Reversal Wins [/b>: Count where reversal trade reached target • [b>Failed Reversal Win Rate [/b>: Wins / Total Failed [b>Win Definition for Reversals: [/b> • Failed bull → reversal short reaches ORB mid • Failed bear → reversal long reaches ORB mid [b>Extension Tracking: [/b> • [b>Average Extension Reached [/b>: Mean of maximum extension achieved across all breakout days • [b>Max Extension Overall [/b>: Largest extension ever achieved in lookback period [b>Example: 🎨 THREE DISPLAY MODES [b>Design Philosophy: [/b> Not all traders need all features. Beginners want simplicity. Professionals want everything. ORB Fusion adapts. [b>SIMPLE MODE: [/b> [b>Shows: [/b> • Primary ORB levels (High, Mid, Low) • ORB box • Breakout signals (triangles) • Failed breakout signals (crosses) • Basic dashboard (ORB status, breakout status, setup) • VWAP [b>Hides: [/b> • Session ORBs (Asian, London, NY) • IB levels and extensions • ORB extensions beyond basic levels • Gap analysis visuals • Statistics dashboard • Momentum candle coloring • Narrative dashboard [b>Use Case: [/b> • Traders who want clean chart • Focus on core ORB concept only • Mobile trading (less screen space) [b>STANDARD MODE: [b>Shows Everything in Simple Plus: [/b> • Session ORBs (Asian, London, NY) • IB levels (high, low, mid) • IB extensions • ORB extensions (1.272x, 1.5x, 1.618x, 2.0x) • Gap analysis and fill targets • VWAP bands (1σ and 2σ) • Momentum candle coloring • Context section in dashboard • Narrative dashboard [b>Hides: [/b> • Advanced extensions (2.618x, 3.0x) • Detailed statistics dashboard [b>Use Case: [/b> • Most traders • Balance between information and clarity • Covers 90% of use cases [b>ADVANCED MODE: [b>Shows Everything: • All session ORBs • All IB levels and extensions • All ORB extensions (including 2.618x and 3.0x) • Full gap analysis • VWAP with both 1σ and 2σ bands • Momentum candle coloring • Complete statistics dashboard • Narrative dashboard • All context metrics [b>Use Case: [/b> • Professional traders • System developers • Those who want maximum information density [b>Switching Modes: [/b> Single dropdown input: "Display Mode" → Simple / Standard / Advanced Entire indicator adapts instantly. No need to toggle 20 individual settings. 📖 NARRATIVE DASHBOARD [b>Innovation: Plain-English Market State [/b> Most indicators show data. ORB Fusion explains what the data [b>means [/b>. [b>Narrative Components: [/b> [b>1. Phase: [/b> • "📍 Building ORB..." (during ORB session) • "📊 Trading Phase" (after ORB complete) • "⏳ Pre-Market" (before ORB session) [b>2. Status (Current Observation): [/b> • "⚠️ Failed breakout - reversal likely" • "🚀 Bullish momentum in play" • "📉 Bearish momentum in play" • "⚖️ Consolidating in range" • "👀 Monitoring for setup" [b>3. Next Level: Tells you what to watch for: • "🎯 1.5x @ 5854.00" (next extension target) • "Watch ORB levels" (inside range, await breakout) [b>4. Setup: [/b> Current trade setup + grade: • "LONG " (bullish breakout, A-grade) • "🔥 SHORT REVERSAL " (failed bull breakout, A+-grade) • "WAIT " (no setup) [b>5. Reason: [/b> Why this setup exists: • "ORB Bullish Breakout" • "Failed Bear Breakout - High Probability Reversal" • "Range Fade - Near High" [b>6. Tip (Market Insight): Contextual advice: • "🔥 TREND DAY - Trail stops" (day type is trending) • "🔄 ROTATION - Fade extremes" (day type is rotating) • "📊 Gap unfilled - magnet level" (gap creates target) • "📈 Normal conditions" (no special context) [b>Example Narrative: ``` 📖 ORB Narrative ━━━━━━━━━━━━━━━━ Phase | 📊 Trading Phase Status | 🚀 Bullish momentum in play Next | 🎯 1.5x @ 5854.00 📈 Setup | LONG Reason | ORB Bullish Breakout 💡 Tip | 🔥 TREND DAY - Trail stops ``` [b>Glance Interpretation: [/b> "We're in trading phase. Bullish breakout happened (momentum in play). Next target is 1.5x extension at 5854. Current setup is LONG with A-grade. It's a trend day, so trail stops (don't take early profits)." Complete market state communicated in 6 lines. No interpretation needed. [b>Why This Matters: Beginner traders struggle with "So what?" question. Indicators show lines and signals, but what does it mean [/b>? Narrative dashboard bridges this gap. Professional traders benefit too—rapid context assessment during fast-moving markets. No time to analyze; glance at narrative, get action plan. 🔔 INTELLIGENT ALERT SYSTEM [b>Four Alert Types: [/b> [b>1. Breakout Alert: [/b> [b>Trigger: [/b> ORB breakout confirmed (bull or bear) [b>Message: [/b> ``` 🚀 ORB BULLISH BREAKOUT Price: 5851.00 Volume Confirmed Grade: A ``` [b>Frequency: [/b> Once per bar (prevents spam) [b>2. Failed Breakout Alert: [/b> [b>Trigger: [/b> Breakout fails, reversal setup generated [b>Message: [/b> ``` 🔥 FAILED BULLISH BREAKOUT! HIGH PROBABILITY SHORT REVERSAL Entry: 5848.00 Stop: 5854.00 T1: 5846.00 T2: 5842.00 Historical Win Rate: 73% ``` [b>Why Comprehensive? [/b> Failed breakout alerts include complete trade plan. You can execute immediately from alert—no need to check chart. [b>3. Extension Alert: [b>Trigger: [/b> Price reaches extension level for first time [b>Message: [/b> ``` 🎯 Bull Extension 1.5x reached @ 5854.00 ``` [b>Use: [/b> Profit-taking reminder. When extension hit, consider scaling out. [b>4. IB Break Alert: [/b> [b>Trigger: [/b> Price breaks above IB high or below IB low [b>Message: [/b> ``` 📊 IB HIGH BROKEN - Potential Trend Day ``` [b>Use: [/b> Day type classification. IB break suggests trend day developing—adjust strategy to trend-following mode. [b>Alert Management: [/b> Each alert type can be enabled/disabled independently. Prevents notification overload. [b>Cooldown Logic: [/b> Alerts won't fire if same alert type triggered within last bar. Prevents: • "Breakout" alert every tick during choppy breakout • Multiple "extension" alerts if price oscillates at level Ensures: One clean alert per event. ⚙️ KEY PARAMETERS EXPLAINED [b>Opening Range Settings: [/b> • [b>ORB Timeframe [/b> (5/15/30/60 min): Duration of opening range window - 30 min recommended for most traders • [b>Use RTH Only [/b> (ON/OFF): Only trade during regular trading hours - ON recommended (avoids thin overnight markets) • [b>Use LTF Precision [/b> (ON/OFF): Sample 1-minute bars for accuracy - ON recommended (critical for charts >1 minute) • [b>Precision TF [/b> (1/5 min): Timeframe for LTF sampling - 1 min recommended (most accurate) [b>Session ORBs: [/b> • [b>Show Asian/London/NY ORB [/b> (ON/OFF): Display multi-session ranges - OFF in Simple mode - ON in Standard/Advanced if trading 24hr markets • [b>Session Windows [/b>: Time ranges for each session ORB - Defaults align with major session opens [b>Initial Balance: [/b> • [b>Show IB [/b> (ON/OFF): Display Initial Balance levels - ON recommended for day type classification • [b>IB Session Window [/b> (0930-1030): First hour of trading - Default is standard for US equities • [b>Show IB Extensions [/b> (ON/OFF): Project IB extension targets - ON recommended (identifies trend days) • [b>IB Extensions 1-4 [/b> (0.5x, 1.0x, 1.5x, 2.0x): Extension multipliers - Defaults are Market Profile standard [b>ORB Extensions: [/b> • [b>Show Extensions [/b> (ON/OFF): Project ORB extension targets - ON recommended (defines profit targets) • [b>Enable Individual Extensions [/b> (1.272x, 1.5x, 1.618x, 2.0x, 2.618x, 3.0x) - Enable 1.272x, 1.5x, 1.618x, 2.0x minimum - Disable 2.618x and 3.0x unless trading very volatile instruments [b>Breakout Detection: • [b>Confirmation Method [/b> (Close/Wick/Body): - Close recommended (best balance) - Wick for scalping - Body for conservative • [b>Require Volume Confirmation [/b> (ON/OFF): - ON recommended (increases reliability) • [b>Volume Multiplier [/b> (1.0-3.0): - 1.5x recommended - Lower for thin instruments - Higher for heavy volume instruments [b>Failed Breakout System: [/b> • [b>Enable Failed Breakouts [/b> (ON/OFF): - ON strongly recommended (highest edge) • [b>Bars to Confirm Failure [/b> (2-10): - 3 bars recommended - 2 for aggressive (more signals, more false failures) - 5+ for conservative (fewer signals, higher quality) • [b>Failure Buffer [/b> (0.0-0.5 ATR): - 0.1 ATR recommended - Filters noise during consolidation near ORB level • [b>Show Reversal Targets [/b> (ON/OFF): - ON recommended (visualizes trade plan) • [b>Reversal Target Mults [/b> (0.5x, 1.0x, 1.5x): - Defaults are tested values - Adjust based on average daily range [b>Gap Analysis: • [b>Show Gap Analysis [/b> (ON/OFF): - ON if trading instruments that gap frequently - OFF for 24hr markets (forex, crypto—no gaps) • [b>Gap Fill Target [/b> (ON/OFF): - ON to visualize previous close (gap fill level) [b>VWAP: • [b>Show VWAP [/b> (ON/OFF): - ON recommended (key institutional level) • [b>Show VWAP Bands [/b> (ON/OFF): - ON in Standard/Advanced - OFF in Simple • [b>Band Multipliers (1.0σ, 2.0σ): - Defaults are standard - 1σ = normal range, 2σ = extreme [b>Day Type: [/b> • [b>Show Day Type Analysis [/b> (ON/OFF): - ON recommended (critical for strategy adaptation) • [b>Trend Day Threshold [/b> (1.0-2.5 IB mult): - 1.5x recommended - When price extends >1.5x IB, classifies as Trend Day [b>Enhanced Visuals: • [b>Show Momentum Candles [/b> (ON/OFF): - ON for visual context - OFF if chart gets too colorful • [b>Show Gradient Zone Fills [/b> (ON/OFF): - ON for professional look - OFF for minimalist chart • [b>Label Display Mode [/b> (All/Adaptive/Minimal): - Adaptive recommended (shows nearby labels only) - All for information density - Minimal for clean chart • [b>Label Proximity [/b> (1.0-5.0 ATR): - 3.0 ATR recommended - Labels beyond this distance are hidden (Adaptive mode) [b>🎓 PROFESSIONAL USAGE PROTOCOL [/b> [b>Phase 1: Learning the System (Week 1) [/b> [b>Goal: [/b> Understand ORB concepts and dashboard interpretation [b>Setup: [/b> • Display Mode: STANDARD • ORB Timeframe: 30 minutes • Enable ALL features (IB, extensions, failed breakouts, VWAP, gap analysis) • Enable statistics tracking [b>Actions: [/b> • Paper trade ONLY—no real money • Observe ORB formation every day (9:30-10:00 AM ET for US markets) • Note when ORB breakouts occur and if they extend • Note when breakouts fail and reversals happen • Watch day type classification evolve during session • Track statistics—which setups are working? [b>Key Learning: [/b> • How often do breakouts reach 1.5x extension? (typically 50-60% of confirmed breakouts) • How often do breakouts fail? (typically 30-40%) • Which setup grade (A/B/C) actually performs best? (should see A-grade outperforming) • What day type produces best results? (trend days favor breakouts, rotation days favor fades) [b>Phase 2: Parameter Optimization (Week 2) [/b> [b>Goal: [/b> Tune system to your instrument and timeframe [b>ORB Timeframe Selection: • Run 5 days with 15-minute ORB • Run 5 days with 30-minute ORB • Compare: Which captures better breakouts on your instrument? • Typically: 30-minute optimal for most, 15-minute for very liquid (ES, SPY) [b>Volume Confirmation Testing: • Run 5 days WITH volume confirmation • Run 5 days WITHOUT volume confirmation • Compare: Does volume confirmation increase win rate? • If win rate improves by >5%: Keep volume confirmation ON • If no improvement: Turn OFF (avoid missing valid breakouts) [b>Failed Breakout Bars: [b>Goal: [/b> Develop personal trading rules based on system signals [b>Setup Selection Rules: [/b> Define which setups you'll trade: • [b>Conservative: [/b> Only A+ and A grades • [b>Balanced: [/b> A+, A, B+ grades • [b>Aggressive: [/b> All grades B and above Test each approach for 5-10 trades, compare results. [b>Position Sizing by Grade: [/b> Consider risk-weighting by setup quality: • A+ grade: 100% position size • A grade: 75% position size • B+ grade: 50% position size • B grade: 25% position size Example: If max risk is $1000/trade: • A+ setup: Risk $1000 • A setup: Risk $750 • B+ setup: Risk $500 This matches bet sizing to edge. [b>Day Type Adaptation: [/b> Create rules for different day types: Trend Days: • Take ALL breakout signals (A/B/C grades) • Hold for 2.0x extension minimum • Trail stops aggressively (1.0 ATR trail) • DON'T fade—reversals unlikely Rotation Days: • ONLY take failed breakout reversals • Ignore initial breakout signals (likely to fail) • Take profits quickly (0.5x extension) • Focus on fade setups (Fade High/Fade Low) Normal Days: • Take A/A+ breakout signals only • Take ALL failed breakout reversals (high probability) • Target 1.0-1.5x extensions • Partial profit-taking at extensions Time-of-Day Rules: [/b> Breakouts at different times have different probabilities: 10:00-10:30 AM (Early Breakout): • ORB just completed • Fresh breakout • Probability: Moderate (50-55% reach 1.0x) • Strategy: Conservative position sizing 10:30-12:00 PM (Mid-Morning): • Momentum established • Volume still healthy • Probability: High (60-65% reach 1.0x) • Strategy: Standard position sizing 12:00-2:00 PM (Lunch Doldrums): • Volume dries up • Whipsaw risk increases • Probability: Low (40-45% reach 1.0x) • Strategy: Avoid new entries OR reduce size 50% 2:00-4:00 PM (Afternoon Session): • Late-day positioning • EOD squeezes possible • Probability: Moderate-High (55-60%) • Strategy: Watch for IB break—if trending all day, follow [b>Phase 4: Live Micro-Sizing (Month 2) [/b> [b>Goal: [/b> Validate paper trading results with minimal risk [b>Setup: [/b> • 10-20% of intended full position size • Take ONLY A+ and A grade setups • Follow stop loss and targets religiously [b>Execution: [/b> • Execute from alerts OR from dashboard setup box • Entry: Close of signal bar OR next bar market order • Stop: Use exact stop from setup (don't widen) • Targets: Scale out at T1/T2/T3 as indicated [b>Tracking: [/b> • Log every trade: Entry, Exit, Grade, Outcome, Day Type • Calculate: Win rate, Average R-multiple, Max consecutive losses • Compare to paper trading results (should be within 15%) [b>Red Flags: [/b> • Win rate <45%: System not suitable for this instrument/timeframe • Major divergence from paper trading: Execution issues (slippage, late entries, emotional exits) • Max consecutive losses >8: Hitting rough patch OR market regime changed [b>Phase 5: Scaling Up (Months 3-6) [b>Goal: [/b> Gradually increase to full position size [b>Progression: [/b> • Month 3: 25-40% size (if micro-sizing profitable) • Month 4: 40-60% size • Month 5: 60-80% size • Month 6: 80-100% size [b>Milestones Required to Scale Up: [/b> • Minimum 30 trades at current size • Win rate ≥48% • Profit factor ≥1.2 • Max drawdown <20% • Emotional control (no revenge trading, no FOMO) [b>Advanced Techniques: [b>Multi-Timeframe ORB: Assumes first 30-60 minutes establish value. Violation: Market opens after major news, price discovery continues for hours (opening range meaningless). 2. [b>Volume Indicates Conviction: ES, NQ, RTY, SPY, QQQ—high liquidity, clean ORB formation, reliable extensions • [b>Large-Cap Stocks: AAPL, MSFT, TSLA, NVDA (>$5B market cap, >5M daily volume) • [b>Liquid Futures: CL (crude oil), GC (gold), 6E (EUR/USD), ZB (bonds)—24hr markets benefit from session ORBs • [b>Major Forex Pairs: [/b> EUR/USD, GBP/USD, USD/JPY—London/NY session ORBs work well [b>Performs Poorly On: [/b> • [b>Illiquid Stocks: <$1M daily volume, wide spreads, gappy price action • [b>Penny Stocks: [/b> Manipulated, pump-and-dump, no real price discovery • [b>Low-Volume ETFs: Exotic sector ETFs, leveraged products with thin volume • [b>Crypto on Sketchy Exchanges: Wash trading, spoofing invalidates volume analysis • [b>Earnings Days: [/b> ORB completes before earnings release, then completely resets (useless) • Binary Event Days: FDA approvals, court rulings—discontinuous price action [b>Known Weaknesses: [/b> • [b>Slow Starts: ORB doesn't complete until 10:00 AM (30-min ORB). Early morning traders have no signals for 30 minutes. Consider using 15-minute ORB if this is problematic. • [b>Failure Detection Lag: [/b> Failed breakout requires 3+ bars to confirm. By the time system signals reversal, price may have already moved significantly back inside range. Manual traders watching in real-time can enter earlier. • [b>Extension Overshoot: [/b> System projects extensions mathematically (1.5x, 2.0x, etc.). Actual moves may stop short (1.3x) or overshoot (2.2x). Extensions are targets, not magnets. • [b>Day Type Misclassification: [/b> Early in session, day type is "Developing." By the time it's classified definitively (often 11:00 AM+), half the day is over. Strategy adjustments happen late. • [b>Gap Assumptions: [/b> System assumes gaps want to fill. Strong trend days never fill gaps (gap becomes support/resistance forever). Blindly trading toward gaps can backfire on trend days. • [b>Volume Data Quality: Forex doesn't have centralized volume (uses tick volume as proxy—less reliable). Crypto volume is often fake (wash trading). Volume confirmation less effective on these instruments. • [b>Multi-Session Complexity: [/b> When using Asian/London/NY ORBs simultaneously, chart becomes cluttered. Requires discipline to focus on relevant session for current time. [b>Risk Factors: [/b> • [b>Opening Gaps: Large gaps (>2%) can create distorted ORBs. Opening range might be unusually wide or narrow, making extensions unreliable. • [b>Low Volatility Environments:[/b> When VIX <12, opening ranges can be tiny (0.2-0.3%). Extensions are equally tiny. Profit targets don't justify commission/slippage. • [b>High Volatility Environments:[/b> When VIX >30, opening ranges are huge (2-3%+). Extensions project unrealistic targets. Failed breakouts happen faster (volatility whipsaw). • [b>Algorithm Dominance:[/b> In heavily algorithmic markets (ES during overnight session), ORB levels can be manipulated—algos pin price to ORB high/low intentionally. Breakouts become stop-runs rather than genuine directional moves. [b>⚠️ RISK DISCLOSURE[/b> Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Opening Range Breakout strategies, while based on sound market structure principles, do not guarantee profits and can result in significant losses. The ORB Fusion indicator implements professional trading concepts including Opening Range theory, Market Profile Initial Balance analysis, Fibonacci extensions, and failed breakout reversal logic. These methodologies have theoretical foundations but past performance—whether backtested or live—is not indicative of future results. Opening Range theory assumes the first 30-60 minutes of trading establish a meaningful value area and that breakouts from this range signal directional conviction. This assumption may not hold during: • Major news events (FOMC, NFP, earnings surprises) • Market structure changes (circuit breakers, trading halts) • Low liquidity periods (holidays, early closures) • Algorithmic manipulation or spoofing Failed breakout detection relies on patterns of trapped participant behavior. While historically these patterns have shown statistical edges, market conditions change. Institutional algorithms, changing market structure, or regime shifts can reduce or eliminate edges that existed historically. Initial Balance classification (trend day vs rotation day vs normal day) is a heuristic framework, not a deterministic prediction. Day type can change mid-session. Early classification may prove incorrect as the day develops. Extension projections (1.272x, 1.5x, 1.618x, 2.0x, etc.) are probabilistic targets derived from Fibonacci ratios and empirical market behavior. They are not "support and resistance levels" that price must reach or respect. Markets can stop short of extensions, overshoot them, or ignore them entirely. Volume confirmation assumes high volume indicates institutional participation and conviction. In algorithmic markets, volume can be artificially high (HFT activity) or artificially low (dark pools, internalization). Volume is a proxy, not a guarantee of conviction. LTF precision sampling improves ORB accuracy by using 1-minute bars but introduces additional data dependencies. If 1-minute data is unavailable, inaccurate, or delayed, ORB calculations will be incorrect. The grading system (A+/A/B+/B/C/D) and confidence scores aggregate multiple factors (volume, VWAP, day type, IB expansion, gap context) into a single assessment. This is a mechanical calculation, not artificial intelligence. The system cannot adapt to unprecedented market conditions or events outside its programmed logic. Real trading involves slippage, commissions, latency, partial fills, and rejected orders not present in indicator calculations. ORB Fusion generates signals at bar close; actual fills occur with delay. Opening range forms during highest volatility (first 30 minutes)—spreads widen, slippage increases. Execution quality significantly impacts realized results. Statistics tracking (win rates, extension levels reached, day type distribution) is based on historical bars in your lookback window. If lookback is small (<50 bars) or market regime changed, statistics may not represent future probabilities. Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively (100+ trades minimum) before risking capital. Start with micro position sizing (5-10% of intended size) for 50+ trades to validate execution quality matches expectations. Never risk more than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every single trade without exception. Understand that most retail traders lose money—sophisticated indicators do not change this fundamental reality. They systematize analysis but cannot eliminate risk. The developer makes no warranties regarding profitability, suitability, accuracy, reliability, or fitness for any purpose. Users assume full responsibility for all trading decisions, parameter selections, risk management, and outcomes. By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses. [b>═══════════════════════════════════════════════════════════════════════════════[/b> [b>CLOSING STATEMENT[/b> [b>═══════════════════════════════════════════════════════════════════════════════[/b> Opening Range Breakout is not a trick. It's a framework. The first 30-60 minutes reveal where participants believe value lies. Breakouts signal directional conviction. Failures signal trapped participants. Extensions define profit targets. Day types dictate strategy. Failed breakouts create the highest-probability reversals. ORB Fusion doesn't predict the future—it identifies [b>structure[/b>, detects [b>breakouts[/b>, recognizes [b>failures[/b>, and generates [b>probabilistic trade plans[/b> with defined risk and reward. The edge is not in the opening range itself. The edge is in recognizing when the market respects structure (follow breakouts) versus when it violates structure (fade breakouts). The edge is in detecting failures faster than discretionary traders. The edge is in systematic classification that prevents catastrophic errors—like fading a trend day or holding through rotation. Most indicators draw lines. ORB Fusion implements a complete institutional trading methodology: Opening Range theory, Market Profile classification, failed breakout intelligence, Fibonacci projections, volume confirmation, gap psychology, and real-time performance tracking. Whether you're a beginner learning market structure or a professional seeking systematic ORB implementation, this system provides the framework. "The market's first word is its opening range. Everything after is commentary." — ORB Fusion
Pine Script® indicator
by DskyzInvestments
10
Impulse Reactor RSI-SMA Trend Indicator [ApexLegion]Impulse Reactor RSI-SMA Trend Indicator Introduction and Theoretical Background Design Rationale Standard indicators frequently generate binary 'BUY' or 'SELL' signals without accounting for the broader market context. This often results in erratic "Flip-Flop" behavior, where signals are triggered indiscriminately regardless of the prevailing volatility regime. Impulse Reactor was engineered to address this limitation by unifying two critical requirements: Quantitative Rigor and Execution Flexibility. The Solution Composite Analytical Framework This script is not a simple visual overlay of existing indicators. It is an algorithmic synthesis designed to function as a unified decision-making engine. The primary objective was to implement rigorous quantitative analysis (Volatility Normalization, Structural Filtering) directly within an alert-enabled framework. This architecture is designed to process signals through strict, multi-factor validation protocols before generating real-time notifications, allowing users to focus on structurally validated setups without manual monitoring. How It Works This is not a simple visual mashup. It utilizes a cross-validation algorithm where the Trend Structure acts as a gatekeeper for Momentum signals: Logic over Lag: Unlike simple moving average crossovers, this script uses a 15-layer Gradient Ribbon to detect "Laminar Flow." If the ribbon is knotted (Compression), the system mathematically suppresses all signals. Volatility Normalization: The core calculation adapts to ATR (Average True Range). This means the indicator automatically expands in volatile markets and contracts in quiet ones, maintaining accuracy without constant manual tweaking. Adaptive Signal Thresholding: It incorporates an 'Anti-Greed' algorithm (Dynamic Thresholding) that automatically adjusts entry criteria based on trend duration. This logic aims to mitigate the risk of entering positions during periods of statistical trend exhaustion. Why Use It? Market State Decoding: The gradient Ribbon visualizes the underlying trend phase in real-time. ◦ Cyan/Blue Flow: Strong Bullish Trend (Laminar Flow). ◦ Magenta/Pink Flow: Strong Bearish Trend. ◦ Compressed/Knotted: When the ribbon lines are tightly squeezed or overlapping, it signals Consolidation. The system filters signals here to avoid chop. Noise Reduction: The goal is not to catch every pivot, but to isolate high-confidence setups. The logic explicitly filters out minor fluctuations to help maintain position alignment with the broader trend. ⚖️ Chapter 1: System Architecture Introduction: Composite Analytical Framework System Overview Impulse Reactor serves as a comprehensive technical analysis engine designed to synthesize three distinct market dimensions—Momentum, Volatility, and Trend Structure—into a unified decision-making framework. Unlike traditional methods that analyze these metrics in isolation, this system functions as a central processing unit that integrates disparate data streams to construct a coherent model of market behavior. Operational Objective The primary objective is to transition from single-dimensional signal generation to a multi-factor assessment model. By fusing data from the Impulse Core (Volatility), Gradient Oscillator (Momentum), and Structural Baseline (Trend), the system aims to filter out stochastic noise and identify high-probability trade setups grounded in quantitative confluence. Market Microstructure Analysis: Limitations of Conventional Models Extensive backtesting and quantitative analysis have identified three critical inefficiencies in standard oscillator-based strategies: • Bounded Oscillator Limitations (The "Oscillation Trap"): Traditional indicators such as RSI or Stochastics are mathematically constrained between fixed values (0 to 100). In strong trending environments, these metrics often saturate in "overbought" or "oversold" zones. Consequently, traders relying on static thresholds frequently exit structurally valid positions prematurely or initiate counter-trend trades against prevailing momentum, resulting in suboptimal performance. • Quantitative Blindness to Quality: Standard moving averages and trend indicators often fail to distinguish the qualitative nature of price movement. They treat low-volume drift and high-velocity expansion identically. This inability to account for "Volatility Quality" leads to delayed responsiveness during critical market events. • Fractal Dissonance (Timeframe Disconnect): Financial markets exhibit fractal characteristics where trends on lower timeframes may contradict higher timeframe structures. Manual integration of multi-timeframe analysis increases cognitive load and susceptibility to human error, often resulting in conflicting biases at the point of execution. Core Design Principles To mitigate the aforementioned systemic inefficiencies, Impulse Reactor employs a modular architecture governed by three foundational principles: Principle A: Volatility Precursor Analysis Market mechanics demonstrate that volatility expansion often functions as a leading indicator for directional price movement. The system is engineered to detect "Volatility Deviation" — specifically, the divergence between short-term and long-term volatility baselines—prior to its manifestation in price action. This allows for entry timing aligned with the expansion phase of market volatility. Principle B: Momentum Density Visualization The system replaces singular momentum lines with a "Momentum Density" model utilizing a 15-layer Simple Moving Average (SMA) Ribbon. • Concept: This visualization represents the aggregate strength and consistency of the trend. • Application: A fully aligned and expanded ribbon indicates a robust trend structure ("Laminar Flow") capable of withstanding minor counter-trend noise, whereas a compressed ribbon signals consolidation or structural weakness. Principle C: Adaptive Confluence Protocols Signal validity is strictly governed by a multi-dimensional confluence logic. The system suppresses signal generation unless there is synchronized confirmation across all three analytical vectors: 1. Volatility: Confirmed expansion via the Impulse Core. 2. Momentum: Directional alignment via the Hybrid Oscillator. 3. Structure: Trend validation via the Baseline. This strict filtering mechanism significantly reduces false positives in non-trending (choppy) environments while maintaining sensitivity to genuine breakouts. 🔍 Chapter 2: Core Modules & Algorithmic Logic Module A: Impulse Core (Normalized Volatility Deviation) Operational Logic The Impulse Core functions as a volatility-normalized momentum gauge rather than a standard oscillator. It is designed to identify "Volatility Contraction" (Squeeze) and "Volatility Expansion" phases by quantifying the divergence between short-term and long-term volatility states. Volatility Z-Score Normalization The formula implements a custom normalization algorithm. Unlike standard oscillators that rely on absolute price changes, this logic calculates the Z-Score of the Volatility Spread. ◦ Numerator: (atr_f - atr_s) captures the raw momentum of volatility expansion. ◦ Denominator: (std_f + 1e-6) standardizes this value against historical variance. ◦ Result: This allows the indicator scales consistently across assets (e.g., Bitcoin vs. Euro) without manual recalibration. f_impulse() => atr_f = ta.atr(fastLen) // Fast Volatility Baseline atr_s = ta.atr(slowLen) // Slow Volatility Baseline std_f = ta.stdev(atr_f, devLen) // Volatility Standard Deviation (atr_f - atr_s) / (std_f + 1e-6) // Normalized Differential Calculation Algorithmic Framework • Differential Calculation: The system computes the spread between a Fast Volatility Baseline (ATR-10) and a Slow Volatility Baseline (ATR-30). • Normalization Protocol: To standardize consistency across diverse asset classes (e.g., Forex vs. Crypto), the raw differential is divided by the standard deviation of the volatility itself over a 30-period lookback. • Signal Generation: ◦ Contraction (Squeeze): When the Fast ATR compresses below the Slow ATR, it registers a potential volatility buildup phase. ◦ Expansion (Release): A rapid divergence of the Fast ATR above the Slow ATR signals a confirmed volatility expansion, validating the strength of the move. Module B: Gradient Oscillator (RSI-SMA Hybrid) Design Rationale To mitigate the "noise" and "false reversal" signals common in single-line oscillators (like standard RSI), this module utilizes a 15-Layer Gradient Ribbon to visualize momentum density and persistence. Technical Architecture • Ribbon Array: The system generates 15 sequential Simple Moving Averages (SMA) applied to a volatility-adjusted RSI source. The length of each layer increases incrementally. • State Analysis: Momentum Alignment (Laminar Flow): When all 15 layers are expanded and parallel, it indicates a robust trend where buying/selling pressure is distributed evenly across multiple timeframes. This state helps filter out premature "overbought/oversold" signals. • Consolidation (Compression): When the distance between the fastest layer (Layer 1) and the slowest layer (Layer 15) approaches zero or the layers intersect, the system identifies a "Non-Tradable Zone," preventing entries during choppy market conditions. // Laminar Flow Validation f_validate_trend() => // Calculate spread between Ribbon layers ribbon_spread = ta.stdev(ribbon_array, 15) // Only allow signals if Ribbon is expanded (Laminar Flow) is_flowing = ribbon_spread > min_expansion_threshold // If compressed (Knotted), force signal to false is_flowing ? signal : na Module C: Adaptive Signal Filtering (Behavioral Bias Mitigation) This subsystem, operating as an algorithmic "Anti-Greed" Mechanism, addresses the statistical tendency for signal degradation following prolonged trends. Dynamic Threshold Adjustment • Win Streak Detection: The algorithm internally tracks the outcome of closed trade cycles. • Sensitivity Multiplier: Upon detecting consecutive successful signals in the same direction, a Penalty_Factor is applied to the entry logic. • Operational Impact: This effectively raises the Required_Slope threshold for subsequent signals. For example, after three consecutive bullish signals, the system requires a 30% steeper trend angle to validate a fourth entry. This enforces stricter discipline during extended trends to reduce the probability of entering at the point of trend exhaustion. Anti-Greed Logic: Dynamic Threshold Calculation f_adjust_threshold(base_slope, win_streak) => // Adds a 10% penalty to the difficulty for every consecutive win penalty_factor = 0.10 risk_scaler = 1 + (win_streak * penalty_factor) // Returns the new, harder-to-reach threshold base_slope * risk_scaler Module D: Trend Baseline (Triple-Smoothed Structure) The Trend Baseline serves as the structural filter for all signals. It employs a Triple-Smoothed Hybrid Algorithm designed to balance lag reduction with noise filtration. Smoothing Stages 1. Volatility Banding: Utilizes a SuperTrend-based calculation to establish the upper and lower boundaries of price action. 2. Weighted Filter: Applies a Weighted Moving Average (WMA) to prioritize recent price data. 3. Exponential Smoothing: A final Exponential Moving Average (EMA) pass is applied to create a seamless baseline curve. Functionality This "Heavy" baseline resists minor intraday volatility spikes while remaining responsive to sustained structural shifts. A signal is only considered valid if the price action maintains structural integrity relative to this baseline 🚦 Chapter 3: Risk Management & Exit Protocols Quantitative Risk Management (TP/SL & Trailing) Foundational Architecture: Volatility-Adjusted Geometry Unlike strategies relying on static nominal values, Impulse Reactor establishes dynamic risk boundaries derived from quantitative volatility metrics. This design aligns trade invalidation levels mathematically with the current market regime. • ATR-Based Dynamic Bracketing: The protocol calculates Stop-Loss and Take-Profit levels by applying Fibonacci coefficients (Default: 0.786 for SL / 1.618 for TP) to the Average True Range (ATR). ◦ High Volatility Environments: The risk bands automatically expand to accommodate wider variance, preventing premature exits caused by standard market noise. ◦ Low Volatility Environments: The bands contract to tighten risk parameters, thereby dynamically adjusting the Risk-to-Reward (R:R) geometry. • Close-Validation Protocol ("Soft Stop"): Institutional algorithms frequently execute liquidity sweeps—driving prices briefly below key support levels to accumulate inventory. ◦ Mechanism: When the "Soft Stop" feature is enabled, the system filters out intraday volatility spikes. The stop-loss is conditional; execution is triggered only if the candle closes beyond the invalidation threshold. ◦ Strategic Advantage: This logic distinguishes between momentary price wicks and genuine structural breakdowns, preserving positions during transient volatility. • Step-Function Trailing Mechanism: To protect unrealized PnL while allowing for normal price breathing, a two-phase trailing methodology is employed: ◦ Phase 1 (Activation): The trailing function remains dormant until the price advances by a pre-defined percentage threshold. ◦ Phase 2 (Dynamic Floor): Once armed, the stop level creates a moving floor, adjusting relative to price action while maintaining a volatility-based (ATR) buffer to systematically protect unrealized PnL. • Algorithmic Exit Protocols (Dynamic Liquidity Analysis) ◦ Rationale: Inefficiencies of Static Targets Static "Take Profit" levels often result in suboptimal exits. They compel traders to close positions based on arbitrary figures rather than evolving market structure, potentially capping upside during significant trends or retaining positions while the underlying trend structure deteriorates. ◦ Solution: Structural Integrity Assessment The system utilizes a Dynamic Liquidity Engine to continuously audit the validity of the position. Instead of targeting a specific price point, the algorithm evaluates whether the trend remains statistically robust. Multi-Factor Exit Logic (The Tri-Vector System) The Smart Exit protocol executes only when specific algorithmic invalidation criteria are met: • 1. Momentum Exhaustion (Confluence Decay): The system monitors a 168-hour rolling average of the Confluence Score. A significant deviation below this historical baseline indicates momentum exhaustion, signaling that the driving force behind the trend has dissipated prior to a price reversal. This enables preemptive exits before a potential drawdown. • 2. Statistical Over-Extension (Mean Reversion): Utilizing the core volatility logic, the system identifies instances where price deviates beyond 2.0 standard deviations from the mean. While the trend may be technically bullish, this statistical anomaly suggests a high probability of mean reversion (elastic snap-back), triggering a defensive exit to capitalize on peak valuation. • 3. Oscillator Rejection (Immediate Pivot): To manage sudden V-shaped volatility, the system monitors RSI pivots. If a sharp "Pivot High" or divergence is detected, the protocol triggers an immediate "Peak Exit," bypassing standard trend filters to secure liquidity during high-velocity reversals. 🎨 Chapter 4: Visualization Guide Gradient Oscillator Ribbon The 15-layer SMA ribbon visualized via plot(r1...r15) represents the "Momentum Density" of the market. • Visuals: ◦ Cyan/Blue Ribbon: Indicates Bullish Momentum. ◦ Pink/Magenta Ribbon: Indicates Bearish Momentum. • Interpretation: ◦ Laminar Flow: When the ribbon expands widely and flows in parallel, it signifies a robust trend where momentum is distributed evenly across timeframes. This is the ideal state for trend-following. ◦ Compression (Consolidation): If the ribbon becomes narrow, twisted, or knotted, it indicates a "Non-Tradable Zone" where the market lacks a unified direction. Traders are advised to wait for clarity. ◦ Over-Extension: If the top layer crosses the Overbought (85) or Oversold (15) lines, it visually warns of potential market overheating. Trend Baseline The thick, color-changing line plotted via plot(baseline) represents the Structural Backbone of the market. • Visuals: Changes color based on the trend direction (Blue for Bullish, Pink for Bearish). • Interpretation: Structural Filter: Long positions are statistically favored only when price action sustains above this baseline, while short positions are favored below it. Dynamic Support/Resistance: The baseline acts as a dynamic support level during uptrends and resistance during downtrends. Entry Signals & Labels Text labels ("Long Entry", "Short Entry") appear when the system detects high-probability setups grounded in quantitative confluence. • Visuals: Labeled signals appear above/below specific candles. • Interpretation: These signals represent moments where Volatility (Expansion), Momentum (Alignment), and Structure (Trend) are synchronized. Smart Exit: Labels such as "Smart Exit" or "Peak Exit" appear when the system detects momentum exhaustion or structural decay, prompting a defensive exit to preserve capital. Dynamic TP/SL Boxes The semi-transparent colored zones drawn via fill() represent the risk management geometry. • Visuals: Colored boxes extending from the entry point to the Take Profit (TP) and Stop Loss (SL) levels. • Function: Volatility-Adjusted Geometry: Unlike static price targets, these boxes expand during high volatility (to prevent wicks from stopping you out) and contract during low volatility (to optimize Risk-to-Reward ratios). SAR + MACD Glow Small glowing shapes appearing above or below candles. • Visuals: Triangle or circle glows near the price bars. • Interpretation: This visual indicates a secondary confirmation where Parabolic SAR and MACD align with the main trend direction. It serves as an additional confluence factor to increase confidence in the trade setup. Support/Resistance Table A small table located at the bottom-right of the chart. • Function: Automatically identifies and displays recent Pivot Highs (Resistance) and Pivot Lows (Support). • Interpretation: These levels can be used as potential targets for Take Profit or invalidation points for manual Stop Loss adjustments. 🖥️ Chapter 5: Dashboard & Operational Guide Integrated Analytics Panel (Dashboard Overview) To facilitate rapid decision-making without manual calculation, the system aggregates critical market dimensions into a unified "Heads-Up Display" (HUD). This panel monitors real-time metrics across multiple timeframes and analytical vectors. A. Intermediate Structure (12H Trend) • Function: Anchors the intraday analysis to the broader market structure using a 12-hour rolling window. • Interpretation: ◦ Bullish (> +0.5%): Indicates a positive structural bias. Long setups align with the macro flow. ◦ Bearish (< -0.5%): Indicates structural weakness. Short setups are statistically favored. ◦ Neutral: Represents a ranging environment where the Confluence Score becomes the primary weighting factor. B. Composite Confluence Score (Signal Confidence) • Definition: A probability metric derived from the synchronization of Volatility (Impulse Core), Momentum (Ribbon), and Trend (Baseline). • Grading Scale: Strong Buy/Sell (> 7.0 / < 3.0): Indicates full alignment across all three vectors. Represents a "Prime Setup" eligible for standard position sizing. Buy/Sell (5.0–7.0 / 3.0–5.0): Indicates a valid trend but with moderate volatility confirmation. Neutral: Signals conflicting data (e.g., Bullish Momentum vs. Bearish Structure). Trading is not recommended ("No-Trade Zone"). C. Statistical Deviation Status (Mean Reversion) • Logic: Utilizes Bollinger Band deviation principles to quantify how far price has stretched from the statistical mean (20 SMA). • Alert States: Over-Extended (> 2.0 SD): Warning that price is statistically likely to revert to the mean (Elastic Snap-back), even if the trend remains technically valid. New entries are discouraged in this zone. Normal: Price is within standard distribution limits, suitable for trend-following entries. D. Volatility Regime Classification • Metric: Compares current ATR against a 100-period historical baseline to categorize the market state. • Regimes: Low Volatility (Lvl < 1.0): Market Compression. Often precedes volatility expansion events. Mid Volatility (Lvl 1.0 - 1.5): Standard operating environment. High Volatility (Lvl > 1.5): Elevated market stress. Risk parameters should be adjusted (e.g., reduced position size) to account for increased variance. E. Performance Telemetry • Function: Displays the historical reliability of the Trend Baseline for the current asset and timeframe. • Operational Threshold: If the displayed Win Rate falls below 40%, it suggests the current market behavior is incoherent (choppy) and does not respect trend logic. In such cases, switching assets or timeframes is recommended. Operational Protocols & Signal Decoding Visual Interpretation Standards • Laminar Flow (Trade Confirmation): A valid trend is visually confirmed when the 15-layer SMA Ribbon is fully expanded and parallel. This indicates distributed momentum across timeframes. • Consolidation (No-Trade): If the ribbon appears twisted, knotted, or compressed, the market lacks a unified directional vector. • Baseline Interaction: The Triple-Smoothed Baseline acts as a dynamic support/resistance filter. Long positions remain valid only while price sustains above this structure. System Calibration (Settings) • Adaptive Signal Filtering (Prev. Anti-Greed): Enabled by default. This logic automatically raises the required trend slope threshold following consecutive wins to mitigate behavioral bias. • Impulse Sensitivity: Controls the reactivity of the Volatility Core. Higher settings capture faster moves but may introduce more noise. ⚙️ Chapter 6: System Configuration & Alert Guide This section provides a complete breakdown of every adjustable setting within Impulse Reactor to assist you in tailoring the engine to your specific needs. 🌐 LANGUAGE SETTINGS (Localization) ◦ Select Language (Default: English): Function: Instantly translates all chart labels, dashboard texts into your preferred language. Supported: English, Korean, Chinese, Spanish ⚡ IMPULSE CORE SETTINGS (Volatility Engine) ◦ Deviation Lookback (Default: 30): The period used to calculate the standard deviation of volatility. Role: Sets the baseline for normalizing momentum. Higher values make the core smoother but slower to react. ◦ Fast Pulse Length (Default: 10): The short-term ATR period. Role: Detects rapid volatility expansion. ◦ Slow Pulse Length (Default: 30): The long-term ATR baseline. Role: Establishes the background volatility level. The core signal is derived from the divergence between Fast and Slow pulses. 🎯 TP/SL SETTINGS (Risk Management) ◦ SL/TP Fibonacci (Default: 0.786 / 1.618): Selects the Fibonacci ratio used for risk calculation. ◦ SL/TP Multiplier (Default: 1.5 / 2): Applies a multiplier to the ATR-based bands. Role: Expands or contracts the Take Profit and Stop Loss boxes. Increase these values for higher volatility assets (like Altcoins) to avoid premature stop-outs. ◦ ATR Length (Default: 14): The lookback period for calculating the Average True Range used in risk geometry. ◦ Use Soft Stop (Close Basis): Role: If enabled, Stop Loss alerts only trigger if a candle closes beyond the invalidation level. This prevents being stopped out by wick manipulations. 🔊 RIBBON SETTINGS (Momentum Visualization) ◦ Show SMA Ribbon: Toggles the visibility of the 15-layer gradient ribbon. ◦ Ribbon Line Count (Default: 15): The number of SMA lines in the ribbon array. ◦ Ribbon Start Length (Default: 2) & Step (Default: 1): Defines the spread of the ribbon. Role: Controls the "thickness" of the momentum density visualization. A wider step creates a broader ribbon, useful for higher timeframes. 📎 DISPLAY OPTIONS ◦ Show Entry Lines / TP/SL Box / Position Labels / S/R Levels / Dashboard: Toggles individual visual elements on the chart to reduce clutter. ◦ Show SAR+MACD Glow: Enables the secondary confirmation shapes (triangles/circles) above/below candles. 📈 TREND BASELINE (Structural Filter) ◦ Supertrend Factor (Default: 12) & ATR Period (Default: 90): Controls the sensitivity of the underlying Supertrend algorithm used for the baseline calculation. ◦ WMA Length (40) & EMA Length (14): The smoothing periods for the Triple-Smoothed Baseline. ◦ Min Trend Duration (Default: 10): The minimum number of bars the trend must be established before a signal is considered valid. 🧠 SMART EXIT (Dynamic Liquidity) ◦ Use Smart Exit: Enables the momentum exhaustion logic. ◦ Exit Threshold Score (Default: 3): The sensitivity level for triggering a Smart Exit. Lower values trigger earlier exits. ◦ Average Period (168) & Min Hold Bars (5): Defines the rolling window for momentum decay analysis and the minimum duration a trade must be held before Smart Exit logic activates. 🛡️ TRAILING STOP (Step) ◦ Use Trailing Stop: Activates the step-function trailing mechanism. ◦ Step 1 Activation % (0.5) & Offset % (0.5): The price must move 0.5% in your favor to arm the first trail level, which sets a stop 0.5% behind price. ◦ Step 2 Activation % (1) & Offset % (0.2): Once price moves 1%, the trail tightens to 0.2%, securing the position. 🌀 SAR & MACD SETTINGS (Secondary Confirmation) ◦ SAR Start/Increment/Max: Standard Parabolic SAR parameters. ◦ SAR Score Scaling (ATR): Adjusts how much weight the SAR signal has in the overall confluence score. ◦ MACD Fast/Slow/Signal: Standard MACD parameters used for the "Glow" signals. 🔄 ANTI-GREED LOGIC (Behavioral Bias) ◦ Strict Entry after Win: Enables the negative feedback loop. ◦ Strict Multiplier (Default: 1.1): Increases the entry difficulty by 10% after each win. Role: Prevents overtrading and entering at the top of an extended trend. 🌍 HTF FILTER (Multi-Timeframe) ◦ Use Auto-Adaptive HTF Filter: Automatically selects a higher timeframe (e.g., 1H -> 4H) to filter signals. ◦ Bypass HTF on Steep Trigger: Allows an entry even against the HTF trend if the local momentum slope is exceptionally steep (catch powerful reversals). 📉 RSI PEAK & CHOPPINESS ◦ RSI Peak Exit (Instant): Triggers an immediate exit if a sharp RSI pivot (V-shape) is detected. ◦ Choppiness Filter: Suppresses signals if the Choppiness Index is above the threshold (Default: 60), indicating a flat market. 📐 SLOPE TRIGGER LOGIC ◦ Force Entry on Steep Slope: Overrides other filters if the price angle is extremely vertical (high velocity). ◦ Slope Sensitivity (1.5): The angle required to trigger this override. ⛔ FLAT MARKET FILTER (ADX & ATR) ◦ Use ADX Filter: Blocks signals if ADX is below the threshold (Default: 20), indicating no trend. ◦ Use ATR Flat Filter: Blocks signals if volatility drops below a critical level (dead market). 🔔 Alert Configuration Guide Impulse Reactor is designed with a comprehensive suite of alert conditions, allowing you to automate your trading or receive real-time notifications for specific market events. How to Set Up: Click the "Alert" (Clock) icon in the TradingView toolbar. Select "Impulse Reactor " from the Condition dropdown. Choose one of the specific trigger conditions below: 🚀 Entry Signals (Trend Initiation) Long Entry: Trigger: Fires when a confirmed Bullish Setup is detected (Momentum + Volatility + Structure align). Usage: Use this to enter new Long positions. Short Entry: Trigger: Fires when a confirmed Bearish Setup is detected. Usage: Use this to enter new Short positions. 🎯 Profit Taking (Target Levels) Long TP: Trigger: Fires when price hits the calculated Take Profit level for a Long trade. Usage: Automate partial or full profit taking. Short TP: Trigger: Fires when price hits the calculated Take Profit level for a Short trade. Usage: Automate partial or full profit taking. 🛡️ Defensive Exits (Risk Management) Smart Exit: Trigger: Fires when the system detects momentum decay or statistical exhaustion (even if the trend hasn't fully reversed). Usage: Recommended for tightening stops or closing positions early to preserve gains. Overbought / Oversold: Trigger: Fires when the ribbon extends into extreme zones. Usage: Warning signal to prepare for a potential reversal or pullback. 💡 Secondary Confirmation (Confluence) SAR+MACD Bullish: Trigger: Fires when Parabolic SAR and MACD align bullishly with the main trend. Usage: Ideal for Pyramiding (adding to an existing winning position). SAR+MACD Bearish: Trigger: Fires when Parabolic SAR and MACD align bearishly. Usage: Ideal for adding to short positions. ⚠️ Chapter 7: Conclusion & Risk Disclosure Methodological Synthesis Impulse Reactor represents a shift from reactive price tracking to proactive energy analysis. By decomposing market activity into its atomic components — Volatility, Momentum, and Structure — and reconstructing them into a coherent decision model, the system aims to provide a quantitative framework for market engagement. It is designed not to predict the future, but to identify high-probability conditions where kinetic energy and trend structure align. Disclaimer & Risk Warnings ◦ Educational Purpose Only This indicator, including all associated code, documentation, and visual outputs, is provided strictly for educational and informational purposes. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. ◦ No Guarantee of Performance Past performance is not indicative of future results. All metrics displayed on the dashboard (including "Win Rate" and "P&L") are theoretical calculations based on historical data. These figures do not account for real-world trading factors such as slippage, liquidity gaps, spread costs, or broker commissions. ◦ High-Risk Warning Trading cryptocurrencies, futures, and leveraged financial products involves a substantial risk of loss. The use of leverage can amplify both gains and losses. Users acknowledge that they are solely responsible for their trading decisions and should conduct independent due diligence before executing any trades. ◦ Software Limitations The software is provided "as is" without warranty. Users should be aware that market data feeds on analysis platforms may experience latency or outages, which can affect signal generation accuracy.
Pine Script® indicator
by ApexLegion
17
Force Pulse█ OVERVIEW Force Pulse is a fast-reacting oscillator that measures the internal strength of market sides by analyzing the aggregated dominance of bulls and bears based on candle size. The indicator normalizes this difference into a 0–100 range, generates signals (OB/OS, midline cross, MA midline cross), and detects divergences between price and the oscillator. It also offers advanced visualization, signal markers, and alerts, making it a versatile tool suitable for many trading styles. █ CONCEPTS Force Pulse was designed as a universal tool that can be applied to various trading strategies depending on its settings: - increasing the period lengths and smoothing transforms it into a momentum/trend indicator, revealing a stable dominance of one market side. - Lowering these parameters turns it into a peak/low detector, ideal for contrarian and mean-reversion strategies. The oscillator analyzes the relationship between the sum of bullish and bearish candles over a selected period, based on: - candle body size, or - average candle body size (AVG Body). Depending on the selected mode, OB/OS levels should be adjusted, as value dynamics differ between modes. The output is normalized to 0–100, where: > 50 – bullish dominance, < 50 – bearish dominance. The additional MA line is derived from smoothed oscillator values and serves as a signal line for midline crosses and as a trend filter. The indicator also detects divergences (HL/LL) between price and the oscillator. █ FEATURES Bull & Bear Strength: - Calculations are based on Body or AVG Body – mode selection requires adjusting OB/OS levels. - Bullish and bearish candle values are summed separately. - All results are normalized to the 0–100 scale. Force Pulse Oscillator: - The main line reflects the current dominance of either market side. Dynamic colors: - Green – above 50, - Red – below 50. Signal MA: - SMA based on oscillator values functions as a signal line. - Helps detect momentum shifts and generates signals via midline crosses. - Can serve as a trend confirmation filter. Overbought / Oversold: - Configurable OB/OS levels, also for the MA line. - Dynamic OB/OS line colors: when the MA line exceeds the defined threshold (e.g., MA > maOverbought or MA < maOversold), OB/OS lines change color (red/green). - This often signals a potential reversal or correction and may act as additional confirmation for oscillator-generated signals. Divergences: - Detection based on swing pivots: - Bullish: price LL, oscillator HL - Bearish: price HH, oscillator LH - Displayed as “Bull” / “Bear” labels. Signals: Supports multiple signal types: - Overbought/Oversold Cross - Midline Cross - MA Midline Cross (based on the signal MA line) - Signals appear as triangles above/below the oscillator. Visualization: - Gradient options for lines and levels. - Full customization of colors, transparency, and line thickness. Alerts available for: - Divergences - OB/OS crossings - Midline crossings - MA midline crossings █ HOW TO USE Add the indicator to your TradingView chart → Indicators → search “Force Pulse” Parameter Configuration Calculation Settings: - Calculation Period (lookback) – defines the strength calculation window. Force Mode (Body / AVG Body): - Body – faster response, higher sensitivity. - AVG Body – more stable output; adjust band levels and periods to your strategy. - EMA Smoothing (smoothLen) – reduces oscillator noise. - MA Length – length of the signal line (SMA). Threshold Levels: - Set Overbought/Oversold levels for both the oscillator and the MA line. - Adjust levels depending on Body / AVG Body mode. Divergence Detection: - Enable/disable divergence detection. - PivotLength affects both delay and signal quality. - Signal Settings: Choose one or multiple signal types. - Style & Colors: Full control over color schemes, gradients, and transparency. Signal Interpretation BUY: - Oscillator leaves oversold (OS crossover). - Midline cross upward. - MA crosses the midline from below. - Bullish divergence. SELL: - Oscillator leaves overbought (drops below OB). - Midline cross downward. - MA crosses the midline from above. - Bearish divergence. Trend / Momentum: -Longer periods and stronger smoothing → stable directional signals. -MA as a trend filter: e.g., signal line above the midline (50) and MA pointing upward indicates continuation of a bullish impulse. Contrarian / Mean Reversion: - Short periods → rapid detection of peaks and troughs; ideal for contrarian signals and pullback entries. █ APPLICATIONS - Trend Trading: Using midline and MA midline crosses to determine direction. - Reversal Trading: OB/OS levels and divergences help identify reversals. - Scalping & Intraday: Short settings + signal line above the midline with bullish MA → shows short-term impulse and continuation. - Swing Trading: Longer MA and higher lookback provide a stable view of market-side dominance. - Momentum Analysis: Force Pulse highlights the strength of the wave before price movement occurs. █ NOTES - In strong trends, the oscillator may stay in extreme zones for a long time — this reflects dominance, not necessarily a reversal signal. - Divergences are more reliable on higher timeframes. - OB/OS levels should be tailored to Body/AVG Body mode and the instrument. - Best results come from combining the indicator with other tools (S/R, market structure, volume).
Pine Script® indicator
by Uncle_the_shooter
8
VMDM - Volume, Momentum & Divergence Master [BullByte]VMDM - Volume, Momentum and Divergence Master Educational Multi-Layer Market Structure Analysis System Multi-factor divergence engine that scores RSI momentum, volume pressure, and institutional footprints into one non-repainting confluence rating (0-100). WHAT THIS INDICATOR IS VMDM is an educational indicator designed to teach traders how to recognize high-probability reversal and continuation patterns by analyzing four independent market dimensions simultaneously. Instead of relying on a single indicator that may produce frequent false signals, VMDM creates a confluence-based scoring system that weights multiple confirmation factors, helping you understand which setups have stronger technical backing and which are lower quality. This is NOT a trading system or signal generator. It is a learning tool that visualizes complex market structure concepts in an accessible format for both coders and non-coders. THE PROBLEM IT SOLVES Most traders face these common challenges: Challenge 1 - Indicator Overload: Running RSI, volume analysis, and divergence detection separately creates chart clutter and conflicting signals. You waste time cross-referencing multiple windows trying to determine if all factors align. Challenge 2 - False Divergences: Standard divergence indicators trigger on every minor pivot, creating noise. Many divergences fail because they lack supporting evidence from volume or market structure. Challenge 3 - Missed Context: A bullish RSI divergence means nothing if it occurs during weak volume or in the middle of strong distribution. Context determines quality. Challenge 4 - Repainting Confusion: Many divergence scripts repaint, showing perfect historical signals that never actually triggered in real-time, leading to false confidence. Challenge 5 - Institutional Pattern Recognition: Absorption zones, stop hunts, and exhaustion patterns are taught in trading education but difficult to identify systematically without manual analysis. VMDM addresses all five challenges by combining complementary analytical layers into one transparent, non-repainting, confluence-weighted system with visual clarity. WHY THIS SPECIFIC COMBINATION - MASHUP JUSTIFICATION This indicator is NOT a random mashup of popular indicators. Each of the four layers serves a specific analytical purpose and together they create a complete market structure assessment framework. THE FOUR ANALYTICAL LAYERS LAYER 1 - RSI MOMENTUM DIVERGENCE (Trend Exhaustion Detection) Purpose: Identifies when price momentum is weakening before price itself reverses. Why RSI: The Relative Strength Index measures momentum on a bounded 0-100 scale, making divergence detection mathematically consistent across all assets and timeframes. Unlike raw price oscillators, RSI normalizes momentum regardless of volatility regime. How It Contributes: Divergence between price pivots and RSI pivots reveals early momentum exhaustion. A lower price low with a higher RSI low (bullish regular divergence) signals sellers are losing strength even as price makes new lows. This is the PRIMARY signal generator in VMDM. Limitation If Used Alone: RSI divergence by itself produces many false signals because momentum can remain weak during continued trends. It needs confirmation from volume and structural evidence. LAYER 2 - VOLUME PRESSURE ANALYSIS (Buying vs Selling Intensity) Purpose: Quantifies whether the current bar's volume reflects buying pressure or selling pressure based on where price closed within the bar's range. Methodology: Instead of just measuring volume size, VMDM calculates WHERE in the bar range the close occurred. A close near the high on high volume indicates strong buying absorption. A close near the low indicates selling pressure. The calculation accounts for wick size (wicks reduce pressure quality) and uses percentile ranking over a lookback period to normalize pressure strength on a 0-100 scale. Formula Concept: Buy Pressure = Volume × (Close - Low) / (High - Low) × Wick Quality Factor Sell Pressure = Volume × (High - Close) / (High - Low) × Wick Quality Factor Net Pressure = Buy Pressure - Sell Pressure Pressure Strength = Percentile Rank of Net Pressure over lookback period Why Percentile Ranking: Absolute volume varies by asset and session. Percentile ranking makes 85th percentile pressure on low-volume crypto comparable to 85th percentile pressure on high-volume forex. How It Contributes: When a bullish divergence occurs at a pivot low AND pressure strength is above 60 (strong buying), this adds 25 confluence points. It confirms that the divergence is occurring during actual accumulation, not just weak selling. Limitation If Used Alone: Pressure analysis shows current bar intensity but cannot identify trend exhaustion or reversal timing. High buying pressure can exist during a strong uptrend with no reversal imminent. LAYER 3 - BEHAVIORAL FOOTPRINT PATTERNS (Volume Anomaly Detection) CRITICAL DISCLAIMER: The terms "institutional footprint," "absorption," "stop hunt," and "exhaustion" used in this indicator are EDUCATIONAL LABELS for specific price and volume behavioral patterns. These patterns are detected through technical analysis of publicly available price, volume, and bar structure data. This indicator does NOT have access to actual institutional order flow, market maker data, broker stop-loss locations, or any non-public data source. These pattern names are used because they are common terminology in trading education to describe these technical behaviors. The analysis is interpretive and based on observable price action, not privileged information. Purpose: Detect volume anomalies and price patterns that historically correlate with potential reversal zones or trend continuation failure. Pattern Type 1 - Absorption (Labeled as "ACCUMULATION" or "DISTRIBUTION") Detection Criteria: Volume is more than 2x the moving average AND bar range is less than 50 percent of the average bar range. Interpretation: High volume compressed into a tight range suggests large participants are absorbing supply (accumulation) or distribution (distribution) without allowing price to move significantly. This often precedes directional moves once absorption completes. Visual: Colored box zone highlighting the absorption area. Pattern Type 2 - Stop Hunt (Labeled as "BULL HUNT" or "BEAR HUNT") Detection Criteria: Price penetrates a recent 10-bar high or low by a small margin (0.2 percent), then closes back inside the range on above-average volume (1.5x+). Interpretation: Price briefly spikes beyond recent structure (likely triggering stop losses placed just beyond obvious levels) then reverses. This is a classic false breakout pattern often seen before reversals. Visual: Label at the wick extreme showing hunt direction. Pattern Type 3 - Exhaustion (Labeled as "SELL EXHAUST" or "BUY EXHAUST") Detection Criteria: Lower wick is more than 2.5x the body size with volume above 1.8x average and RSI below 35 (sell exhaustion), OR upper wick more than 2.5x body size with volume above 1.8x average and RSI above 65 (buy exhaustion). Interpretation: Large wicks with high volume and extreme RSI suggest aggressive buying or selling was met with equally aggressive rejection. This exhaustion often marks short-term extremes. Visual: Label showing exhaustion type. How These Contribute: When a divergence forms at a pivot AND one of these behavioral patterns is active, the confluence score increases by 20 points. This confirms the divergence is occurring during structural anomaly activity, not just normal price flow. Limitation If Used Alone: These patterns can occur mid-trend and do not indicate direction without momentum context. Absorption in a strong uptrend may just be continuation accumulation. LAYER 4 - CONFLUENCE SCORING MATRIX (Quality Weighting System) Purpose: Translate all detected conditions into a single 0-100 quality score so you can objectively compare setups. Scoring Breakdown: Divergence Present: +30 points (primary signal) Pressure Confirmation: +25 points (volume supports direction) Behavioral Footprint Active: +20 points (structural anomaly present) RSI Extreme: +15 points (RSI below 30 or above 70 at pivot) Volume Spike: +10 points (current volume above 1.5x average) Maximum Possible Score: 100 points Why These Weights: The weights reflect reliability hierarchy based on backtesting observation. Divergence is the core signal (30 points), but without volume confirmation (25 points) many fail. Behavioral patterns add meaningful context (20 points). RSI extremes and volume spikes are secondary confirmations (15 and 10 points). Quality Tiers: 90-100: TEXTBOOK (all factors aligned) 75-89: HIGH QUALITY (strong confluence) 60-74: VALID (meets minimum threshold) Below 60: DEVELOPING (not displayed unless threshold lowered) How It Contributes: The confluence score allows you to filter noise. You can set your minimum quality threshold in settings. Higher thresholds (75+) show fewer but higher-quality patterns. Lower thresholds (50-60) show more patterns but include lower-confidence setups. This teaches you to distinguish strong setups from weak ones. Limitation: Confluence scoring is historical observation-based, not predictive guarantee. A 95-point setup can still fail. The score represents technical alignment, not future certainty. WHY THIS COMBINATION WORKS TOGETHER Each layer addresses a limitation in the others: RSI Divergence identifies WHEN momentum is exhausting (timing) Volume Pressure confirms WHETHER the exhaustion is accompanied by opposite-side accumulation (confirmation) Behavioral Footprint shows IF structural anomalies support the reversal hypothesis (context) Confluence Scoring weights ALL factors into an objective quality metric (filtering) Using only RSI divergence gives you timing without confirmation. Using only volume pressure gives you intensity without directional context. Using only pattern detection gives you anomalies without trend exhaustion context. Using all four together creates a complete analytical framework where each layer compensates for the others' weaknesses. This is not a mashup for the sake of combining indicators. It is a structured analytical system where each component has a defined role in a multi-dimensional market assessment process. HOW TO READ THE INDICATOR - VISUAL ELEMENTS GUIDE VMDM displays up to five visual layer types. You can enable or disable each layer independently in settings under "Visual Layers." VISUAL LAYER 1 - MARKET STRUCTURE (Pivot Points and Lines) What You See: Small labels at swing highs and lows marked "PH" (Pivot High) and "PL" (Pivot Low) with horizontal dashed lines extending right from each pivot. What It Means: These are CONFIRMED pivots, not real-time. A pivot low appears AFTER the required right-side confirmation bars pass (default 3 bars). This creates a delay but prevents repainting. The pivot only appears once it is mathematically confirmed. The horizontal lines represent support (from pivot lows) and resistance (from pivot highs) levels where price previously found significant rejection. Color Coding: Green label and line: Pivot Low (potential support) Red label and line: Pivot High (potential resistance) How To Use: These pivots are the foundation for divergence detection. Divergence is only calculated between confirmed pivots, ensuring all signals are non-repainting. The lines help you see historical structure levels. VISUAL LAYER 2 - PRESSURE ZONES (Background Color) What You See: Subtle background color shading on bars - light green or light red tint. What It Means: This visualizes volume pressure strength in real-time. Color Coding: Light Green Background: Pressure Strength above 70 (strong buying pressure - price closing near highs on volume) Light Red Background: Pressure Strength below 30 (strong selling pressure - price closing near lows on volume) No Color: Neutral pressure (pressure between 30-70) How To Use: When a bullish divergence pattern appears during green pressure zones, it suggests the divergence is forming during accumulation. When a bearish divergence appears during red zones, distribution is occurring. Pressure zones help you filter divergences - those forming in supportive pressure environments have higher probability. VISUAL LAYER 3 - DIVERGENCE LINES (Dotted Connectors) What You See: Dotted lines connecting two pivot points (either two pivot lows or two pivot highs). What It Means: A divergence has been detected between those two pivots. The line connects the price pivots where RSI showed opposite behavior. Color Coding: Bright Green Line: Bullish divergence (regular or hidden) Bright Red Line: Bearish divergence (regular or hidden) How To Use: The divergence line appears ONLY after the second pivot is confirmed (delayed by right-side confirmation bars). This is intentional to prevent repainting. When you see the line appear, it means: For Bullish Regular Divergence: Price made a lower low (second pivot lower than first) RSI made a higher low (RSI at second pivot higher than first) Interpretation: Downtrend losing momentum For Bullish Hidden Divergence: Price made a higher low (second pivot higher than first) RSI made a lower low (RSI at second pivot lower than first) Interpretation: Uptrend continuation likely (pullback within uptrend) For Bearish Regular Divergence: Price made a higher high (second pivot higher than first) RSI made a lower high (RSI at second pivot lower than first) Interpretation: Uptrend losing momentum For Bearish Hidden Divergence: Price made a lower high (second pivot lower than first) RSI made a higher high (RSI at second pivot higher than first) Interpretation: Downtrend continuation likely (bounce within downtrend) If "Show Consolidated Analysis Label" is disabled, a small label will appear on the divergence line showing the divergence type abbreviation. VISUAL LAYER 4 - BEHAVIORAL FOOTPRINT MARKERS What You See: Boxes, labels, and markers at specific bars showing pattern detection. ABSORPTION ZONES (Boxes): Colored rectangular boxes spanning one or more bars. Purple Box: Accumulation absorption zone (high volume, tight range, bullish close) Red Box: Distribution absorption zone (high volume, tight range, bearish close) If absorption continues for multiple consecutive bars, the box extends and a counter appears in the label showing how many bars the absorption lasted. What It Means: Large volume is being absorbed without significant price movement. This often precedes directional breakouts once the absorption phase completes. STOP HUNT MARKERS (Labels): Small labels below or above wicks labeled "BULL HUNT" or "BEAR HUNT" (may show bar count if consecutive). What It Means: BULL HUNT : Price spiked below recent lows then reversed back up on volume - likely triggered sell stops before reversing BEAR HUNT : Price spiked above recent highs then reversed back down on volume - likely triggered buy stops before reversing EXHAUSTION MARKERS (Labels): Labels showing "SELL EXHAUST" or "BUY EXHAUST." What It Means: SELL EXHAUST : Large lower wick with high volume and low RSI - aggressive selling met with strong rejection BUY EXHAUST : Large upper wick with high volume and high RSI - aggressive buying met with strong rejection How To Use: These markers help you identify WHERE structural anomalies occurred. When a divergence signal appears AT THE SAME TIME as one of these patterns, the confluence score increases. You are looking for alignment - divergence + behavioral pattern + pressure confirmation = high-quality setup. VISUAL LAYER 5 - CONSOLIDATED ANALYSIS LABEL (Main Pattern Signal) What You See: A large label appearing at pivot points (or in real-time mode, at current bar) containing full pattern analysis. Label Appearance: Depending on your "Use Compact Label Format" setting: COMPACT MODE (Single Line): Example: "BULLISH REGULAR | Q:HIGH QUALITY C:82" Breakdown: BULLISH REGULAR: Divergence type detected Q:HIGH QUALITY: Pattern quality tier C:82: Confluence score (82 out of 100) FULL MODE (Multi-Line Detailed): Example: PATTERN DETECTED ------------------- BULLISH REGULAR Quality: HIGH QUALITY Price: Lower Low Momentum: Higher Low Signal: Weakening Downtrend CONFLUENCE: 82/100 ------------------- Divergence: 30 Pressure: 25 Institutional: 20 RSI Extreme: 0 Volume: 10 Breakdown: Top section: Pattern type and quality Middle section: Divergence explanation (what price did vs what RSI did) Bottom section: Confluence score with itemized breakdown showing which factors contributed Label Position: In Confirmed modes: Label appears AT the pivot point (delayed by confirmation bars) In Real-time mode: Label appears at current bar as conditions develop Label Color: Gold: Textbook quality (90+ confluence) Green: High quality (75-89 confluence) Blue: Valid quality (60-74 confluence) How To Use: This is your primary decision-making label. When it appears: Check the divergence type (regular divergences are reversal signals, hidden divergences are continuation signals) Review the quality tier (textbook and high quality have better historical win rates) Examine the confluence breakdown to see which factors are present and which are missing Look at the chart context (trend, support/resistance, timeframe) Use this information to assess whether the setup aligns with your strategy The label does NOT tell you to buy or sell. It tells you a technical pattern has formed and provides the quality assessment. Your trading decision must incorporate risk management, market context, and your strategy rules. UNDERSTANDING THE THREE DETECTION MODES VMDM offers three signal detection modes in settings to accommodate different trading styles and learning objectives. MODE 1: "Confluence Only (Real-Time)" How It Works: Displays signals AS THEY DEVELOP on the current bar without waiting for pivot confirmation. The system calculates confluence score from pressure, volume, RSI extremes, and behavioral patterns. Divergence signals are NOT required in this mode. Delay: ZERO - signals appear immediately. Use Case: Real-time scanning for high-confluence zones without divergence requirement. Useful for intraday traders who want immediate alerts when multiple factors align. Tradeoff: More frequent signals but includes setups without confirmed divergence. Higher false signal rate. Signals can change as the bar develops (not repainting in historical bars, but current bar updates). Visual Behavior: Labels appear at the current bar. No divergence lines unless divergence happens to be present. MODE 2: "Divergence + Confluence (Confirmed)" - DEFAULT RECOMMENDED How It Works: Full system engagement. Signals appear ONLY when: A pivot is confirmed (requires right-side confirmation bars to pass) Divergence is detected between current pivot and previous pivot Total confluence score meets or exceeds your minimum threshold Delay: Equal to your "Pivot Right Bars" setting (default 3 bars). This means signals appear 3 bars AFTER the actual pivot formed. Use Case: Highest-quality, non-repainting signals for swing traders and learners who want to study confirmed pattern completion. Tradeoff: Delayed signals. You will not receive the signal until confirmation occurs. In fast-moving markets, price may have already moved significantly by the time the signal appears. Visual Behavior: Labels appear at the historical pivot location (in the past). Divergence lines connect the two pivots. This is the most educational mode because it shows completed, confirmed patterns. Non-Repainting Guarantee: Yes. Once a signal appears, it never disappears or changes. MODE 3: "Divergence + Confluence (Relaxed)" How It Works: Same as Confirmed mode but with adaptive thresholds. If confluence is very high (10 points above threshold), the signal may appear even if some factors are weak. If divergence is present but confluence is slightly below threshold (within 10 points), it may still appear. Delay: Same as Confirmed mode (right-side confirmation bars). Use Case: Slightly more signals than Confirmed mode for traders willing to accept near-threshold setups. Tradeoff: More signals but lower average quality than Confirmed mode. Visual Behavior: Same as Confirmed mode. DASHBOARD GUIDE - READING THE METRICS The dashboard appears in the corner of your chart (position selectable in settings) and provides real-time market state analysis. You can choose between four dashboard detail levels in settings: Off, Compact, Optimized (default), Full. DASHBOARD ROW EXPLANATIONS ROW 1 - Header Information Left: Current symbol and timeframe Center: "VMDM " Right: Version number ROW 2 - Mode and Delay Shows which detection mode you are using and the signal delay. Example: "CONFIRMED | Delay: 3 bars" This reminds you that signals in confirmed mode appear 3 bars after the pivot forms. ROW 3 - Market Regime Format: "TREND UP HV" or "RANGING NV" First Part - Trend State: TREND UP: 20 EMA above 50 EMA with strong separation TREND DOWN: 20 EMA below 50 EMA with strong separation RANGING: EMAs close together, low trend strength TRANSITION: Between trending and ranging states Second Part - Volatility State: HV: High Volatility (current ATR more than 1.3x the 50-bar average ATR) NV: Normal Volatility (current ATR between 0.7x and 1.3x average) LV: Low Volatility (current ATR less than 0.7x average) Third Column: Volatility ratio (example: "1.45x" means current ATR is 1.45 times normal) How To Use: Regime context helps you interpret signals. Reversal divergences are more reliable in ranging or transitional regimes. Continuation divergences (hidden) are more reliable in trending regimes. High volatility means wider stops may be needed. ROW 4 - Pressure Shows current volume pressure state. Format: "BUYING | ██████████░░░░░░░░░" States: BUYING : Pressure strength above 60 (closes near highs) SELLING : Pressure strength below 40 (closes near lows) NEUTRAL : Pressure strength between 40-60 Bar Visualization: Each block represents 10 percentile points. A full bar (10 filled blocks) = 100th percentile pressure. Color: Green for buying, red for selling, gray for neutral. How To Use: When pressure aligns with divergence direction (bullish divergence during buying pressure), confluence is stronger. ROW 5 - Volume and RSI Format: "1.8x | RSI 68 | OB" First Value: Current volume ratio (1.8x = volume is 1.8 times the moving average) Second Value: Current RSI reading Third Value: RSI state OB: Overbought (RSI above 70) OS: Oversold (RSI below 30) Blank: Neutral RSI How To Use: Volume spikes (above 1.5x) during divergence formation add confluence. RSI extremes at pivots add confluence. ROW 6 - Behavioral Footprint Format: "BULL HUNT | 2 bars" Shows the most recent behavioral pattern detected and how long ago. States: ACCUMULATION / DISTRIBUTION: Absorption detected BULL HUNT / BEAR HUNT: Stop hunt detected SELL EXHAUST / BUY EXHAUST: Exhaustion detected SCANNING: No recent pattern NOW: Pattern is active on current bar How To Use: When footprint activity is recent (within 50 bars) or active now, it adds context to divergence signals forming in that area. ROW 7 - Current Pattern Shows the divergence type currently detected (if any). Examples: "BULLISH REGULAR", "BEARISH HIDDEN", "Scanning..." Quality: Shows pattern quality (TEXTBOOK, HIGH QUALITY, VALID) How To Use: This tells you what type of signal is active. Regular divergences are reversal setups. Hidden divergences are continuation setups. ROW 8 - Session Summary Format: "14 events | A3 H8 E3" First Value: Total institutional events this session Breakdown: A: Absorption events H: Stop hunt events E: Exhaustion events How To Use: High event counts suggest an active, volatile session with frequent structural anomalies. Low counts suggest quiet, orderly price action. ROW 9 - Confluence Score (Optimized/Full mode only) Format: "78/100 | ████████░░" Shows current real-time confluence score even if no pattern is confirmed yet. How To Use: Watch this in real-time to see how close you are to pattern formation. When it exceeds your threshold and divergence forms, a signal will appear (after confirmation delay). ROW 10 - Patterns Studied (Optimized/Full mode only) Format: "47 patterns | 12 bars ago" First Value: Total confirmed patterns detected since chart loaded Second Value: How many bars since the last confirmed pattern appeared How To Use: Helps you understand pattern frequency on your selected symbol and timeframe. If many bars have passed since last pattern, market may be trending without reversal opportunities. ROW 11 - Bull/Bear Ratio (Optimized/Full mode only) Format: "28:19 | BULL" Shows count of bullish vs bearish patterns detected. Balance: BULL: More bullish patterns detected (suggests market has had more bullish reversals/continuations) BEAR: More bearish patterns detected BAL: Equal counts How To Use: Extreme imbalances can indicate directional bias in the studied period. A heavily bullish ratio in a downtrend might suggest frequent failed rallies (bearish continuation). Context matters. ROW 12 - Volume Ratio Detail (Optimized/Full mode only) Shows current volume vs average volume in absolute terms. Example: "1.4x | 45230 / 32300" How To Use: Confirms whether current activity is above or below normal. ROW 13 - Last Institutional Event (Full mode only) Shows the most recent institutional pattern type and how many bars ago it occurred. Example: "DISTRIBUTION | 23 bars" How To Use: Tracks recency of last anomaly for context. SETTINGS GUIDE - EVERY PARAMETER EXPLAINED PERFORMANCE SECTION Enable All Visuals (Master Toggle) Default: ON What It Does: Master kill switch for ALL visual elements (labels, lines, boxes, background colors, dashboard). When OFF, only plot outputs remain (invisible unless you open data window). When To Change: Turn OFF on mobile devices, 1-second charts, or slow computers to improve performance. You can still receive alerts even with visuals disabled. Impact: Dramatic performance improvement when OFF, but you lose all visual feedback. Maximum Object History Default: 50 | Range: 10-100 What It Does: Limits how many of each object type (labels, lines, boxes) are kept in memory. Older objects beyond this limit are deleted. When To Change: Lower to 20-30 on fast timeframes (1-minute charts) to prevent slowdown. Increase to 100 on daily charts if you want more historical pattern visibility. Impact: Lower values = better performance but less historical visibility. Higher values = more history visible but potential slowdown on fast timeframes. Alert Cooldown (Bars) Default: 5 | Range: 1-50 What It Does: Minimum number of bars that must pass before another alert of the same type can fire. Prevents alert spam when multiple patterns form in quick succession. When To Change: Increase to 20+ on 1-minute charts to reduce noise. Decrease to 1-2 on daily charts if you want every pattern alerted. Impact: Higher cooldown = fewer alerts. Lower cooldown = more alerts. USER EXPERIENCE SECTION Show Enhanced Tooltips Default: ON What It Does: Enables detailed hover-over tooltips on labels and visual elements. When To Change: Turn OFF if you encounter Pine Script compilation errors related to tooltip arguments (rare, platform-specific issue). Impact: Minimal. Just adds helpful hover text. MARKET STRUCTURE DETECTION SECTION Pivot Left Bars Default: 3 | Range: 2-10 What It Does: Number of bars to the LEFT of the center bar that must be higher (for pivot low) or lower (for pivot high) than the center bar for a pivot to be valid. Example: With value 3, a pivot low requires the center bar's low to be lower than the 3 bars to its left. When To Change: Increase to 5-7 on noisy timeframes (1-minute charts) to filter insignificant pivots Decrease to 2 on slow timeframes (daily charts) to catch more pivots Impact: Higher values = fewer, more significant pivots = fewer signals. Lower values = more frequent pivots = more signals but more noise. Pivot Right Bars Default: 3 | Range: 2-10 What It Does: Number of bars to the RIGHT of the center bar that must pass for confirmation. This creates the non-repainting delay. Example: With value 3, a pivot is confirmed 3 bars AFTER it forms. When To Change: Increase to 5-7 for slower, more confirmed signals (better for swing trading) Decrease to 2 for faster signals (better for intraday, but still non-repainting) Impact: Higher values = longer delay but more reliable confirmation. Lower values = faster signals but less confirmation. This setting directly controls your signal delay in Confirmed and Relaxed modes. Minimum Confluence Score Default: 60 | Range: 40-95 What It Does: The threshold score required for a pattern to be displayed. Patterns with confluence scores below this threshold are not shown. When To Change: Increase to 75+ if you only want high-quality textbook setups (fewer signals) Decrease to 50-55 if you want to see more developing patterns (more signals, lower average quality) Impact: This is your primary signal filter. Higher threshold = fewer, higher-quality signals. Lower threshold = more signals but includes weaker setups. Recommended starting point is 60-65. TECHNICAL PERIODS SECTION RSI Period Default: 14 | Range: 5-50 What It Does: Lookback period for RSI calculation. When To Change: Decrease to 9-10 for faster, more sensitive RSI that detects shorter-term momentum changes Increase to 21-28 for slower, smoother RSI that filters noise Impact: Lower values make RSI more volatile (more frequent extremes and divergences). Higher values make RSI smoother (fewer but more significant divergences). 14 is industry standard. Volume Moving Average Period Default: 20 | Range: 10-200 What It Does: Lookback period for calculating average volume. Current volume is compared to this average to determine volume ratio. When To Change: Decrease to 10-14 for shorter-term volume comparison (more sensitive to recent volume changes) Increase to 50-100 for longer-term volume comparison (smoother, less sensitive) Impact: Lower values make volume ratio more volatile. Higher values make it more stable. 20 is standard. ATR Period Default: 14 | Range: 5-100 What It Does: Lookback period for Average True Range calculation used for volatility measurement and label positioning. When To Change: Rarely needs adjustment. Use 7-10 for faster volatility response, 21-28 for slower. Impact: Affects volatility ratio calculation and visual label spacing. Minimal impact on signals. Pressure Percentile Lookback Default: 50 | Range: 10-300 What It Does: Lookback period for calculating volume pressure percentile ranking. Your current pressure is ranked against the pressure of the last X bars. When To Change: Decrease to 20-30 for shorter-term pressure context (more responsive to recent changes) Increase to 100-200 for longer-term pressure context (smoother rankings) Impact: Lower values make pressure strength more sensitive to recent bars. Higher values provide more stable, long-term pressure assessment. Capped at 300 for performance reasons. SIGNAL DETECTION SECTION Signal Detection Mode Default: "Divergence + Confluence (Confirmed)" Options: Confluence Only (Real-time) Divergence + Confluence (Confirmed) Divergence + Confluence (Relaxed) What It Does: Selects which detection logic mode to use (see "Understanding The Three Detection Modes" section above). When To Change: Use Confirmed for learning and non-repainting signals. Use Real-time for live scanning without divergence requirement. Use Relaxed for slightly more signals than Confirmed. Impact: Fundamentally changes when and how signals appear. VISUAL LAYERS SECTION All toggles default to ON. Each controls visibility of one visual layer: Show Market Structure: Pivot markers and support/resistance lines Show Pressure Zones: Background color shading Show Divergence Lines: Dotted lines connecting pivots Show Institutional Footprint Markers: Absorption boxes, hunt labels, exhaustion labels Show Consolidated Analysis Label: Main pattern detection label Use Compact Label Format Default: OFF What It Does: Switches consolidated label between single-line compact format and multi-line detailed format. When To Change: Turn ON if you find full labels too large or distracting. Impact: Visual clarity vs. information density tradeoff. DASHBOARD SECTION Dashboard Mode Default: "Optimized" Options: Off, Compact, Optimized, Full What It Does: Controls how much information the dashboard displays. Off: No dashboard Compact: 8 rows (essential metrics only) Optimized: 12 rows (recommended balance) Full: 13 rows (every available metric) Dashboard Position Default: "Top Right" Options: Top Right, Top Left, Bottom Right, Bottom Left What It Does: Screen corner where dashboard appears. HOW TO USE VMDM - PRACTICAL WORKFLOW STEP 1 - INITIAL SETUP Add VMDM to your chart Select your detection mode (Confirmed recommended for learning) Set your minimum confluence score (start with 60-65) Adjust pivot parameters if needed (default 3/3 is good for most timeframes) Enable the visual layers you want to see STEP 2 - CHART ANALYSIS Let the indicator load and analyze historical data Review the patterns that appear historically Examine the confluence scores - notice which patterns had higher scores Observe which patterns occurred during supportive pressure zones Notice the divergence line connections - understand what price vs RSI did STEP 3 - PATTERN RECOGNITION LEARNING When a consolidated analysis label appears: Read the divergence type (regular or hidden, bullish or bearish) Check the quality tier (textbook, high quality, or valid) Review the confluence breakdown - which factors contributed Look at the chart context - where is price relative to structure, trend, etc. Observe the behavioral footprint markers nearby - do they support the pattern STEP 4 - REAL-TIME MONITORING Watch the dashboard for real-time regime and pressure state Monitor the current confluence score in the dashboard When it approaches your threshold, be alert for potential pattern formation When a new pattern appears (after confirmation delay), evaluate it using the workflow above Use your trading strategy rules to decide if the setup aligns with your criteria STEP 5 - POST-PATTERN OBSERVATION After a pattern appears: Mark the level on your chart Observe what price does after the pattern completes Did price respect the reversal/continuation signal What was the confluence score of patterns that worked vs. those that failed Learn which quality tiers and confluence levels produce better results on your specific symbol and timeframe RECOMMENDED TIMEFRAMES AND ASSET CLASSES VMDM is timeframe-agnostic and works on any asset with volume data. However, optimal performance varies: BEST TIMEFRAMES 15-Minute to 1-Hour: Ideal balance of signal frequency and reliability. Pivot confirmation delay is acceptable. Sufficient volume data for pressure analysis. 4-Hour to Daily: Excellent for swing trading. Very high-quality signals. Lower frequency but higher significance. Recommended for learning because patterns are clearer. 1-Minute to 5-Minute: Works but requires adjustment. Increase pivot bars to 5-7 for filtering. Decrease max object history to 30 for performance. Expect more noise. Weekly/Monthly: Works but very infrequent signals. Increase confluence threshold to 70+ to ensure only major patterns appear. BEST ASSET CLASSES Forex Majors: Excellent volume data and clear trends. Pressure analysis works well. Crypto (Major Pairs): Good volume data. High volatility makes divergences more pronounced. Works very well. Stock Indices (SPY, QQQ, etc.): Excellent. Clean price action and reliable volume. Individual Stocks: Works well on high-volume stocks. Low-volume stocks may produce unreliable pressure readings. Commodities (Gold, Oil, etc.): Works well. Clear trends and reactions. WHAT THIS INDICATOR CANNOT DO - LIMITATIONS LIMITATION 1 - It Does Not Predict The Future VMDM identifies when technical conditions align historically associated with potential reversals or continuations. It does not predict what will happen next. A textbook 95-confluence pattern can still fail if fundamental events, news, or larger timeframe structure override the setup. LIMITATION 2 - Confirmation Delay Means You Miss Early Entry In Confirmed and Relaxed modes, the non-repainting design means you receive signals AFTER the pivot is confirmed. Price may have already moved significantly by the time you receive the signal. This is the tradeoff for non-repainting reliability. You can use Real-time mode for faster signals but sacrifice divergence confirmation. LIMITATION 3 - It Does Not Tell You Position Sizing or Risk Management VMDM provides technical pattern analysis. It does not calculate stop loss levels, take profit targets, or position sizing. You must apply your own risk management rules. Never risk more than you can afford to lose based on a technical signal. LIMITATION 4 - Volume Pressure Analysis Requires Reliable Volume Data On assets with thin volume or unreliable volume reporting, pressure analysis may be inaccurate. Stick to major liquid assets with consistent volume data. LIMITATION 5 - It Cannot Detect Fundamental Events VMDM is purely technical. It cannot predict earnings reports, central bank decisions, geopolitical events, or other fundamental catalysts that can override technical patterns. LIMITATION 6 - Divergence Requires Two Pivots The indicator cannot detect divergence until at least two pivots of the same type have formed. In strong trends without pullbacks, you may go long periods without signals. LIMITATION 7 - Institutional Pattern Names Are Interpretive The behavioral footprint patterns are named using common trading education terminology, but they are detected through technical analysis, not actual institutional data access. The patterns are interpretations based on price and volume behavior. CONCEPT FOUNDATION - WHY THIS APPROACH WORKS MARKET PRINCIPLE 1 - Momentum Divergence Precedes Price Reversal Price is the final output of market forces, but momentum (the rate of change in those forces) shifts first. When price makes a new low but the momentum behind that move is weaker (higher RSI low), it signals that sellers are losing strength even though they temporarily pushed price lower. This precedes reversal. This is a fundamental principle in technical analysis taught by Charles Dow, widely observed in market behavior. MARKET PRINCIPLE 2 - Volume Reveals Conviction Price can move on low volume (low conviction) or high volume (high conviction). When price makes a new low on declining volume while RSI shows improving momentum, it suggests the new low is not confirmed by participant conviction. Adding volume pressure analysis to momentum divergence adds a confirmation layer that filters false divergences. MARKET PRINCIPLE 3 - Anomalies Mark Structural Extremes When volume spikes significantly but range contracts (absorption), or when price spikes beyond structure then reverses (stop hunt), or when aggressive moves are met with large-wick rejection (exhaustion), these anomalies often mark short-term extremes. Combining these structural observations with momentum analysis creates context. MARKET PRINCIPLE 4 - Confluence Improves Probability No single technical factor is reliable in isolation. RSI divergence alone fails frequently. Volume analysis alone cannot time entries. Combining multiple independent factors into a weighted system increases the probability that observed patterns have structural significance rather than random noise. THE EDUCATIONAL VALUE By visualizing all four layers simultaneously and breaking down the confluence scoring transparently, VMDM teaches you to think in terms of multi-dimensional analysis rather than single-indicator reliance. Over time, you will learn to recognize these patterns manually and understand which combinations produce better results on your traded assets. INSTITUTIONAL TERMINOLOGY - IMPORTANT CLARIFICATION This indicator uses the following terms that are common in trading education: Institutional Footprint Absorption (Accumulation / Distribution) Stop Hunt Exhaustion CRITICAL DISCLAIMER: These terms are EDUCATIONAL LABELS for specific price action and volume behavior patterns detected through technical analysis of publicly available chart data (open, high, low, close, volume). This indicator does NOT have access to: Actual institutional order flow or order book data Market maker positions or intentions Broker stop-loss databases Non-public trading data Proprietary institutional information The patterns labeled as "institutional footprint" are interpretations based on observable price and volume behavior that educational trading literature often associates with potential large-participant activity. The detection is algorithmic pattern recognition, not privileged data access. When this indicator identifies "absorption," it means it detected high volume within a small range - a condition that MAY indicate large orders being filled but is not confirmation of actual institutional participation. When it identifies a "stop hunt," it means price briefly penetrated a structural level then reversed - a pattern that MAY have triggered stop losses but is not confirmation that stops were specifically targeted. When it identifies "exhaustion," it means high volume with large rejection wicks - a pattern that MAY indicate aggressive participation meeting strong opposition but is not confirmation of institutional involvement. These are technical analysis interpretations, not factual statements about market participant identity or intent. DISCLAIMER AND RISK WARNING EDUCATIONAL PURPOSE ONLY This indicator is designed as an educational tool to help traders learn to recognize technical patterns, understand multi-factor analysis, and practice systematic market observation. It is NOT a trading system, signal service, or financial advice. NO PERFORMANCE GUARANTEE Past pattern behavior does not guarantee future results. A pattern that historically preceded price movement in one direction may fail in the future due to changing market conditions, fundamental events, or random variance. Confluence scores reflect historical technical alignment, not future certainty. TRADING INVOLVES SUBSTANTIAL RISK Trading financial instruments involves substantial risk of loss. You can lose more than your initial investment. Never trade with money you cannot afford to lose. Always use proper risk management including stop losses, position sizing, and portfolio diversification. NO PREDICTIVE CLAIMS This indicator does NOT predict future price movement. It identifies when technical conditions align in patterns that historically have been associated with potential reversals or continuations. Market behavior is probabilistic, not deterministic. BACKTESTING LIMITATIONS If you backtest trading strategies using this indicator, ensure you account for: Realistic commission costs Realistic slippage (difference between signal price and actual fill price) Sufficient sample size (minimum 100 trades for statistical relevance) Reasonable position sizing (risking no more than 1-2 percent of account per trade) The confirmation delay inherent in the indicator (you cannot enter at the exact pivot in Confirmed mode) Backtests that do not account for these factors will produce unrealistic results. AUTHOR LIABILITY The author (BullByte) is not responsible for any trading losses incurred using this indicator. By using this indicator, you acknowledge that all trading decisions are your sole responsibility and that you understand the risks involved. NOT FINANCIAL ADVICE Nothing in this indicator, its code, its description, or its visual outputs constitutes financial, investment, or trading advice. Consult a licensed financial advisor before making investment decisions. FREQUENTLY ASKED QUESTIONS Q: Why do signals appear in the past, not at the current bar A: In Confirmed and Relaxed modes, signals appear at confirmed pivots, which requires waiting for right-side confirmation bars (default 3). This creates a delay but prevents repainting. Use Real-time mode if you want current-bar signals without pivot confirmation. Q: Can I use this for automated trading A: You can create alert-based automation, but understand that Confirmed mode signals appear AFTER the pivot with delay, so your entry will not be at the pivot price. Real-time mode signals can change as the current bar develops. Automation requires careful consideration of these factors. Q: How do I know which confluence score to use A: Start with 60. Observe which patterns work on your symbol/timeframe. If too many false signals, increase to 70-75. If too few signals, decrease to 55. Quality vs. quantity tradeoff. Q: Do regular divergences mean I should enter a reversal trade immediately A: No. Regular divergences indicate momentum exhaustion, which is a WARNING sign that trend may reverse, not a confirmation that it will. Use confluence score, market context, support/resistance, and your strategy rules to make entry decisions. Many divergences fail. Q: What's the difference between regular and hidden divergence A: Regular divergence = price and momentum move in opposite directions at extremes = potential reversal signal. Hidden divergence = price and momentum move in opposite directions during pullbacks = potential continuation signal. Hidden divergence suggests the pullback is just a correction within the larger trend. Q: Why does the pressure zone color sometimes conflict with the divergence direction A: Pressure is real-time current bar analysis. Divergence is confirmed pivot analysis from the past. They measure different things at different times. A bullish divergence confirmed 3 bars ago might appear during current selling pressure. This is normal. Q: Can I use this on stocks without volume data A: No. Volume is required for pressure analysis and behavioral pattern detection. Use only on assets with reliable volume reporting. Q: How often should I expect signals A: Depends on timeframe and settings. Daily charts might produce 5-10 signals per month. 1-hour charts might produce 20-30. 15-minute charts might produce 50-100. Adjust confluence threshold to control frequency. Q: Can I modify the code A: Yes, this is open source. You can modify for personal use. If you publish a modified version, please credit the original and ensure your publication meets TradingView guidelines. Q: What if I disagree with a pattern's confluence score A: The scoring weights are based on general observations and may not suit your specific strategy or asset. You can modify the code to adjust weights if you have data-driven reasons to do so. Final Notes VMDM - Volume, Momentum and Divergence Master is an educational multi-layer market analysis system designed to teach systematic pattern recognition through transparent, confluence-weighted signal detection. By combining RSI momentum divergence, volume pressure quantification, behavioral footprint pattern recognition, and quality scoring into a unified framework, it provides a comprehensive learning environment for understanding market structure. Use this tool to develop your analytical skills, understand how multiple technical factors interact, and learn to distinguish high-quality setups from noise. Remember that technical analysis is probabilistic, not predictive. No indicator replaces proper education, risk management, and trading discipline. Trade responsibly. Learn continuously. Risk only what you can afford to lose. -BullByte
Pine Script® indicator
by BullByte
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