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bar color changeThis Pine v5 code allows you to distinguish between candles on the chart. The body/wick/frame of the "live" candle that hasn't yet closed is colored white. When a live candle is present, the body of the immediately preceding candle is colored green with offset = -1. All other candles remain gray (#2e2e2e). plotcandle fixes the wick/frame so that the live and previous candles are selected when following the trend. If there are other conflicting scripts, the most recently added one quickly takes precedence.
Pine Script®指標
由justcaredit提供
Bars pattern MLThis script implements a K-Nearest Neighbors (KNN)-based machine learning model to predict future price movements in financial markets. It analyzes past price action using Euclidean distance and selects the most similar historical patterns to estimate future price changes. Unlike traditional KNN implementations, this approach optimizes distance calculations by maintaining a dynamically updated list of the closest neighbors, ensuring efficient selection without the need for sorting. The model generates a forecasted price trajectory based on incremental predictions, which are visualized on the chart using polylines for better interpretability.
Pine Script®指標
由tkarolak提供
2
Volume HighlightBar colouring: this indicator is simple but effective, it repaints higher than normal candles a certain colour (by default gold/yellow) it helps to know what are valuable areas to trade around for longs and shorts. Changing the volume multiplier manually helps you to screen volume relevant to the timeframe you are trading on. For example, some charts 1min the best filter/setting would be 12-35 multiplier where others like btc 1-4 hourly, the filter/setting might be 8-12. The key is having only the highest/most relevant 3-4 volume candles showing as they often represent supports and resistances.
Pine Script®指標
由retrosoul提供
Pivot Points And Breakout Price Action With LuckyNickVaBar Color Candle Aligned with pivot points swing high and swing lows For Those Who Are Familiar with Trading The Breakouts Of Highs & Lows Of Structure. Pivots are said to be key areas in the market where price shows heavy reaction to where reversals make occur. At these points there are swing Highs & swing lows that traders may be able to find opportunity in the market. This Script is a combination of pivot points and Barcolor signals for the breakout.
Pine Script®指標
由Nickafella提供
3
Bars CharacteristicsThis code is for defining or explaining market conditions via micro trend and the characterized bars. lines 5,6: show the conditions for a normal trend, means market can go in the direction that it has in the past. lines 11,12: show the conditions for kind of the trend having cumulative energy itself, mean market can go in the same direction. lines 18,19: show the conditions for kind of the trend having overbought/sold concept, means it's better exit from the market or to look for the other clues. lines 24,25: show some kind of noise not a stable trend, it's better not to enter the market.
Pine Script®指標
由SuYan提供
WhenWasThePriceAction Bars of largest range (volatility) * see moments of strongest price action immediately * colored & upDown by candle color * amplifier: you see only the bull runs, and subsequent dumps Very nice on the 5 years scale of BITSTAMP:BTCUSD - nothing comparable to 2013 has happened yet. Internals: squared_range = pow(high-low, 2) That is essentially it already. The rest are details: * gauge with (in case of Bitcoin exponentially rising) price * show in red for negative candles * take even higher polynomial (than 2) to show only the very largest values * allow some user input (but there is not much more that can be chosen here.) Sorry for such a simple formula - but sometimes the easiest things are powerful. Please give feedback. www.tradingview.com and/or in the cryptocurrency chat. Thanks.
Pine Script®指標
由akd提供
3
Movement Efficiency - [Rider Algo]Movement Efficiency v2 — Rider Algo Developed by Rider Algo 🔬 WHAT IS THIS INDICATOR? Movement Efficiency measures how much of the market’s total movement actually translates into net directional progress . In simple terms: Is price advancing cleanly toward a destination, or thrashing around going nowhere? Most oscillators tell you how far or how fast price moved. This one asks a different question: how efficiently did it move? A market that travels 1,000 points to advance 800 points net = clean trend . A market that travels 1,000 points but only advances 100 points net = chop (even if candles look “exciting”). This distinction is critical for choosing the right strategy at the right time. --- 📐 HOW IT WORKS The indicator combines two layers of analysis: Layer 1 — Efficiency Ratio (Kaufman’s ER) The foundation is a well-known formula from quantitative finance: ER = |Close − Close | / Σ|Close − Close | over n bars This produces a value between 0 and 1 : ≈ 1.0 → price moved in a straight line (maximum efficiency). ≈ 0.0 → price moved a lot but ended near where it started (pure noise). Layer 2 — Wick Absorption Penalty (Microstructure Enhancement) Classic ER has a blind spot: it only uses closing prices . Two candles can have identical closes but tell totally different stories: Strong body + tiny wicks = clean push Tiny body + massive wicks = absorption / rejection This indicator: Computes a wick-to-body ratio per bar Averages it over the lookback Applies a soft penalty (square root to avoid over-punishment) Outputs an adjusted efficiency score that detects “dirty” advances classic ER misses --- 🎯 THREE MARKET REGIMES The indicator classifies the market into three zones : 🟢 TREND ZONE (green background) Efficiency is above the trend threshold → price is advancing cleanly with minimal noise. Best environment for trend-following (breakouts, MA crosses, momentum). Directional triangle signals: ▲ Green triangle = Bullish trend detected (upward efficient advance) ▼ Red triangle = Bearish trend detected (downward efficient decline) 🔴 CHOP ZONE (red background) Efficiency is below the chop threshold → lots of movement, no progress. Danger zone for trend-followers (whipsaws, failed breakouts, stop hunts). ▼ Orange triangle = Market entering chop regime 🟡 TRANSITION ZONE (no background) Efficiency is between thresholds → market is shifting. Watch for the next triangle to confirm direction. --- ⚙️ ASSET PROFILES (Pre-Tuned Thresholds) Different assets have different “normal” efficiency. The indicator includes four profiles: Crypto / High Volatility — relaxed thresholds Trend: 0.40 | Chop: 0.18 Designed for assets where large wicks and volatility are normal. Stocks / Medium Volatility — balanced thresholds Trend: 0.48 | Chop: 0.22 Forex / Low Volatility — stricter thresholds Trend: 0.55 | Chop: 0.28 Forex tends to print cleaner candles → higher bar for “trend”. Custom — set your own thresholds for any instrument/timeframe. --- 📊 INFORMATION PANEL The top-right dashboard shows: Efficiency — current adjusted efficiency score (0 to 1) Regime — TREND ▲ (bull), TREND ▼ (bear), CHOP ✗ , or TRANSITION ~ Percentile — where current reading sits vs last 200 bars Example: 85% = higher efficiency than 85% of recent history Wick Noise — average wick-to-body ratio (higher = more absorption/rejection) Profile — active asset profile --- 🔧 KEY SETTINGS Efficiency Period (default: 10 ) — lookback window Lower = more reactive | Higher = smoother 10 works well for weekly charts, 14 for daily. Smoothing Length (default: 3 ) — EMA applied to final output Reduces noise without significant lag. Wick Penalty Weight (default: 0.25 ) — how aggressively wicks reduce efficiency 0 = classic ER only | toward 1.0 = stricter microstructure filtering Color Mode — Gradient (continuous red→green) or Zones (discrete + directional bias) Show Raw ER — overlay classic ER (no wick penalty) for comparison --- 💡 PRACTICAL APPLICATIONS 1) Strategy Selection Filter Use as a meta-filter above your main strategy: Only take trend-following setups in TREND (green) Only take mean-reversion setups in CHOP (red) This can dramatically reduce false signals. 2) Regime Change Early Warning Watch for transitions: CHOP entry (orange triangle) Then a trend triangle (green/red) These often precede strong moves (consolidation → expansion). 3) Trend Quality Assessment Not all trends are equal: 0.65 + low wick noise = higher reliability 0.42 + high wick noise = “dirty” trend / less trustworthy Use Percentile to contextualize. 4) Risk Management If efficiency drops from TREND into TRANSITION , consider: Reducing position size Tightening stops Avoiding aggressive breakout chasing --- ⚠️ IMPORTANT NOTES This indicator does NOT repaint. Uses confirmed bar data + current bar → safe for backtesting and live trading. Not a buy/sell signal generator. It identifies the market regime so you can choose the right approach. Defaults are calibrated for crypto on weekly timeframes. For other assets/timeframes, start with the correct Asset Profile . No single indicator is a complete trading system. Always use risk management + confirmation.
Pine Script®指標
由RiderAlgo提供
BISI - SIBI (FVG) + Order Blocks + iFVG Impulsive Candles This indicator highlights three core price-action elements used in imbalance and liquidity-based trading: Fair Value Gaps (FVG) — BISI / SIBI • Bullish FVG (BISI): when the current low is above the high from two bars ago (low > high ). • Bearish FVG (SIBI): when the current high is below the low from two bars ago (high < low ). Each FVG is drawn as a box with customizable text, color, opacity, and borders. Order Blocks (OB) — Displacement Logic • Bullish OB: previous candle is bearish and current close breaks above the previous high (displacement). • Bearish OB: previous candle is bullish and current close breaks below the previous low. The Order Block is defined by candle high/low and drawn as a box. iFVG Candle Coloring (Optional) Bars are colored when an FVG or strict iFVG condition is detected. This allows quick visual identification of imbalance candles without overcrowding the chart. AUTO STRETCH & AUTO DELETE — Clean Chart Logic • Stretch: FVG and OB boxes extend to the right until mitigation. • Auto-delete: once a zone is considered “filled,” it can be automatically removed to keep charts clean. Definition of “filled” used in this script: A zone is considered touched when the candle body (Open/Close range) crosses the box range: min(open, close) < top AND max(open, close) > bottom. This is intentionally stricter than wick-based fills to avoid premature invalidations. KEY SETTINGS • Lookback Bars — Limits detection range for performance and clarity. • Max PD Arrays — Controls maximum stored zones to prevent overload. • Stretch Until Filled — Extends zones dynamically. • Auto-delete When Filled — Removes mitigated zones. • Opacity & Borders — Full visual customization. • Min Penetration (iFVG) — Controls strictness of iFVG qualification. BEST PRACTICES • FVGs are commonly used as retracement zones after displacement, especially within HTF structure alignment. • Order Blocks are most effective when preceded by liquidity sweeps or strong expansion. • iFVG coloring helps identify impulsive behavior quickly without clutter. LIMITATIONS • This is a visual analytical tool, not a standalone entry system. • Fill logic is based on body penetration (not wick-based). • Very low timeframes with large lookback settings may impact performance.
Pine Script®指標
由RAFENTech提供
BarCoreLibrary "BarCore" BarCore is a foundational library for technical analysis, providing essential functions for evaluating the structural properties of candlesticks and inter-bar relationships. It prioritizes ratio-based metrics (0.0 to 1.0) over absolute prices, making it asset-agnostic and ideal for robust pattern recognition, momentum analysis, and volume-weighted pressure evaluation. Key modules: - Structure & Range: High-precision bar and body metrics with relative positioning. - Wick Dynamics: Absolute and relative wick analysis for identifying price rejection. - Inter-bar Logic: Containment, coverage, and quantitative price overlap (Ratio-based). - Gap Intelligence: Real body and price gaps with customizable significance thresholds. - Flow & Pressure: Volume-weighted buying/selling pressure and Money Flow metrics. isBuyingBar()   Checks if the bar is a bullish (up) bar, where close is greater than open.   Returns: bool True if the bar closed higher than it opened. isSellingBar()   Checks if the bar is a bearish (down) bar, where close is less than open.   Returns: bool True if the bar closed lower than it opened. barMidpoint()   Calculates the absolute midpoint of the bar's total range (High + Low) / 2.   Returns: float The midpoint price of the bar. barRange()   Calculates the absolute size of the bar's total range (High to Low).   Returns: float The absolute difference between high and low. barRangeMidpoint()   Calculates half of the bar's total range size.   Returns: float Half the bar's range size. realBodyHigh()   Returns the higher price between the open and close.   Returns: float The top of the real body. realBodyLow()   Returns the lower price between the open and close.   Returns: float The bottom of the real body. realBodyMidpoint()   Calculates the absolute midpoint of the bar's real body.   Returns: float The midpoint price of the real body. realBodyRange()   Calculates the absolute size of the bar's real body.   Returns: float The absolute difference between open and close. realBodyRangeMidpoint()   Calculates half of the bar's real body size.   Returns: float Half the real body size. upperWickRange()   Calculates the absolute size of the upper wick.   Returns: float The range from high to the real body high. lowerWickRange()   Calculates the absolute size of the lower wick.   Returns: float The range from the real body low to low. openRatio()   Returns the location of the open price relative to the bar's total range (0.0 at low to 1.0 at high).   Returns: float The ratio of the distance from low to open, divided by the total range. closeRatio()   Returns the location of the close price relative to the bar's total range (0.0 at low to 1.0 at high).   Returns: float The ratio of the distance from low to close, divided by the total range. realBodyRatio()   Calculates the ratio of the real body size to the total bar range.   Returns: float The real body size divided by the bar range. Returns 0 if barRange is 0. upperWickRatio()   Calculates the ratio of the upper wick size to the total bar range.   Returns: float The upper wick size divided by the bar range. Returns 0 if barRange is 0. lowerWickRatio()   Calculates the ratio of the lower wick size to the total bar range.   Returns: float The lower wick size divided by the bar range. Returns 0 if barRange is 0. upperWickToBodyRatio()   Calculates the ratio of the upper wick size to the real body size.   Returns: float The upper wick size divided by the real body size. Returns 0 if realBodyRange is 0. lowerWickToBodyRatio()   Calculates the ratio of the lower wick size to the real body size.   Returns: float The lower wick size divided by the real body size. Returns 0 if realBodyRange is 0. totalWickRatio()   Calculates the ratio of the total wick range (Upper Wick + Lower Wick) to the total bar range.   Returns: float The total wick range expressed as a ratio of the bar's total range. Returns 0 if barRange is 0. isBodyExpansion()   Checks if the current bar's real body range is larger than the previous bar's real body range (body expansion).   Returns: bool True if realBodyRange() > realBodyRange() . isBodyContraction()   Checks if the current bar's real body range is smaller than the previous bar's real body range (body contraction).   Returns: bool True if realBodyRange() < realBodyRange() . isWithinPrevBar(inclusive)   Checks if the current bar's range is entirely within the previous bar's range.   Parameters:      inclusive (bool) : If true, allows equality (<=, >=). Default is false.   Returns: bool True if High < High AND Low > Low . isCoveringPrevBar(inclusive)   Checks if the current bar's range fully covers the entire previous bar's range.   Parameters:      inclusive (bool) : If true, allows equality (<=, >=). Default is false.   Returns: bool True if High > High AND Low < Low . isWithinPrevBody(inclusive)   Checks if the current bar's real body is entirely inside the previous bar's real body.   Parameters:      inclusive (bool) : If true, allows equality (<=, >=). Default is false.   Returns: bool True if the current body is contained inside the previous body. isCoveringPrevBody(inclusive)   Checks if the current bar's real body fully covers the previous bar's real body.   Parameters:      inclusive (bool) : If true, allows equality (<=, >=). Default is false.   Returns: bool True if the current body fully covers the previous body. isOpenWithinPrevBody(inclusive)   Checks if the current bar's open price falls within the real body range of the previous bar.   Parameters:      inclusive (bool) : If true, includes the boundary prices. Default is false.   Returns: bool True if the open price is between the previous bar's real body high and real body low. isCloseWithinPrevBody(inclusive)   Checks if the current bar's close price falls within the real body range of the previous bar.   Parameters:      inclusive (bool) : If true, includes the boundary prices. Default is false.   Returns: bool True if the close price is between the previous bar's real body high and real body low. isPrevOpenWithinBody(inclusive)   Checks if the previous bar's open price falls within the current bar's real body range.   Parameters:      inclusive (bool) : If true, includes the boundary prices. Default is false.   Returns: bool True if open is between the current bar's real body high and real body low. isPrevCloseWithinBody(inclusive)   Checks if the previous bar's closing price falls within the current bar's real body range.   Parameters:      inclusive (bool) : If true, includes the boundary prices. Default is false.   Returns: bool True if close is between the current bar's real body high and real body low. isOverlappingPrevBar()   Checks if there is any price overlap between the current bar's range and the previous bar's range.   Returns: bool True if the current bar's range has any intersection with the previous bar's range. bodyOverlapRatio()   Calculates the percentage of the current real body that overlaps with the previous real body.   Returns: float The overlap ratio (0.0 to 1.0). 1.0 means the current body is entirely within the previous body's price range. isCompletePriceGapUp()   Checks for a complete price gap up where the current bar's low is strictly above the previous bar's high, meaning there is zero price overlap between the two bars.   Returns: bool True if the current low is greater than the previous high. isCompletePriceGapDown()   Checks for a complete price gap down where the current bar's high is strictly below the previous bar's low, meaning there is zero price overlap between the two bars.   Returns: bool True if the current high is less than the previous low. isRealBodyGapUp()   Checks for a gap between the current and previous real bodies.   Returns: bool True if the current body is completely above the previous body. isRealBodyGapDown()   Checks for a gap between the current and previous real bodies.   Returns: bool True if the current body is completely below the previous body. gapRatio()   Calculates the percentage difference between the current open and the previous close, expressed as a decimal ratio.   Returns: float The gap ratio (positive for gap up, negative for gap down). Returns 0 if the previous close is 0. gapPercentage()   Calculates the percentage difference between the current open and the previous close.   Returns: float The gap percentage (positive for gap up, negative for gap down). Returns 0 if previous close is 0. isGapUp()   Checks for a basic gap up, where the current bar's open is strictly higher than the previous bar's close. This is the minimum condition for a gap up.   Returns: bool True if the current open is greater than the previous close (i.e., gapRatio is positive). isGapDown()   Checks for a basic gap down, where the current bar's open is strictly lower than the previous bar's close. This is the minimum condition for a gap down.   Returns: bool True if the current open is less than the previous close (i.e., gapRatio is negative). isSignificantGapUp(minRatio)   Checks if the current bar opened significantly higher than the previous close, as defined by a minimum percentage ratio.   Parameters:      minRatio (float) : The minimum required gap percentage ratio. Default is 0.03 (3%).   Returns: bool True if the gap ratio (open vs. previous close) is greater than or equal to the minimum ratio. isSignificantGapDown(minRatio)   Checks if the current bar opened significantly lower than the previous close, as defined by a minimum percentage ratio.   Parameters:      minRatio (float) : The minimum required gap percentage ratio. Default is 0.03 (3%).   Returns: bool True if the absolute value of the gap ratio (open vs. previous close) is greater than or equal to the minimum ratio. trueRangeComponentHigh()   Calculates the absolute distance from the current bar's High to the previous bar's Close, representing one of the components of the True Range.   Returns: float The absolute difference: |High - Close |. trueRangeComponentLow()   Calculates the absolute distance from the current bar's Low to the previous bar's Close, representing one of the components of the True Range.   Returns: float The absolute difference: |Low - Close |. isUpperWickDominant(minRatio)   Checks if the upper wick is significantly long relative to the total range.   Parameters:      minRatio (float) : Minimum ratio of the wick to the total bar range. Default is 0.7 (70%).   Returns: bool True if the upper wick dominates the bar's range. isUpperWickNegligible(maxRatio)   Checks if the upper wick is very small relative to the total range.   Parameters:      maxRatio (float) : Maximum ratio of the wick to the total bar range. Default is 0.05 (5%).   Returns: bool True if the upper wick is negligible. isLowerWickDominant(minRatio)   Checks if the lower wick is significantly long relative to the total range.   Parameters:      minRatio (float) : Minimum ratio of the wick to the total bar range. Default is 0.7 (70%).   Returns: bool True if the lower wick dominates the bar's range. isLowerWickNegligible(maxRatio)   Checks if the lower wick is very small relative to the total range.   Parameters:      maxRatio (float) : Maximum ratio of the wick to the total bar range. Default is 0.05 (5%).   Returns: bool True if the lower wick is negligible. isSymmetric(maxTolerance)   Checks if the upper and lower wicks are roughly equal in length.   Parameters:      maxTolerance (float) : Maximum allowable percentage difference between the two wicks. Default is 0.15 (15%).   Returns: bool True if wicks are symmetric within the tolerance level. isMarubozuBody(minRatio)   Candle with a very large body relative to the total range (minimal wicks).   Parameters:      minRatio (float) : Minimum body size ratio. Default is 0.9 (90%).   Returns: bool True if the bar has minimal wicks (Marubozu body). isLargeBody(minRatio)   Candle with a large body relative to the total range.   Parameters:      minRatio (float) : Minimum body size ratio. Default is 0.6 (60%).   Returns: bool True if the bar has a large body. isSmallBody(maxRatio)   Candle with a small body relative to the total range.   Parameters:      maxRatio (float) : Maximum body size ratio. Default is 0.4 (40%).   Returns: bool True if the bar has small body. isDojiBody(maxRatio)   Candle with a very small body relative to the total range (indecision).   Parameters:      maxRatio (float) : Maximum body size ratio. Default is 0.1 (10%).   Returns: bool True if the bar has a very small body. isLowerWickExtended(minRatio)   Checks if the lower wick is significantly extended relative to the real body size.   Parameters:      minRatio (float) : Minimum required ratio of the lower wick length to the real body size. Default is 2.0 (Lower wick must be at least twice the body's size).   Returns: bool True if the lower wick's length is at least `minRatio` times the size of the real body. isUpperWickExtended(minRatio)   Checks if the upper wick is significantly extended relative to the real body size.   Parameters:      minRatio (float) : Minimum required ratio of the upper wick length to the real body size. Default is 2.0 (Upper wick must be at least twice the body's size).   Returns: bool True if the upper wick's length is at least `minRatio` times the size of the real body. isStrongBuyingBar(minCloseRatio, maxOpenRatio)   Checks for a bar with strong bullish momentum (open near low, close near high), indicating high conviction.   Parameters:      minCloseRatio (float) : Minimum required ratio for the close location (relative to range, e.g., 0.7 means close must be in the top 30%). Default is 0.7 (70%).      maxOpenRatio (float) : Maximum allowed ratio for the open location (relative to range, e.g., 0.3 means open must be in the bottom 30%). Default is 0.3 (30%).   Returns: bool True if the bar is bullish, opened in the low extreme, and closed in the high extreme. isStrongSellingBar(maxCloseRatio, minOpenRatio)   Checks for a bar with strong bearish momentum (open near high, close near low), indicating high conviction.   Parameters:      maxCloseRatio (float) : Maximum allowed ratio for the close location (relative to range, e.g., 0.3 means close must be in the bottom 30%). Default is 0.3 (30%).      minOpenRatio (float) : Minimum required ratio for the open location (relative to range, e.g., 0.7 means open must be in the top 30%). Default is 0.7 (70%).   Returns: bool True if the bar is bearish, opened in the high extreme, and closed in the low extreme. isWeakBuyingBar(maxCloseRatio, maxBodyRatio)   Identifies a bar that is technically bullish but shows significant weakness, characterized by a failure to close near the high and a small body size.   Parameters:      maxCloseRatio (float) : Maximum allowed ratio for the close location relative to the range (e.g., 0.6 means the close must be in the bottom 60% of the bar's range). Default is 0.6 (60%).      maxBodyRatio (float) : Maximum allowed ratio for the real body size relative to the bar's range (e.g., 0.4 means the body is small). Default is 0.4 (40%).   Returns: bool True if the bar is bullish, but its close is weak and its body is small. isWeakSellingBar(minCloseRatio, maxBodyRatio)   Identifies a bar that is technically bearish but shows significant weakness, characterized by a failure to close near the low and a small body size.   Parameters:      minCloseRatio (float) : Minimum required ratio for the close location relative to the range (e.g., 0.4 means the close must be in the top 60% of the bar's range). Default is 0.4 (40%).      maxBodyRatio (float) : Maximum allowed ratio for the real body size relative to the bar's range (e.g., 0.4 means the body is small). Default is 0.4 (40%).   Returns: bool True if the bar is bearish, but its close is weak and its body is small. balanceOfPower()   Measures the net pressure of buyers vs. sellers within the bar, normalized to the bar's range.   Returns: float A value between -1.0 (strong selling) and +1.0 (strong buying), representing the strength and direction of the close relative to the open. buyingPressure()   Measures the net buying volume pressure based on the close location and volume.   Returns: float A numerical value representing the volume weighted buying pressure. sellingPressure()   Measures the net selling volume pressure based on the close location and volume.   Returns: float A numerical value representing the volume weighted selling pressure. moneyFlowMultiplier()   Calculates the Money Flow Multiplier (MFM), which is the price component of Money Flow and CMF.   Returns: float A normalized value from -1.0 (strong selling) to +1.0 (strong buying), representing the net directional pressure. moneyFlowVolume()   Calculates the Money Flow Volume (MFV), which is the Money Flow Multiplier weighted by the bar's volume.   Returns: float A numerical value representing the volume-weighted money flow. Positive = buying dominance; negative = selling dominance. isAccumulationBar()   Checks for basic accumulation on the current bar, requiring both positive Money Flow Volume and a buying bar (closing higher than opening).   Returns: bool True if the bar exhibits buying dominance through its internal range location and is a buying bar. isDistributionBar()   Checks for basic distribution on the current bar, requiring both negative Money Flow Volume and a selling bar (closing lower than opening).   Returns: bool True if the bar exhibits selling dominance through its internal range location and is a selling bar.
Pine Script®腳本庫
由sgbpulse提供
Adaptive Genesis Engine [AGE]ADAPTIVE GENESIS ENGINE (AGE) Pure Signal Evolution Through Genetic Algorithms Where Darwin Meets Technical Analysis 🧬 WHAT YOU'RE GETTING - THE PURE INDICATOR This is a technical analysis indicator - it generates signals, visualizes probability, and shows you the evolutionary process in real-time. This is NOT a strategy with automatic execution - it's a sophisticated signal generation system that you control . What This Indicator Does: Generates Long/Short entry signals with probability scores (35-88% range) Evolves a population of up to 12 competing strategies using genetic algorithms Validates strategies through walk-forward optimization (train/test cycles) Visualizes signal quality through premium gradient clouds and confidence halos Displays comprehensive metrics via enhanced dashboard Provides alerts for entries and exits Works on any timeframe, any instrument, any broker What This Indicator Does NOT Do: Execute trades automatically Manage positions or calculate position sizes Place orders on your behalf Make trading decisions for you This is pure signal intelligence. AGE tells you when and how confident it is. You decide whether and how much to trade. 🔬 THE SCIENCE: GENETIC ALGORITHMS MEET TECHNICAL ANALYSIS What Makes This Different - The Evolutionary Foundation Most indicators are static - they use the same parameters forever, regardless of market conditions. AGE is alive . It maintains a population of competing strategies that evolve, adapt, and improve through natural selection principles: Birth: New strategies spawn through crossover breeding (combining DNA from fit parents) plus random mutation for exploration Life: Each strategy trades virtually via shadow portfolios, accumulating wins/losses, tracking drawdown, and building performance history Selection: Strategies are ranked by comprehensive fitness scoring (win rate, expectancy, drawdown control, signal efficiency) Death: Weak strategies are culled periodically, with elite performers (top 2 by default) protected from removal Evolution: The gene pool continuously improves as successful traits propagate and unsuccessful ones die out This is not curve-fitting. Each new strategy must prove itself on out-of-sample data through walk-forward validation before being trusted for live signals. 🧪 THE DNA: WHAT EVOLVES Every strategy carries a 10-gene chromosome controlling how it interprets market data: Signal Sensitivity Genes Entropy Sensitivity (0.5-2.0): Weight given to market order/disorder calculations. Low values = conservative, require strong directional clarity. High values = aggressive, act on weaker order signals. Momentum Sensitivity (0.5-2.0): Weight given to RSI/ROC/MACD composite. Controls responsiveness to momentum shifts vs. mean-reversion setups. Structure Sensitivity (0.5-2.0): Weight given to support/resistance positioning. Determines how much price location within swing range matters. Probability Adjustment Genes Probability Boost (-0.10 to +0.10): Inherent bias toward aggressive (+) or conservative (-) entries. Acts as personality trait - some strategies naturally optimistic, others pessimistic. Trend Strength Requirement (0.3-0.8): Minimum trend conviction needed before signaling. Higher values = only trades strong trends, lower values = acts in weak/sideways markets. Volume Filter (0.5-1.5): Strictness of volume confirmation. Higher values = requires strong volume, lower values = volume less important. Risk Management Genes ATR Multiplier (1.5-4.0): Base volatility scaling for all price levels. Controls whether strategy uses tight or wide stops/targets relative to ATR. Stop Multiplier (1.0-2.5): Stop loss tightness. Lower values = aggressive profit protection, higher values = more breathing room. Target Multiplier (1.5-4.0): Profit target ambition. Lower values = quick scalping exits, higher values = swing trading holds. Adaptation Gene Regime Adaptation (0.0-1.0): How much strategy adjusts behavior based on detected market regime (trending/volatile/choppy). Higher values = more reactive to regime changes. The Magic: AGE doesn't just try random combinations. Through tournament selection and fitness-weighted crossover, successful gene combinations spread through the population while unsuccessful ones fade away. Over 50-100 bars, you'll see the population converge toward genes that work for YOUR instrument and timeframe. 📊 THE SIGNAL ENGINE: THREE-LAYER SYNTHESIS Before any strategy generates a signal, AGE calculates probability through multi-indicator confluence: Layer 1 - Market Entropy (Information Theory) Measures whether price movements exhibit directional order or random walk characteristics: The Math: Shannon Entropy = -Σ(p × log(p)) Market Order = 1 - (Entropy / 0.693) What It Means: High entropy = choppy, random market → low confidence signals Low entropy = directional market → high confidence signals Direction determined by up-move vs down-move dominance over lookback period (default: 20 bars) Signal Output: -1.0 to +1.0 (bearish order to bullish order) Layer 2 - Momentum Synthesis Combines three momentum indicators into single composite score: Components: RSI (40% weight): Normalized to -1/+1 scale using (RSI-50)/50 Rate of Change (30% weight): Percentage change over lookback (default: 14 bars), clamped to ±1 MACD Histogram (30% weight): Fast(12) - Slow(26), normalized by ATR Why This Matters: RSI catches mean-reversion opportunities, ROC catches raw momentum, MACD catches momentum divergence. Weighting favors RSI for reliability while keeping other perspectives. Signal Output: -1.0 to +1.0 (strong bearish to strong bullish) Layer 3 - Structure Analysis Evaluates price position within swing range (default: 50-bar lookback): Position Classification: Bottom 20% of range = Support Zone → bullish bounce potential Top 20% of range = Resistance Zone → bearish rejection potential Middle 60% = Neutral Zone → breakout/breakdown monitoring Signal Logic: At support + bullish candle = +0.7 (strong buy setup) At resistance + bearish candle = -0.7 (strong sell setup) Breaking above range highs = +0.5 (breakout confirmation) Breaking below range lows = -0.5 (breakdown confirmation) Consolidation within range = ±0.3 (weak directional bias) Signal Output: -1.0 to +1.0 (bearish structure to bullish structure) Confluence Voting System Each layer casts a vote (Long/Short/Neutral). The system requires minimum 2-of-3 agreement (configurable 1-3) before generating a signal: Examples: Entropy: Bullish, Momentum: Bullish, Structure: Neutral → Signal generated (2 long votes) Entropy: Bearish, Momentum: Neutral, Structure: Neutral → No signal (only 1 short vote) All three bullish → Signal generated with +5% probability bonus This is the key to quality. Single indicators give too many false signals. Triple confirmation dramatically improves accuracy. 📈 PROBABILITY CALCULATION: HOW CONFIDENCE IS MEASURED Base Probability: Raw_Prob = 50% + (Average_Signal_Strength × 25%) Then AGE applies strategic adjustments: Trend Alignment: Signal with trend: +4% Signal against strong trend: -8% Weak/no trend: no adjustment Regime Adaptation: Trending market (efficiency >50%, moderate vol): +3% Volatile market (vol ratio >1.5x): -5% Choppy market (low efficiency): -2% Volume Confirmation: Volume > 70% of 20-bar SMA: no change Volume below threshold: -3% Volatility State (DVS Ratio): High vol (>1.8x baseline): -4% (reduce confidence in chaos) Low vol (<0.7x baseline): -2% (markets can whipsaw in compression) Moderate elevated vol (1.0-1.3x): +2% (trending conditions emerging) Confluence Bonus: All 3 indicators agree: +5% 2 of 3 agree: +2% Strategy Gene Adjustment: Probability Boost gene: -10% to +10% Regime Adaptation gene: scales regime adjustments by 0-100% Final Probability: Clamped between 35% (minimum) and 88% (maximum) Why These Ranges? Below 35% = too uncertain, better not to signal Above 88% = unrealistic, creates overconfidence Sweet spot: 65-80% for quality entries 🔄 THE SHADOW PORTFOLIO SYSTEM: HOW STRATEGIES COMPETE Each active strategy maintains a virtual trading account that executes in parallel with real-time data: Shadow Trading Mechanics Entry Logic: Calculate signal direction, probability, and confluence using strategy's unique DNA Check if signal meets quality gate: Probability ≥ configured minimum threshold (default: 65%) Confluence ≥ configured minimum (default: 2 of 3) Direction is not zero (must be long or short, not neutral) Verify signal persistence: Base requirement: 2 bars (configurable 1-5) Adapts based on probability: high-prob signals (75%+) enter 1 bar faster, low-prob signals need 1 bar more Adjusts for regime: trending markets reduce persistence by 1, volatile markets add 1 Apply additional filters: Trend strength must exceed strategy's requirement gene Regime filter: if volatile market detected, probability must be 72%+ to override Volume confirmation required (volume > 70% of average) If all conditions met for required persistence bars, enter shadow position at current close price Position Management: Entry Price: Recorded at close of entry bar Stop Loss: ATR-based distance = ATR × ATR_Mult (gene) × Stop_Mult (gene) × DVS_Ratio Take Profit: ATR-based distance = ATR × ATR_Mult (gene) × Target_Mult (gene) × DVS_Ratio Position: +1 (long) or -1 (short), only one at a time per strategy Exit Logic: Check if price hit stop (on low) or target (on high) on current bar Record trade outcome in R-multiples (profit/loss normalized by ATR) Update performance metrics: Total trades counter incremented Wins counter (if profit > 0) Cumulative P&L updated Peak equity tracked (for drawdown calculation) Maximum drawdown from peak recorded Enter cooldown period (default: 8 bars, configurable 3-20) before next entry allowed Reset signal age counter to zero Walk-Forward Tracking: During position lifecycle, trades are categorized: Training Phase (first 250 bars): Trade counted toward training metrics Testing Phase (next 75 bars): Trade counted toward testing metrics (out-of-sample) Live Phase (after WFO period): Trade counted toward overall metrics Why Shadow Portfolios? No lookahead bias (uses only data available at the bar) Realistic execution simulation (entry on close, stop/target checks on high/low) Independent performance tracking for true fitness comparison Allows safe experimentation without risking capital Each strategy learns from its own experience 🏆 FITNESS SCORING: HOW STRATEGIES ARE RANKED Fitness is not just win rate. AGE uses a comprehensive multi-factor scoring system: Core Metrics (Minimum 3 trades required) Win Rate (30% of fitness): WinRate = Wins / TotalTrades Normalized directly (0.0-1.0 scale) Total P&L (30% of fitness): Normalized_PnL = (PnL + 300) / 600 Clamped 0.0-1.0. Assumes P&L range of -300R to +300R for normalization scale. Expectancy (25% of fitness): Expectancy = Total_PnL / Total_Trades Normalized_Expectancy = (Expectancy + 30) / 60 Clamped 0.0-1.0. Rewards consistency of profit per trade. Drawdown Control (15% of fitness): Normalized_DD = 1 - (Max_Drawdown / 15) Clamped 0.0-1.0. Penalizes strategies that suffer large equity retracements from peak. Sample Size Adjustment Quality Factor: <50 trades: 1.0 (full weight, small sample) 50-100 trades: 0.95 (slight penalty for medium sample) 100 trades: 0.85 (larger penalty for large sample) Why penalize more trades? Prevents strategies from gaming the system by taking hundreds of tiny trades to inflate statistics. Favors quality over quantity. Bonus Adjustments Walk-Forward Validation Bonus: if (WFO_Validated): Fitness += (WFO_Efficiency - 0.5) × 0.1 Strategies proven on out-of-sample data receive up to +10% fitness boost based on test/train efficiency ratio. Signal Efficiency Bonus (if diagnostics enabled): if (Signals_Evaluated > 10): Pass_Rate = Signals_Passed / Signals_Evaluated Fitness += (Pass_Rate - 0.1) × 0.05 Rewards strategies that generate high-quality signals passing the quality gate, not just profitable trades. Final Fitness: Clamped at 0.0 minimum (prevents negative fitness values) Result: Elite strategies typically achieve 0.50-0.75 fitness. Anything above 0.60 is excellent. Below 0.30 is prime candidate for culling. 🔬 WALK-FORWARD OPTIMIZATION: ANTI-OVERFITTING PROTECTION This is what separates AGE from curve-fitted garbage indicators. The Three-Phase Process Every new strategy undergoes a rigorous validation lifecycle: Phase 1 - Training Window (First 250 bars, configurable 100-500): Strategy trades normally via shadow portfolio All trades count toward training performance metrics System learns which gene combinations produce profitable patterns Tracks independently: Training_Trades, Training_Wins, Training_PnL Phase 2 - Testing Window (Next 75 bars, configurable 30-200): Strategy continues trading without any parameter changes Trades now count toward testing performance metrics (separate tracking) This is out-of-sample data - strategy has never seen these bars during "optimization" Tracks independently: Testing_Trades, Testing_Wins, Testing_PnL Phase 3 - Validation Check: Minimum_Trades = 5 (configurable 3-15) IF (Train_Trades >= Minimum AND Test_Trades >= Minimum): WR_Efficiency = Test_WinRate / Train_WinRate Expectancy_Efficiency = Test_Expectancy / Train_Expectancy WFO_Efficiency = (WR_Efficiency + Expectancy_Efficiency) / 2 IF (WFO_Efficiency >= 0.55): // configurable 0.3-0.9 Strategy.Validated = TRUE Strategy receives fitness bonus ELSE: Strategy receives 30% fitness penalty ELSE: Validation deferred (insufficient trades in one or both periods) What Validation Means Validated Strategy (Green "✓ VAL" in dashboard): Performed at least 55% as well on unseen data compared to training data Gets fitness bonus: +(efficiency - 0.5) × 0.1 Receives priority during tournament selection for breeding More likely to be chosen as active trading strategy Unvalidated Strategy (Orange "○ TRAIN" in dashboard): Failed to maintain performance on test data (likely curve-fitted to training period) Receives 30% fitness penalty (0.7x multiplier) Makes strategy prime candidate for culling Can still trade but with lower selection probability Insufficient Data (continues collecting): Hasn't completed both training and testing periods yet OR hasn't achieved minimum trade count in both periods Validation check deferred until requirements met Why 55% Efficiency Threshold? If a strategy earned 10R during training but only 5.5R during testing, it still proved an edge exists beyond random luck. Requiring 100% efficiency would be unrealistic - market conditions change between periods. But requiring >50% ensures the strategy didn't completely degrade on fresh data. The Protection: Strategies that work great on historical data but fail on new data are automatically identified and penalized. This prevents the population from being polluted by overfitted strategies that would fail in live trading. 🌊 DYNAMIC VOLATILITY SCALING (DVS): ADAPTIVE STOP/TARGET PLACEMENT AGE doesn't use fixed stop distances. It adapts to current volatility conditions in real-time. Four Volatility Measurement Methods 1. ATR Ratio (Simple Method): Current_Vol = ATR(14) / Close Baseline_Vol = SMA(Current_Vol, 100) Ratio = Current_Vol / Baseline_Vol Basic comparison of current ATR to 100-bar moving average baseline. 2. Parkinson (High-Low Range Based): For each bar: HL = log(High / Low) Parkinson_Vol = sqrt(Σ(HL²) / (4 × Period × log(2))) More stable than close-to-close volatility. Captures intraday range expansion without overnight gap noise. 3. Garman-Klass (OHLC Based): HL_Term = 0.5 × ² CO_Term = (2×log(2) - 1) × ² GK_Vol = sqrt(Σ(HL_Term - CO_Term) / Period) Most sophisticated estimator. Incorporates all four price points (open, high, low, close) plus gap information. 4. Ensemble Method (Default - Median of All Three): Ratio_1 = ATR_Current / ATR_Baseline Ratio_2 = Parkinson_Current / Parkinson_Baseline Ratio_3 = GK_Current / GK_Baseline DVS_Ratio = Median(Ratio_1, Ratio_2, Ratio_3) Why Ensemble? Takes median to avoid outliers and false spikes If ATR jumps but range-based methods stay calm, median prevents overreaction If one method fails, other two compensate Most robust approach across different market conditions Sensitivity Scaling Scaled_Ratio = (Raw_Ratio) ^ Sensitivity Sensitivity 0.3: Cube root - heavily dampens volatility impact Sensitivity 0.5: Square root - moderate dampening Sensitivity 0.7 (Default): Balanced response to volatility changes Sensitivity 1.0: Linear - full 1:1 volatility impact Sensitivity 1.5: Exponential - amplified response to volatility spikes Safety Clamps: Final DVS Ratio always clamped between 0.5x and 2.5x baseline to prevent extreme position sizing or stop placement errors. How DVS Affects Shadow Trading Every strategy's stop and target distances are multiplied by the current DVS ratio: Stop Loss Distance: Stop_Distance = ATR × ATR_Mult (gene) × Stop_Mult (gene) × DVS_Ratio Take Profit Distance: Target_Distance = ATR × ATR_Mult (gene) × Target_Mult (gene) × DVS_Ratio Example Scenario: ATR = 10 points Strategy's ATR_Mult gene = 2.5 Strategy's Stop_Mult gene = 1.5 Strategy's Target_Mult gene = 2.5 DVS_Ratio = 1.4 (40% above baseline volatility - market heating up) Stop = 10 × 2.5 × 1.5 × 1.4 = 52.5 points (vs. 37.5 in normal vol) Target = 10 × 2.5 × 2.5 × 1.4 = 87.5 points (vs. 62.5 in normal vol) Result: During volatility spikes: Stops automatically widen to avoid noise-based exits, targets extend for bigger moves During calm periods: Stops tighten for better risk/reward, targets compress for realistic profit-taking Strategies adapt risk management to match current market behavior 🧬 THE EVOLUTIONARY CYCLE: SPAWN, COMPETE, CULL Initialization (Bar 1) AGE begins with 4 seed strategies (if evolution enabled): Seed Strategy #0 (Balanced): All sensitivities at 1.0 (neutral) Zero probability boost Moderate trend requirement (0.4) Standard ATR/stop/target multiples (2.5/1.5/2.5) Mid-level regime adaptation (0.5) Seed Strategy #1 (Momentum-Focused): Lower entropy sensitivity (0.7), higher momentum (1.5) Slight probability boost (+0.03) Higher trend requirement (0.5) Tighter stops (1.3), wider targets (3.0) Seed Strategy #2 (Entropy-Driven): Higher entropy sensitivity (1.5), lower momentum (0.8) Slight probability penalty (-0.02) More trend tolerant (0.6) Wider stops (1.8), standard targets (2.5) Seed Strategy #3 (Structure-Based): Balanced entropy/momentum (0.8/0.9), high structure (1.4) Slight probability boost (+0.02) Lower trend requirement (0.35) Moderate risk parameters (1.6/2.8) All seeds start with WFO validation bypassed if WFO is disabled, or must validate if enabled. Spawning New Strategies Timing (Adaptive): Historical phase: Every 30 bars (configurable 10-100) Live phase: Every 200 bars (configurable 100-500) Automatically switches to live timing when barstate.isrealtime triggers Conditions: Current population < max population limit (default: 8, configurable 4-12) At least 2 active strategies exist (need parents) Available slot in population array Selection Process: Run tournament selection 3 times with different seeds Each tournament: randomly sample active strategies, pick highest fitness Best from 3 tournaments becomes Parent 1 Repeat independently for Parent 2 Ensures fit parents but maintains diversity Crossover Breeding: For each of 10 genes: Parent1_Fitness = fitness Parent2_Fitness = fitness Weight1 = Parent1_Fitness / (Parent1_Fitness + Parent2_Fitness) Gene1 = parent1's value Gene2 = parent2's value Child_Gene = Weight1 × Gene1 + (1 - Weight1) × Gene2 Fitness-weighted crossover ensures fitter parent contributes more genetic material. Mutation: For each gene in child: IF (random < mutation_rate): Gene_Range = GENE_MAX - GENE_MIN Noise = (random - 0.5) × 2 × mutation_strength × Gene_Range Mutated_Gene = Clamp(Child_Gene + Noise, GENE_MIN, GENE_MAX) Historical mutation rate: 20% (aggressive exploration) Live mutation rate: 8% (conservative stability) Mutation strength: 12% of gene range (configurable 5-25%) Initialization of New Strategy: Unique ID assigned (total_spawned counter) Parent ID recorded Generation = max(parent generations) + 1 Birth bar recorded (for age tracking) All performance metrics zeroed Shadow portfolio reset WFO validation flag set to false (must prove itself) Result: New strategy with hybrid DNA enters population, begins trading in next bar. Competition (Every Bar) All active strategies: Calculate their signal based on unique DNA Check quality gate with their thresholds Manage shadow positions (entries/exits) Update performance metrics Recalculate fitness score Track WFO validation progress Strategies compete indirectly through fitness ranking - no direct interaction. Culling Weak Strategies Timing (Adaptive): Historical phase: Every 60 bars (configurable 20-200, should be 2x spawn interval) Live phase: Every 400 bars (configurable 200-1000, should be 2x spawn interval) Minimum Adaptation Score (MAS): Initial MAS = 0.10 MAS decays: MAS × 0.995 every cull cycle Minimum MAS = 0.03 (floor) MAS represents the "survival threshold" - strategies below this fitness level are vulnerable. Culling Conditions (ALL must be true): Population > minimum population (default: 3, configurable 2-4) At least one strategy has fitness < MAS Strategy's age > culling interval (prevents premature culling of new strategies) Strategy is not in top N elite (default: 2, configurable 1-3) Culling Process: Find worst strategy: For each active strategy: IF (age > cull_interval): Fitness = base_fitness IF (not WFO_validated AND WFO_enabled): Fitness × 0.7 // 30% penalty for unvalidated IF (Fitness < MAS AND Fitness < worst_fitness_found): worst_strategy = this_strategy worst_fitness = Fitness IF (worst_strategy found): Count elite strategies with fitness > worst_fitness IF (elite_count >= elite_preservation_count): Deactivate worst_strategy (set active flag = false) Increment total_culled counter Elite Protection: Even if a strategy's fitness falls below MAS, it survives if fewer than N strategies are better. This prevents culling when population is generally weak. Result: Weak strategies removed from population, freeing slots for new spawns. Gene pool improves over time. Selection for Display (Every Bar) AGE chooses one strategy to display signals: Best fitness = -1 Selected = none For each active strategy: Fitness = base_fitness IF (WFO_validated): Fitness × 1.3 // 30% bonus for validated strategies IF (Fitness > best_fitness): best_fitness = Fitness selected_strategy = this_strategy Display selected strategy's signals on chart Result: Only the highest-fitness (optionally validated-boosted) strategy's signals appear as chart markers. Other strategies trade invisibly in shadow portfolios. 🎨 PREMIUM VISUALIZATION SYSTEM AGE includes sophisticated visual feedback that standard indicators lack: 1. Gradient Probability Cloud (Optional, Default: ON) Multi-layer gradient showing signal buildup 2-3 bars before entry: Activation Conditions: Signal persistence > 0 (same directional signal held for multiple bars) Signal probability ≥ minimum threshold (65% by default) Signal hasn't yet executed (still in "forming" state) Visual Construction: 7 gradient layers by default (configurable 3-15) Each layer is a line-fill pair (top line, bottom line, filled between) Layer spacing: 0.3 to 1.0 × ATR above/below price Outer layers = faint, inner layers = bright Color transitions from base to intense based on layer position Transparency scales with probability (high prob = more opaque) Color Selection: Long signals: Gradient from theme.gradient_bull_mid to theme.gradient_bull_strong Short signals: Gradient from theme.gradient_bear_mid to theme.gradient_bear_strong Base transparency: 92%, reduces by up to 8% for high-probability setups Dynamic Behavior: Cloud grows/shrinks as signal persistence increases/decreases Redraws every bar while signal is forming Disappears when signal executes or invalidates Performance Note: Computationally expensive due to linefill objects. Disable or reduce layers if chart performance degrades. 2. Population Fitness Ribbon (Optional, Default: ON) Histogram showing fitness distribution across active strategies: Activation: Only draws on last bar (barstate.islast) to avoid historical clutter Visual Construction: 10 histogram layers by default (configurable 5-20) Plots 50 bars back from current bar Positioned below price at: lowest_low(100) - 1.5×ATR (doesn't interfere with price action) Each layer represents a fitness threshold (evenly spaced min to max fitness) Layer Logic: For layer_num from 0 to ribbon_layers: Fitness_threshold = min_fitness + (max_fitness - min_fitness) × (layer / layers) Count strategies with fitness ≥ threshold Height = ATR × 0.15 × (count / total_active) Y_position = base_level + ATR × 0.2 × layer Color = Gradient from weak to strong based on layer position Line_width = Scaled by height (taller = thicker) Visual Feedback: Tall, bright ribbon = healthy population, many fit strategies at high fitness levels Short, dim ribbon = weak population, few strategies achieving good fitness Ribbon compression (layers close together) = population converging to similar fitness Ribbon spread = diverse fitness range, active selection pressure Use Case: Quick visual health check without opening dashboard. Ribbon growing upward over time = population improving. 3. Confidence Halo (Optional, Default: ON) Circular polyline around entry signals showing probability strength: Activation: Draws when new position opens (shadow_position changes from 0 to ±1) Visual Construction: 20-segment polyline forming approximate circle Center: Low - 0.5×ATR (long) or High + 0.5×ATR (short) Radius: 0.3×ATR (low confidence) to 1.0×ATR (elite confidence) Scales with: (probability - min_probability) / (1.0 - min_probability) Color Coding: Elite (85%+): Cyan (theme.conf_elite), large radius, minimal transparency (40%) Strong (75-85%): Strong green (theme.conf_strong), medium radius, moderate transparency (50%) Good (65-75%): Good green (theme.conf_good), smaller radius, more transparent (60%) Moderate (<65%): Moderate green (theme.conf_moderate), tiny radius, very transparent (70%) Technical Detail: Uses chart.point array with index-based positioning 5-bar horizontal spread for circular appearance (±5 bars from entry) Curved=false (Pine Script polyline limitation) Fill color matches line color but more transparent (88% vs line's transparency) Purpose: Instant visual probability assessment. No need to check dashboard - halo size/brightness tells the story. 4. Evolution Event Markers (Optional, Default: ON) Visual indicators of genetic algorithm activity: Spawn Markers (Diamond, Cyan): Plots when total_spawned increases on current bar Location: bottom of chart (location.bottom) Color: theme.spawn_marker (cyan/bright blue) Size: tiny Indicates new strategy just entered population Cull Markers (X-Cross, Red): Plots when total_culled increases on current bar Location: bottom of chart (location.bottom) Color: theme.cull_marker (red/pink) Size: tiny Indicates weak strategy just removed from population What It Tells You: Frequent spawning early = population building, active exploration Frequent culling early = high selection pressure, weak strategies dying fast Balanced spawn/cull = healthy evolutionary churn No markers for long periods = stable population (evolution plateaued or optimal genes found) 5. Entry/Exit Markers Clear visual signals for selected strategy's trades: Long Entry (Triangle Up, Green): Plots when selected strategy opens long position (position changes 0 → +1) Location: below bar (location.belowbar) Color: theme.long_primary (green/cyan depending on theme) Transparency: Scales with probability: Elite (85%+): 0% (fully opaque) Strong (75-85%): 10% Good (65-75%): 20% Acceptable (55-65%): 35% Size: small Short Entry (Triangle Down, Red): Plots when selected strategy opens short position (position changes 0 → -1) Location: above bar (location.abovebar) Color: theme.short_primary (red/pink depending on theme) Transparency: Same scaling as long entries Size: small Exit (X-Cross, Orange): Plots when selected strategy closes position (position changes ±1 → 0) Location: absolute (at actual exit price if stop/target lines enabled) Color: theme.exit_color (orange/yellow depending on theme) Transparency: 0% (fully opaque) Size: tiny Result: Clean, probability-scaled markers that don't clutter chart but convey essential information. 6. Stop Loss & Take Profit Lines (Optional, Default: ON) Visual representation of shadow portfolio risk levels: Stop Loss Line: Plots when selected strategy has active position Level: shadow_stop value from selected strategy Color: theme.short_primary with 60% transparency (red/pink, subtle) Width: 2 Style: plot.style_linebr (breaks when no position) Take Profit Line: Plots when selected strategy has active position Level: shadow_target value from selected strategy Color: theme.long_primary with 60% transparency (green, subtle) Width: 2 Style: plot.style_linebr (breaks when no position) Purpose: Shows where shadow portfolio would exit for stop/target Helps visualize strategy's risk/reward ratio Useful for manual traders to set similar levels Disable for cleaner chart (recommended for presentations) 7. Dynamic Trend EMA Gradient-colored trend line that visualizes trend strength: Calculation: EMA(close, trend_length) - default 50 period (configurable 20-100) Slope calculated over 10 bars: (current_ema - ema ) / ema × 100 Color Logic: Trend_direction: Slope > 0.1% = Bullish (1) Slope < -0.1% = Bearish (-1) Otherwise = Neutral (0) Trend_strength = abs(slope) Color = Gradient between: - Neutral color (gray/purple) - Strong bullish (bright green) if direction = 1 - Strong bearish (bright red) if direction = -1 Gradient factor = trend_strength (0 to 1+ scale) Visual Behavior: Faint gray/purple = weak/no trend (choppy conditions) Light green/red = emerging trend (low strength) Bright green/red = strong trend (high conviction) Color intensity = trend strength magnitude Transparency: 50% (subtle, doesn't overpower price action) Purpose: Subconscious awareness of trend state without checking dashboard or indicators. 8. Regime Background Tinting (Subtle) Ultra-low opacity background color indicating detected market regime: Regime Detection: Efficiency = directional_movement / total_range (over trend_length bars) Vol_ratio = current_volatility / average_volatility IF (efficiency > 0.5 AND vol_ratio < 1.3): Regime = Trending (1) ELSE IF (vol_ratio > 1.5): Regime = Volatile (2) ELSE: Regime = Choppy (0) Background Colors: Trending: theme.regime_trending (dark green, 92-93% transparency) Volatile: theme.regime_volatile (dark red, 93% transparency) Choppy: No tint (normal background) Purpose: Subliminal regime awareness Helps explain why signals are/aren't generating Trending = ideal conditions for AGE Volatile = fewer signals, higher thresholds applied Choppy = mixed signals, lower confidence Important: Extremely subtle by design. Not meant to be obvious, just subconscious context. 📊 ENHANCED DASHBOARD Comprehensive real-time metrics in single organized panel (top-right position): Dashboard Structure (5 columns × 14 rows) Header Row: Column 0: "🧬 AGE PRO" + phase indicator (🔴 LIVE or ⏪ HIST) Column 1: "POPULATION" Column 2: "PERFORMANCE" Column 3: "CURRENT SIGNAL" Column 4: "ACTIVE STRATEGY" Column 0: Market State Regime (📈 TREND / 🌊 CHAOS / ➖ CHOP) DVS Ratio (current volatility scaling factor, format: #.##) Trend Direction (▲ BULL / ▼ BEAR / ➖ FLAT with color coding) Trend Strength (0-100 scale, format: #.##) Column 1: Population Metrics Active strategies (count / max_population) Validated strategies (WFO passed / active total) Current generation number Total spawned (all-time strategy births) Total culled (all-time strategy deaths) Column 2: Aggregate Performance Total trades across all active strategies Aggregate win rate (%) - color-coded: Green (>55%) Orange (45-55%) Red (<45%) Total P&L in R-multiples - color-coded by positive/negative Best fitness score in population (format: #.###) MAS - Minimum Adaptation Score (cull threshold, format: #.###) Column 3: Current Signal Status Status indicator: "▲ LONG" (green) if selected strategy in long position "▼ SHORT" (red) if selected strategy in short position "⏳ FORMING" (orange) if signal persisting but not yet executed "○ WAITING" (gray) if no active signal Confidence percentage (0-100%, format: #.#%) Quality assessment: "🔥 ELITE" (cyan) for 85%+ probability "✓ STRONG" (bright green) for 75-85% "○ GOOD" (green) for 65-75% "- LOW" (dim) for <65% Confluence score (X/3 format) Signal age: "X bars" if signal forming "IN TRADE" if position active "---" if no signal Column 4: Selected Strategy Details Strategy ID number (#X format) Validation status: "✓ VAL" (green) if WFO validated "○ TRAIN" (orange) if still in training/testing phase Generation number (GX format) Personal fitness score (format: #.### with color coding) Trade count P&L and win rate (format: #.#R (##%) with color coding) Color Scheme: Panel background: theme.panel_bg (dark, low opacity) Panel headers: theme.panel_header (slightly lighter) Primary text: theme.text_primary (bright, high contrast) Secondary text: theme.text_secondary (dim, lower contrast) Positive metrics: theme.metric_positive (green) Warning metrics: theme.metric_warning (orange) Negative metrics: theme.metric_negative (red) Special markers: theme.validated_marker, theme.spawn_marker Update Frequency: Only on barstate.islast (current bar) to minimize CPU usage Purpose: Quick overview of entire system state No need to check multiple indicators Trading decisions informed by population health, regime state, and signal quality Transparency into what AGE is thinking 🔍 DIAGNOSTICS PANEL (Optional, Default: OFF) Detailed signal quality tracking for optimization and debugging: Panel Structure (3 columns × 8 rows) Position: Bottom-right corner (doesn't interfere with main dashboard) Header Row: Column 0: "🔍 DIAGNOSTICS" Column 1: "COUNT" Column 2: "%" Metrics Tracked (for selected strategy only): Total Evaluated: Every signal that passed initial calculation (direction ≠ 0) Represents total opportunities considered ✓ Passed: Signals that passed quality gate and executed Green color coding Percentage of evaluated signals Rejection Breakdown: ⨯ Probability: Rejected because probability < minimum threshold Most common rejection reason typically ⨯ Confluence: Rejected because confluence < minimum required (e.g., only 1 of 3 indicators agreed) ⨯ Trend: Rejected because signal opposed strong trend Indicates counter-trend protection working ⨯ Regime: Rejected because volatile regime detected and probability wasn't high enough to override Shows regime filter in action ⨯ Volume: Rejected because volume < 70% of 20-bar average Indicates volume confirmation requirement Color Coding: Passed count: Green (success metric) Rejection counts: Red (failure metrics) Percentages: Gray (neutral, informational) Performance Cost: Slight CPU overhead for tracking counters. Disable when not actively optimizing settings. How to Use Diagnostics Scenario 1: Too Few Signals Evaluated: 200 Passed: 10 (5%) ⨯ Probability: 120 (60%) ⨯ Confluence: 40 (20%) ⨯ Others: 30 (15%) Diagnosis: Probability threshold too high for this strategy's DNA. Solution: Lower min probability from 65% to 60%, or allow strategy more time to evolve better DNA. Scenario 2: Too Many False Signals Evaluated: 200 Passed: 80 (40%) Strategy win rate: 45% Diagnosis: Quality gate too loose, letting low-quality signals through. Solution: Raise min probability to 70%, or increase min confluence to 3 (all indicators must agree). Scenario 3: Regime-Specific Issues ⨯ Regime: 90 (45% of rejections) Diagnosis: Frequent volatile regime detection blocking otherwise good signals. Solution: Either accept fewer trades during chaos (recommended), or disable regime filter if you want signals regardless of market state. Optimization Workflow: Enable diagnostics Run 200+ bars Analyze rejection patterns Adjust settings based on data Re-run and compare pass rate Disable diagnostics when satisfied ⚙️ CONFIGURATION GUIDE 🧬 Evolution Engine Settings Enable AGE Evolution (Default: ON): ON: Full genetic algorithm (recommended for best results) OFF: Uses only 4 seed strategies, no spawning/culling (static population for comparison testing) Max Population (4-12, Default: 8): Higher = more diversity, more exploration, slower performance Lower = faster computation, less exploration, risk of premature convergence Sweet spot: 6-8 for most use cases 4 = minimum for meaningful evolution 12 = maximum before diminishing returns Min Population (2-4, Default: 3): Safety floor - system never culls below this count Prevents population extinction during harsh selection Should be at least half of max population Elite Preservation (1-3, Default: 2): Top N performers completely immune to culling Ensures best genes always survive 1 = minimal protection, aggressive selection 2 = balanced (recommended) 3 = conservative, slower gene pool turnover Historical: Spawn Interval (10-100, Default: 30): Bars between spawning new strategies during historical data Lower = faster evolution, more exploration Higher = slower evolution, more evaluation time per strategy 30 bars = ~1-2 hours on 15min chart Historical: Cull Interval (20-200, Default: 60): Bars between culling weak strategies during historical data Should be 2x spawn interval for balanced churn Lower = aggressive selection pressure Higher = patient evaluation Live: Spawn Interval (100-500, Default: 200): Bars between spawning during live trading Much slower than historical for stability Prevents population chaos during live trading 200 bars = ~1.5 trading days on 15min chart Live: Cull Interval (200-1000, Default: 400): Bars between culling during live trading Should be 2x live spawn interval Conservative removal during live trading Historical: Mutation Rate (0.05-0.40, Default: 0.20): Probability each gene mutates during breeding (20% = 2 out of 10 genes on average) Higher = more exploration, slower convergence Lower = more exploitation, faster convergence but risk of local optima 20% balances exploration vs exploitation Live: Mutation Rate (0.02-0.20, Default: 0.08): Mutation rate during live trading Much lower for stability (don't want population to suddenly degrade) 8% = mostly inherits parent genes with small tweaks Mutation Strength (0.05-0.25, Default: 0.12): How much genes change when mutated (% of gene's total range) 0.05 = tiny nudges (fine-tuning) 0.12 = moderate jumps (recommended) 0.25 = large leaps (aggressive exploration) Example: If gene range is 0.5-2.0, 12% strength = ±0.18 possible change 📈 Signal Quality Settings Min Signal Probability (0.55-0.80, Default: 0.65): Quality gate threshold - signals below this never generate 0.55-0.60 = More signals, accept lower confidence (higher risk) 0.65 = Institutional-grade balance (recommended) 0.70-0.75 = Fewer but higher-quality signals (conservative) 0.80+ = Very selective, very few signals (ultra-conservative) Min Confluence Score (1-3, Default: 2): Required indicator agreement before signal generates 1 = Any single indicator can trigger (not recommended - too many false signals) 2 = Requires 2 of 3 indicators agree (RECOMMENDED for balance) 3 = All 3 must agree (very selective, few signals, high quality) Base Persistence Bars (1-5, Default: 2): Base bars signal must persist before entry System adapts automatically: High probability signals (75%+) enter 1 bar faster Low probability signals (<68%) need 1 bar more Trending regime: -1 bar (faster entries) Volatile regime: +1 bar (more confirmation) 1 = Immediate entry after quality gate (responsive but prone to whipsaw) 2 = Balanced confirmation (recommended) 3-5 = Patient confirmation (slower but more reliable) Cooldown After Trade (3-20, Default: 8): Bars to wait after exit before next entry allowed Prevents overtrading and revenge trading 3 = Minimal cooldown (active trading) 8 = Balanced (recommended) 15-20 = Conservative (position trading) Entropy Length (10-50, Default: 20): Lookback period for market order/disorder calculation Lower = more responsive to regime changes (noisy) Higher = more stable regime detection (laggy) 20 = works across most timeframes Momentum Length (5-30, Default: 14): Period for RSI/ROC calculations 14 = standard (RSI default) Lower = more signals, less reliable Higher = fewer signals, more reliable Structure Length (20-100, Default: 50): Lookback for support/resistance swing range 20 = short-term swings (day trading) 50 = medium-term structure (recommended) 100 = major structure (position trading) Trend EMA Length (20-100, Default: 50): EMA period for trend detection and direction bias 20 = short-term trend (responsive) 50 = medium-term trend (recommended) 100 = long-term trend (position trading) ATR Period (5-30, Default: 14): Period for volatility measurement 14 = standard ATR Lower = more responsive to vol changes Higher = smoother vol calculation 📊 Volatility Scaling (DVS) Settings Enable DVS (Default: ON): Dynamic volatility scaling for adaptive stop/target placement Highly recommended to leave ON OFF only for testing fixed-distance stops DVS Method (Default: Ensemble): ATR Ratio: Simple, fast, single-method (good for beginners) Parkinson: High-low range based (good for intraday) Garman-Klass: OHLC based (sophisticated, considers gaps) Ensemble: Median of all three (RECOMMENDED - most robust) DVS Memory (20-200, Default: 100): Lookback for baseline volatility comparison 20 = very responsive to vol changes (can overreact) 100 = balanced adaptation (recommended) 200 = slow, stable baseline (minimizes false vol signals) DVS Sensitivity (0.3-1.5, Default: 0.7): How much volatility affects scaling (power-law exponent) 0.3 = Conservative, heavily dampens vol impact (cube root) 0.5 = Moderate dampening (square root) 0.7 = Balanced response (recommended) 1.0 = Linear, full 1:1 vol response 1.5 = Aggressive, amplified response (exponential) 🔬 Walk-Forward Optimization Settings Enable WFO (Default: ON): Out-of-sample validation to prevent overfitting Highly recommended to leave ON OFF only for testing or if you want unvalidated strategies Training Window (100-500, Default: 250): Bars for in-sample optimization 100 = fast validation, less data (risky) 250 = balanced (recommended) - about 1-2 months on daily, 1-2 weeks on 15min 500 = patient validation, more data (conservative) Testing Window (30-200, Default: 75): Bars for out-of-sample validation Should be ~30% of training window 30 = minimal test (fast validation) 75 = balanced (recommended) 200 = extensive test (very conservative) Min Trades for Validation (3-15, Default: 5): Required trades in BOTH training AND testing periods 3 = minimal sample (risky, fast validation) 5 = balanced (recommended) 10+ = conservative (slow validation, high confidence) WFO Efficiency Threshold (0.3-0.9, Default: 0.55): Minimum test/train performance ratio required 0.30 = Very loose (test must be 30% as good as training) 0.55 = Balanced (recommended) - test must be 55% as good 0.70+ = Strict (test must closely match training) Higher = fewer validated strategies, lower risk of overfitting 🎨 Premium Visuals Settings Visual Theme: Neon Genesis: Cyberpunk aesthetic (cyan/magenta/purple) Carbon Fiber: Industrial look (blue/red/gray) Quantum Blue: Quantum computing (blue/purple/pink) Aurora: Northern lights (teal/orange/purple) ⚡ Gradient Probability Cloud (Default: ON): Multi-layer gradient showing signal buildup Turn OFF if chart lags or for cleaner look Cloud Gradient Layers (3-15, Default: 7): More layers = smoother gradient, more CPU intensive Fewer layers = faster, blockier appearance 🎗️ Population Fitness Ribbon (Default: ON): Histogram showing fitness distribution Turn OFF for cleaner chart Ribbon Layers (5-20, Default: 10): More layers = finer fitness detail Fewer layers = simpler histogram ⭕ Signal Confidence Halo (Default: ON): Circular indicator around entry signals Size/brightness scales with probability Minimal performance cost 🔬 Evolution Event Markers (Default: ON): Diamond (spawn) and X (cull) markers Shows genetic algorithm activity Minimal performance cost 🎯 Stop/Target Lines (Default: ON): Shows shadow portfolio stop/target levels Turn OFF for cleaner chart (recommended for screenshots/presentations) 📊 Enhanced Dashboard (Default: ON): Comprehensive metrics panel Should stay ON unless you want zero overlays 🔍 Diagnostics Panel (Default: OFF): Detailed signal rejection tracking Turn ON when optimizing settings Turn OFF during normal use (slight performance cost) 📈 USAGE WORKFLOW - HOW TO USE THIS INDICATOR Phase 1: Initial Setup & Learning Add AGE to your chart Recommended timeframes: 15min, 30min, 1H (best signal-to-noise ratio) Works on: 5min (day trading), 4H (swing trading), Daily (position trading) Load 1000+ bars for sufficient evolution history Let the population evolve (100+ bars minimum) First 50 bars: Random exploration, poor results expected Bars 50-150: Population converging, fitness improving Bars 150+: Stable performance, validated strategies emerging Watch the dashboard metrics Population should grow toward max capacity Generation number should advance regularly Validated strategies counter should increase Best fitness should trend upward toward 0.50-0.70 range Observe evolution markers Diamond markers (cyan) = new strategies spawning X markers (red) = weak strategies being culled Frequent early activity = healthy evolution Activity slowing = population stabilizing Be patient. Evolution takes time. Don't judge performance before 150+ bars. Phase 2: Signal Observation Watch signals form Gradient cloud builds up 2-3 bars before entry Cloud brightness = probability strength Cloud thickness = signal persistence Check signal quality Look at confidence halo size when entry marker appears Large bright halo = elite setup (85%+) Medium halo = strong setup (75-85%) Small halo = good setup (65-75%) Verify market conditions Check trend EMA color (green = uptrend, red = downtrend, gray = choppy) Check background tint (green = trending, red = volatile, clear = choppy) Trending background + aligned signal = ideal conditions Review dashboard signal status Current Signal column shows: Status (Long/Short/Forming/Waiting) Confidence % (actual probability value) Quality assessment (Elite/Strong/Good) Confluence score (2/3 or 3/3 preferred) Only signals meeting ALL quality gates appear on chart. If you're not seeing signals, population is either still learning or market conditions aren't suitable. Phase 3: Manual Trading Execution When Long Signal Fires: Verify confidence level (dashboard or halo size) Confirm trend alignment (EMA sloping up, green color) Check regime (preferably trending or choppy, avoid volatile) Enter long manually on your broker platform Set stop loss at displayed stop line level (if lines enabled), or use your own risk management Set take profit at displayed target line level, or trail manually Monitor position - exit if X marker appears (signal reversal) When Short Signal Fires: Same verification process Confirm downtrend (EMA sloping down, red color) Enter short manually Use displayed stop/target levels or your own AGE tells you WHEN and HOW CONFIDENT. You decide WHETHER and HOW MUCH. Phase 4: Set Up Alerts (Never Miss a Signal) Right-click on indicator name in legend Select "Add Alert" Choose condition: "AGE Long" = Long entry signal fired "AGE Short" = Short entry signal fired "AGE Exit" = Position reversal/exit signal Set notification method: Sound alert (popup on chart) Email notification Webhook to phone/trading platform Mobile app push notification Name the alert (e.g., "AGE BTCUSD 15min Long") Save alert Recommended: Set alerts for both long and short, enable mobile push notifications. You'll get alerted in real-time even if not watching charts. Phase 5: Monitor Population Health Weekly Review: Check dashboard Population column: Active count should be near max (6-8 of 8) Validated count should be >50% of active Generation should be advancing (1-2 per week typical) Check dashboard Performance column: Aggregate win rate should be >50% (target: 55-65%) Total P&L should be positive (may fluctuate) Best fitness should be >0.50 (target: 0.55-0.70) MAS should be declining slowly (normal adaptation) Check Active Strategy column: Selected strategy should be validated (✓ VAL) Personal fitness should match best fitness Trade count should be accumulating Win rate should be >50% Warning Signs: Zero validated strategies after 300+ bars = settings too strict or market unsuitable Best fitness stuck <0.30 = population struggling, consider parameter adjustment No spawning/culling for 200+ bars = evolution stalled (may be optimal or need reset) Aggregate win rate <45% sustained = system not working on this instrument/timeframe Health Check Pass: 50%+ strategies validated Best fitness >0.50 Aggregate win rate >52% Regular spawn/cull activity Selected strategy validated Phase 6: Optimization (If Needed) Enable Diagnostics Panel (bottom-right) for data-driven tuning: Problem: Too Few Signals Evaluated: 200 Passed: 8 (4%) ⨯ Probability: 140 (70%) Solutions: Lower min probability: 65% → 60% or 55% Reduce min confluence: 2 → 1 Lower base persistence: 2 → 1 Increase mutation rate temporarily to explore new genes Check if regime filter is blocking signals (⨯ Regime high?) Problem: Too Many False Signals Evaluated: 200 Passed: 90 (45%) Win rate: 42% Solutions: Raise min probability: 65% → 70% or 75% Increase min confluence: 2 → 3 Raise base persistence: 2 → 3 Enable WFO if disabled (validates strategies before use) Check if volume filter is being ignored (⨯ Volume low?) Problem: Counter-Trend Losses ⨯ Trend: 5 (only 5% rejected) Losses often occur against trend Solutions: System should already filter trend opposition May need stronger trend requirement Consider only taking signals aligned with higher timeframe trend Use longer trend EMA (50 → 100) Problem: Volatile Market Whipsaws ⨯ Regime: 100 (50% rejected by volatile regime) Still getting stopped out frequently Solutions: System is correctly blocking volatile signals Losses happening because vol filter isn't strict enough Consider not trading during volatile periods (respect the regime) Or disable regime filter and accept higher risk Optimization Workflow: Enable diagnostics Run 200+ bars with current settings Analyze rejection patterns and win rate Make ONE change at a time (scientific method) Re-run 200+ bars and compare results Keep change if improvement, revert if worse Disable diagnostics when satisfied Never change multiple parameters at once - you won't know what worked. Phase 7: Multi-Instrument Deployment AGE learns independently on each chart: Recommended Strategy: Deploy AGE on 3-5 different instruments Different asset classes ideal (e.g., ES futures, EURUSD, BTCUSD, SPY, Gold) Each learns optimal strategies for that instrument's personality Take signals from all 5 charts Natural diversification reduces overall risk Why This Works: When one market is choppy, others may be trending Different instruments respond to different news/catalysts Portfolio-level win rate more stable than single-instrument Evolution explores different parameter spaces on each chart Setup: Same settings across all charts (or customize if preferred) Set alerts for all Take every validated signal across all instruments Position size based on total account (don't overleverage any single signal) ⚠️ REALISTIC EXPECTATIONS - CRITICAL READING What AGE Can Do ✅ Generate probability-weighted signals using genetic algorithms ✅ Evolve strategies in real-time through natural selection ✅ Validate strategies on out-of-sample data (walk-forward optimization) ✅ Adapt to changing market conditions automatically over time ✅ Provide comprehensive metrics on population health and signal quality ✅ Work on any instrument, any timeframe, any broker ✅ Improve over time as weak strategies are culled and fit strategies breed What AGE Cannot Do ❌ Win every trade (typical win rate: 55-65% at best) ❌ Predict the future with certainty (markets are probabilistic, not deterministic) ❌ Work perfectly from bar 1 (needs 100-150 bars to learn and stabilize) ❌ Guarantee profits under all market conditions ❌ Replace your trading discipline and risk management ❌ Execute trades automatically (this is an indicator, not a strategy) ❌ Prevent all losses (drawdowns are normal and expected) ❌ Adapt instantly to regime changes (re-learning takes 50-100 bars) Performance Realities Typical Performance After Evolution Stabilizes (150+ bars): Win Rate: 55-65% (excellent for trend-following systems) Profit Factor: 1.5-2.5 (realistic for validated strategies) Signal Frequency: 5-15 signals per 100 bars (quality over quantity) Drawdown Periods: 20-40% of time in equity retracement (normal trading reality) Max Consecutive Losses: 5-8 losses possible even with 60% win rate (probability says this is normal) Evolution Timeline: Bars 0-50: Random exploration, learning phase - poor results expected, don't judge yet Bars 50-150: Population converging, fitness climbing - results improving Bars 150-300: Stable performance, most strategies validated - consistent results Bars 300+: Mature population, optimal genes dominant - best results Market Condition Dependency: Trending Markets: AGE excels - clear directional moves, high-probability setups Choppy Markets: AGE struggles - fewer signals generated, lower win rate Volatile Markets: AGE cautious - higher rejection rate, wider stops, fewer trades Market Regime Changes: When market shifts from trending to choppy overnight Validated strategies can become temporarily invalidated AGE will adapt through evolution, but not instantly Expect 50-100 bar re-learning period after major regime shifts Fitness may temporarily drop then recover This is NOT a holy grail. It's a sophisticated signal generator that learns and adapts using genetic algorithms. Your success depends on: Patience during learning periods (don't abandon after 3 losses) Proper position sizing (risk 0.5-2% per trade, not 10%) Following signals consistently (cherry-picking defeats statistical edge) Not abandoning system prematurely (give it 200+ bars minimum) Understanding probability (60% win rate means 40% of trades WILL lose) Respecting market conditions (trending = trade more, choppy = trade less) Managing emotions (AGE is emotionless, you need to be too) Expected Drawdowns: Single-strategy max DD: 10-20% of equity (normal) Portfolio across multiple instruments: 5-15% (diversification helps) Losing streaks: 3-5 consecutive losses expected periodically No indicator eliminates risk. AGE manages risk through: Quality gates (rejecting low-probability signals) Confluence requirements (multi-indicator confirmation) Persistence requirements (no knee-jerk reactions) Regime awareness (reduced trading in chaos) Walk-forward validation (preventing overfitting) But it cannot prevent all losses. That's inherent to trading. 🔧 TECHNICAL SPECIFICATIONS Platform: TradingView Pine Script v5 Indicator Type: Overlay indicator (plots on price chart) Execution Type: Signals only - no automatic order placement Computational Load: Moderate to High (genetic algorithms + shadow portfolios) 8 strategies × shadow portfolio simulation = significant computation Premium visuals add additional load (gradient cloud, fitness ribbon) TradingView Resource Limits (Built-in Caps): Max Bars Back: 500 (sufficient for WFO and evolution) Max Labels: 100 (plenty for entry/exit markers) Max Lines: 150 (adequate for stop/target lines) Max Boxes: 50 (not heavily used) Max Polylines: 100 (confidence halos) Recommended Chart Settings: Timeframe: 15min to 1H (optimal signal/noise balance) 5min: Works but noisier, more signals 4H/Daily: Works but fewer signals Bars Loaded: 1000+ (ensures sufficient evolution history) Replay Mode: Excellent for testing without risk Performance Optimization Tips: Disable gradient cloud if chart lags (most CPU intensive visual) Disable fitness ribbon if still laggy Reduce cloud layers from 7 to 3 Reduce ribbon layers from 10 to 5 Turn off diagnostics panel unless actively tuning Close other heavy indicators to free resources Browser/Platform Compatibility: Works on all modern browsers (Chrome, Firefox, Safari, Edge) Mobile app supported (full functionality on phone/tablet) Desktop app supported (best performance) Web version supported (may be slower on older computers) Data Requirements: Real-time or delayed data both work No special data feeds required Works with TradingView's standard data Historical + live data seamlessly integrated 🎓 THEORETICAL FOUNDATIONS AGE synthesizes advanced concepts from multiple disciplines: Evolutionary Computation Genetic Algorithms (Holland, 1975): Population-based optimization through natural selection metaphor Tournament Selection: Fitness-based parent selection with diversity preservation Crossover Operators: Fitness-weighted gene recombination from two parents Mutation Operators: Random gene perturbation for exploration of new parameter space Elitism: Preservation of top N performers to prevent loss of best solutions Adaptive Parameters: Different mutation rates for historical vs. live phases Technical Analysis Support/Resistance: Price structure within swing ranges Trend Following: EMA-based directional bias Momentum Analysis: RSI, ROC, MACD composite indicators Volatility Analysis: ATR-based risk scaling Volume Confirmation: Trade activity validation Information Theory Shannon Entropy (1948): Quantification of market order vs. disorder Signal-to-Noise Ratio: Directional information vs. random walk Information Content: How much "information" a price move contains Statistics & Probability Walk-Forward Analysis: Rolling in-sample/out-of-sample optimization Out-of-Sample Validation: Testing on unseen data to prevent overfitting Monte Carlo Principles: Shadow portfolio simulation with realistic execution Expectancy Theory: Win rate × avg win - loss rate × avg loss Probability Distributions: Signal confidence quantification Risk Management ATR-Based Stops: Volatility-normalized risk per trade Volatility Regime Detection: Market state classification (trending/choppy/volatile) Drawdown Control: Peak-to-trough equity measurement R-Multiple Normalization: Performance measurement in risk units Machine Learning Concepts Online Learning: Continuous adaptation as new data arrives Fitness Functions: Multi-objective optimization (win rate + expectancy + drawdown) Exploration vs. Exploitation: Balance between trying new strategies and using proven ones Overfitting Prevention: Walk-forward validation as regularization Novel Contribution: AGE is the first TradingView indicator to apply genetic algorithms to real-time indicator parameter optimization while maintaining strict anti-overfitting controls through walk-forward validation. Most "adaptive" indicators simply recalibrate lookback periods or thresholds. AGE evolves entirely new strategies through competitive selection - it's not parameter tuning, it's Darwinian evolution of trading logic itself. The combination of: Genetic algorithm population management Shadow portfolio simulation for realistic fitness evaluation Walk-forward validation to prevent overfitting Multi-indicator confluence for signal quality Dynamic volatility scaling for adaptive risk ...creates a system that genuinely learns and improves over time while avoiding the curse of curve-fitting that plagues most optimization approaches. 🏗️ DEVELOPMENT NOTES This project represents months of intensive development, facing significant technical challenges: Challenge 1: Making Genetics Actually Work Early versions spawned garbage strategies that polluted the gene pool: Random gene combinations produced nonsensical parameter sets Weak strategies survived too long, dragging down population No clear convergence toward optimal solutions Solution: Comprehensive fitness scoring (4 factors: win rate, P&L, expectancy, drawdown) Elite preservation (top 2 always protected) Walk-forward validation (unproven strategies penalized 30%) Tournament selection (fitness-weighted breeding) Adaptive culling (MAS decay creates increasing selection pressure) Challenge 2: Balancing Evolution Speed vs. Stability Too fast = population chaos, no convergence. Too slow = can't adapt to regime changes. Solution: Dual-phase timing: Fast evolution during historical (30/60 bar intervals), slow during live (200/400 bar intervals) Adaptive mutation rates: 20% historical, 8% live Spawn/cull ratio: Always 2:1 to prevent population collapse Challenge 3: Shadow Portfolio Accuracy Needed realistic trade simulation without lookahead bias: Can't peek at future bars for exits Must track multiple portfolios simultaneously Stop/target checks must use bar's high/low correctly Solution: Entry on close (realistic) Exit checks on current bar's high/low (realistic) Independent position tracking per strategy Cooldown periods to prevent unrealistic rapid re-entry ATR-normalized P&L (R-multiples) for fair comparison across volatility regimes Challenge 4: Pine Script Compilation Limits Hit TradingView's execution limits multiple times: Too many array operations Too many variables Too complex conditional logic Solution: Optimized data structures (single DNA array instead of 8 separate arrays) Minimal visual overlays (only essential plots) Efficient fitness calculations (vectorized where possible) Strategic use of barstate.islast to minimize dashboard updates Challenge 5: Walk-Forward Implementation Standard WFO is difficult in Pine Script: Can't easily "roll forward" through historical data Can't re-optimize strategies mid-stream Must work in real-time streaming environment Solution: Age-based phase detection (first 250 bars = training, next 75 = testing) Separate metric tracking for train vs. test Efficiency calculation at fixed interval (after test period completes) Validation flag persists for strategy lifetime Challenge 6: Signal Quality Control Early versions generated too many signals with poor win rates: Single indicators produced excessive noise No trend alignment No regime awareness Instant entries on single-bar spikes Solution: Three-layer confluence system (entropy + momentum + structure) Minimum 2-of-3 agreement requirement Trend alignment checks (penalty for counter-trend) Regime-based probability adjustments Persistence requirements (signals must hold multiple bars) Volume confirmation Quality gate (probability + confluence thresholds) The Result A system that: Truly evolves (not just parameter sweeps) Truly validates (out-of-sample testing) Truly adapts (ongoing competition and breeding) Stays within TradingView's platform constraints Provides institutional-quality signals Maintains transparency (full metrics dashboard) Development time: 3+ months of iterative refinement Lines of code: ~1500 (highly optimized) Test instruments: ES, NQ, EURUSD, BTCUSD, SPY, AAPL Test timeframes: 5min, 15min, 1H, Daily 🎯 FINAL WORDS The Adaptive Genesis Engine is not just another indicator - it's a living system that learns, adapts, and improves through the same principles that drive biological evolution. Every bar it observes adds to its experience. Every strategy it spawns explores new parameter combinations. Every strategy it culls removes weakness from the gene pool. This is evolution in action on your charts. You're not getting a static formula locked in time. You're getting a system that thinks , that competes , that survives through natural selection. The strongest strategies rise to the top. The weakest die. The gene pool improves generation after generation. AGE doesn't claim to predict the future - it adapts to whatever the future brings. When markets shift from trending to choppy, from calm to volatile, from bullish to bearish - AGE evolves new strategies suited to the new regime. Use it on any instrument. Any timeframe. Any market condition. AGE will adapt. This indicator gives you the pure signal intelligence. How you choose to act on it - position sizing, risk management, execution discipline - that's your responsibility. AGE tells you when and how confident . You decide whether and how much . Trust the process. Respect the evolution. Let Darwin work. "In markets, as in nature, it is not the strongest strategies that survive, nor the most intelligent - but those most responsive to change." Taking you to school. — Dskyz, Trade with insight. Trade with anticipation. — Happy Holiday's
Pine Script®指標
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ValueAtTime█ OVERVIEW This library is a Pine Script® programming tool for accessing historical values in a time series using UNIX timestamps . Its data structure and functions index values by time, allowing scripts to retrieve past values based on absolute timestamps or relative time offsets instead of relying on bar index offsets. █ CONCEPTS UNIX timestamps In Pine Script®, a UNIX timestamp is an integer representing the number of milliseconds elapsed since January 1, 1970, at 00:00:00 UTC (the UNIX Epoch ). The timestamp is a unique, absolute representation of a specific point in time. Unlike a calendar date and time, a UNIX timestamp's meaning does not change relative to any time zone . This library's functions process series values and corresponding UNIX timestamps in pairs , offering a simplified way to identify values that occur at or near distinct points in time instead of on specific bars. Storing and retrieving time-value pairs This library's `Data` type defines the structure for collecting time and value information in pairs. Objects of the `Data` type contain the following two fields:  • `times` – An array of "int" UNIX timestamps for each recorded value.  • `values` – An array of "float" values for each saved timestamp. Each index in both arrays refers to a specific time-value pair. For instance, the `times` and `values` elements at index 0 represent the first saved timestamp and corresponding value. The library functions that maintain `Data` objects queue up to one time-value pair per bar into the object's arrays, where the saved timestamp represents the bar's opening time . Because the `times` array contains a distinct UNIX timestamp for each item in the `values` array, it serves as a custom mapping for retrieving saved values. All the library functions that return information from a `Data` object use this simple two-step process to identify a value based on time:  1. Perform a binary search on the `times` array to find the earliest saved timestamp closest to the specified time or offset and get the element's index.  2. Access the element from the `values` array at the retrieved index, returning the stored value corresponding to the found timestamp. Value search methods There are several techniques programmers can use to identify historical values from corresponding timestamps. This library's functions include three different search methods to locate and retrieve values based on absolute times or relative time offsets: Timestamp search   Find the value with the earliest saved timestamp closest to a specified timestamp. Millisecond offset search Find the value with the earliest saved timestamp closest to a specified number of milliseconds behind the current bar's opening time. This search method provides a time-based alternative to retrieving historical values at specific bar offsets. Period offset search Locate the value with the earliest saved timestamp closest to a defined period offset behind the current bar's opening time. The function calculates the span of the offset based on a period string . The "string" must contain one of the following unit tokens:  • "D" for days  • "W" for weeks  • "M" for months  • "Y" for years  • "YTD" for year-to-date, meaning the time elapsed since the beginning of the bar's opening year in the exchange time zone. The period string can include a multiplier prefix for all supported units except "YTD" (e.g., "2W" for two weeks). Note that the precise span covered by the "M", "Y", and "YTD" units varies across time. The "1M" period can cover 28, 29, 30, or 31 days, depending on the bar's opening month and year in the exchange time zone. The "1Y" period covers 365 or 366 days, depending on leap years. The "YTD" period's span changes with each new bar, because it always measures the time from the start of the current bar's opening year. █ CALCULATIONS AND USE This library's functions offer a flexible, structured approach to retrieving historical values at or near specific timestamps, millisecond offsets, or period offsets for different analytical needs. See below for explanations of the exported functions and how to use them. Retrieving single values The library includes three functions that retrieve a single stored value using timestamp, millisecond offset, or period offset search methods:  • `valueAtTime()` – Locates the saved value with the earliest timestamp closest to a specified timestamp.  • `valueAtTimeOffset()` – Finds the saved value with the earliest timestamp closest to the specified number of milliseconds behind the current bar's opening time.  • `valueAtPeriodOffset()` – Finds the saved value with the earliest timestamp closest to the period-based offset behind the current bar's opening time. Each function has two overloads for advanced and simple use cases. The first overload searches for a value in a user-specified `Data` object created by the `collectData()` function (see below). It returns a tuple containing the found value and the corresponding timestamp. The second overload maintains a `Data` object internally to store and retrieve values for a specified `source` series. This overload returns a tuple containing the historical `source` value, the corresponding timestamp, and the current bar's `source` value, making it helpful for comparing past and present values from requested contexts. Retrieving multiple values The library includes the following functions to retrieve values from multiple historical points in time, facilitating calculations and comparisons with values retrieved across several intervals:  • `getDataAtTimes()` – Locates a past `source` value for each item in a `timestamps` array. Each retrieved value's timestamp represents the earliest time closest to one of the specified timestamps.  • `getDataAtTimeOffsets()` – Finds a past `source` value for each item in a `timeOffsets` array. Each retrieved value's timestamp represents the earliest time closest to one of the specified millisecond offsets behind the current bar's opening time.  • `getDataAtPeriodOffsets()` – Finds a past value for each item in a `periods` array. Each retrieved value's timestamp represents the earliest time closest to one of the specified period offsets behind the current bar's opening time. Each function returns a tuple with arrays containing the found `source` values and their corresponding timestamps. In addition, the tuple includes the current `source` value and the symbol's description, which also makes these functions helpful for multi-interval comparisons using data from requested contexts. Processing period inputs When writing scripts that retrieve historical values based on several user-specified period offsets, the most concise approach is to create a single text input that allows users to list each period, then process the "string" list into an array for use in the `getDataAtPeriodOffsets()` function. This library includes a `getArrayFromString()` function to provide a simple way to process strings containing comma-separated lists of periods. The function splits the specified `str` by its commas and returns an array containing every non-empty item in the list with surrounding whitespaces removed. View the example code to see how we use this function to process the value of a text area input . Calculating period offset times Because the exact amount of time covered by a specified period offset can vary, it is often helpful to verify the resulting times when using the `valueAtPeriodOffset()` or `getDataAtPeriodOffsets()` functions to ensure the calculations work as intended for your use case. The library's `periodToTimestamp()` function calculates an offset timestamp from a given period and reference time. With this function, programmers can verify the time offsets in a period-based data search and use the calculated offset times in additional operations. For periods with "D" or "W" units, the function calculates the time offset based on the absolute number of milliseconds the period covers (e.g., `86400000` for "1D"). For periods with "M", "Y", or "YTD" units, the function calculates an offset time based on the reference time's calendar date in the exchange time zone. Collecting data All the `getDataAt*()` functions, and the second overloads of the `valueAt*()` functions, collect and maintain data internally, meaning scripts do not require a separate `Data` object when using them. However, the first overloads of the `valueAt*()` functions do not collect data, because they retrieve values from a user-specified `Data` object. For cases where a script requires a separate `Data` object for use with these overloads or other custom routines, this library exports the `collectData()` function. This function queues each bar's `source` value and opening timestamp into a `Data` object and returns the object's ID. This function is particularly useful when searching for values from a specific series more than once. For instance, instead of using multiple calls to the second overloads of `valueAt*()` functions with the same `source` argument, programmers can call `collectData()` to store each bar's `source` and opening timestamp, then use the returned `Data` object's ID in calls to the first `valueAt*()` overloads to reduce memory usage. The `collectData()` function and all the functions that collect data internally include two optional parameters for limiting the saved time-value pairs to a sliding window: `timeOffsetLimit` and `timeframeLimit`. When either has a non-na argument, the function restricts the collected data to the maximum number of recent bars covered by the specified millisecond- and timeframe-based intervals. NOTE : All calls to the functions that collect data for a `source` series can execute up to once per bar or realtime tick, because each stored value requires a unique corresponding timestamp. Therefore, scripts cannot call these functions iteratively within a loop . If a call to these functions executes more than once inside a loop's scope, it causes a runtime error. █ EXAMPLE CODE The example code at the end of the script demonstrates one possible use case for this library's functions. The code retrieves historical price data at user-specified period offsets, calculates price returns for each period from the retrieved data, and then populates a table with the results. The example code's process is as follows:   1. Input a list of periods – The user specifies a comma-separated list of period strings in the script's "Period list" input (e.g., "1W, 1M, 3M, 1Y, YTD"). Each item in the input list represents a period offset from the latest bar's opening time.   2. Process the period list – The example calls `getArrayFromString()` on the first bar to split the input list by its commas and construct an array of period strings.   3. Request historical data – The code uses a call to `getDataAtPeriodOffsets()` as the `expression` argument in a request.security() call to retrieve the closing prices of "1D" bars for each period included in the processed `periods` array.   4. Display information in a table – On the latest bar, the code uses the retrieved data to calculate price returns over each specified period, then populates a two-row table with the results. The cells for each return percentage are color-coded based on the magnitude and direction of the price change. The cells also include tooltips showing the compared daily bar's opening date in the exchange time zone. █ NOTES  • This library's architecture relies on a user-defined type (UDT) for its data storage format. UDTs are blueprints from which scripts create objects , i.e., composite structures with fields containing independent values or references of any supported type.  • The library functions search through a `Data` object's `times` array using the array.binary_search_leftmost() function, which is more efficient than looping through collected data to identify matching timestamps. Note that this built-in works only for arrays with elements sorted in ascending order .  • Each function that collects data from a `source` series updates the values and times stored in a local `Data` object's arrays. If a single call to these functions were to execute in a loop , it would store multiple values with an identical timestamp, which can cause erroneous search behavior. To prevent looped calls to these functions, the library uses the `checkCall()` helper function in their scopes. This function maintains a counter that increases by one each time it executes on a confirmed bar. If the count exceeds the total number of bars, indicating the call executes more than once in a loop, it raises a runtime error .  • Typically, when requesting higher-timeframe data with request.security() while using barmerge.lookahead_on as the `lookahead` argument, the `expression` argument should be offset with the history-referencing operator to prevent lookahead bias on historical bars. However, the call in this script's example code enables lookahead without offsetting the `expression` because the script displays results only on the last historical bar and all realtime bars, where there is no future data to leak into the past. This call ensures the displayed results use the latest data available from the context on realtime bars. Look first. Then leap. █ EXPORTED TYPES Data   A structure for storing successive timestamps and corresponding values from a dataset.   Fields:      times (array) : An "int" array containing a UNIX timestamp for each value in the `values` array.      values (array) : A "float" array containing values corresponding to the timestamps in the `times` array. █ EXPORTED FUNCTIONS getArrayFromString(str)   Splits a "string" into an array of substrings using the comma (`,`) as the delimiter. The function trims surrounding whitespace characters from each substring, and it excludes empty substrings from the result.   Parameters:      str (series string) : The "string" to split into an array based on its commas.   Returns: (array) An array of trimmed substrings from the specified `str`. periodToTimestamp(period, referenceTime)   Calculates a UNIX timestamp representing the point offset behind a reference time by the amount of time within the specified `period`.   Parameters:      period (series string) : The period string, which determines the time offset of the returned timestamp. The specified argument must contain a unit and an optional multiplier (e.g., "1Y", "3M", "2W", "YTD"). Supported units are: - "Y" for years. - "M" for months. - "W" for weeks. - "D" for days. - "YTD" (Year-to-date) for the span from the start of the `referenceTime` value's year in the exchange time zone. An argument with this unit cannot contain a multiplier.      referenceTime (series int) : The millisecond UNIX timestamp from which to calculate the offset time.   Returns: (int) A millisecond UNIX timestamp representing the offset time point behind the `referenceTime`. collectData(source, timeOffsetLimit, timeframeLimit)   Collects `source` and `time` data successively across bars. The function stores the information within a `Data` object for use in other exported functions/methods, such as `valueAtTimeOffset()` and `valueAtPeriodOffset()`. Any call to this function cannot execute more than once per bar or realtime tick.   Parameters:      source (series float) : The source series to collect. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.      timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.      timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.   Returns: (Data) A `Data` object containing collected `source` values and corresponding timestamps over the allowed time range. method valueAtTime(data, timestamp)   (Overload 1 of 2) Retrieves value and time data from a `Data` object's fields at the index of the earliest timestamp closest to the specified `timestamp`. Callable as a method or a function.   Parameters:      data (series Data) : The `Data` object containing the collected time and value data.      timestamp (series int) : The millisecond UNIX timestamp to search. The function returns data for the earliest saved timestamp that is closest to the value.   Returns: ( ) A tuple containing the following data from the `Data` object: - The stored value corresponding to the identified timestamp ("float"). - The earliest saved timestamp that is closest to the specified `timestamp` ("int"). valueAtTime(source, timestamp, timeOffsetLimit, timeframeLimit)   (Overload 2 of 2) Retrieves `source` and time information for the earliest bar whose opening timestamp is closest to the specified `timestamp`. Any call to this function cannot execute more than once per bar or realtime tick.   Parameters:      source (series float) : The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.      timestamp (series int) : The millisecond UNIX timestamp to search. The function returns data for the earliest bar whose timestamp is closest to the value.      timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.      timeframeLimit (simple string) : (simple string) Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.   Returns: ( ) A tuple containing the following data: - The `source` value corresponding to the identified timestamp ("float"). - The earliest bar's timestamp that is closest to the specified `timestamp` ("int"). - The current bar's `source` value ("float"). method valueAtTimeOffset(data, timeOffset)   (Overload 1 of 2) Retrieves value and time data from a `Data` object's fields at the index of the earliest saved timestamp closest to `timeOffset` milliseconds behind the current bar's opening time. Callable as a method or a function.   Parameters:      data (series Data) : The `Data` object containing the collected time and value data.      timeOffset (series int) : The millisecond offset behind the bar's opening time. The function returns data for the earliest saved timestamp that is closest to the calculated offset time.   Returns: ( ) A tuple containing the following data from the `Data` object: - The stored value corresponding to the identified timestamp ("float"). - The earliest saved timestamp that is closest to `timeOffset` milliseconds before the current bar's opening time ("int"). valueAtTimeOffset(source, timeOffset, timeOffsetLimit, timeframeLimit)   (Overload 2 of 2) Retrieves `source` and time information for the earliest bar whose opening timestamp is closest to `timeOffset` milliseconds behind the current bar's opening time. Any call to this function cannot execute more than once per bar or realtime tick.   Parameters:      source (series float) : The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.      timeOffset (series int) : The millisecond offset behind the bar's opening time. The function returns data for the earliest bar's timestamp that is closest to the calculated offset time.      timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.      timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.   Returns: ( ) A tuple containing the following data: - The `source` value corresponding to the identified timestamp ("float"). - The earliest bar's timestamp that is closest to `timeOffset` milliseconds before the current bar's opening time ("int"). - The current bar's `source` value ("float"). method valueAtPeriodOffset(data, period)   (Overload 1 of 2) Retrieves value and time data from a `Data` object's fields at the index of the earliest timestamp closest to a calculated offset behind the current bar's opening time. The calculated offset represents the amount of time covered by the specified `period`. Callable as a method or a function.   Parameters:      data (series Data) : The `Data` object containing the collected time and value data.      period (series string) : The period string, which determines the calculated time offset. The specified argument must contain a unit and an optional multiplier (e.g., "1Y", "3M", "2W", "YTD"). Supported units are: - "Y" for years. - "M" for months. - "W" for weeks. - "D" for days. - "YTD" (Year-to-date) for the span from the start of the current bar's year in the exchange time zone. An argument with this unit cannot contain a multiplier.   Returns: ( ) A tuple containing the following data from the `Data` object: - The stored value corresponding to the identified timestamp ("float"). - The earliest saved timestamp that is closest to the calculated offset behind the bar's opening time ("int"). valueAtPeriodOffset(source, period, timeOffsetLimit, timeframeLimit)   (Overload 2 of 2) Retrieves `source` and time information for the earliest bar whose opening timestamp is closest to a calculated offset behind the current bar's opening time. The calculated offset represents the amount of time covered by the specified `period`. Any call to this function cannot execute more than once per bar or realtime tick.   Parameters:      source (series float) : The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.      period (series string) : The period string, which determines the calculated time offset. The specified argument must contain a unit and an optional multiplier (e.g., "1Y", "3M", "2W", "YTD"). Supported units are: - "Y" for years. - "M" for months. - "W" for weeks. - "D" for days. - "YTD" (Year-to-date) for the span from the start of the current bar's year in the exchange time zone. An argument with this unit cannot contain a multiplier.      timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.      timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.   Returns: ( ) A tuple containing the following data: - The `source` value corresponding to the identified timestamp ("float"). - The earliest bar's timestamp that is closest to the calculated offset behind the current bar's opening time ("int"). - The current bar's `source` value ("float"). getDataAtTimes(timestamps, source, timeOffsetLimit, timeframeLimit)   Retrieves `source` and time information for each bar whose opening timestamp is the earliest one closest to one of the UNIX timestamps specified in the `timestamps` array. Any call to this function cannot execute more than once per bar or realtime tick.   Parameters:      timestamps (array) : An array of "int" values representing UNIX timestamps. The function retrieves `source` and time data for each element in this array.      source (series float) : The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.      timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.      timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.   Returns: ( ) A tuple of the following data: - An array containing a `source` value for each identified timestamp (array). - An array containing an identified timestamp for each item in the `timestamps` array (array). - The current bar's `source` value ("float"). - The symbol's description from `syminfo.description` ("string"). getDataAtTimeOffsets(timeOffsets, source, timeOffsetLimit, timeframeLimit)   Retrieves `source` and time information for each bar whose opening timestamp is the earliest one closest to one of the time offsets specified in the `timeOffsets` array. Each offset in the array represents the absolute number of milliseconds behind the current bar's opening time. Any call to this function cannot execute more than once per bar or realtime tick.   Parameters:      timeOffsets (array) : An array of "int" values representing the millisecond time offsets used in the search. The function retrieves `source` and time data for each element in this array. For example, the array ` ` specifies that the function returns data for the timestamps closest to one day and one week behind the current bar's opening time.      source (float) : (series float) The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.      timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.      timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.   Returns: ( ) A tuple of the following data: - An array containing a `source` value for each identified timestamp (array). - An array containing an identified timestamp for each offset specified in the `timeOffsets` array (array). - The current bar's `source` value ("float"). - The symbol's description from `syminfo.description` ("string"). getDataAtPeriodOffsets(periods, source, timeOffsetLimit, timeframeLimit)   Retrieves `source` and time information for each bar whose opening timestamp is the earliest one closest to a calculated offset behind the current bar's opening time. Each calculated offset represents the amount of time covered by a period specified in the `periods` array. Any call to this function cannot execute more than once per bar or realtime tick.   Parameters:      periods (array) : An array of period strings, which determines the time offsets used in the search. The function retrieves `source` and time data for each element in this array. For example, the array ` ` specifies that the function returns data for the timestamps closest to one day, week, and month behind the current bar's opening time. Each "string" in the array must contain a unit and an optional multiplier. Supported units are: - "Y" for years. - "M" for months. - "W" for weeks. - "D" for days. - "YTD" (Year-to-date) for the span from the start of the current bar's year in the exchange time zone. An argument with this unit cannot contain a multiplier.      source (float) : (series float) The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.      timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.      timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.   Returns: ( ) A tuple of the following data: - An array containing a `source` value for each identified timestamp (array). - An array containing an identified timestamp for each period specified in the `periods` array (array). - The current bar's `source` value ("float"). - The symbol's description from `syminfo.description` ("string").
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RSMPatternLibLibrary "RSMPatternLib" RSM Pattern Library - All chart patterns from PATTERNS.md Implements: Candlestick patterns, Support/Resistance, Gaps, Triangles, Volume Divergence, and more ALL PATTERNS ARE OWN IMPLEMENTATION - No external dependencies EDGE CASES HANDLED: - Zero/tiny candle bodies - Missing volume data - Low bar count scenarios - Integer division issues - Price normalization for different instruments bullishEngulfing(minBodyRatio, minPrevBodyRatio)   Detects Bullish Engulfing pattern   Parameters:      minBodyRatio (float) : Minimum body size as ratio of total range (default 0.3)      minPrevBodyRatio (float) : Minimum previous candle body ratio to filter dojis (default 0.1)   Returns: bool True when bullish engulfing detected EDGE CASES: Handles doji previous candle, zero range, tiny bodies bearishEngulfing(minBodyRatio, minPrevBodyRatio)   Detects Bearish Engulfing pattern   Parameters:      minBodyRatio (float) : Minimum body size as ratio of total range (default 0.3)      minPrevBodyRatio (float) : Minimum previous candle body ratio to filter dojis (default 0.1)   Returns: bool True when bearish engulfing detected EDGE CASES: Handles doji previous candle, zero range, tiny bodies doji(maxBodyRatio, minRangeAtr)   Detects Doji candle (indecision)   Parameters:      maxBodyRatio (float) : Maximum body size as ratio of total range (default 0.1)      minRangeAtr (float) : Minimum range as multiple of ATR to filter flat candles (default 0.3)   Returns: bool True when doji detected EDGE CASES: Filters out no-movement bars, handles zero range shootingStar(wickMultiplier, maxLowerWickRatio, minBodyAtrRatio)   Detects Shooting Star (bearish reversal)   Parameters:      wickMultiplier (float) : Upper wick must be at least this times the body (default 2.0)      maxLowerWickRatio (float) : Lower wick max as ratio of body (default 0.5)      minBodyAtrRatio (float) : Minimum body size as ratio of ATR (default 0.1)   Returns: bool True when shooting star detected EDGE CASES: Handles zero body (uses range-based check), tiny bodies hammer(wickMultiplier, maxUpperWickRatio, minBodyAtrRatio)   Detects Hammer (bullish reversal)   Parameters:      wickMultiplier (float) : Lower wick must be at least this times the body (default 2.0)      maxUpperWickRatio (float) : Upper wick max as ratio of body (default 0.5)      minBodyAtrRatio (float) : Minimum body size as ratio of ATR (default 0.1)   Returns: bool True when hammer detected EDGE CASES: Handles zero body (uses range-based check), tiny bodies invertedHammer(wickMultiplier, maxLowerWickRatio)   Detects Inverted Hammer (bullish reversal after downtrend)   Parameters:      wickMultiplier (float) : Upper wick must be at least this times the body (default 2.0)      maxLowerWickRatio (float) : Lower wick max as ratio of body (default 0.5)   Returns: bool True when inverted hammer detected EDGE CASES: Same as shootingStar but requires bullish close hangingMan(wickMultiplier, maxUpperWickRatio)   Detects Hanging Man (bearish reversal after uptrend)   Parameters:      wickMultiplier (float) : Lower wick must be at least this times the body (default 2.0)      maxUpperWickRatio (float) : Upper wick max as ratio of body (default 0.5)   Returns: bool True when hanging man detected NOTE: Identical to hammer - context (uptrend) determines meaning morningStar(requireGap, minAvgBars)   Detects Morning Star (3-candle bullish reversal)   Parameters:      requireGap (bool) : Whether to require gap between candles (default false for crypto/forex)      minAvgBars (int) : Minimum bars for average body calculation (default 14)   Returns: bool True when morning star pattern detected EDGE CASES: Gap is optional, handles low bar count, uses shifted average eveningStar(requireGap, minAvgBars)   Detects Evening Star (3-candle bearish reversal)   Parameters:      requireGap (bool) : Whether to require gap between candles (default false for crypto/forex)      minAvgBars (int) : Minimum bars for average body calculation (default 14)   Returns: bool True when evening star pattern detected EDGE CASES: Gap is optional, handles low bar count gapUp()   Detects Gap Up   Returns: bool True when current bar opens above previous bar's high gapDown()   Detects Gap Down   Returns: bool True when current bar opens below previous bar's low gapSize()   Returns gap size in price   Returns: float Gap size (positive for gap up, negative for gap down, 0 for no gap) gapPercent()   Returns gap size as percentage   Returns: float Gap size as percentage of previous close gapType(volAvgLen, breakawayMinPct, highVolMult)   Classifies gap type based on volume   Parameters:      volAvgLen (int) : Length for volume average (default 20)      breakawayMinPct (float) : Minimum gap % for breakaway (default 1.0)      highVolMult (float) : Volume multiplier for high volume (default 1.5)   Returns: string Gap type: "Breakaway", "Common", "Continuation", or "None" EDGE CASES: Handles missing volume data, low bar count swingHigh(leftBars, rightBars)   Detects swing high using pivot   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)   Returns: float Swing high price or na swingLow(leftBars, rightBars)   Detects swing low using pivot   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)   Returns: float Swing low price or na higherHigh(leftBars, rightBars, lookback)   Checks if current swing high is higher than previous swing high   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : How many bars back to search for previous pivot (default 50)   Returns: bool True when higher high pattern detected EDGE CASES: Searches backwards for pivots instead of using var (library-safe) higherLow(leftBars, rightBars, lookback)   Checks if current swing low is higher than previous swing low   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : How many bars back to search for previous pivot (default 50)   Returns: bool True when higher low pattern detected lowerHigh(leftBars, rightBars, lookback)   Checks if current swing high is lower than previous swing high   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : How many bars back to search for previous pivot (default 50)   Returns: bool True when lower high pattern detected lowerLow(leftBars, rightBars, lookback)   Checks if current swing low is lower than previous swing low   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : How many bars back to search for previous pivot (default 50)   Returns: bool True when lower low pattern detected bullishTrend(leftBars, rightBars, lookback)   Detects Bullish Trend (HH + HL within lookback)   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : Lookback period (default 50)   Returns: bool True when making higher highs AND higher lows bearishTrend(leftBars, rightBars, lookback)   Detects Bearish Trend (LH + LL within lookback)   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : Lookback period (default 50)   Returns: bool True when making lower highs AND lower lows nearestResistance(lookback, leftBars, rightBars)   Finds nearest resistance level above current price   Parameters:      lookback (int) : Number of bars to look back (default 50)      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)   Returns: float Nearest resistance level or na EDGE CASES: Pre-computes pivots, handles bounds properly nearestSupport(lookback, leftBars, rightBars)   Finds nearest support level below current price   Parameters:      lookback (int) : Number of bars to look back (default 50)      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)   Returns: float Nearest support level or na resistanceBreakout(lookback, leftBars, rightBars)   Detects resistance breakout   Parameters:      lookback (int) : Number of bars to look back (default 50)      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)   Returns: bool True when price breaks above resistance EDGE CASES: Uses previous bar's resistance to avoid lookahead supportBreakdown(lookback, leftBars, rightBars)   Detects support breakdown   Parameters:      lookback (int) : Number of bars to look back (default 50)      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)   Returns: bool True when price breaks below support bullishVolumeDivergence(leftBars, rightBars, lookback)   Detects Bullish Volume Divergence (price makes lower low, volume decreases)   Parameters:      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)      lookback (int) : Bars to search for previous pivot (default 50)   Returns: bool True when bullish volume divergence detected EDGE CASES: Library-safe (no var), searches for previous pivot bearishVolumeDivergence(leftBars, rightBars, lookback)   Detects Bearish Volume Divergence (price makes higher high, volume decreases)   Parameters:      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)      lookback (int) : Bars to search for previous pivot (default 50)   Returns: bool True when bearish volume divergence detected rangeContracting(lookback)   Detects if price is in a contracting range (triangle formation)   Parameters:      lookback (int) : Bars to analyze (default 20)   Returns: bool True when range is contracting EDGE CASES: Uses safe integer division, checks minimum lookback ascendingTriangle(lookback, flatTolerance)   Detects Ascending Triangle (flat top, rising bottom)   Parameters:      lookback (int) : Bars to analyze (default 20)      flatTolerance (float) : Max normalized slope for "flat" line (default 0.002)   Returns: bool True when ascending triangle detected EDGE CASES: Safe division, normalized slope, minimum lookback descendingTriangle(lookback, flatTolerance)   Detects Descending Triangle (falling top, flat bottom)   Parameters:      lookback (int) : Bars to analyze (default 20)      flatTolerance (float) : Max normalized slope for "flat" line (default 0.002)   Returns: bool True when descending triangle detected symmetricalTriangle(lookback, minSlope)   Detects Symmetrical Triangle (converging trend lines)   Parameters:      lookback (int) : Bars to analyze (default 20)      minSlope (float) : Minimum normalized slope magnitude (default 0.0005)   Returns: bool True when symmetrical triangle detected doubleBottom(tolerance, minSpanBars, lookback)   Detects Double Bottom (W pattern) - OWN IMPLEMENTATION Two swing lows at similar price levels with a swing high between them   Parameters:      tolerance (float) : Max price difference between lows as % (default 3)      minSpanBars (int) : Minimum bars between the two lows (default 5)      lookback (int) : Max bars to search for pattern (default 100)   Returns: bool True when double bottom detected doubleTop(tolerance, minSpanBars, lookback)   Detects Double Top (M pattern) - OWN IMPLEMENTATION Two swing highs at similar price levels with a swing low between them   Parameters:      tolerance (float) : Max price difference between highs as % (default 3)      minSpanBars (int) : Minimum bars between the two highs (default 5)      lookback (int) : Max bars to search for pattern (default 100)   Returns: bool True when double top detected tripleBottom(tolerance, minSpanBars, lookback)   Detects Triple Bottom - OWN IMPLEMENTATION Three swing lows at similar price levels   Parameters:      tolerance (float) : Max price difference between lows as % (default 3)      minSpanBars (int) : Minimum total bars for pattern (default 10)      lookback (int) : Max bars to search for pattern (default 150)   Returns: bool True when triple bottom detected tripleTop(tolerance, minSpanBars, lookback)   Detects Triple Top - OWN IMPLEMENTATION Three swing highs at similar price levels   Parameters:      tolerance (float) : Max price difference between highs as % (default 3)      minSpanBars (int) : Minimum total bars for pattern (default 10)      lookback (int) : Max bars to search for pattern (default 150)   Returns: bool True when triple top detected bearHeadShoulders()   Detects Bearish Head and Shoulders (OWN IMPLEMENTATION) Head is higher than both shoulders, shoulders roughly equal, with valid neckline STRICT VERSION - requires proper structure, neckline, and minimum span   Returns: bool True when bearish H&S detected bullHeadShoulders()   Detects Bullish (Inverse) Head and Shoulders (OWN IMPLEMENTATION) Head is lower than both shoulders, shoulders roughly equal, with valid neckline STRICT VERSION - requires proper structure, neckline, and minimum span   Returns: bool True when bullish H&S detected bearAscHeadShoulders()   Detects Bearish Ascending Head and Shoulders (variant)   Returns: bool True when pattern detected bullAscHeadShoulders()   Detects Bullish Ascending Head and Shoulders (variant)   Returns: bool True when pattern detected bearDescHeadShoulders()   Detects Bearish Descending Head and Shoulders (variant)   Returns: bool True when pattern detected bullDescHeadShoulders()   Detects Bullish Descending Head and Shoulders (variant)   Returns: bool True when pattern detected isSwingLow()   Re-export: Detects swing low   Returns: bool True when swing low detected isSwingHigh()   Re-export: Detects swing high   Returns: bool True when swing high detected swingHighPrice(idx)   Re-export: Gets swing high price at index   Parameters:      idx (int) : Index (0 = most recent)   Returns: float Swing high price swingLowPrice(idx)   Re-export: Gets swing low price at index   Parameters:      idx (int) : Index (0 = most recent)   Returns: float Swing low price swingHighBarIndex(idx)   Re-export: Gets swing high bar index   Parameters:      idx (int) : Index (0 = most recent)   Returns: int Bar index of swing high swingLowBarIndex(idx)   Re-export: Gets swing low bar index   Parameters:      idx (int) : Index (0 = most recent)   Returns: int Bar index of swing low cupBottom(smoothLen, minDepthAtr, maxDepthAtr)   Detects Cup and Handle pattern formation Uses price acceleration and depth analysis   Parameters:      smoothLen (int) : Smoothing length for price (default 10)      minDepthAtr (float) : Minimum cup depth as ATR multiple (default 1.0)      maxDepthAtr (float) : Maximum cup depth as ATR multiple (default 5.0)   Returns: bool True when potential cup bottom detected EDGE CASES: Added depth filter, ATR validation cupHandle(lookback, maxHandleRetraceRatio)   Detects potential handle formation after cup   Parameters:      lookback (int) : Bars to look back for cup (default 30)      maxHandleRetraceRatio (float) : Maximum handle retracement of cup depth (default 0.5)   Returns: bool True when handle pattern detected bullishPatternCount()   Returns count of bullish patterns detected   Returns: int Number of bullish patterns currently active bearishPatternCount()   Returns count of bearish patterns detected   Returns: int Number of bearish patterns currently active detectedPatterns()   Returns string description of detected patterns   Returns: string Comma-separated list of detected patterns
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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®指標
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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.
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