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FVE Volatility color-coded Volume bar The FVE is a pure volume indicator. Unlike most of the other indicators (except OBV), price change doesn?t come into the equation for the FVE (price is not multiplied by volume), but is only used to determine whether money is flowing in or out of the stock. This is contrary to the current trend in the design of modern money flow indicators. The author decided against a price-volume indicator for the following reasons: - A pure volume indicator has more power to contradict. - The number of buyers or sellers (which is assessed by volume) will be the same, regardless of the price fluctuation. - Price-volume indicators tend to spike excessively at breakouts or breakdowns. This study is an addition to FVE indicator. Indicator plots different-coloured volume bars depending on volatility.
Indicador Pine Script®
por HPotter
2
Custom Indicator Clearly Shows If Bulls or Bears are in Control!The Two Versions of this Indicator I learned from Two Famous and Highly Successful Traders. This Indicator shows With No Lag Clear Up and Down Trends in Market by Documenting Clearly If Bulls or Bears are in Control. The Version In SubChart 1 Shows Consecutive Closes if the Current Close is Greater than of Less than the Midpoint of the Previous Bar (Why Midpoint Explained in Detail in 1st Post). The Version in SubChart 2 Shows Consecutive Closes that are Greater than or Less Than the Previous Close (Will Discuss Specific Uses in 1st Post). Works on Stocks, Forex, Futures, on All Timeframes.
Indicador Pine Script®
por ChrisMoody
37
VWAP filtered MACD Bars with positive MACD histogram value and closing above VWAP are colored, long positions should be taken in areas made of those bars. Similarly, bars with negative MACD histogram value and closing below VWAP are also colored, short positions should be taken there. This indicator by default should be a part of your trend following trading system. In the setting you can change colors Above grow: positive and rising MACD histogram value Above fall: positive and falling MACD histogram value Below fall: negative and falling MACD histogram value Below grow: negative and rising MACD histogram value
Indicador Pine Script®
por phk28
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.
Indicador Pine Script®
por justcaredit
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.
Indicador Pine Script®
por tkarolak
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.
Indicador Pine Script®
por retrosoul
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.
Indicador Pine Script®
por Nickafella
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.
Indicador Pine Script®
por SuYan
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.
Indicador Pine Script®
por akd
3
FX Momentum Breakout Detector# FX Momentum Breakout Strategy A TradingView Pine Script indicator that detects momentum breakouts in forex pairs and automatically executes trades via SignalStack integration. The strategy uses EMA crossovers, swing structure breaks, and Fibonacci retracement levels for entry, stop loss, and take profit placement. ## Overview This strategy identifies bullish and bearish momentum breakouts by combining: - **EMA (Exponential Moving Average)** for trend direction - **Swing High/Low** structure breaks for entry signals - **Fibonacci retracement levels** for stop loss and take profit - **Volume and time filters** to improve signal quality - **Dynamic position sizing** based on Fibonacci stop distance and risk percentage ### Key Features - ✅ **Automated Order Execution**: Direct integration with SignalStack for hands-free trading - ✅ **Risk-Based Position Sizing**: Automatically calculates lot size based on stop distance and account risk - ✅ **Fibonacci-Based TP/SL**: Uses Fibonacci 0.5 levels for take profit and stop loss - ✅ **Time Window Filter**: Only trades during active market hours (7AM-7PM Japan Time) - ✅ **Volume Filter**: Requires volume above 10-day moving average - ✅ **Single Alert System**: One alert handles both long and short signals ## Strategy Logic ### Entry Conditions **Long (Buy) Signal:** - Price crosses above EMA 20, OR - Price breaks above swing high structure - AND: Minimum 3 consecutive bull bars (strong momentum) - AND: Price is above EMA 20 (if EMA filter enabled) - AND: Volume is above 10-day MA - AND: Time is within 7AM-7PM JST window **Short (Sell) Signal:** - Price crosses below EMA 20, OR - Price breaks below swing low structure - AND: Minimum 3 consecutive bear bars (strong momentum) - AND: Price is below EMA 20 (if EMA filter enabled) - AND: Volume is above 10-day MA - AND: Time is within 7AM-7PM JST window ### Stop Loss & Take Profit - **Long Positions:** - Take Profit: Fibonacci 0.5 level above entry (`fib_up_0_5`) - Stop Loss: Fibonacci 0.5 level below entry (`fib_dn_0_5`) - **Short Positions:** - Take Profit: Fibonacci 0.5 level below entry (`fib_dn_0_5`) - Stop Loss: Fibonacci 0.5 level above entry (`fib_up_0_5`) ### Position Sizing Position size is calculated dynamically based on: 1. **Account Balance**: Your account size in USD (default: $125,000) 2. **Risk Percentage**: Risk per trade (default: 1.0%) 3. **Stop Loss Distance**: Distance from entry to Fibonacci stop level (in pips) **Formula:** ``` Risk in Dollars = Account Balance × (Risk % / 100) Stop Loss (pips) = |Entry Price - Stop Loss Price| / Pip Size Position Size (lots) = Risk $ / (Stop Loss (pips) × $10 per pip per lot) ``` The strategy rounds to 0.01 lot increments (micro lots) for precise position sizing. ## Setup Instructions ### Prerequisites 1. **TradingView Account**: Pro plan or higher (required for webhook alerts) 2. **SignalStack Account**: Active account with connected broker (e.g., OANDA) 3. **SignalStack Webhook URL**: Get this from your SignalStack dashboard ### Step 1: Add Strategy to TradingView 1. Open TradingView and navigate to your chart 2. Click "Pine Editor" (bottom panel) 3. Copy the code from `v2.0_fx_breakout_strategy.md` 4. Paste into Pine Editor 5. Click "Save" and then "Add to Chart" ### Step 2: Configure Strategy Inputs In the strategy settings panel, configure: **Technical Parameters:** - **EMA Length**: Default 20 (trend filter) - **Swing High/Low Lookback**: Default 7 bars - **Min Consecutive Bull/Bear Bars**: Default 3 (momentum requirement) - **Require EMA Filter**: Default `true` (price must be on correct side of EMA) **Risk Management:** - **Account Balance (USD)**: Your account size (default: 125,000) - **Risk Per Trade (%)**: Risk percentage per trade (default: 1.0%) - **ATR Length**: Default 14 (for informational ATR display) **Filters:** - **Volume MA Length**: Default 10 (volume filter period) - **Enable Webhook Alerts**: Set to `true` for automated trading - **Alert Frequency**: `once_per_bar_close` (recommended) - **Asset Label**: Leave empty to use chart symbol, or override if needed ### Step 3: Create TradingView Alert 1. Click the "Alerts" icon (bell) at the top of the chart, or press `Alt+A` (Windows) / `Option+A` (Mac) 2. Click "Create Alert" or the "+" button 3. Select the chart with your strategy **Alert Configuration:** **Condition Tab:** - **Condition**: Select "FX Momentum Breakout Detector" (your strategy name) - **Trigger**: "Once Per Bar Close" (matches strategy setting) - **Expiration**: Set as needed (or leave unlimited) **Notifications Tab:** - **Webhook URL**: Paste your SignalStack webhook URL - **Message**: Leave as default (strategy generates JSON automatically) 4. Save the alert with a descriptive name (e.g., "EURUSD Breakout SignalStack") ### Step 4: Verify SignalStack Connection 1. Check your SignalStack dashboard for incoming webhooks 2. Verify the broker connection is active 3. Test with a paper trading account first For detailed SignalStack setup, see (./SIGNALSTACK_SETUP.md). ## Webhook Payload Format The strategy sends a JSON payload in SignalStack format. Primary fields: ```json { "symbol": "EURUSD", "action": "buy", "quantity": 2.78, "take_profit": 1.0895, "stop_loss": 1.0805, "ticker": "EURUSD", "ticker_id": "OANDA:EURUSD", "base": "EUR", "quote": "USD", "timeframe": "15", "price": 1.0850, "ema20": 1.0820, "range": 0.0050, "breakout_price": 1.0850, "fib_up_0_5": 1.0895, "fib_dn_0_5": 1.0805, "atr_pips": 25.0, "stop_loss_pips": 45.0, "position_size_lots": 2.78, "risk_dollars": 1250.0, "signal": "bullish momentum breakout", "bar_time": "2024-01-15T10:30:00" } ``` **SignalStack Required Fields:** - `symbol`: Trading symbol - `action`: "buy" or "sell" - `quantity`: Position size in lots - `take_profit`: Take profit price - `stop_loss`: Stop loss price ## Testing Use the included test script to verify webhook integration: ```bash # Test both Discord and SignalStack python test_webhook.py # Test Discord only python test_webhook.py --discord # Test SignalStack only python test_webhook.py --signalstack ``` The test script sends sample payloads matching the strategy format and verifies webhook delivery. ## Configuration Examples ### Conservative Setup (Lower Risk) - Account Balance: 125,000 USD - Risk Per Trade: 0.5% - EMA Length: 20 - Min Bull/Bear Bars: 4 - Require EMA Filter: `true` ### Aggressive Setup (Higher Risk) - Account Balance: 125,000 USD - Risk Per Trade: 2.0% - EMA Length: 15 - Min Bull/Bear Bars: 2 - Require EMA Filter: `false` ### Multiple Currency Pairs To trade multiple pairs: 1. Add the strategy to each chart 2. Create a separate alert for each pair 3. Use the same SignalStack webhook URL for all alerts 4. SignalStack routes orders based on the `symbol` field ## Time Window Filter The strategy only trades during **7AM-7PM Japan Time (JST)**, which corresponds to: - **UTC**: 22:00 (previous day) to 10:00 (same day) - This covers the Asian and early European trading sessions To modify the time window, edit the `timeWindowFilter` calculation in the strategy code. ## Position Sizing Examples ### Example 1: EURUSD Long - Account Balance: $125,000 - Risk: 1.0% = $1,250 - Entry Price: 1.0850 - Stop Loss (fib_dn_0_5): 1.0805 - Stop Distance: 45 pips - Position Size: $1,250 / (45 pips × $10) = **2.78 lots** ### Example 2: GBPUSD Short - Account Balance: $125,000 - Risk: 1.0% = $1,250 - Entry Price: 1.2650 - Stop Loss (fib_up_0_5): 1.2700 - Stop Distance: 50 pips - Position Size: $1,250 / (50 pips × $10) = **2.50 lots** ## Troubleshooting ### Alert Not Triggering 1. **Check Strategy Settings:** - Ensure "Enable Webhook Alerts" is `true` - Verify time window (7AM-7PM JST) - Check volume filter (must be above 10-day MA) 2. **Check Alert Settings:** - Verify webhook URL is correct - Ensure alert is active (not expired) - Check alert frequency matches strategy setting ### Webhook Not Received by SignalStack 1. **Verify URL:** - Check SignalStack dashboard for correct webhook URL - Ensure URL is complete (no truncation) 2. **Check Payload Format:** - SignalStack expects `symbol`, `action`, `quantity`, `take_profit`, `stop_loss` - Verify these fields are present in the payload 3. **Test Webhook:** - Use TradingView's "Test Alert" feature - Check SignalStack logs for incoming requests - Run `test_webhook.py` to verify format ### OANDA Authentication Error If you receive a 401 Unauthorized error: 1. **Check OANDA API Token Permissions:** - Log in to OANDA - Go to "My Account" > "My Services" > "Manage API Access" - Ensure token has **Trading** permissions (not just read-only) 2. **Update SignalStack Configuration:** - Go to SignalStack dashboard - Navigate to OANDA broker connection settings - Update API token with a token that has trading permissions - Verify account ID matches your OANDA account For detailed troubleshooting, see (./SIGNALSTACK_SETUP.md). ### Position Size Issues 1. **Check Account Balance Input:** - Verify account balance matches your actual account size - Ensure risk percentage is appropriate (1% recommended) 2. **Verify Stop Loss Calculation:** - Stop loss is based on Fibonacci 0.5 level - Position size automatically adjusts to maintain risk percentage - Check that pip size is correct for your currency pair ## Files - **v2.0_fx_breakout_strategy.md**: Pine Script strategy code - **test_webhook.py**: Python test script for webhook validation - **SIGNALSTACK_SETUP.md**: Detailed SignalStack configuration guide - **design.md**: Strategy design notes and considerations ## Risk Disclaimer ⚠️ **Trading forex involves substantial risk of loss. This strategy is provided for educational purposes only.** - Always test with paper trading before using real funds - Past performance does not guarantee future results - Use appropriate risk management (1-2% risk per trade recommended) - Monitor positions and adjust stop losses as needed - This strategy does not guarantee profits ## Support - **SignalStack Documentation**: Check SignalStack's official docs for webhook requirements - **TradingView Support**: For alert/webhook issues in TradingView - **Strategy Issues**: Review the strategy code comments for configuration options ## License This strategy is provided as-is for personal use. Modify and adapt as needed for your trading requirements.
Indicador Pine Script®
por kevin_stoll
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.
Biblioteca Pine Script®
por sgbpulse
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
Indicador Pine Script®
por DskyzInvestments
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