Range Bar Gaps DetectorRange Bar Gaps Detector
Overview
The Range Bar Gaps Detector identifies price gaps across multiple range bar sizes (12, 24, 60, and 120) on any trading instrument, helping traders spot potential support/resistance zones or breakout opportunities. Designed for Pine Script v6, this indicator detects gaps on range bars and exports data for use in companion scripts like Range Bar Gaps Overlap, making it ideal for multi-timeframe gap analysis.
Key Features
Multi-Range Gap Detection: Identifies gaps on 12, 24, 60, and 120-range bars, capturing both bullish (gap up) and bearish (gap down) price movements.
Customizable Sensitivity: Includes a user-defined minimum deviation (default: 10% of 14-period SMA) for 12-range gaps to filter out noise.
7-Day Lookback: Automatically prunes gaps older than 7 days to focus on recent, relevant price levels.
Data Export: Serializes up to 10 gaps per range (tops, bottoms, start bars, highest/lowest prices, and age) for seamless integration with overlap analysis scripts.
Debugging Support: Plots gap counts and aggregation data in the Data Window for easy verification of detected gaps.
How It Works
The indicator aggregates price movements to simulate higher range bars (24, 60, 120) from a base range bar chart. It detects gaps when the price jumps significantly between bars, ensuring gaps meet the minimum deviation threshold for 12-range bars. Gaps are stored in arrays, serialized for external use, and pruned after 7 days to maintain efficiency.
Usage
Add to your range bar chart (e.g., 12-range) to detect gaps across multiple ranges.
Use alongside the Range Bar Gaps Overlap indicator to visualize gaps and their overlaps as boxes on the chart.
Check the Data Window to confirm gap counts and sizes for each range (12, 24, 60, 120).
Adjust the "Minimal Deviation (%) for 12-Range" input to control gap detection sensitivity.
Settings
Minimal Deviation (%) for 12-Range: Set the minimum gap size for 12-range bars (default: 10% of 14-period SMA).
Range Sizes: Fixed at 24, 60, and 120 for higher range bar aggregation.
Notes
Ensure the script is published under your TradingView username (e.g., GreenArrow2005) for use with companion scripts.
Best used on range bar charts to maintain consistent gap detection.
For advanced overlap analysis, pair with the Range Bar Gaps Overlap indicator to highlight zones where gaps from different ranges align.
Ideal For
Traders seeking to identify key price levels for support/resistance or breakout strategies.
Multi-timeframe analysts combining gap data across various range bar sizes.
Developers building custom indicators that leverage gap data for advanced charting.
Cerca negli script per "涨幅超过60日均线的股票"
Liquid Pulse Liquid Pulse by Dskyz (DAFE) Trading Systems
Liquid Pulse is a trading algo built by Dskyz (DAFE) Trading Systems for futures markets like NQ1!, designed to snag high-probability trades with tight risk control. it fuses a confluence system—VWAP, MACD, ADX, volume, and liquidity sweeps—with a trade scoring setup, daily limits, and VIX pauses to dodge wild volatility. visuals include simple signals, VWAP bands, and a dashboard with stats.
Core Components for Liquid Pulse
Volume Sensitivity (volumeSensitivity) controls how much volume spikes matter for entries. options: 'Low', 'Medium', 'High' default: 'High' (catches small spikes, good for active markets) tweak it: 'Low' for calm markets, 'High' for chaos.
MACD Speed (macdSpeed) sets the MACD’s pace for momentum. options: 'Fast', 'Medium', 'Slow' default: 'Medium' (solid balance) tweak it: 'Fast' for scalping, 'Slow' for swings.
Daily Trade Limit (dailyTradeLimit) caps trades per day to keep risk in check. range: 1 to 30 default: 20 tweak it: 5-10 for safety, 20-30 for action.
Number of Contracts (numContracts) sets position size. range: 1 to 20 default: 4 tweak it: up for big accounts, down for small.
VIX Pause Level (vixPauseLevel) stops trading if VIX gets too hot. range: 10 to 80 default: 39.0 tweak it: 30 to avoid volatility, 50 to ride it.
Min Confluence Conditions (minConditions) sets how many signals must align. range: 1 to 5 default: 2 tweak it: 3-4 for strict, 1-2 for more trades.
Min Trade Score (Longs/Shorts) (minTradeScoreLongs/minTradeScoreShorts) filters trade quality. longs range: 0 to 100 default: 73 shorts range: 0 to 100 default: 75 tweak it: 80-90 for quality, 60-70 for volume.
Liquidity Sweep Strength (sweepStrength) gauges breakouts. range: 0.1 to 1.0 default: 0.5 tweak it: 0.7-1.0 for strong moves, 0.3-0.5 for small.
ADX Trend Threshold (adxTrendThreshold) confirms trends. range: 10 to 100 default: 41 tweak it: 40-50 for trends, 30-35 for weak ones.
ADX Chop Threshold (adxChopThreshold) avoids chop. range: 5 to 50 default: 20 tweak it: 15-20 to dodge chop, 25-30 to loosen.
VWAP Timeframe (vwapTimeframe) sets VWAP period. options: '15', '30', '60', '240', 'D' default: '60' (1-hour) tweak it: 60 for day, 240 for swing, D for long.
Take Profit Ticks (Longs/Shorts) (takeProfitTicksLongs/takeProfitTicksShorts) sets profit targets. longs range: 5 to 100 default: 25.0 shorts range: 5 to 100 default: 20.0 tweak it: 30-50 for trends, 10-20 for chop.
Max Profit Ticks (maxProfitTicks) caps max gain. range: 10 to 200 default: 60.0 tweak it: 80-100 for big moves, 40-60 for tight.
Min Profit Ticks to Trail (minProfitTicksTrail) triggers trailing. range: 1 to 50 default: 7.0 tweak it: 10-15 for big gains, 5-7 for quick locks.
Trailing Stop Ticks (trailTicks) sets trail distance. range: 1 to 50 default: 5.0 tweak it: 8-10 for room, 3-5 for fast locks.
Trailing Offset Ticks (trailOffsetTicks) sets trail offset. range: 1 to 20 default: 2.0 tweak it: 1-2 for tight, 5-10 for loose.
ATR Period (atrPeriod) measures volatility. range: 5 to 50 default: 9 tweak it: 14-20 for smooth, 5-9 for reactive.
Hardcoded Settings volLookback: 30 ('Low'), 20 ('Medium'), 11 ('High') volThreshold: 1.5 ('Low'), 1.8 ('Medium'), 2 ('High') swingLen: 5
Execution Logic Overview trades trigger when confluence conditions align, entering long or short with set position sizes. exits use dynamic take-profits, trailing stops after a profit threshold, hard stops via ATR, and a time stop after 100 bars.
Features Multi-Signal Confluence: needs VWAP, MACD, volume, sweeps, and ADX to line up.
Risk Control: ATR-based stops (capped 15 ticks), take-profits (scaled by volatility), and trails.
Market Filters: VIX pause, ADX trend/chop checks, volatility gates. Dashboard: shows scores, VIX, ADX, P/L, win %, streak.
Visuals Simple signals (green up triangles for longs, red down for shorts) and VWAP bands with glow. info table (bottom right) with MACD momentum. dashboard (top right) with stats.
Chart and Backtest:
NQ1! futures, 5-minute chart. works best in trending, volatile conditions. tweak inputs for other markets—test thoroughly.
Backtesting: NQ1! Frame: Jan 19, 2025, 09:00 — May 02, 2025, 16:00 Slippage: 3 Commission: $4.60
Fee Typical Range (per side, per contract)
CME Exchange $1.14 – $1.20
Clearing $0.10 – $0.30
NFA Regulatory $0.02
Firm/Broker Commis. $0.25 – $0.80 (retail prop)
TOTAL $1.60 – $2.30 per side
Round Turn: (enter+exit) = $3.20 – $4.60 per contract
Disclaimer this is for education only. past results don’t predict future wins. trading’s risky—only use money you can lose. backtest and validate before going live. (expect moderators to nitpick some random chart symbol rule—i’ll fix and repost if they pull it.)
About the Author Dskyz (DAFE) Trading Systems crafts killer trading algos. Liquid Pulse is pure research and grit, built for smart, bold trading. Use it with discipline. Use it with clarity. Trade smarter. I’ll keep dropping badass strategies ‘til i build a brand or someone signs me up.
2025 Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
Constance Brown RSI with Composite IndexConstance Brown RSI with Composite Index
Overview
This indicator combines Constance Brown's RSI interpretation methodology with a Composite Index and ATR Distance to VWAP measurement to provide a comprehensive trading tool. It helps identify trends, momentum shifts, overbought/oversold conditions, and potential reversal points.
Key Features
Color-coded RSI zones for immediate trend identification
Composite Index for momentum analysis and divergence detection
ATR Distance to VWAP for identifying extreme price deviations
Automatic divergence detection for early reversal warnings
Pre-configured alerts for key trading signals
How to Use This Indicator
Trend Identification
The RSI line changes color based on its position:
Blue zone (RSI > 50): Bullish trend - look for buying opportunities
Purple zone (RSI < 50): Bearish trend - look for selling opportunities
Gray zone (RSI 40-60): Neutral/transitional market - prepare for potential breakout
The 40-50 area (light blue fill) acts as support during uptrends, while the 50-60 area (light purple fill) acts as resistance during downtrends.
// From the code:
upTrendZone = rsiValue > 50 and rsiValue <= 90
downTrendZone = rsiValue < 50 and rsiValue >= 10
neutralZone = rsiValue > 40 and rsiValue < 60
rsiColor = neutralZone ? neutralRSI : upTrendZone ? upTrendRSI : downTrendRSI
Momentum Analysis
The Composite Index (fuchsia line) provides momentum confirmation:
Values above 50 indicate positive momentum
Values below 40 indicate negative momentum
Crossing above/below these thresholds signals potential momentum shifts
// From the code:
compositeIndexRaw = rsiChange / ta.stdev(rsiValue, rsiLength)
compositeIndex = ta.sma(compositeIndexRaw, compositeSmoothing)
compositeScaled = compositeIndex * 10 + 50 // Scaled to fit 0-100 range
Overbought/Oversold Detection
The ATR Distance to VWAP table in the top-right corner shows how far price has moved from VWAP in terms of ATR units:
Extreme positive values (orange/red): Potentially overbought
Extreme negative values (purple/red): Potentially oversold
Near zero (gray): Price near average value
// From the code:
priceDistance = (close - vwapValue) / ta.atr(atrPeriod)
// Color coding based on distance value
Divergence Trading
The indicator automatically detects divergences between the Composite Index and price:
Bullish divergence: Price makes lower low but Composite Index makes higher low
Bearish divergence: Price makes higher high but Composite Index makes lower high
// From the code:
divergenceBullish = ta.lowest(compositeIndex, rsiLength) > ta.lowest(close, rsiLength)
divergenceBearish = ta.highest(compositeIndex, rsiLength) < ta.highest(close, rsiLength)
Trading Strategies
Trend Following
1. Identify the trend using RSI color:
Blue = Uptrend, Purple = Downtrend
2. Wait for pullbacks to support/resistance zones:
In uptrends: Buy when RSI pulls back to 40-50 zone and bounces
In downtrends: Sell when RSI rallies to 50-60 zone and rejects
3. Confirm with Composite Index:
Uptrends: Composite Index stays above 50 or quickly returns above it
Downtrends: Composite Index stays below 50 or quickly returns below it
4. Manage risk using ATR Distance:
Take profits when ATR Distance reaches extreme values
Place stops beyond recent swing points
Reversal Trading
1. Look for divergences
Bullish: Price makes lower low but Composite Index makes higher low
Bearish: Price makes higher high but Composite Index makes lower high
2. Confirm with ATR Distance:
Extreme readings suggest potential reversals
3. Wait for RSI zone transition:
Bullish: RSI crosses above 40 (purple to neutral/blue)
Bearish: RSI crosses below 60 (blue to neutral/purple)
4. Enter after confirmation:
Use candlestick patterns for precise entry
Place stops beyond the divergence point
Four pre-configured alerts are available:
Momentum High: Composite Index above 50
Momentum Low: Composite Index below 40
Bullish Divergence: Composite Index higher low
Bearish Divergence: Composite Index lower high
Customization
Adjust these parameters to optimize for your trading style:
RSI Length: Default 14, lower for more sensitivity, higher for fewer signals
Composite Index Smoothing: Default 10, lower for quicker signals, higher for less noise
ATR Period: Default 14, affects the ATR Distance to VWAP calculation
This indicator works well across various markets and timeframes, though the default settings are optimized for daily charts. Adjust parameters for shorter or longer timeframes as needed.
Happy trading!
RSI3M3+ v.1.8RSI3M3+ v.1.8 Indicator
This script is an advanced trading indicator based on Walter J. Bressert's cycle analysis methodology, combined with an RSI (Relative Strength Index) variation. Let me break it down and explain how it works.
Core Concepts
The RSI3M3+ indicator combines:
A short-term RSI (3-period)
A 3-period moving average to smooth the RSI
Bressert's cycle analysis principles to identify optimal trading points
RSI3M3+ Indicator VisualizationImage Walter J. Bressert's Cycle Analysis Concepts
Walter Bressert was a pioneer in cycle analysis trading who believed markets move in cyclical patterns that can be measured and predicted. His key principles integrated into this indicator include:
Trading Cycles: Markets move in cycles with measurable time spans from low to low
Timing Bands: Projected periods when the next cyclical low or high is anticipated
Oscillator Use: Using oscillators like RSI to confirm cycle position
Entry/Exit Rules: Specific rules for trade entry and exit based on cycle position
Key Parameters in the Script
Basic RSI Parameters
Required bars: Minimum number of bars needed (default: 20)
Overbought region: RSI level considered overbought (default: 70)
Oversold region: RSI level considered oversold (default: 30)
Bressert-Specific Parameters
Cycle Detection Length: Lookback period for cycle identification (default: 30)
Minimum/Maximum Cycle Length: Expected cycle duration in days (default: 15-30)
Buy Line: Lower threshold for buy signals (default: 40)
Sell Line: Upper threshold for sell signals (default: 60)
How the Indicator Works
RSI3M3 Calculation:
Calculates a 3-period RSI (sRSI)
Smooths it with a 3-period moving average (sMA)
Cycle Detection:
Identifies bottoms: When the RSI is below the buy line (40) and starting to turn up
Identifies tops: When the RSI is above the sell line (60) and starting to turn down
Records these points to calculate cycle lengths
Timing Bands:
Projects when the next cycle bottom or top should occur
Creates visual bands on the chart showing these expected time windows
Signal Generation:
Buy signals occur when the RSI turns up from below the oversold level (30)
Sell signals occur when the RSI turns down from above the overbought level (70)
Enhanced by Bressert's specific timing rules
Bressert's Five Trading Rules (Implemented in the Script)
Cycle Timing: The low must be 15-30 market days from the previous Trading Cycle bottom
Prior Top Validation: A Trading Cycle high must have occurred with the oscillator above 60
Oscillator Behavior: The oscillator must drop below 40 and turn up
Entry Trigger: Entry is triggered by a rise above the price high of the upturn day
Protective Stop: Place stop slightly below the Trading Cycle low (implemented as 99% of bottom price)
How to Use the Indicator
Reading the Chart
Main Plot Area:
Green line: 3-period RSI
Red line: 3-period moving average of the RSI
Horizontal bands: Oversold (30) and Overbought (70) regions
Dotted lines: Buy line (40) and Sell line (60)
Yellow vertical bands: Projected timing windows for next cycle bottom
Signals:
Green up arrows: Buy signals
Red down arrows: Sell signals
Trading Strategy
For Buy Signals:
Wait for the RSI to drop below the buy line (40)
Look for an upturn in the RSI from below this level
Enter the trade when price rises above the high of the upturn day
Place a protective stop at 99% of the Trading Cycle low
For Sell Signals:
Wait for the RSI to rise above the sell line (60)
Look for a downturn in the RSI from above this level
Consider exiting or taking profits when a sell signal appears
Alternative exit: When price moves below the low of the downturn day
Cycle Timing Enhancement:
Pay attention to the yellow timing bands
Signals occurring within these bands have higher probability of success
Signals outside these bands may be less reliable
Practical Tips for Using RSI3M3+
Timeframe Selection:
The indicator works best on daily charts for intermediate-term trading
Can be used on weekly charts for longer-term position trading
On intraday charts, adjust cycle lengths accordingly
Market Applicability:
Works well in trending markets with clear cyclical behavior
Less effective in choppy, non-trending markets
Consider additional indicators for trend confirmation
Parameter Adjustment:
Different markets may have different natural cycle lengths
You may need to adjust the min/max cycle length parameters
Higher volatility markets may need wider overbought/oversold levels
Trade Management:
Enter trades when all Bressert's conditions are met
Use the protective stop as defined (99% of cycle low)
Consider taking partial profits at the projected cycle high timing
Advanced Techniques
Multiple Timeframe Analysis:
Confirm signals with the same indicator on higher timeframes
Enter in the direction of the larger cycle when smaller and larger cycles align
Divergence Detection:
Look for price making new lows while RSI makes higher lows (bullish)
Look for price making new highs while RSI makes lower highs (bearish)
Confluence with Price Action:
Combine with support/resistance levels
Use with candlestick patterns for confirmation
Consider volume confirmation of cycle turns
This RSI3M3+ indicator combines the responsiveness of a short-term RSI with the predictive power of Bressert's cycle analysis, offering traders a sophisticated tool for identifying high-probability trading opportunities based on market cycles and momentum shifts.
THANK YOU FOR PREVIOUS CODER THAT EFFORT TO CREATE THE EARLIER VERSION THAT MAKE WALTER J BRESSERT CONCEPT IN TRADINGVIEW @ADutchTourist
JL - DWM OHLCThis indicator plots the following price levels on your chart automatically AND will not show up if you are using a timeframe bigger than 60 minutes, 1 day, or 1 week.
Here are the price levels that are automatically plotted for you, and so you know the styling is different for Daily, Weekly, Monthly levels so you can easily distinguish between them:
- Prior Day: High / Low / Close
- Current Day: Open
- Prior Week: High / Low / Close
- Current Week: Open
- Prior Month: High / Low / Close
- Current Month: Open
These plots are timeframe dependent and will not plot on subsequently higher timeframes, here is how they work:
Daily Price Levels are only shown on timeframes that are smaller than 60 minutes.
Weekly Price Levels are only shown on timeframes smaller than 1 Day.
Monthly Price Levels are only shown on timeframes smaller than 1 Week.
This way, you can turn on the indicator and not have to think about turning off certain price levels if you switch to a larger / longer timeframe than what you typically use.
For example, Daily OHLC price levels will quickly clutter the 60 minute chart, and likely you don't need to know the HLC of the Prior Day if you are looking at the 60 minute chart. Therefor it may be helpful to automatically hide the Daily price level plots, and only show the Weekly and Monthly plots on the 60 minute timeframe.
I hope you find this indicator helpful, thanks for reading.
Simultaneous INSIDE Bar Break IndicatorSimultaneous Inside Bar Break Indicator (SIBBI) for The Strat Community
Overview:
The Simultaneous Inside Bar Break Indicator (SIBBI) is designed to help traders using The Strat methodology identify one of the most powerful breakout patterns: the Simultaneous Inside Bar Break across multiple symbols. This indicator detects when all four user-selected symbols form inside bars on the previous candle and then break those inside bars in the same direction (either bullish or bearish) on the current candle.
Inside bars represent consolidation periods where price action does not break the high or low of the previous candle. When a simultaneous break occurs across multiple symbols, this often signals a strong move in the market, making this a key actionable signal in The Strat trading strategy.
Key Features:
Multi-Symbol Analysis: You can track up to four different symbols simultaneously. By default, the indicator comes with SPY, QQQ, IWM, and DIA, but you can modify these to track any other assets or symbols.
Inside Bar Detection: The indicator checks whether all four symbols have inside bars on the previous candle. It only triggers when all symbols meet this condition, making it a highly specific and reliable signal.
Simultaneous Break Detection: Once all symbols have inside bars, the indicator waits for a breakout in the same direction across all four symbols. A simultaneous bullish break (prices breaking above the previous candle’s high) triggers a green label, while a simultaneous bearish break (prices breaking below the previous candle’s low) triggers a red label.
Dynamic Label Timeframe: The indicator dynamically adjusts the timeframe in the label based on the user’s selected timeframe. This allows traders to know precisely which timeframe the break is occurring on. If the user selects "Chart Timeframe," the indicator will evolve with the current chart's timeframe, making it more versatile.
Timeframe Flexibility: The indicator can be set to analyze any timeframe—15-minute, 30-minute, 60-minute, daily, weekly, and so on. It only works for the specific timeframe you set it to in the settings. If set to "Chart Timeframe," the label will adapt dynamically based on the timeframe you are currently viewing.
Customizable Labels: The user can choose the size of the labels (tiny, small, or normal), ensuring that the visual output is tailored to individual preferences and chart layouts.
Best Use Case:
The Simultaneous Inside Bar Break Indicator is particularly powerful when applied to multiple timeframes. Here’s how to use it for maximum impact:
Multi-Timeframe Setup: Set the indicator on various timeframes (e.g., 15-minute, 30-minute, 60-minute, and daily) across multiple charts. This allows you to monitor different timeframes and identify when lower timeframe breaks trigger potential moves on higher timeframes.
Anticipating Strong Moves: When a simultaneous inside bar break occurs on one timeframe (e.g., 30-minute), keep an eye on the higher timeframes (e.g., 60-minute or daily) to see if those timeframes also break. This stacking of inside bar breaks can signal powerful market moves.
Higher Conviction Signals: The indicator is designed to provide high-conviction signals. Since it requires all four symbols to break in the same direction simultaneously, it reduces false signals and focuses on higher probability setups, which is crucial for traders using The Strat to time their trades effectively.
How the Indicator Works:
Inside Bar Formation: The indicator first checks that all four selected symbols had inside bars in the previous bar (i.e., the current high and low are contained within the previous bar’s high and low).
Simultaneous Break Detection: After detecting inside bars, the indicator checks if all four symbols break out in the same direction—bullish (breaking above the previous bar’s high) or bearish (breaking below the previous bar’s low).
Label Display: When a simultaneous inside bar break occurs, a label is plotted on the chart—either green for a bullish break (below the candle) or red for a bearish break (above the candle). The label will display the timeframe you set in the settings (e.g., "IBSB 60" for a 60-minute break).
Chart Timeframe Option: If you prefer, you can set the indicator to evolve with the chart’s current timeframe. In this mode, the label will not show a specific timeframe but will still display the simultaneous inside bar break when it occurs.
Recommendations for Usage:
Focus on Multiple Timeframes: The Strat methodology is all about understanding the relationship between different timeframes. Use this indicator on multiple timeframes to get a better picture of potential moves.
Pair with Other Strat Techniques: This indicator is most powerful when combined with other Strat tools, such as broadening formations, timeframe continuity, and actionable signals (e.g., 2-2 reversals). The simultaneous inside bar break can help confirm or invalidate other signals.
Customize Symbols and Timeframes: Although the default symbols are SPY, QQQ, IWM, and DIA, feel free to replace them with symbols more relevant to your trading. This indicator works well across equities, indices, futures, and forex pairs.
How to Set It Up:
Select Symbols: Choose four symbols that you want to track. These can be index ETFs (like SPY and QQQ), individual stocks, or any other tradable instruments.
Set Timeframe: In the indicator’s settings, choose a specific timeframe (e.g., 15-minute, 30-minute, daily). The label will reflect the selected timeframe, making it clear which time-based break you are seeing.
Optional - Chart Timeframe Mode: If you want the indicator to adapt to the chart’s current timeframe, select the "Chart Timeframe" option in the settings. The indicator will plot the breaks without showing a specific timeframe in the label.
Customize Label Size: Depending on your chart layout and personal preference, you can adjust the size of the labels (tiny, small, or normal) in the settings.
Conclusion:
The Simultaneous Inside Bar Break Indicator is a powerful tool for traders using The Strat methodology, offering a highly specific and reliable signal that can indicate potential large market moves. By monitoring multiple symbols and timeframes, you can gain deeper insight into the market's behavior and act with greater confidence. This indicator is ideal for traders looking to catch high-conviction moves and align their trades with broader market continuity.
Note: The indicator works best when paired with multi-timeframe analysis, allowing you to see how breaks on lower timeframes might influence larger trends. For traders who prefer simplicity, setting it to the "Chart Timeframe" mode offers flexibility while maintaining the core benefits of this indicator.
Financial Radar Chart by zdmreRadar chart is often used when you want to display data across several unique dimensions. Although there are exceptions, these dimensions are usually quantitative, and typically range from zero to a maximum value. Each dimension’s range is normalized to one another, so that when we draw our spider chart, the length of a line from zero to a dimension’s maximum value will be the similar for every dimension.
This Charts are useful for seeing which variables are scoring high or low within a dataset, making them ideal for displaying performance.
How is the score formed?
Debt Paying Ability
if Debt_to_Equity < %10 : 100
elif < 20% : 90
elif < 30% : 80
elif < 40% : 70
elif < 50% : 60
elif < 60% : 50
elif < 70% : 40
elif < 80% : 30
elif < 90% : 20
elif < 100% : 10
else: 0
ROIC
if Return_on_Invested_Capital > %50 : 100
elif > 40% : 90
elif > 30% : 80
elif > 20% : 70
elif > 10% : 50
elif > 5% : 20
else: 0
ROE
if Return_on_Equity > %50 : 100
elif > 40% : 90
elif > 30% : 80
elif > 20% : 70
elif > 10% : 50
elif > 5% : 20
else: 0
Operating Ability
if Operating_Margin > %50 : 100
elif > 30% : 90
elif > 20% : 80
elif > 15% : 60
elif > 10% : 40
elif > 0 : 20
else: 0
EV/EBITDA
if Enterprise_Value_to_EBITDA < 3 : 100
elif < 5 : 80
elif < 7 : 70
elif < 8 : 60
elif < 10 : 40
elif < 12 : 20
else: 0
FREE CASH Ability
if Price_to_Free_Cash_Flow < 5 : 100
elif < 7 : 90
elif < 10 : 80
elif < 16 : 60
elif < 18 : 50
elif < 20 : 40
elif < 22 : 30
elif < 30 : 20
elif < 40 : 15
elif < 50 : 10
elif < 60 : 5
else: 0
GROWTH Ability
if Revenue_One_Year_Growth > %20 : 100
elif > 16% : 90
elif > 14% : 80
elif > 12% : 70
elif > 10% : 50
elif > 7% : 40
elif > 4% : 30
elif > 2% : 20
elif > 0 : 10
else: 0
[blackcat] L1 Old Duck HeadLevel 1
Background
The old duck head is a classic form formed by a series of behaviors such as bankers opening positions, washing dishes, and pulling over the top of the duck head.
Function
A form of stock candles:
(1) Moving averages using 5, 10 and 60 parameters. When the 5-day and 10-day moving averages crossed the 60-day moving average, a duck neck was formed.
(2) The high point when the stock price fell back formed a duck head.
(3) When the stock price fell back soon, the 5-day and 10-day moving averages again turned up to form a duckbill.
(4) Duck nose refers to the hole formed when the 5-day moving average crosses the 10-day moving average and the two lines cross again.
Market significance:
(1) When the dealer starts to collect chips, the stock price rises slowly, and the 5-day and 10-day moving averages cross the 60-day moving average, forming a duck neck.
(2) When the stock price of the banker shakes the position and starts to pull back, the high point of the stock price forms the top of the duck's head.
(3) When the dealer builds a position again to collect chips, the stock price rises again, forming a duck bill.
Operation method:
(1) Buy when the 5-day and 10-day moving averages cross the 60-day moving average and form a duck neck.
(2) Buy on dips near the sesame point of trading volume near the duckbill.
(3) Intervene when the stock price crosses the top of the duck's head in heavy volume.
The top of the duck’s head should be a little far away from the 60-day moving average, otherwise it means that the dealer is not willing to open a position at this old duck’s head, and the bottom of the old duck’s head must be heavy. Small is better, nothing is the strongest! There must be a lot of sesame dots under the nostrils of the duck, otherwise it means that the dealer has poor control. There must be ventilation under the duck bill, the higher the ventilation, the better!
Remarks
Feedbacks are appreciated.
Edge of MomentumThe script was designed for the purpose of catching the rocket portion of a move (the edge of momentum).
Long
--When RSI closes over 60, take long order 1 tick above that bar. The closed bar above RSI 60 will be colored "green" or whatever color the user chooses. (RSI > 60)
--On a long position, exit will be a closed bar below the ema (low, 10) . The closed bar below the ema will be colored "yellow." (Price < ema)
--Note: On a long position there is no need to exit when a closed bar is colored "purple." RSI is just below 60 but above 40. Pullback or chop
Short
--When RSI closes below 40, take a short order 1 tick below that bar. The closed bar below RSI 40 will be colored "red." RSI<40)
--On a short position, exit will be a closed bar above the ema (low, 10). The closed bar above the ema will be colored "purple." (Price > ema)
--Note: On a short position there is no need to exit when a closed bar is colored "yellow."
Note: You may see a series of purple and yellow bars, that is simply chop. I define chop as RSI moving between 60 and 40.
Trade should only be taken above green colored candle(long) and below red colored candle (short). No position should be taken off yellow or purple candle (chop)
Again this is designed to catch the momentum part of a move, and to help reduce some entries during chop. It is a simple systems that beginning traders can use and profit from.
Note: I don't no shit about coding scripts I just learn from reading others.
Enjoy. If you decide to use please drop me a line...suggestions/comments, etc.
Best of luck in all you do.
3 Duck's Trading System from Babypips.comThe 3 Duck's Trading System from Babypips.com
The 3 Duck's Trading System is the most popular and active trading system thread on the the babypips.com forum. It is a system that is mainly for beginners because it teaches you discipline, learning to cope with price moving against your position and learning to stay in a trade and keep profits running. For the thread and more info on the 3 Duck's Trading System click here
How does it work?
The system is a very simple enter/exit based on the 60 SMA of 3 different time frames: 4 hour, 1 hour and 5 minute.
The Rules, er, the Ducks! The Ducks must all be in a row for a trade to take place!
Duck 1 - To go long, price must be above the 60 SMA on the 4 hour chart.
Duck 2 - To go long, price must be above the 60 SMA on the 1 hour chart.
Duck 3 - To go long, price must cross above the 60 SMA on the 5 minute chart and the 60 SMA of the 5 minute chart must be below that of the 4 hour and 1 hour chart. (obviously the reverse for shorting)
YOU MUST USE THIS SYSTEM ONLY ON THE 5 MINUTE CHART.
I say this because I have already charted all of the Ducks into the 5 minute chart so you don't have to flip back and forth.
I have also added some inputs for profit targets, stop targets, trailing stops and times to trade for backtesting.
If you have any questions or comments, please let me know! If you see I messed up on something, please let me know!
Also a VERY special thanks to the babypips.com user Captain_Currency . He wrote this strategy 10 years ago (2007 was 10 years ago?!) and he is still active on the thread and posting results and offering help!
VWAP MA HLOC securities Jayy update fix This version replaces previous versions that stopped functioning as a result of a TradingView script update.
This script complies with the current script syntax.
for intraday securities default is 9:30 am to 4 pm Eastern Other session choices are provided in the format dialogue box.
script plots VWAP, yesterday's high, low, open and close (HLOC), the day befores HLOC - if desired, today's open and todays high and low.
Also signals inside bars (high is less than or equal to the previous
bar's high and the low is greater than or equal to
the previous low) the : true inside bars have a maroon triangle below the bar as well as a ">" above the bar.
If subsequent bars are inside the last bar before the last true inside bar they also are marked with an ">"
Also plots the 20 ema for different time periods (as per Al Brooks), If you trade the 5 min then you will
likely be interested in the 20 ema for 15 mins and 60 mins
the following is a list of the higher timeframe 20 emas
1 minute 5, 15, 60 period 20 ema
5 minute 15, 60 period 20 ema
15 minute 60, 120 , 240 period 20 ema
60 minute 120, 240 period 20 ema
120 minute 240, D period 20 ema
240 minute D period 20 ema
Jayy
Liquidity Sweep + FVG Entry Model//@version=5
indicator("Liquidity Sweep + FVG Entry Model", overlay = true, max_labels_count = 500, max_lines_count = 500)
// Just to confirm indicator is loaded, always plot close:
plot(close, color = color.new(color.white, 0))
// ─────────────────────────────────────────────
// PARAMETERS
// ─────────────────────────────────────────────
len = input.int(5, "Liquidity Lookback")
tpMultiplier = input.float(2.0, "TP Distance Multiplier")
// ─────────────────────────────────────────────
// LIQUIDITY SWEEP DETECTION
// ─────────────────────────────────────────────
lowestPrev = ta.lowest(low, len)
highestPrev = ta.highest(high, len)
sweepLow = low < lowestPrev and close > lowestPrev
sweepHigh = high > highestPrev and close < highestPrev
// Plot liquidity levels
plot(lowestPrev, "Liquidity Low", color = color.new(color.blue, 40), style = plot.style_line)
plot(highestPrev, "Liquidity High", color = color.new(color.red, 40), style = plot.style_line)
// ─────────────────────────────────────────────
// DISPLACEMENT DETECTION
// ─────────────────────────────────────────────
bullDisp = sweepLow and close > open and close > close
bearDisp = sweepHigh and close < open and close < close
// ─────────────────────────────────────────────
// FAIR VALUE GAP (FVG)
// ─────────────────────────────────────────────
bullFVG = low > high
bearFVG = high < low
// we’ll store the last FVG lines
var line fvgTop = na
var line fvgBottom = na
// clear old FVG lines when new one appears
if bullFVG or bearFVG
if not na(fvgTop)
line.delete(fvgTop)
if not na(fvgBottom)
line.delete(fvgBottom)
// Bullish FVG box
if bullFVG
fvgTop := line.new(bar_index , high , bar_index, high , extend = extend.right, color = color.new(color.green, 60))
fvgBottom := line.new(bar_index , low, bar_index, low, extend = extend.right, color = color.new(color.green, 60))
// Bearish FVG box
if bearFVG
fvgTop := line.new(bar_index , low , bar_index, low , extend = extend.right, color = color.new(color.red, 60))
fvgBottom := line.new(bar_index , high, bar_index, high, extend = extend.right, color = color.new(color.red, 60))
// ─────────────────────────────────────────────
// ENTRY, SL, TP CONDITIONS
// ─────────────────────────────────────────────
var line slLine = na
var line tp1Line = na
var line tp2Line = na
f_deleteLineIfExists(line_id) =>
if not na(line_id)
line.delete(line_id)
if bullDisp and bullFVG
sl = low
tp1 = close + (close - sl) * tpMultiplier
tp2 = close + (close - sl) * (tpMultiplier * 1.5)
f_deleteLineIfExists(slLine)
f_deleteLineIfExists(tp1Line)
f_deleteLineIfExists(tp2Line)
slLine := line.new(bar_index, sl, bar_index + 1, sl, extend = extend.right, color = color.red)
tp1Line := line.new(bar_index, tp1, bar_index + 1, tp1, extend = extend.right, color = color.green)
tp2Line := line.new(bar_index, tp2, bar_index + 1, tp2, extend = extend.right, color = color.green)
label.new(bar_index, close, "BUY Entry\nFVG Retest\nSL Below Sweep",
style = label.style_label_up, color = color.new(color.green, 0), textcolor = color.white)
if bearDisp and bearFVG
sl = high
tp1 = close - (sl - close) * tpMultiplier
tp2 = close - (sl - close) * (tpMultiplier * 1.5)
f_deleteLineIfExists(slLine)
f_deleteLineIfExists(tp1Line)
f_deleteLineIfExists(tp2Line)
slLine := line.new(bar_index, sl, bar_index + 1, sl, extend = extend.right, color = color.red)
tp1Line := line.new(bar_index, tp1, bar_index + 1, tp1, extend = extend.right, color = color.green)
tp2Line := line.new(bar_index, tp2, bar_index + 1, tp2, extend = extend.right, color = color.green)
label.new(bar_index, close, "SELL Entry\nFVG Retest\nSL Above Sweep",
style = label.style_label_down, color = color.new(color.red, 0), textcolor = color.white)
Mirpapa_Lib_BoxLibrary "Mirpapa_Lib_Box"
GetHTFrevised(_tf, _case)
GetHTFrevised
@description Retrieve a specific bar value from a Higher Time Frame (HTF) series.
Parameters:
_tf (string) : string The target HTF string (examples: "60", "1D").
_case (string) : string Case string determining which OHLC value to request.
@return float Returns the requested HTF value or na if _case does not match.
GetHTFrevised(_tf)
Parameters:
_tf (string)
GetHTFoffsetToLTFoffset(_offset, _chartTf, _htfTf)
GetHTFoffsetToLTFoffset
@description Adjust an HTF offset to an LTF offset by calculating the ratio of timeframes.
Parameters:
_offset (int) : int The HTF bar offset (0 means current HTF bar).
_chartTf (string) : string The current chart's timeframe (e.g., "5", "15", "1D").
_htfTf (string) : string The High Time Frame string (e.g., "60", "1D").
@return int The corresponding LTF bar index. Returns 0 if the result is negative.
GetHtfFromLabel(_label)
GetHtfFromLabel
@description Convert a Korean HTF label into a Pine Script timeframe string.
Parameters:
_label (string) : string The Korean label (e.g., "5분", "1시간").
@return string Returns the corresponding Pine Script timeframe (e.g., "5", "60").
IsChartTFcomparisonHTF(_chartTf, _htfTf)
IsChartTFcomparisonHTF
@description Determine whether a given HTF is greater than or equal to the current chart timeframe.
Parameters:
_chartTf (string) : string Current chart timeframe (e.g., "5", "15", "1D").
_htfTf (string) : string HTF timeframe (e.g., "60", "1D").
@return bool True if HTF ≥ chartTF, false otherwise.
IsCondition(_boxType, _isBull, _pricePrev, _priceNow)
IsCondition
@description FOB, FVG 조건 체크.\
_boxType: "fob"(Fair Order Block) 또는 "fvg"(Fair Value Gap).\
_isBull: true(상승 패턴), false(하락 패턴).\
상승 시 현재 가격이 이전 가격보다 높으면 true, 하락 시 이전 가격이 현재 가격보다 높으면 true 반환.
Parameters:
_boxType (string) : 박스 타입 ("fob", "fvg")
_isBull (bool) : 상승(true) 또는 하락(false)
_pricePrev (float) : 이전 가격
_priceNow (float) : 현재 가격
Returns: bool 조건 만족 여부
IsCondition(_boxType, _high2, _high1, _high0, _low2, _low1, _low0)
IsCondition
@description Sweep 조건 체크 (Swing High/Low 동시 발생).\
_boxType: "sweep" 또는 "breachBoth".\
조건: high2 < high1 > high0 (Swing High) AND low2 > low1 < low0 (Swing Low).\
중간 캔들이 양쪽보다 높고 낮은 지점을 동시에 형성할 때 true 반환.
Parameters:
_boxType (string) : 박스 타입 ("sweep", "breachBoth")
_high2 (float)
_high1 (float)
_high0 (float)
_low2 (float)
_low1 (float)
_low0 (float)
Returns: bool 조건 만족 여부
IsCondition(_boxType, _isBull, _open1, _close1, _high1, _low1, _open0, _close0, _low2, _low3, _high2, _high3)
IsCondition
@description RB (Rejection Block) 조건 체크.\
_boxType: "rb" (Rejection Block).\
상승 RB: candle1=음봉, candle0=양봉, low3>low1 AND low2>low1, close1*1.001>open0, open1close0.\
이전 캔들의 거부 후 현재 캔들이 반대 방향으로 전환될 때 true 반환.
Parameters:
_boxType (string) : 박스 타입 ("rb")
_isBull (bool) : 상승(true) 또는 하락(false)
_open1 (float)
_close1 (float)
_high1 (float)
_low1 (float)
_open0 (float)
_close0 (float)
_low2 (float)
_low3 (float)
_high2 (float)
_high3 (float)
Returns: bool 조건 만족 여부
IsCondition(_boxType, _isBull, _open2, _close1, _open1, _close0)
IsCondition
@description SOB (Strong Order Block) 조건 체크.\
_boxType: "sob" (Strong Order Block).\
상승 SOB: 양봉2 => 음봉1 => 양봉0, open2 > close1 AND open1 < close0.\
하락 SOB: 음봉2 => 양봉1 => 음봉0, open2 < close1 AND open1 > close0.\
3개 캔들 패턴으로 강한 주문 블록 형성 시 true 반환.
Parameters:
_boxType (string) : 박스 타입 ("sob")
_isBull (bool) : 상승(true) 또는 하락(false)
_open2 (float) : 2개 이전 캔들 open
_close1 (float) : 1개 이전 캔들 close
_open1 (float) : 1개 이전 캔들 open
_close0 (float) : 현재 캔들 close
Returns: bool 조건 만족 여부
CreateBox(_boxType, _boxState, _breachModeStart, _breachModeEnd, _isBull, _leftTime, _rightTime, _top, _bottom, _useLine, _colorBG, _colorBD, _colorText, _customText)
CreateBox
@description 박스 생성 (값 전달 방식 - 모든 좌표 직접 지정).\
호출자가 모든 좌표와 시간을 계산하여 전달.\
HTF/LTF 구분 불필요, tf/cache 계산 없음.\
Parameters:
_boxType (string) : 박스 타입 ("sob", "fob", "rb", "custom" 등)
_boxState (string) : 박스 상태 ("pending", "start", "extension")
_breachModeStart (string) : 시작 돌파 처리 방식
_breachModeEnd (string) : 종료 돌파 처리 방식
_isBull (bool) : 상승(true) 또는 하락(false)
_leftTime (int) : 박스 시작 시간
_rightTime (int) : 박스 종료 시간
_top (float) : 박스 상단 가격
_bottom (float) : 박스 하단 가격
_useLine (bool) : 중간선 표시 여부
_colorBG (color) : 박스 배경색
_colorBD (color) : 박스 테두리색
_colorText (color) : 텍스트 색상
_customText (string) : 커스텀 텍스트
Returns: 성공 여부와 박스 데이터
CreateBox(_boxType, _boxState, _tf, _isBull, _useLine, _colorBG, _colorBD, _colorText, _cache)
CreateBox (자동 계산 방식)
@description 박스 생성 (tf/cache 기반 자동 좌표 계산).\
라이브러리가 boxType에 따라 좌표를 자동 계산.\
Parameters:
_boxType (string) : 박스 타입
_boxState (string) : 박스 상태 ("pending", "start", "extension")
_tf (string) : 시간대
_isBull (bool) : 상승(true) 또는 하락(false)
_useLine (bool) : 중간선 표시 여부
_colorBG (color) : 박스 배경색
_colorBD (color) : 박스 테두리색
_colorText (color) : 텍스트 색상
_cache (HTFCache) : HTF 캐시 데이터
Returns: 성공 여부와 박스 데이터
ProcessBoxDatas(_openBoxes, _closedBoxes, _useMidLine, _closeCount, _baseColor, _pendingTransparency, _startTransparency, _endTransparency, _currentBarIndex, _currentLow, _currentHigh, _currentClose, _currentTime)
ProcessBoxDatas
@description 박스 확장 및 상태별 돌파 처리.\
열린 박스들을 현재 bar까지 확장하고, 상태별 돌파 조건 체크.\
PENDING: 시작 조건 체크 → START 전환\
START: 종료 조건 체크 → END 전환\
EXTENSION: 무한 확장\
종료된 박스는 _closedBoxes로 이동하고 _colorClose 색상 적용.\
Parameters:
_openBoxes (array) : 열린 박스 배열
_closedBoxes (array) : 닫힌 박스 배열
_useMidLine (bool) : 중간선 표시 여부
_closeCount (int) : 돌파 카운트 (이 횟수만큼 돌파 시 종료)
_baseColor (color) : 기본 색상
_pendingTransparency (int) : 대기 상태 불투명도
_startTransparency (int) : 시작 상태 불투명도
_endTransparency (int) : 종료 상태 불투명도
_currentBarIndex (int) : 현재 bar_index
_currentLow (float) : 현재 low
_currentHigh (float) : 현재 high
_currentClose (float) : 현재 close
_currentTime (int) : 현재 time
Returns: bool 항상 true
BoxType
BoxType
Fields:
FOB (series string)
FVG (series string)
IFVG (series string)
SOB (series string)
RB (series string)
BB (series string)
MB (series string)
SWEEP (series string)
CUSTOM (series string)
BreachMode
BreachMode
Fields:
BOTH_HIGH_LOW (series string)
BOTH_CLOSE (series string)
DIRECTIONAL_HIGH_LOW (series string)
DIRECTIONAL_CLOSE (series string)
NEAR_HIGH (series string)
NEAR_CLOSE (series string)
NEAR_LOW (series string)
FAR_HIGH (series string)
FAR_CLOSE (series string)
FAR_LOW (series string)
BoxState
BoxState
Fields:
PENDING (series string)
START (series string)
END (series string)
EXTENSION (series string)
BoxData
BoxData
Fields:
_type (series string) : 박스 타입
_breachModeStart (series string) : 시작 돌파 처리 방식
_breachModeEnd (series string) : 종료 돌파 처리 방식
_boxState (series string) : 박스 상태
_isBull (series bool) : 상승(true) 또는 하락(false) 방향
_box (series box)
_line (series line)
_boxTop (series float)
_boxBot (series float)
_boxMid (series float)
_topBreached (series bool)
_bottomBreached (series bool)
_breakCount (series int)
HTFCache
Fields:
_timeframe (series string)
_lastBarIndex (series int)
_isNewBar (series bool)
_barIndex (series int)
_open (series float)
_high (series float)
_low (series float)
_close (series float)
_open1 (series float)
_close1 (series float)
_high1 (series float)
_low1 (series float)
_open2 (series float)
_close2 (series float)
_high2 (series float)
_low2 (series float)
_high3 (series float)
_low3 (series float)
_time1 (series int)
_time2 (series int)
猛の掟・本物っぽいTradingViewスクリーナー 完全版//@version=5
indicator("猛の掟・本物っぽいTradingViewスクリーナー 完全版", overlay=false, max_labels_count=500, max_lines_count=500)
// =============================
// 入力パラメータ
// =============================
emaLenShort = input.int(5, "短期EMA", minval=1)
emaLenMid = input.int(13, "中期EMA", minval=1)
emaLenLong = input.int(26, "長期EMA", minval=1)
macdFastLen = input.int(12, "MACD Fast", minval=1)
macdSlowLen = input.int(26, "MACD Slow", minval=1)
macdSignalLen = input.int(9, "MACD Signal", minval=1)
macdZeroTh = input.float(0.2, "MACDゼロライン近辺とみなす許容値", step=0.05)
volMaLen = input.int(5, "出来高平均日数", minval=1)
volMinRatio = input.float(1.3, "出来高倍率(初動判定しきい値)", step=0.1)
volStrongRatio = input.float(1.5, "出来高倍率(本物/三点シグナル用)", step=0.1)
highLookback = input.int(60, "直近高値の参照本数", minval=10)
pullbackMin = input.float(5.0, "押し目最小 ", step=0.5)
pullbackMax = input.float(15.0, "押し目最大 ", step=0.5)
breakLookback = input.int(15, "レジブレ後とみなす本数", minval=1)
wickBodyMult = input.float(2.0, "ピンバー:下ヒゲが実体の何倍以上か", step=0.5)
// 表示設定
showPanel = input.bool(true, "下パネルにスコアを表示する")
showTable = input.bool(true, "右上に8条件チェック表を表示する")
// =============================
// 基本指標計算
// =============================
emaShort = ta.ema(close, emaLenShort)
emaMid = ta.ema(close, emaLenMid)
emaLong = ta.ema(close, emaLenLong)
= ta.macd(close, macdFastLen, macdSlowLen, macdSignalLen)
volMa = ta.sma(volume, volMaLen)
volRatio = volMa > 0 ? volume / volMa : 0.0
recentHigh = ta.highest(high, highLookback)
prevHigh = ta.highest(high , highLookback)
pullbackPct = recentHigh > 0 ? (recentHigh - close) / recentHigh * 100.0 : 0.0
// ローソク足要素
body = math.abs(close - open)
upperWick = high - math.max(open, close)
lowerWick = math.min(open, close) - low
// =============================
// A：トレンド条件
// =============================
emaUp = emaShort > emaShort and emaMid > emaMid and emaLong > emaLong
goldenOrder = emaShort > emaMid and emaMid > emaLong
aboveEma2 = close > emaLong and close > emaLong
trendOK = emaUp and goldenOrder and aboveEma2
// =============================
// B：MACD条件
// =============================
macdGC = ta.crossover(macdLine, macdSignal)
macdNearZero = math.abs(macdLine) <= macdZeroTh
macdUp = macdLine > macdLine
macdOK = macdGC and macdNearZero and macdUp
// =============================
// C：出来高条件
// =============================
volInitOK = volRatio >= volMinRatio // 8条件用
volStrongOK = volRatio >= volStrongRatio // 三点シグナル用
volumeOK = volInitOK
// =============================
// D：ローソク足パターン
// =============================
isBullPinbar = lowerWick > wickBodyMult * body and lowerWick > upperWick and close >= open
isBullEngulf = close > open and open < close and close > open
isBigBullCross = close > emaShort and close > emaMid and open < emaShort and open < emaMid and close > open
candleOK = isBullPinbar or isBullEngulf or isBigBullCross
// =============================
// E：価格帯（押し目＆レジブレ）
// =============================
pullbackOK = pullbackPct >= pullbackMin and pullbackPct <= pullbackMax
isBreakout = close > prevHigh and close <= prevHigh
barsSinceBreak = ta.barssince(isBreakout)
afterBreakZone = barsSinceBreak >= 0 and barsSinceBreak <= breakLookback
afterBreakPullbackOK = afterBreakZone and pullbackOK and close > emaShort
priceOK = pullbackOK and afterBreakPullbackOK
// =============================
// 8条件の統合
// =============================
allRulesOK = trendOK and macdOK and volumeOK and candleOK and priceOK
// =============================
// 最終三点シグナル
// =============================
longLowerWick = lowerWick > wickBodyMult * body and lowerWick > upperWick
macdGCAboveZero = ta.crossover(macdLine, macdSignal) and macdLine > 0
volumeSpike = volStrongOK
finalThreeSignal = longLowerWick and macdGCAboveZero and volumeSpike
buyConfirmed = allRulesOK and finalThreeSignal
// =====================================================
// スクリーナー用スコア（0=なし, 1=猛, 2=確）
// =====================================================
score = buyConfirmed ? 2 : (allRulesOK ? 1 : 0)
// 色分け（1行で安全な書き方）
col = score == 2 ? color.new(color.yellow, 0) : score == 1 ? color.new(color.lime, 0) : color.new(color.gray, 80)
// -----------------------------------------------------
// ① 視覚用：下パネルのカラム表示
// -----------------------------------------------------
plot(showPanel ? score : na,
title = "猛スコア（0=なし,1=猛,2=確）",
style = plot.style_columns,
color = col,
linewidth = 2)
hline(0, "なし", color=color.new(color.gray, 80))
hline(1, "猛", color=color.new(color.lime, 60))
hline(2, "確", color=color.new(color.yellow, 60))
// -----------------------------------------------------
// ② Data Window 用出力（スクリーナー風）
// -----------------------------------------------------
plot(score, title="Score_0なし1猛2確", color=color.new(color.white, 100), display=display.data_window)
plot(allRulesOK ? 1 : 0, title="A_Trend_OK", color=color.new(color.white, 100), display=display.data_window)
plot(macdOK ? 1 : 0, title="B_MACD_OK", color=color.new(color.white, 100), display=display.data_window)
plot(volumeOK ? 1 : 0, title="C_Volume_OK", color=color.new(color.white, 100), display=display.data_window)
plot(candleOK ? 1 : 0, title="D_Candle_OK", color=color.new(color.white, 100), display=display.data_window)
plot(priceOK ? 1 : 0, title="E_Price_OK", color=color.new(color.white, 100), display=display.data_window)
plot(longLowerWick ? 1 : 0, title="F_Pin下ヒゲ_OK", color=color.new(color.white, 100), display=display.data_window)
plot(macdGCAboveZero ? 1 : 0, title="G_MACDゼロ上", color=color.new(color.white, 100), display=display.data_window)
plot(volumeSpike ? 1 : 0, title="H_出来高1.5倍", color=color.new(color.white, 100), display=display.data_window)
// -----------------------------------------------------
// ③ 右上に「8条件チェック表」を表示（最終バーのみ）
// -----------------------------------------------------
var table info = table.new(position.top_right, 2, 9,
border_width = 1,
border_color = color.new(color.white, 60))
// 1行分の表示用ヘルパー
fRow(string label, bool cond, int row) =>
color bg = cond ? color.new(color.lime, 70) : color.new(color.red, 80)
string txt = cond ? "達成" : "未達"
// 左列：条件名
table.cell(info, 0, row, label, text_color = color.white, bgcolor = color.new(color.black, 0))
// 右列：結果（達成 / 未達）
table.cell(info, 1, row, txt, text_color = color.white, bgcolor = bg)
if barstate.islast and showTable
// ヘッダー（2列とも黒背景）
table.cell(info, 0, 0, "猛の掟 8条件チェック", text_color = color.white, bgcolor = color.new(color.black, 0))
table.cell(info, 1, 0, "", text_color = color.white, bgcolor = color.new(color.black, 0))
fRow("A: トレンド", trendOK, 1)
fRow("B: MACD", macdOK, 2)
fRow("C: 出来高", volumeOK, 3)
fRow("D: ローソク", candleOK, 4)
fRow("E: 押し目/レジブレ", priceOK, 5)
fRow("三点: ヒゲ", longLowerWick, 6)
fRow("三点: MACDゼロ上", macdGCAboveZero,7)
fRow("三点: 出来高1.5倍", volumeSpike, 8)
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
VWAP + EMA9/21/50 + Ichimoku + RSI (M5) - Strict + TPSL//@version=5
indicator("VWAP + EMA9/21/50 + Ichimoku + RSI (M5) - Strict + TPSL", overlay=true, shorttitle="VWAP_EMA_ICH_RSI_TPSL")
// === Inputs ===
emaFastLen = input.int(9, "EMA Fast (9)")
emaMidLen = input.int(21, "EMA Mid (21)")
emaSlowLen = input.int(50, "EMA Slow (50)")
// Ichimoku inputs
tenkanLen = input.int(9, "Tenkan Sen Length")
kijunLen = input.int(26, "Kijun Sen Length")
senkouBLen = input.int(52, "Senkou B Length")
displacement = input.int(26, "Displacement")
// RSI
rsiLen = input.int(14, "RSI Length")
rsiThreshold = input.int(50, "RSI Threshold")
// VWAP option
useSessionVWAP = input.bool(true, "Use Session VWAP (true) / Daily VWAP (false)")
// Volume filter
useVolumeFilter = input.bool(true, "Enable Volume Filter")
volAvgLen = input.int(20, "Volume Avg Length")
volMultiplier = input.float(1.2, "Min Volume > avg *", step=0.1)
// Higher timeframe trend check
useHTF = input.bool(true, "Enable Higher-Timeframe Trend Check")
htfTF = input.string("60", "HTF timeframe (e.g. 60, 240, D)")
// Alerts / webhook
alertOn = input.bool(true, "Enable Alerts")
useWebhook = input.bool(true, "Send webhook on alerts")
webhookURL = input.string("", "Webhook URL (leave blank to set in alert)")
// TP/SL & Trailing inputs
useTP = input.bool(true, "Enable Take Profit (TP)")
tpTypeRR = input.bool(true, "TP as Risk-Reward ratio (true) / Fixed points (false)")
tpRR = input.float(1.5, "TP RR (e.g. 1.5)", step=0.1)
fixedTPpts = input.float(40.0, "Fixed TP (ticks/pips) if not RR")
useSL = input.bool(true, "Enable Stop Loss (SL)")
slTypeATR = input.bool(true, "SL as ATR-based (true) / Fixed points (false)")
atrLen = input.int(14, "ATR Length")
atrMult = input.float(1.5, "ATR Multiplier for SL", step=0.1)
fixedSLpts = input.float(20.0, "Fixed SL (ticks/pips) if not ATR")
useTrailing = input.bool(true, "Enable Trailing Stop")
trailType = input.string("ATR", "Trailing type: ATR or EMA", options= ) // "ATR" or "EMA"
trailATRmult = input.float(1.0, "Trailing ATR Multiplier", step=0.1)
trailEMAlen = input.int(9, "Trailing EMA Length (if EMA chosen)")
trailLockInPts = input.float(5.0, "Trail lock-in (min profit before trail active, pts)")
// Other
showArrows = input.bool(true, "Show Entry Arrows")
// === Calculations ===
ema9 = ta.ema(close, emaFastLen)
ema21 = ta.ema(close, emaMidLen)
ema50 = ta.ema(close, emaSlowLen)
// VWAP
vwapVal = ta.vwap
// Ichimoku
highestHighTenkan = ta.highest(high, tenkanLen)
lowestLowTenkan = ta.lowest(low, tenkanLen)
tenkan = (highestHighTenkan + lowestLowTenkan) / 2
highestHighKijun = ta.highest(high, kijunLen)
lowestLowKijun = ta.lowest(low, kijunLen)
kijun = (highestHighKijun + lowestLowKijun) / 2
highestHighSenkouB = ta.highest(high, senkouBLen)
lowestLowSenkouB = ta.lowest(low, senkouBLen)
senkouB = (highestHighSenkouB + lowestLowSenkouB) / 2
senkouA = (tenkan + kijun) / 2
// RSI
rsi = ta.rsi(close, rsiLen)
// Volume
volAvg = ta.sma(volume, volAvgLen)
volOk = not useVolumeFilter or (volume > volAvg * volMultiplier)
// Higher timeframe trend values
htf_close = request.security(syminfo.tickerid, htfTF, close)
htf_ema50 = request.security(syminfo.tickerid, htfTF, ta.ema(close, emaSlowLen))
htf_rsi = request.security(syminfo.tickerid, htfTF, ta.rsi(close, rsiLen))
htf_bull = htf_close > htf_ema50
htf_bear = htf_close < htf_ema50
htf_ok = not useHTF or (htf_bull and close > ema50) or (htf_bear and close < ema50)
// Trend filters (on current timeframe)
priceAboveVWAP = close > vwapVal
priceAboveEMA50 = close > ema50
priceAboveCloud = close > senkouA and close > senkouB
bullTrend = priceAboveVWAP and priceAboveEMA50 and priceAboveCloud
bearTrend = not priceAboveVWAP and not priceAboveEMA50 and not priceAboveCloud
// Pullback detection (price near EMA21 within tolerance)
tolPerc = input.float(0.35, "Pullback tolerance (%)", step=0.05) / 100.0
nearEMA21 = math.abs(close - ema21) <= ema21 * tolPerc
// Entry conditions
emaCrossUp = ta.crossover(ema9, ema21)
emaCrossDown = ta.crossunder(ema9, ema21)
longConditionBasic = bullTrend and (nearEMA21 or close >= vwapVal) and emaCrossUp and rsi > rsiThreshold
shortConditionBasic = bearTrend and (nearEMA21 or close <= vwapVal) and emaCrossDown and rsi < rsiThreshold
longCondition = longConditionBasic and volOk and htf_ok and (not useHTF or htf_bull) and (rsi > rsiThreshold)
shortCondition = shortConditionBasic and volOk and htf_ok and (not useHTF or htf_bear) and (rsi < rsiThreshold)
// More strict: require Tenkan > Kijun for bull and Tenkan < Kijun for bear
ichimokuAlign = (tenkan > kijun) ? 1 : (tenkan < kijun ? -1 : 0)
longCondition := longCondition and (ichimokuAlign == 1)
shortCondition := shortCondition and (ichimokuAlign == -1)
// ATR for SL / trailing
atr = ta.atr(atrLen)
// --- Trade management state variables ---
var float activeLongEntry = na
var float activeShortEntry = na
var float activeLongSL = na
var float activeShortSL = na
var float activeLongTP = na
var float activeShortTP = na
var float activeLongTrail = na
var float activeShortTrail = na
// Function to convert fixed points to price (assumes chart in points as price units)
fixedToPriceLong(p) => p
fixedToPriceShort(p) => p
// On signal, set entry, SL and TP
if longCondition
activeLongEntry := close
// SL
if useSL
if slTypeATR
activeLongSL := close - atr * atrMult
else
activeLongSL := close - fixedToPriceLong(fixedSLpts)
else
activeLongSL := na
// TP
if useTP
if tpTypeRR and useSL and not na(activeLongSL)
risk = activeLongEntry - activeLongSL
activeLongTP := activeLongEntry + risk * tpRR
else
activeLongTP := activeLongEntry + fixedToPriceLong(fixedTPpts)
else
activeLongTP := na
// reset short
activeShortEntry := na
activeShortSL := na
activeShortTP := na
// init trailing
activeLongTrail := activeLongSL
if shortCondition
activeShortEntry := close
if useSL
if slTypeATR
activeShortSL := close + atr * atrMult
else
activeShortSL := close + fixedToPriceShort(fixedSLpts)
else
activeShortSL := na
if useTP
if tpTypeRR and useSL and not na(activeShortSL)
riskS = activeShortSL - activeShortEntry
activeShortTP := activeShortEntry - riskS * tpRR
else
activeShortTP := activeShortEntry - fixedToPriceShort(fixedTPpts)
else
activeShortTP := na
// reset long
activeLongEntry := na
activeLongSL := na
activeLongTP := na
// init trailing
activeShortTrail := activeShortSL
// Trailing logic (update only when in profit beyond 'lock-in')
if not na(activeLongEntry) and useTrailing
// current unrealized profit in points
currProfitPts = close - activeLongEntry
if currProfitPts >= trailLockInPts
// declare candidate before use to avoid undeclared identifier errors
float candidate = na
if trailType == "ATR"
candidate := close - atr * trailATRmult
else
candidate := close - ta.ema(close, trailEMAlen)
// move trail stop up but never below initial SL
activeLongTrail := math.max(nz(activeLongTrail, activeLongSL), candidate)
// ensure trail never goes below initial SL if SL exists
if useSL and not na(activeLongSL)
activeLongTrail := math.max(activeLongTrail, activeLongSL)
// update SL to trailing
activeLongSL := activeLongTrail
if not na(activeShortEntry) and useTrailing
currProfitPtsS = activeShortEntry - close
if currProfitPtsS >= trailLockInPts
// declare candidateS before use
float candidateS = na
if trailType == "ATR"
candidateS := close + atr * trailATRmult
else
candidateS := close + ta.ema(close, trailEMAlen)
activeShortTrail := math.min(nz(activeShortTrail, activeShortSL), candidateS)
if useSL and not na(activeShortSL)
activeShortTrail := math.min(activeShortTrail, activeShortSL)
activeShortSL := activeShortTrail
// Detect TP/SL hits (for plotting & alerts)
longTPHit = not na(activeLongTP) and close >= activeLongTP
longSLHit = not na(activeLongSL) and close <= activeLongSL
shortTPHit = not na(activeShortTP) and close <= activeShortTP
shortSLHit = not na(activeShortSL) and close >= activeShortSL
if longTPHit or longSLHit
// reset long state after hit
activeLongEntry := na
activeLongSL := na
activeLongTP := na
activeLongTrail := na
if shortTPHit or shortSLHit
activeShortEntry := na
activeShortSL := na
activeShortTP := na
activeShortTrail := na
// Plot EMAs
p_ema9 = plot(ema9, title="EMA9", linewidth=1)
plot(ema21, title="EMA21", linewidth=1)
plot(ema50, title="EMA50", linewidth=2)
// Plot VWAP
plot(vwapVal, title="VWAP", linewidth=2, style=plot.style_line)
// Plot Ichimoku lines (Tenkan & Kijun)
plot(tenkan, title="Tenkan", linewidth=1)
plot(kijun, title="Kijun", linewidth=1)
// Plot cloud (senkouA & senkouB shifted forward)
plot(senkouA, title="Senkou A", offset=displacement, transp=60)
plot(senkouB, title="Senkou B", offset=displacement, transp=60)
fill(plot(senkouA, offset=displacement), plot(senkouB, offset=displacement), color = senkouA > senkouB ? color.new(color.green, 80) : color.new(color.red, 80))
// Plot active trade lines
plotshape(not na(activeLongEntry), title="Active Long", location=location.belowbar, color=color.new(color.green, 0), style=shape.circle, size=size.tiny)
plotshape(not na(activeShortEntry), title="Active Short", location=location.abovebar, color=color.new(color.red, 0), style=shape.circle, size=size.tiny)
plot(activeLongSL, title="Long SL", color=color.red, linewidth=2)
plot(activeLongTP, title="Long TP", color=color.green, linewidth=2)
plot(activeShortSL, title="Short SL", color=color.red, linewidth=2)
plot(activeShortTP, title="Short TP", color=color.green, linewidth=2)
// Arrows / labels
if showArrows
if longCondition
label.new(bar_index, low, "BUY", style=label.style_label_up, color=color.green, textcolor=color.white, size=size.small)
if shortCondition
label.new(bar_index, high, "SELL", style=label.style_label_down, color=color.red, textcolor=color.white, size=size.small)
// Alerts
// alertcondition must be declared in global scope so TradingView can create alerts from them
alertcondition(longCondition, "VWAP+EMA+Ichimoku+RSI — BUY (STRICT)", "BUY signal from VWAP+EMA+Ichimoku+RSI (STRICT)")
alertcondition(shortCondition, "VWAP+EMA+Ichimoku+RSI — SELL (STRICT)", "SELL signal from VWAP+EMA+Ichimoku+RSI (STRICT)")
// Runtime alerts (still use alert() to trigger immediate alerts; webhook is added in TradingView Alert dialog)
if alertOn
if longCondition
alert("VWAP+EMA+Ichimoku+RSI — BUY (STRICT)", alert.freq_once_per_bar_close)
if shortCondition
alert("VWAP+EMA+Ichimoku+RSI — SELL (STRICT)", alert.freq_once_per_bar_close)
// Alerts for TP/SL hits
if longTPHit
alert("LONG TP HIT", alert.freq_once_per_bar_close)
if longSLHit
alert("LONG SL HIT", alert.freq_once_per_bar_close)
if shortTPHit
alert("SHORT TP HIT", alert.freq_once_per_bar_close)
if shortSLHit
alert("SHORT SL HIT", alert.freq_once_per_bar_close)
// Info table
var table info = table.new(position.top_right, 1, 8)
if barstate.islast
table.cell(info, 0, 0, text = 'Trend: ' + (bullTrend ? 'Bull' : bearTrend ? 'Bear' : 'Neutral'))
table.cell(info, 0, 1, text = 'EMA9/21/50: ' + str.tostring(ema9, format.mintick) + ' / ' + str.tostring(ema21, format.mintick) + ' / ' + str.tostring(ema50, format.mintick))
table.cell(info, 0, 2, text = 'VWAP: ' + str.tostring(vwapVal, format.mintick))
table.cell(info, 0, 3, text = 'RSI: ' + str.tostring(rsi, format.mintick))
table.cell(info, 0, 4, text = 'Vol OK: ' + (volOk ? 'Yes' : 'No'))
table.cell(info, 0, 5, text = 'HTF: ' + htfTF + ' ' + (htf_bull ? 'Bull' : htf_bear ? 'Bear' : 'Neutral'))
table.cell(info, 0, 6, text = 'ActiveLong: ' + (not na(activeLongEntry) ? 'Yes' : 'No'))
table.cell(info, 0, 7, text = 'ActiveShort: ' + (not na(activeShortEntry) ? 'Yes' : 'No'))
// End of script
ORB_RDORB_RD - Opening Range Box (Ryan DeBraal)
This indicator automatically draws a high/low box for the first portion of
each trading day, automatically stepping the range window from 15, 30, 45,
up to 60 minutes after the session starts. The box updates live as the range
forms, then optionally extends across the rest of the session.
FEATURES
-----------------------------------------------------------------------------
• Opening Range Detection
- Automatically ladders the range window: 0–15, 0–30, 0–45, 0–60 minutes
- Automatic reset at each new trading day
- Live high/low updates while inside the 0–60 minute window
• Auto-Drawing Range Box
- Draws a dynamic rectangle as the range forms
- Top and bottom update with every new high/low
- Extends sideways in real time during formation
- Optional full-day extension after the 60-minute range finalizes
• Customizable Visuals
- Adjustable fill transparency
- Mild green tint by default for clarity
PURPOSE
-----------------------------------------------------------------------------
This tool highlights the evolving opening range, a widely used intraday
reference for breakout traders, mean-reversion setups, and session structure
analysis. Ideal for:
• Identifying early support and resistance
• Framing breakout and pullback decisions
• Tracking intraday trend bias after the morning range
Grok/Claude Turtle Soup Alert SystemReplaces previous Turtle Soup Strategy/Indicator as Tradingview will not let me update it.
# 🥣 Turtle Soup Strategy (Enhanced)
## A Mean-Reversion Strategy Based on Failed Breakouts
---
## Historical Origins
### The Original Turtle Traders (1983-1988)
The Turtle Trading system is one of the most famous experiments in trading history. In 1983, legendary commodities trader **Richard Dennis** made a bet with his partner **William Eckhardt** about whether great traders were born or made. Dennis believed trading could be taught; Eckhardt believed it was innate.
To settle the debate, Dennis recruited 23 ordinary people through newspaper ads—including a professional blackjack player, a fantasy game designer, and an accountant—and taught them his trading system in just two weeks. He called them "Turtles" after turtle farms he had visited in Singapore, saying *"We are going to grow traders just like they grow turtles in Singapore."*
The results were extraordinary. Over the next five years, the Turtles reportedly earned over **$175 million in profits**. The experiment proved Dennis right: trading could indeed be taught.
#### The Original Turtle Rules:
- **Entry:** Buy when price breaks above the 20-day high (System 1) or 55-day high (System 2)
- **Exit:** Sell when price breaks below the 10-day low (System 1) or 20-day low (System 2)
- **Stop Loss:** 2x ATR (Average True Range) from entry
- **Position Sizing:** Based on volatility (ATR)
- **Philosophy:** Pure trend-following—catch big moves by riding breakouts
The Turtle system was a **trend-following** strategy that assumed breakouts would lead to sustained trends. It worked brilliantly in trending markets but suffered during choppy, range-bound conditions.
---
### The Turtle Soup Strategy (1990s)
In the 1990s, renowned trader **Linda Bradford Raschke** (along with Larry Connors) observed something interesting: many of the breakouts that the Turtle system traded actually *failed*. Price would spike above the 20-day high, trigger Turtle buy orders, then immediately reverse—trapping the breakout traders.
Raschke realized these failed breakouts were predictable and tradeable. She developed the **Turtle Soup** strategy, which does the *exact opposite* of the original Turtle system:
> *"Instead of buying the breakout, we wait for it to fail—then fade it."*
The name "Turtle Soup" is a clever play on words: the strategy essentially "eats" the Turtles by trading against them when their breakouts fail.
#### Original Turtle Soup Rules:
- **Setup:** Price makes a new 20-day high (or low)
- **Qualifier:** The previous 20-day high must be at least 3-4 days old (not a fresh breakout)
- **Entry Trigger:** Price reverses back inside the channel (failed breakout)
- **Entry:** Go SHORT (against the failed breakout above), or LONG (against the failed breakdown below)
- **Philosophy:** Mean-reversion—fade false breakouts and profit from trapped traders
#### Turtle Soup Plus One Variant:
Raschke also developed a more conservative variant called "Turtle Soup Plus One" which waits for the *next bar* after the breakout to confirm the failure before entering. This reduces false signals but may miss some opportunities.
---
## Our Enhanced Turtle Soup Strategy
We have taken the classic Turtle Soup concept and enhanced it with modern technical indicators and filters to improve signal quality and adapt to today's markets.
### Core Logic Preserved
The fundamental strategy remains true to Raschke's original concept:
| Turtle (Original) | Turtle Soup (Our Strategy) |
|-------------------|---------------------------|
| BUY breakout above 20-day high | SHORT when that breakout FAILS |
| SELL breakout below 20-day low | LONG when that breakdown FAILS |
| Trend-following | Mean-reversion |
| "The trend is your friend" | "Failed breakouts trap traders" |
---
### Enhancements & Improvements
#### 1. RSI Exhaustion Filter
**Addition:** RSI must confirm exhaustion before entry
- **For SHORT entries:** RSI > 60 (buyers exhausted)
- **For LONG entries:** RSI < 40 (sellers exhausted)
**Why:** The original Turtle Soup had no momentum filter. Adding RSI ensures we only fade breakouts when the market is showing signs of exhaustion, significantly reducing false signals. This enhancement was inspired by later traders who found RSI extremes (originally 90/10, softened to 60/40) dramatically improved win rates.
#### 2. ADX Trending Filter
**Addition:** ADX must be > 20 for trades to execute
**Why:** While the original Turtle Soup was designed for ranging markets, we found that requiring *some* trend strength (ADX > 20) actually improves results. This ensures we're trading in markets with enough directional movement to create meaningful failed breakouts, rather than random noise in dead markets.
#### 3. Heikin Ashi Smoothing
**Addition:** Optional Heikin Ashi calculations for breakout detection
**Why:** Heikin Ashi candles smooth out price noise and make trend reversals more visible. When enabled, the strategy uses HA values to detect breakouts and failures, reducing whipsaws from erratic price spikes.
#### 4. Dynamic Donchian Channels with Regime Detection
**Addition:** Color-coded channels based on market regime
- 🟢 **Green:** Bullish regime (uptrend + DI+ > DI- + OBV bullish)
- 🔴 **Red:** Bearish regime (downtrend + DI- > DI+ + OBV bearish)
- 🟡 **Yellow:** Neutral regime
**Why:** Visual regime detection helps traders understand the broader market context. The original Turtle Soup had no regime awareness—our enhancement lets traders see at a glance whether conditions favor the strategy.
#### 5. Volume Spike Detection (Optional)
**Addition:** Optional filter requiring volume surge on the breakout bar
**Why:** Failed breakouts are more significant when they occur on high volume. A volume spike on the breakout bar (default 1.2x average) indicates more traders got trapped, creating stronger reversal potential.
#### 6. ATR-Based Stops and Targets
**Addition:** Configurable ATR-based stop losses and profit targets
- **Stop Loss:** 1.5x ATR (default)
- **Profit Target:** 2.0x ATR (default)
**Why:** The original Turtle Soup used fixed stop placement. ATR-based stops adapt to current volatility, providing tighter stops in calm markets and wider stops in volatile conditions.
#### 7. Signal Cooldown
**Addition:** Minimum bars between trades (default 5)
**Why:** Prevents overtrading during choppy conditions where multiple failed breakouts might occur in quick succession.
#### 8. Real-Time Info Panel
**Addition:** Comprehensive dashboard showing:
- Current regime (Bullish/Bearish/Neutral)
- RSI value and zone
- ADX value and trending status
- Breakout status
- Bars since last high/low
- Current setup status
- Position status
**Why:** Gives traders instant visibility into all strategy conditions without needing to check multiple indicators.
---
## Entry Rules Summary
### SHORT Entry (Fading Failed Breakout Above)
1. ✅ Price breaks ABOVE the 20-period Donchian high
2. ✅ Previous 20-period high was at least 1 bar ago
3. ✅ Price closes back BELOW the Donchian high (failed breakout)
4. ✅ RSI > 60 (exhausted buyers)
5. ✅ ADX > 20 (trending market)
6. ✅ Cooldown period met
→ **Enter SHORT**, betting the breakout will fail
### LONG Entry (Fading Failed Breakdown Below)
1. ✅ Price breaks BELOW the 20-period Donchian low
2. ✅ Previous 20-period low was at least 1 bar ago
3. ✅ Price closes back ABOVE the Donchian low (failed breakdown)
4. ✅ RSI < 40 (exhausted sellers)
5. ✅ ADX > 20 (trending market)
6. ✅ Cooldown period met
→ **Enter LONG**, betting the breakdown will fail
---
## Exit Rules
1. **ATR Stop Loss:** Position closed if price moves 1.5x ATR against entry
2. **ATR Profit Target:** Position closed if price moves 2.0x ATR in favor
3. **Channel Exit:** Position closed if price breaks the exit channel in the opposite direction
4. **Mid-Channel Exit:** Position closed if price returns to channel midpoint
---
## Best Market Conditions
The Turtle Soup strategy performs best when:
- ✅ Markets are prone to false breakouts
- ✅ Volatility is moderate (not too low, not extreme)
- ✅ Price is oscillating within a broader range
- ✅ There are clear support/resistance levels
The strategy may struggle when:
- ❌ Strong trends persist (breakouts follow through)
- ❌ Volatility is extremely low (no meaningful breakouts)
- ❌ Markets are in news-driven directional moves
---
## Default Settings
| Parameter | Default | Description |
|-----------|---------|-------------|
| Lookback Period | 20 | Donchian channel period |
| Min Bars Since Extreme | 1 | Bars since last high/low |
| RSI Length | 14 | RSI calculation period |
| RSI Short Level | 60 | RSI must be above this for shorts |
| RSI Long Level | 40 | RSI must be below this for longs |
| ADX Length | 14 | ADX calculation period |
| ADX Threshold | 20 | Minimum ADX for trades |
| ATR Period | 20 | ATR calculation period |
| ATR Stop Multiplier | 1.5 | Stop loss distance in ATR |
| ATR Target Multiplier | 2.0 | Profit target distance in ATR |
| Cooldown Period | 5 | Minimum bars between trades |
| Volume Multiplier | 1.2 | Volume spike threshold |
---
## Philosophy
> *"The Turtle system made millions by following breakouts. The Turtle Soup strategy makes money when those breakouts fail. In trading, there's always someone on the other side of the trade—this strategy profits by being the smart money that fades the trapped breakout traders."*
The beauty of the Turtle Soup strategy is its elegant simplicity: it exploits a known, repeatable pattern (failed breakouts) while using modern filters (RSI, ADX) to improve timing and reduce false signals.
---
## Credits
- **Original Turtle System:** Richard Dennis & William Eckhardt (1983)
- **Turtle Soup Strategy:** Linda Bradford Raschke & Larry Connors (1990s)
- **RSI Enhancement:** Various traders who discovered RSI extremes improve reversal detection
- **This Implementation:** Enhanced with Heikin Ashi smoothing, regime detection, ADX filtering, and comprehensive visualization
---
*"We're not following the turtles—we're making soup out of them."* 🥣
Dr. Barbara Star: Dual Strategies Combined [Merged] - geminiDr. Barbara Star: Dual Strategy Suite (Merged)
Overview
This script integrates two distinct but complementary trading methodologies developed by Dr. Barbara Star: "Capture Direction & Momentum" and "Profit with Dual Oscillators & Bands." While both strategies utilize price channels to filter noise, they approach entry and exit timing from different angles—one focusing on momentum shifts (Stochastic/EMA) and the other on cyclical price deviations (DPO/Bollinger Bands).
This tool allows the user to run either strategy independently or combine them to find high-confluence setups where momentum and cyclical structure align.
Strategy A: Capture Direction & Momentum
Source: Capture Direction And Momentum
1. Purpose & Theory
The goal of this method is to filter out the "noise" of choppy markets and identify the specific point where price direction aligns with momentum strength. It moves away from trying to catch exact tops or bottoms and instead focuses on catching the "meat" of the trend (continuation).
2. Implementation
Structure (The Channel): A 13-period SMA of the Highs and Lows creates a "No Trade Zone". When price is inside this channel, the market is considered directionless.
Direction (5 EMA): A fast 5-period EMA acts as a directional trigger. When it breaks outside the SMA channel, it signals acceleration.
Momentum (Modified Stochastic): A Slow Stochastic (14,2) is used, but with a crucial modification: the overbought/oversold levels are shifted to 40 and 60 (instead of 20/80).
3. How to Use It
The "Trend Zones" (Background Colors):
Green Background (Bullish): The 5 EMA is above the channel AND the Stochastic is > 60. This is the "Go" zone.
Red Background (Bearish): The 5 EMA is below the channel AND the Stochastic is < 40.
Yellow Background: The "No Trade Zone." The price is consolidating, or the indicators disagree.
The Continuation Signal (Marked by "U" or "D"):
Why it matters: This is the most powerful setup in the system. It detects when price pulls back (retracement) but momentum remains strong.
The Signal: If the 5 EMA dips back into the SMA channel (weakness) but the Stochastic stays above 60 (strength), a blue "U" (Up) marker appears. This indicates the pullback is likely a buying opportunity, not a reversal. Conversely, a yellow "D" appears in downtrends if Stoch stays below 40.
Exits (Marked by "X"):
Signals to take profit when the 5 EMA closes back inside the channel and the Stochastic crosses back into the neutral 40–60 zone.
Strategy B: Dual Oscillators & Bands
Source: Profit With Dual Oscillators & Bands
1. Purpose & Theory
This strategy uses "Dual Bollinger Bands" to define the volatility structure of the trend and "Dual Detrended Price Oscillators" (DPO) to time the entries based on cycle shifts.
2. Implementation
Structure (Dual Bands):
Inner Bands (1 SD): These define the "Trend Channel." Strong trends tend to ride between the 1 SD and 3 SD bands.
Outer Bands (3 SD): These represent extremes (containing 99.5% of price action). Hits here often signal exhaustion.
Timing (Dual DPOs):
Long Oscillator (DPO 20): Identifies the broader trend direction (Positive = Bullish).
Short Oscillator (DPO 9): Identifies shorter-term timing and potential divergences.
3. How to Use It
Identifying the Trend State:
Strong Uptrend: Price holds above the Upper Inner Band (1 SD).
Strong Downtrend: Price holds below the Lower Inner Band (1 SD).
Transition/Neutral: Price is stuck between the Upper and Lower Inner bands.
Entry Signals (Triangles on Chart & Circles in Pane):
Aggressive Entry: When the fast DPO 9 crosses zero. This signals early momentum shifts.
Conservative Entry: Wait for the slow DPO 20 to cross zero, confirming the broader trend has shifted.
Visuals: The script plots triangles on the main chart when these cross. In the lower pane, a Blue Circle indicates a bullish cross and a Yellow Circle indicates a bearish cross.
Continuation Setup:
Similar to Strategy A, look for moments where the DPO 9 dips below zero (pullback) while the DPO 20 remains above zero (trend intact). This is often a reload opportunity.
Combined Mode: The "Power Couple"
When selecting "Both" in the settings, the indicator merges these tools for maximum confirmation:
Visual filtering: The lower pane automatically scales the DPO lines to fit inside the 0–100 Stochastic range (centering the DPO zero line at 50). This allows you to read both momentum and cycles in a single glance.
Confluence Trading:
Look for the Background to turn Green (Strategy A Trend) coincident with a Blue Triangle/Circle (Strategy B Momentum Cross).
Use the Inner Bollinger Bands (Strategy B) as your trailing stop-loss while riding the SMA Channel (Strategy A) trend.
Reference Settings
Strategy A: SMA Channel (13), EMA (5), Stochastic (14, 2, 40/60 levels).
Strategy B: Bollinger Bands (20 SMA, 1.0 & 3.0 deviations), DPO (9 & 20).
Sources: of the methodologies
1-Stocks & Commodities V. 32:7 (10-16): Profit With Dual Oscillators & Bands by Barbara Star, PhD
2-Stocks & Commodities V. 43:12 (8–12): Capture Direction And Momentum by Barbara Star, PhD
Grok/Claude Turtle Soup Strategy # 🥣 Turtle Soup Strategy (Enhanced)
## A Mean-Reversion Strategy Based on Failed Breakouts
---
## Historical Origins
### The Original Turtle Traders (1983-1988)
The Turtle Trading system is one of the most famous experiments in trading history. In 1983, legendary commodities trader **Richard Dennis** made a bet with his partner **William Eckhardt** about whether great traders were born or made. Dennis believed trading could be taught; Eckhardt believed it was innate.
To settle the debate, Dennis recruited 23 ordinary people through newspaper ads—including a professional blackjack player, a fantasy game designer, and an accountant—and taught them his trading system in just two weeks. He called them "Turtles" after turtle farms he had visited in Singapore, saying *"We are going to grow traders just like they grow turtles in Singapore."*
The results were extraordinary. Over the next five years, the Turtles reportedly earned over **$175 million in profits**. The experiment proved Dennis right: trading could indeed be taught.
#### The Original Turtle Rules:
- **Entry:** Buy when price breaks above the 20-day high (System 1) or 55-day high (System 2)
- **Exit:** Sell when price breaks below the 10-day low (System 1) or 20-day low (System 2)
- **Stop Loss:** 2x ATR (Average True Range) from entry
- **Position Sizing:** Based on volatility (ATR)
- **Philosophy:** Pure trend-following—catch big moves by riding breakouts
The Turtle system was a **trend-following** strategy that assumed breakouts would lead to sustained trends. It worked brilliantly in trending markets but suffered during choppy, range-bound conditions.
---
### The Turtle Soup Strategy (1990s)
In the 1990s, renowned trader **Linda Bradford Raschke** (along with Larry Connors) observed something interesting: many of the breakouts that the Turtle system traded actually *failed*. Price would spike above the 20-day high, trigger Turtle buy orders, then immediately reverse—trapping the breakout traders.
Raschke realized these failed breakouts were predictable and tradeable. She developed the **Turtle Soup** strategy, which does the *exact opposite* of the original Turtle system:
> *"Instead of buying the breakout, we wait for it to fail—then fade it."*
The name "Turtle Soup" is a clever play on words: the strategy essentially "eats" the Turtles by trading against them when their breakouts fail.
#### Original Turtle Soup Rules:
- **Setup:** Price makes a new 20-day high (or low)
- **Qualifier:** The previous 20-day high must be at least 3-4 days old (not a fresh breakout)
- **Entry Trigger:** Price reverses back inside the channel (failed breakout)
- **Entry:** Go SHORT (against the failed breakout above), or LONG (against the failed breakdown below)
- **Philosophy:** Mean-reversion—fade false breakouts and profit from trapped traders
#### Turtle Soup Plus One Variant:
Raschke also developed a more conservative variant called "Turtle Soup Plus One" which waits for the *next bar* after the breakout to confirm the failure before entering. This reduces false signals but may miss some opportunities.
---
## Our Enhanced Turtle Soup Strategy
We have taken the classic Turtle Soup concept and enhanced it with modern technical indicators and filters to improve signal quality and adapt to today's markets.
### Core Logic Preserved
The fundamental strategy remains true to Raschke's original concept:
| Turtle (Original) | Turtle Soup (Our Strategy) |
|-------------------|---------------------------|
| BUY breakout above 20-day high | SHORT when that breakout FAILS |
| SELL breakout below 20-day low | LONG when that breakdown FAILS |
| Trend-following | Mean-reversion |
| "The trend is your friend" | "Failed breakouts trap traders" |
---
### Enhancements & Improvements
#### 1. RSI Exhaustion Filter
**Addition:** RSI must confirm exhaustion before entry
- **For SHORT entries:** RSI > 60 (buyers exhausted)
- **For LONG entries:** RSI < 40 (sellers exhausted)
**Why:** The original Turtle Soup had no momentum filter. Adding RSI ensures we only fade breakouts when the market is showing signs of exhaustion, significantly reducing false signals. This enhancement was inspired by later traders who found RSI extremes (originally 90/10, softened to 60/40) dramatically improved win rates.
#### 2. ADX Trending Filter
**Addition:** ADX must be > 20 for trades to execute
**Why:** While the original Turtle Soup was designed for ranging markets, we found that requiring *some* trend strength (ADX > 20) actually improves results. This ensures we're trading in markets with enough directional movement to create meaningful failed breakouts, rather than random noise in dead markets.
#### 3. Heikin Ashi Smoothing
**Addition:** Optional Heikin Ashi calculations for breakout detection
**Why:** Heikin Ashi candles smooth out price noise and make trend reversals more visible. When enabled, the strategy uses HA values to detect breakouts and failures, reducing whipsaws from erratic price spikes.
#### 4. Dynamic Donchian Channels with Regime Detection
**Addition:** Color-coded channels based on market regime
- 🟢 **Green:** Bullish regime (uptrend + DI+ > DI- + OBV bullish)
- 🔴 **Red:** Bearish regime (downtrend + DI- > DI+ + OBV bearish)
- 🟡 **Yellow:** Neutral regime
**Why:** Visual regime detection helps traders understand the broader market context. The original Turtle Soup had no regime awareness—our enhancement lets traders see at a glance whether conditions favor the strategy.
#### 5. Volume Spike Detection (Optional)
**Addition:** Optional filter requiring volume surge on the breakout bar
**Why:** Failed breakouts are more significant when they occur on high volume. A volume spike on the breakout bar (default 1.2x average) indicates more traders got trapped, creating stronger reversal potential.
#### 6. ATR-Based Stops and Targets
**Addition:** Configurable ATR-based stop losses and profit targets
- **Stop Loss:** 1.5x ATR (default)
- **Profit Target:** 2.0x ATR (default)
**Why:** The original Turtle Soup used fixed stop placement. ATR-based stops adapt to current volatility, providing tighter stops in calm markets and wider stops in volatile conditions.
#### 7. Signal Cooldown
**Addition:** Minimum bars between trades (default 5)
**Why:** Prevents overtrading during choppy conditions where multiple failed breakouts might occur in quick succession.
#### 8. Real-Time Info Panel
**Addition:** Comprehensive dashboard showing:
- Current regime (Bullish/Bearish/Neutral)
- RSI value and zone
- ADX value and trending status
- Breakout status
- Bars since last high/low
- Current setup status
- Position status
**Why:** Gives traders instant visibility into all strategy conditions without needing to check multiple indicators.
---
## Entry Rules Summary
### SHORT Entry (Fading Failed Breakout Above)
1. ✅ Price breaks ABOVE the 20-period Donchian high
2. ✅ Previous 20-period high was at least 1 bar ago
3. ✅ Price closes back BELOW the Donchian high (failed breakout)
4. ✅ RSI > 60 (exhausted buyers)
5. ✅ ADX > 20 (trending market)
6. ✅ Cooldown period met
→ **Enter SHORT**, betting the breakout will fail
### LONG Entry (Fading Failed Breakdown Below)
1. ✅ Price breaks BELOW the 20-period Donchian low
2. ✅ Previous 20-period low was at least 1 bar ago
3. ✅ Price closes back ABOVE the Donchian low (failed breakdown)
4. ✅ RSI < 40 (exhausted sellers)
5. ✅ ADX > 20 (trending market)
6. ✅ Cooldown period met
→ **Enter LONG**, betting the breakdown will fail
---
## Exit Rules
1. **ATR Stop Loss:** Position closed if price moves 1.5x ATR against entry
2. **ATR Profit Target:** Position closed if price moves 2.0x ATR in favor
3. **Channel Exit:** Position closed if price breaks the exit channel in the opposite direction
4. **Mid-Channel Exit:** Position closed if price returns to channel midpoint
---
## Best Market Conditions
The Turtle Soup strategy performs best when:
- ✅ Markets are prone to false breakouts
- ✅ Volatility is moderate (not too low, not extreme)
- ✅ Price is oscillating within a broader range
- ✅ There are clear support/resistance levels
The strategy may struggle when:
- ❌ Strong trends persist (breakouts follow through)
- ❌ Volatility is extremely low (no meaningful breakouts)
- ❌ Markets are in news-driven directional moves
---
## Default Settings
| Parameter | Default | Description |
|-----------|---------|-------------|
| Lookback Period | 20 | Donchian channel period |
| Min Bars Since Extreme | 1 | Bars since last high/low |
| RSI Length | 14 | RSI calculation period |
| RSI Short Level | 60 | RSI must be above this for shorts |
| RSI Long Level | 40 | RSI must be below this for longs |
| ADX Length | 14 | ADX calculation period |
| ADX Threshold | 20 | Minimum ADX for trades |
| ATR Period | 20 | ATR calculation period |
| ATR Stop Multiplier | 1.5 | Stop loss distance in ATR |
| ATR Target Multiplier | 2.0 | Profit target distance in ATR |
| Cooldown Period | 5 | Minimum bars between trades |
| Volume Multiplier | 1.2 | Volume spike threshold |
---
## Philosophy
> *"The Turtle system made millions by following breakouts. The Turtle Soup strategy makes money when those breakouts fail. In trading, there's always someone on the other side of the trade—this strategy profits by being the smart money that fades the trapped breakout traders."*
The beauty of the Turtle Soup strategy is its elegant simplicity: it exploits a known, repeatable pattern (failed breakouts) while using modern filters (RSI, ADX) to improve timing and reduce false signals.
---
## Credits
- **Original Turtle System:** Richard Dennis & William Eckhardt (1983)
- **Turtle Soup Strategy:** Linda Bradford Raschke & Larry Connors (1990s)
- **RSI Enhancement:** Various traders who discovered RSI extremes improve reversal detection
- **This Implementation:** Enhanced with Heikin Ashi smoothing, regime detection, ADX filtering, and comprehensive visualization
---
*"We're not following the turtles—we're making soup out of them."* 🥣
RCV Essentials════════════════════════════════════════════
RCV ESSENTIALS - MULTI-TIMEFRAME & SESSION ANALYSIS TOOL
════════════════════════════════════════════
📊 WHAT THIS INDICATOR DOES
This professional-grade indicator combines two powerful analysis modules:
1. TRADING SESSION TRACKER - Visualizes high/low ranges for major global market sessions (NY Open, London Open, Asian Session, etc.)
2. MULTI-TIMEFRAME CANDLE DISPLAY - Shows up to 8 higher timeframes simultaneously on your chart (15m, 30m, 1H, 4H, 1D, 1W, 1M, 3M)
════════════════════════════════════════════
🎯 KEY FEATURES
════════════════════════════════════════════
TRADING SESSIONS MODULE:
✓ Track up to 6 custom trading sessions simultaneously
✓ Real-time high/low range detection during active sessions
✓ Pre-configured for NYO (7-9am), LNO (2-3am), Asian Session (4:30pm-12am)
✓ 60+ global timezone options
✓ Customizable colors, labels, and transparency
✓ Daily divider lines (optional Sunday skip for traditional markets)
✓ Only displays on ≤30m timeframes for optimal clarity
MULTI-TIMEFRAME CANDLES MODULE:
✓ Display 1-8 higher timeframes with up to 10 candles each
✓ Real-time candle updates (non-repainting)
✓ Fully customizable colors (separate bullish/bearish for body/border/wick)
✓ Adjustable candle width, spacing, and positioning
✓ Smart label system (top/bottom/both, aligned or follow candles)
✓ Automatic timeframe validation (only shows TFs higher than chart)
✓ Memory-optimized with automatic cleanup
════════════════════════════════════════════
🔧 HOW IT WORKS
════════════════════════════════════════════
TECHNICAL IMPLEMENTATION:
Session Tracking Algorithm:
• Detects session start/end using time() function with timezone support
• Continuously monitors and updates high/low during active session
• Finalizes range when session ends using var persistence
• Draws boxes using real-time bar_index positioning
• Maintains session ranges across multiple days for reference
Multi-Timeframe System:
• Uses ta.change(time()) detection to identify new MTF candle formation
• Constructs candles using custom Type definitions (Candle, CandleSet, Config)
• Stores OHLC data in arrays with automatic size management
• Renders using box objects (bodies) and line objects (wicks)
• Updates current candle every tick; historical candles remain static
• Calculates dynamic positioning based on user settings (offset, spacing, width)
Object-Oriented Architecture:
• Custom Type "Candle" - Stores OHLC values, timestamps, visual elements
• Custom Type "CandleSet" - Manages arrays of candles + settings per timeframe
• Custom Type "Config" - Centralizes all display configuration
• Efficient memory management via unshift() for new candles, pop() for old
Performance Optimizations:
• var declarations minimize recalculation overhead
• Conditional execution (sessions only on short timeframes)
• Maximum display limits prevent excessive object creation
• Timeframe validation at barstate.isfirst reduces redundant checks
════════════════════════════════════════════
📈 HOW TO USE
════════════════════════════════════════════
SETUP:
1. Add indicator to chart (works best on 1m-30m timeframes)
2. Open Settings → "Trading Sessions" group
- Enable desired sessions (NYO, LNO, AS, or custom)
- Select your timezone from 60+ options
- Adjust colors and transparency
3. Open Settings → "Multi-TF Candles" group
- Enable timeframes (TF1-TF8)
- Configure each timeframe and display count
- Customize colors and layout
READING THE CHART:
• Session boxes show high/low ranges during active sessions
• MTF candles display to the right of current price
• Labels identify each timeframe (15m, 1H, 4H, etc.)
• Real-time updates on the most recent MTF candle
TRADING APPLICATIONS:
Session Breakout Strategy:
→ Identify session high/low (e.g., Asian session 16:30-00:00)
→ Wait for break above/below range
→ Confirm with higher timeframe candle close
→ Enter in breakout direction, stop at opposite side of range
Multi-Timeframe Confirmation:
→ Spot setup on primary chart (e.g., 5m)
→ Verify 15m, 1H, 4H candles align with trade direction
→ Only take trades where higher TFs confirm
→ Exit when higher TF candles show reversal
Combined Session + MTF:
→ Asian session establishes range overnight
→ London Open breaks Asian high
→ Confirm with bullish 15m + 1H candles
→ Enter long with stop below Asian high
════════════════════════════════════════════
🎨 ORIGINALITY & INNOVATION
════════════════════════════════════════════
What makes this indicator original:
1. INTEGRATED DUAL-MODULE DESIGN
Unlike separate session or MTF indicators, this combines both in a single performance-optimized script, enabling powerful correlation analysis between session behavior and timeframe structure.
2. ADVANCED RENDERING SYSTEM
Uses custom Pine Script v5 Types with dynamic box/line object management instead of basic plot functions. This enables:
• Precise visual control over positioning and spacing
• Real-time updates without repainting
• Efficient memory handling via automatic cleanup
• Support for 8 simultaneous timeframes with independent settings
3. INTELLIGENT SESSION TRACKING
The algorithm continuously recalculates ranges bar-by-bar during active sessions, then preserves the final range. This differs from static zone indicators that simply draw fixed boxes at predefined levels.
4. MODULAR ARCHITECTURE
Custom Type definitions (Candle, CandleSet, Config) create extensible, maintainable code structure while supporting complex multi-timeframe operations with minimal performance impact.
5. PROFESSIONAL FLEXIBILITY
Extensive customization: 6 configurable sessions, 8 timeframe slots, 60+ timezones, granular color/sizing/spacing controls, multiple label positioning modes—adaptable to any market or trading style.
6. SMART VISUAL DESIGN
Automatic timeframe validation, dynamic label alignment options, and intelligent spacing calculations ensure clarity even with multiple timeframes displayed simultaneously.
════════════════════════════════════════════
⚙️ CONFIGURATION OPTIONS
════════════════════════════════════════════
TRADING SESSIONS:
• Session 1-6: On/Off toggles
• Time Ranges: Custom start-end times
• Labels: Custom text for each session
• Colors: Individual color per session
• Timezone: 60+ options (Americas, Europe, Asia, Pacific, Africa)
• Range Transparency: 0-100%
• Outline: Optional border
• Label Display: Show/hide session names
• Daily Divider: Dotted lines at day changes
• Skip Sunday: For traditional markets vs 24/7 crypto
MULTI-TF CANDLES:
• Timeframes 1-8: Enable/disable individually
• Timeframe Selection: Any TF (seconds to months)
• Display Count: 1-10 candles per timeframe
• Bullish Colors: Body/Border/Wick (independent)
• Bearish Colors: Body/Border/Wick (independent)
• Candle Width: 1-10+ bars
• Right Margin: 0-200+ bars from edge
• TF Spacing: Gap between timeframe groups
• Label Color: Any color
• Label Size: Tiny/Small/Normal/Large/Huge
• Label Position: Top/Bottom/Both
• Label Alignment: Follow Candles or Align
════════════════════════════════════════════
📋 TECHNICAL SPECIFICATIONS
════════════════════════════════════════════
• Pine Script Version: v5
• Chart Overlay: True
• Max Boxes: 500
• Max Lines: 500
• Max Labels: 500
• Max Bars Back: 5000
• Update Frequency: Real-time (every tick)
• Timeframe Compatibility: Chart TF must be lower than selected MTFs
• Session Display: Activates only on ≤30 minute timeframes
• Memory Management: Automatic cleanup via array operations
Ellipse Price Action Indicator v2 (Upgraded)
This upgraded Ellipse Price Action Indicator (EPAI v2) to take high-accuracy trades.
I am explaining it as if you are looking at the chart step by step, so you will understand exactly:
-When to buy
-When to
-When to avoid
-How to read Strength Meter
-How Ellipse zones work
⭐ 1. THE BASICS — What This Indicator Actually Does
This indicator tracks:
✔ The “Elliptical Path” of price
Like a planet revolving around the Sun, price “oscillates” around a center.
The indicator detects this hidden mathematical path using:
Two Focus Points (Fast MA & Slow MA)
Curved Ellipse boundaries
Compression of price
Momentum of trend
Breakout zones
⭐ 2. UNDERSTANDING THE 3 ZONES
🔴 UPPER ZONE = Sell Zone
Price is near the upper ellipse boundary → overbought space.
🟢 LOWER ZONE = Buy Zone
Price near lower ellipse boundary → oversold space.
🔵 CENTRAL ZONE = No Trade Zone
Price swinging inside the ellipse center → noise.
Only trade in UPPER or LOWER zones.
Never in the central zone.
⭐ 3. THE MOST IMPORTANT PART — Strength Meter v2
Strength Meter v2 (0 to 100%) is the core filter.
✔ Above 70% → High winning probability (take trade)
✔ 60–70% → Medium probability (trade if confident)
❌ Below 60% → Avoid trade
Strength combines:
Ellipse compression
Momentum slope
Price position curve
Eccentricity
Trend direction
This alone removes 70% bad trades.
⭐ 4. BUY SETUP (Exact Rules)
You get a BUY only if all conditions match:
① Price goes to lower ellipse zone
② Compression is ON (ellipse is tight)
③ Momentum slope direction = UP
④ Focus Lines Cross Bullish (Fast > Slow)
⑤ Strength v2 ≥ your threshold (default 60%)
⑥ A BUY signal prints (triangle UP)
When these align →
🟢 BUY with high accuracy
Best Accuracy Buy is:
Price in lower zone
Strength ≥ 0.75
Slope UP
Ellipse compressed
⭐ 5. SELL SETUP (Exact Rules)
Same logic reversed:
① Price in upper ellipse zone
② Compression ON
③ Momentum slope DOWN
④ Focus Lines cross bearish (Fast < Slow)
⑤ Strength v2 ≥ threshold
⑥ SELL signal prints (triangle DOWN)
This means:
🔴 SELL with high accuracy
Best Accuracy Sell is:
Price in upper zone
Strength ≥ 0.75
Slope DOWN
Ellipse compressed
⭐ 6. BREAKOUT TRADES (Optional but powerful)
When price breaks above/below ellipse:
🔸 Upper Breakout → SELL (if strength strong)
🔸 Lower Breakout → BUY (if strength strong)
Breakout signals are marked by orange arrows.
Breakouts are taken only if:
Strength v2 > 50%
Slope supports breakout
Compression exists before breakout
Breakout trades catch trend continuation.
⭐ 7. HOW TO CONFIRM A STRONG TRADE
Look at the table on the chart:
✔ Strength v2 ≥ 70% (GREEN)
✔ Compression = GREEN
✔ Slope direction = UP (for buy) or DOWN (for sell)
✔ Zone = LOWER or UPPER
✔ Eccentricity = LOW (<0.5 means smooth trend)
If these line up →
⭐ High-probability entry.
⭐ 8. WHEN YOU SHOULD NOT TRADE
❌ If price is in Central Zone
❌ Strength < 60
❌ No compression detected
❌ Slope is flat or against direction
❌ Only one condition is matching
❌ Eccentricity is too large
(Big ellipse = unpredictable swings)
⭐ 9. What Is the Accuracy Level?
In trending markets → 75% to 85% accuracy
In ranging markets → 50% (use compression filter to avoid)
The indicator is designed to avoid bad market conditions automatically.
⭐ 10. BEST TIMEFRAMES
✔ 5m, 15m, 1H → Intraday
✔ 4H, 1D → Swing Trading
✔ NOT recommended below 1m timeframe
⭐ SUMMARY (EASY VERSION)
🟢 BUY:
Lower zone + compression + bullish slope + strong focus cross + strength ≥ 60
🔴 SELL:
Upper zone + compression + bearish slope + strong focus cross + strength ≥ 60
🟠 Breakout:
Upper/lower breakout + strength ≥ 50
🔵 Avoid:
Central zone or weak strength
Coin Jin Multi SMA+ BB+ SMA forecast Ver2.02This script provides a complete trend-analysis system based on the
5 / 20 / 60 / 112 / 224 / 448 / 896 SMAs.
It precisely detects bullish/bearish alignment and automatically identifies
12 advanced trend-shift signals (Start, End, and Reversal).
Key Features:
● 9 SMA lines (including custom X1 & X2)
Each SMA supports custom color, width, and style (Line/Step/Circles).
● Bollinger Bands with customizable options
Fully adjustable length, source, width, style, fill transparency, and more.
● SMA Forecast (curved projection)
– Slope computed via linear regression
– Predicts up to 30 future bars
– Forced dotted style ensures visibility at all zoom levels
● 12 Advanced Trend Signals (alertcondition)
Automatically detects:
Start of full alignment (with/without SMA 896)
End of alignment
Bull ↔ Bear transitions
Perfect for momentum trading, trend-following, reversal detection, or automated alert systems.
● Labeling last value of each SMA
Each SMA prints a label such as "5", “20”, “60”, “896”, or custom lengths at the latest bar.
이 스크립트는 5 / 20 / 60 / 112 / 224 / 448 / 896 이동평균선을 기반으로
정배열·역배열 상태를 정밀하게 분석하고,
총 12가지 고급 추세 신호(시작·종료·전환) 를 자동으로 감지하는 통합 추세 분석 도구입니다.
주요 기능:
● 9개의 SMA 표시 (커스텀 X1, X2 포함)
각 SMA는 색상·굵기·형태(Line/Step/Circle)를 개별 설정할 수 있습니다.
● 볼린저밴드 표시 및 채우기 옵션
BB 길이, 소스, 타입, 두께, 투명도 등을 자유롭게 조절 가능.
● SMA Forecast (미래 방향 곡선 예측)
– 기울기 기반 선형회귀 슬로프 계산
– 곡선 형태로 미래 30봉까지 예측
– 점선(Dotted) 강제 적용으로 어떤 배율에서도 선명하게 표시
● 12가지 고급 추세 신호(alertcondition)
정배열·역배열의
Start (처음 완성될 때)
End (깨질 때)
Switch (전환)
을 모두 자동 탐지하여 트레이딩뷰 알림으로 받을 수 있음.
● SMA 마지막 가격 라벨 표시
각 SMA 끝 지점에 “5 / 20 / 60 / ... / 896” 식으로 라벨 표시.
