VDUB BB %B REVERSAL_v4.2 revised by JustUncleLThis is an revised Open Public version of Vdub Bollinger Band %B reversal indicator. This version includes optional Divergence Finder with selectable channel width, optional Market Session time highlighting and optional Binary Option expiry markers.
Pesquisar nos scripts por "Divergence"
Open Close Cross Alert R6 by JustUncleLThis revision of this indicator is an Open Public release. The indicator alert based on JayRogers "Open Close Cross Strategy R2" and is used in conjunction with the revised "Open Close Cross Strategy R5".
Description:
This indicator alert created for TradingView alarm sub-system (via the alertcondition() function, which currently does not work in a "Strategy" script). Also this indicator plots the Difference Factor between the Close and Open Moving Averages, this gives a good indication of strength of move. Also included in this release is optional Divergence finder with variable width channel.
MACD + RSI TSA simple strategy that use EMAs convergence/divergence and RSI peeks to take position. Fractals are really useful to positioning your stop loss.
It works well on commodities and forex markets.
CDC RSI DivergenceThis script alerts when a bullish or bearish divergence occurs.
The alert have minor repainting so do not use this as an entry / exit signal
but rather a guideline to be considered with other indicators. (MACD for example)
PPO Divergence AlertsThis is a pretty straightforward script that adds alerts to Pekipek's excellent PPO Divergence indicator and changes the visuals a bit. You can have the alerts fire on these events:
Purple Circle (bullish signal)
Orange Circle (bearish signal)
Green Dot (High Point)
Red Dot (Low Point)
I use this on daily Forex charts to get alerts when purple or orange dots show. As you can see, it catches a lot of big moves, some of which can give insane returns if you're 200x leveraged on the likes of 1Broker.com. In regards to reducing false signals, I'm getting good results with a Williams %R but would love to hear any suggestions.
Edit: Oops. Box #6 in the example should be yellow, not purple.
[RS]Convergence Divergence Impulse Counter V0EXPERIMENTAL:
Counts the number of impulses with the same direction within a larger trend.
Willams %R extreme and MACD DivergenceThis is two different indicators combine into one.
First is the two different period of Williams% 5 mins and 15 mins.
Second is the MACD line of 5 mins chart.
Both will be primarily use on the 5 mins chart, you can use on another time frame if you wish
Williams %:
green = uptrending
red = downtrending
Bright green: overbought condition, look to Short at the end of the signal
Bright Red: oversold condition, look to Long at the end of the signal
MACD:
I also added the MACD line which you can use for divergence
when price move higher and MACD is moving down, expecting price to drop sometime soon or vice versa. See chart for example.
[RS]Leading Momentum Oscilator V0EXPERIMENTAL: Momentum oscilator based on offset, can also be used for divergence/convergence
ridenz rsi"Divergence is when the price of an asset is moving in the opposite direction of a technical indicator, such as an oscillator, or is moving contrary to other data. Divergence warns that the current price trend may be weakening, and in some cases may lead to the price changing direction.
If the stock is rising and making new highs, ideally the RSI is reaching new highs as well. If the stock is making new highs, but the RSI starts making lower highs, this warns the price uptrend may be weakening. This is negative divergence.
Positive divergence is the opposite situation. Imagine the price of a stock is making new lows while the RSI makes higher lows with each swing in the stock price. Investors may conclude that the lower lows in the stock price are losing their downward momentum and a trend reversal may soon follow.
Divergence is one of the common uses of many technical indicators, primarily the oscillators."
Spirit Time SMT 1M DIVDivergences from 90Min-1Min
apparently i have to explain more of what this does.
pretty self explanatory
Hope this enough text
Divergence Channels Indicator — JudasBulletUnique Buy/Sell Indicator. Place stoploss above/below 18 EMA. Low risk/high reward. My No.1 indicator on every chart.
Divergence TridentA Combination of MACD + VFI + WaveTrend
Tradingview hates me and is making me explain this in greater detail so maybe this is enough????
Divergence from Moving AverageSimple script to monitor the difference between closing price and sma(20), sma(20) and sma(60), sma(60) and sma(120). The zero axis provides times when the moving average converge.
Divergence Volume of BiznesFilosofThis indicator warns in advance about impending trouble. And also prepares in advance for a new rally. It also shows the ratio of volumes in dollars to the actual volume of the instrument. We can see when whales accumulate positions and carry extra passengers into the market.
More details about this indicator on my channel in YouTube.
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Этот индикатор предупреждает заранее о надвигающейся беде. А также готовит заранее к новому ралли. Также он показывает соотношение объемов в долларах к фактическому объёму инструмента. Мы можем видеть когда киты накапливают позиции и выносят лишних пассажиров в срынка.
Более подробно про этот индикатор на моём канале в Ютуб.
WaveTrend With Divs & RSI(STOCH) Divs by WeloTradesWaveTrend with Divergences & RSI(STOCH) Divergences by WeloTrades
Overview
The "WaveTrend With Divergences & RSI(STOCH) Divergences" is an advanced Pine Script™ indicator designed for TradingView, offering a multi-dimensional analysis of market conditions. This script integrates several technical indicators—WaveTrend, Money Flow Index (MFI), RSI, and Stochastic RSI—into a cohesive tool that identifies both regular and hidden divergences across these indicators. These divergences can indicate potential market reversals and provide critical trading opportunities.
This indicator is not just a simple combination of popular tools; it offers extensive customization options, organized data presentation, and valuable trading signals that are easy to interpret. Whether you're a day trader or a long-term investor, this script enhances your ability to make informed decisions.
Originality and Usefulness
The originality of this script lies in its integration and the synergy it creates among the indicators used. Rather than merely combining multiple indicators, this script allows them to work together, enhancing each other's strengths. For example, by identifying divergences across WaveTrend, RSI, and Stochastic RSI simultaneously, the script provides multiple layers of confirmation, which reduces the likelihood of false signals and increases the reliability of trading signals.
The usefulness of this script is apparent in its ability to offer a consolidated view of market dynamics. It not only simplifies the analytical process by combining different indicators but also provides deeper insights through its divergence detection features. This comprehensive approach is designed to help traders identify potential market reversals, confirm trends, and ultimately make more informed trading decisions.
How the Components Work Together
1. Cross-Validation of Signals
WaveTrend: This indicator is primarily used to identify overbought and oversold conditions, as well as potential buy and sell signals. WaveTrend's ability to smooth price data and reduce noise makes it a reliable tool for identifying trend reversals.
RSI & Stochastic RSI: These momentum oscillators are used to measure the speed and change of price movements. While RSI identifies general overbought and oversold conditions, Stochastic RSI offers a more granular view by tracking the RSI’s level relative to its high-low range over a period of time. When these indicators align with WaveTrend signals, it adds a layer of confirmation that enhances the reliability of the signals.
Money Flow Index (MFI): This volume-weighted indicator assesses the inflow and outflow of money in an asset, giving insights into buying and selling pressure. By analyzing the MFI alongside WaveTrend and RSI indicators, the script can cross-validate signals, ensuring that buy or sell signals are supported by actual market volume.
Example Bullish scenario:
When a bullish divergence is detected on the RSI and confirmed by a corresponding bullish signal on the WaveTrend, along with an increasing Money Flow Index, the probability of a successful trade setup increases. This cross-validation minimizes the risk of acting on false signals, which might occur when relying on a single indicator.
Example Bearish scenario:
When a bearish divergence is detected on the RSI and confirmed by a corresponding bearish signal on the WaveTrend, along with an decreasing Money Flow Index, the probability of a successful trade setup increases. This cross-validation minimizes the risk of acting on false signals, which might occur when relying on a single indicator.
2. Divergence Detection and Market Reversals
Regular Divergences: Occur when the price action and an indicator (like RSI or WaveTrend) move in opposite directions. Regular bullish divergence signals a potential upward reversal when the price makes a lower low while the indicator makes a higher low. Conversely, regular bearish divergence suggests a downward reversal when the price makes a higher high, but the indicator makes a lower high.
Hidden Divergences: These occur when the price action and indicator move in the same direction, but with different momentum. Hidden bullish divergence suggests the continuation of an uptrend, while hidden bearish divergence suggests the continuation of a downtrend. By detecting these divergences across multiple indicators, the script identifies potential trend reversals or continuations with greater accuracy.
Example: The script might detect a regular bullish divergence on the WaveTrend while simultaneously identifying a hidden bullish divergence on the RSI. This combination suggests that while a trend reversal is possible, the overall market sentiment remains bullish, providing a nuanced view of the market.
A Regular Bullish Divergence Example:
A Hidden Bullish Divergence Example:
A Regular Bearish Divergence Example:
A Hidden Bearish Divergence Example:
3. Trend Strength and Sentiment Analysis
WaveTrend: Measures the strength and direction of the trend. By identifying the extremes of market sentiment (overbought and oversold levels), WaveTrend provides early signals for potential reversals.
Money Flow Index (MFI): Assesses the underlying sentiment by analyzing the flow of money. A rising MFI during an uptrend confirms strong buying pressure, while a falling MFI during a downtrend confirms selling pressure. This helps traders assess whether a trend is likely to continue or reverse.
RSI & Stochastic RSI: Offer a momentum-based perspective on the trend’s strength. High RSI or Stochastic RSI values indicate that the asset may be overbought, suggesting a potential reversal. Conversely, low values indicate oversold conditions, signaling a possible upward reversal.
Example:
During a strong uptrend, the WaveTrend & RSI's might signal overbought conditions, suggesting caution. If the MFI also shows decreasing buying pressure and the RSI reaches extreme levels, these indicators together suggest that the trend might be weakening, and a reversal could be imminent.
Example:
During a strong downtrend, the WaveTrend & RSI's might signal oversold conditions, suggesting caution. If the MFI also shows increasing buying pressure and the RSI reaches extreme levels, these indicators together suggest that the trend might be weakening, and a reversal could be imminent.
Conclusion
The "WaveTrend With Divergences & RSI(STOCH) Divergences" script offers a powerful, integrated approach to technical analysis by combining trend, momentum, and sentiment indicators into a single tool. Its unique value lies in the cross-validation of signals, the ability to detect divergences, and the comprehensive view it provides of market conditions. By offering traders multiple layers of analysis and customization options, this script is designed to enhance trading decisions, reduce false signals, and provide clearer insights into market dynamics.
WAVETREND
Display of WaveTrend:
Display of WaveTrend Setting:
WaveTrend Indicator Explanation
The WaveTrend indicator helps identify overbought and oversold conditions, as well as potential buy and sell signals. Its flexibility allows traders to adapt it to various strategies, making it a versatile tool in technical analysis.
WaveTrend Input Settings:
WT MA Source: Default: HLC3
What it is: The data source used for calculating the WaveTrend Moving Average.
What it does: Determines the input data to smooth price action and filter noise.
Example: Using HLC3 (average of High, Low, Close) provides a smoother data representation compared to using just the closing price.
Length (WT MA Length): Default: 3
What it is: The period used to calculate the Moving Average.
What it does: Adjusts the sensitivity of the WaveTrend indicator, where shorter lengths respond more quickly to price changes.
Example: A length of 3 is ideal for short-term analysis, providing quick reactions to price movements.
WT Channel Length & Average: Default: WT Channel Length = 9, Average = 12
What it is: Lengths used to calculate the WaveTrend channel and its average.
What it does: Smooths out the WaveTrend further, reducing false signals by averaging over a set period.
Example: Higher values reduce noise and help in identifying more reliable trends.
Channel: Style, Width, and Color:
What it is: Customization options for the WaveTrend channel's appearance.
What it does: Adjusts how the channel is displayed, including line style, width, and color.
Example: Choosing an area style with a distinct color can make the WaveTrend indicator clearly visible on the chart.
WT Buy & Sell Signals:
What it is: Settings to enable and customize buy and sell signals based on WaveTrend.
What it does: Allows for the display of buy/sell signals and customization of their shapes and colors.
When it gives a Buy Signal: Generated when the WaveTrend line crosses below an oversold level and then rises back, indicating a potential upward price movement.
When it gives a Sell Signal: Triggered when the WaveTrend line crosses above an overbought level and then declines, suggesting a possible downward trend.
Example: The script identifies these signals based on mean reversion principles, where prices tend to revert to the mean after reaching extremes. Traders can use these signals to time their entries and exits effectively.
WAVETREND OVERBOUGTH AND OVERSOLD LEVELS
Display of WaveTrend with Overbought & Oversold Levels:
Display of WaveTrend Overbought & Oversold Levels Settings:
WaveTrend Overbought & Oversold Levels Explanation
WT OB & OS Levels: Default: OB Level 1 = 53, OB Level 2 = 60, OS Level 1 = -53, OS Level 2 = -60
What it is: The default overbought and oversold levels used by the WaveTrend indicator to signal potential market reversals.
What it does: When the WaveTrend crosses above the OB levels, it indicates an overbought condition, potentially signaling a reversal or selling opportunity. Conversely, when it crosses below the OS levels, it indicates an oversold condition, potentially signaling a reversal or buying opportunity.
Example: A trader might use these levels to time entry or exit points, such as selling when the WaveTrend crosses into the overbought zone or buying when it crosses into the oversold zone.
Show OB/OS Levels: Default: True
What it is: Toggle options to show or hide the overbought and oversold levels on your chart.
What it does: When enabled, these levels will be visually represented on your chart, helping you to easily identify when the market reaches these critical thresholds.
Example: Displaying these levels can help you quickly see when the WaveTrend is approaching or has crossed into overbought or oversold territory, allowing for more informed trading decisions.
Line Style, Width, and Color for OB/OS Levels:
What it is: Options to customize the appearance of the OB and OS levels on your chart, including line style (solid, dotted, dashed), line width, and color.
What it does: These settings allow you to adjust how prominently these levels are displayed on your chart, which can help you better visualize and respond to overbought or oversold conditions.
Example: Setting a thicker, dashed line in a contrasting color can make these levels stand out more clearly, aiding in quick visual identification.
Example of Use:
Scenario: A trader wants to identify potential selling points when the market is overbought. They set the OB levels at 53 and 60, choosing a solid, red line style to make these levels clear on their chart. As the WaveTrend crosses above 53, they monitor for further price action, and upon crossing 60, they consider initiating a sell order.
WAVETREND DIVERGENCES
Display of WaveTrend Divergence:
Display of WaveTrend Divergence Setting:
WaveTrend Divergence Indicator Explanation
The WaveTrend Divergence feature helps identify potential reversal points in the market by highlighting divergences between the price and the WaveTrend indicator. Divergences can signal a shift in market momentum, indicating a possible trend reversal. This component allows traders to visualize and customize divergence detection on their charts.
WaveTrend Divergence Input Settings:
Potential Reversal Range: Default: 28
What it is: The number of bars to look back when detecting potential tops and bottoms.
What it does: Sets the range for identifying possible reversal points based on historical data.
Example: A setting of 28 looks back across the last 28 bars to find reversal points, offering a balance between responsiveness and reliability.
Reversal Minimum LVL OB & OS: Default: OB = 35, OS = -35
What it is: The minimum overbought and oversold levels required for detecting potential reversals.
What it does: Adjusts the thresholds that trigger a reversal signal based on the WaveTrend indicator.
Example: A higher OB level reduces the sensitivity to overbought conditions, potentially filtering out false reversal signals.
Lookback Bar Left & Right: Default: Left = 10, Right = 1
What it is: The number of bars to the left and right used to confirm a top or bottom.
What it does: Helps determine the position of peaks and troughs in the price action.
Example: A larger left lookback captures more extended price action before the peak, while a smaller right lookback focuses on the immediate past.
Lookback Range Min & Max: Default: Min = 5, Max = 60
What it is: The minimum and maximum range for the lookback period when identifying divergences.
What it does: Fine-tunes the detection of divergences by controlling the range over which the indicator looks back.
Example: A wider range increases the chances of detecting divergences across different market conditions.
R.Div Minimum LVL OB & OS: Default: OB = 53, OS = -53
What it is: The threshold levels for detecting regular divergences.
What it does: Adjusts the sensitivity of the regular divergence detection.
Example: Higher thresholds make the detection more conservative, identifying only stronger divergence signals.
H.Div Minimum LVL OB & OS: Default: OB = 20, OS = -20
What it is: The threshold levels for detecting hidden divergences.
What it does: Similar to regular divergence settings but for hidden divergences, which can indicate potential reversals that are less obvious.
Example: Lower thresholds make the hidden divergence detection more sensitive, capturing subtler market shifts.
Divergence Label Options:
What it is: Options to display and customize labels for regular and hidden divergences.
What it does: Allows users to visually differentiate between regular and hidden divergences using customizable labels and colors.
Example: Using different colors and symbols for regular (R) and hidden (H) divergences makes it easier to interpret signals on the chart.
Text Size and Color:
What it is: Customization options for the size and color of divergence labels.
What it does: Adjusts the readability and visibility of divergence labels on the chart.
Example: Larger text size may be preferred for charts with a lot of data, ensuring divergence labels stand out clearly.
FAST & SLOW MONEY FLOW INDEX
Display of Fast & Slow Money Flow:
Display of Fast & Slow Money Flow Setting:
Fast Money Flow Indicator Explanation
The Fast Money Flow indicator helps traders identify the flow of money into and out of an asset over a shorter time frame. By tracking the volume-weighted average of price movements, it provides insights into buying and selling pressure in the market, which can be crucial for making timely trading decisions.
Fast Money Flow Input Settings:
Fast Money Flow: Length: Default: 9
What it is: The period used for calculating the Fast Money Flow.
What it does: Determines the sensitivity of the Money Flow calculation. A shorter length makes the indicator more responsive to recent price changes, while a longer length provides a smoother signal.
Example: A length of 9 is suitable for traders looking to capture quick shifts in market sentiment over a short period.
Fast MFI Area Multiplier: Default: 5
What it is: A multiplier applied to the Money Flow area calculation.
What it does: Adjusts the size of the Money Flow area on the chart, effectively amplifying or reducing the visual impact of the indicator.
Example: A higher multiplier can make the Money Flow more prominent on the chart, aiding in the quick identification of significant money flow changes.
Y Position (Y Pos): Default: 0
What it is: The vertical position adjustment for the Fast Money Flow plot on the chart.
What it does: Allows you to move the Money Flow plot up or down on the chart to avoid overlap with other indicators.
Example: Adjusting the Y Position can be useful if you have multiple indicators on the chart and need to maintain clarity.
Fast MFI Style, Width, and Color:
What it is: Customization options for how the Fast Money Flow is displayed on the chart.
What it does: Enables you to choose between different plot styles (line or area), set the line width, and select colors for positive and negative money flow.
Example: Using different colors for positive (green) and negative (red) money flow helps to visually distinguish between periods of buying and selling pressure.
Slow Money Flow Indicator Explanation
The Slow Money Flow indicator tracks the flow of money into and out of an asset over a longer time frame. It provides a broader perspective on market sentiment, smoothing out short-term fluctuations and highlighting longer-term trends.
Slow Money Flow Input Settings:
Slow Money Flow: Length: Default: 12
What it is: The period used for calculating the Slow Money Flow.
What it does: A longer period smooths out short-term fluctuations, providing a clearer view of the overall money flow trend.
Example: A length of 12 is often used by traders looking to identify sustained trends rather than short-term volatility.
Slow MFI Area Multiplier: Default: 5
What it is: A multiplier applied to the Slow Money Flow area calculation.
What it does: Adjusts the size of the Money Flow area on the chart, helping to emphasize the indicator’s significance.
Example: Increasing the multiplier can help highlight the Money Flow in markets with less volatile price action.
Y Position (Y Pos): Default: 0
What it is: The vertical position adjustment for the Slow Money Flow plot on the chart.
What it does: Allows for vertical repositioning of the Money Flow plot to maintain chart clarity when used with other indicators.
Example: Adjusting the Y Position ensures that the Slow Money Flow indicator does not overlap with other key indicators on the chart.
Slow MFI Style, Width, and Color:
What it is: Customization options for the visual display of the Slow Money Flow on the chart.
What it does: Allows you to choose the plot style (line or area), set the line width, and select colors to differentiate positive and negative money flow.
Example: Customizing the colors for the Slow Money Flow allows traders to quickly distinguish between buying and selling trends in the market.
RSI
Display of RSI:
Display of RSI Setting:
RSI Indicator Explanation
The Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. It is typically used to identify overbought or oversold conditions in the market, providing traders with potential signals for buying or selling.
RSI Input Settings:
RSI Source: Default: Close
What it is: The data source used for calculating the RSI.
What it does: Determines which price data (e.g., close, open) is used in the RSI calculation, affecting how the indicator reflects market conditions.
Example: Using the closing price is standard practice, as it reflects the final agreed-upon price for a given time period.
MA Type (Moving Average Type): Default: SMA
What it is: The type of moving average applied to the RSI for smoothing purposes.
What it does: Changes the smoothing technique of the RSI, impacting how quickly the indicator responds to price movements.
Example: Using an Exponential Moving Average (EMA) will make the RSI more sensitive to recent price changes compared to a Simple Moving Average (SMA).
RSI Length: Default: 14
What it is: The period over which the RSI is calculated.
What it does: Adjusts the sensitivity of the RSI. A shorter length (e.g., 7) makes the RSI more responsive to recent price changes, while a longer length (e.g., 21) smooths out the indicator, reducing the number of signals.
Example: A 14-period RSI is commonly used for identifying overbought and oversold conditions, providing a balance between sensitivity and reliability.
RSI Plot Style, Width, and Color:
What it is: Options to customize the appearance of the RSI line on the chart.
What it does: Allows you to adjust the visual representation of the RSI, including the line width and color.
Example: Setting a thicker line width and a bright color like yellow can make the RSI more visible on the chart, aiding in quick analysis.
Display of RSI with RSI Moving Average:
RSI Moving Average Explanation
The RSI Moving Average adds a smoothing layer to the RSI, helping to filter out noise and provide clearer signals. It is particularly useful for confirming trend strength and identifying potential reversals.
RSI Moving Average Input Settings:
MA Length: Default: 14
What it is: The period over which the Moving Average is calculated on the RSI.
What it does: Adjusts the smoothing of the RSI, helping to reduce false signals and provide a clearer trend indication.
Example: A 14-period moving average on the RSI can smooth out short-term fluctuations, making it easier to spot genuine overbought or oversold conditions.
MA Plot Style, Width, and Color:
What it is: Customization options for how the RSI Moving Average is displayed on the chart.
What it does: Allows you to adjust the line width and color, helping to differentiate the Moving Average from the main RSI line.
Example: Using a contrasting color for the RSI Moving Average (e.g., magenta) can help it stand out against the main RSI line, making it easier to interpret the indicator.
STOCHASTIC RSI
Display of Stochastic RSI:
Display of Stochastic RSI Setting:
Stochastic RSI Indicator Explanation
The Stochastic RSI (Stoch RSI) is a momentum oscillator that measures the level of the RSI relative to its high-low range over a set period of time. It is used to identify overbought and oversold conditions, providing potential buy and sell signals based on momentum shifts.
Stochastic RSI Input Settings:
Stochastic RSI Length: Default: 14
What it is: The period over which the Stochastic RSI is calculated.
What it does: Adjusts the sensitivity of the Stochastic RSI. A shorter length makes the indicator more responsive to recent price changes, while a longer length smooths out the fluctuations, reducing noise.
Example: A length of 14 is commonly used to identify momentum shifts over a medium-term period, providing a balanced view of potential overbought or oversold conditions.
Display of Stochastic RSI %K Line:
Stochastic RSI %K Line Explanation
The %K line in the Stochastic RSI is the main line that tracks the momentum of the RSI over the chosen period. It is the faster-moving component of the Stochastic RSI, often used to identify entry and exit points.
Stochastic RSI %K Input Settings:
%K Length: Default: 3
What it is: The period used for smoothing the %K line of the Stochastic RSI.
What it does: Smoothing the %K line helps reduce noise and provides a clearer signal for potential market reversals.
Example: A smoothing length of 3 is common, offering a balance between responsiveness and noise reduction, making it easier to spot significant momentum shifts.
%K Plot Style, Width, and Color:
What it is: Customization options for the visual representation of the %K line.
What it does: Allows you to adjust the appearance of the %K line on the chart, including line width and color, to fit your visual preferences.
Example: Setting a blue color and a medium width for the %K line makes it stand out clearly on the chart, helping to identify key points of momentum change.
%K Fill Color (Above):
What it is: The fill color that appears above the %K line on the chart.
What it does: Adds visual clarity by shading the area above the %K line, making it easier to interpret the direction and strength of momentum.
Example: Using a light blue fill color above the %K line can help emphasize bullish momentum, making it visually prominent.
Display of Stochastic RSI %D Line:
Stochastic RSI %D Line Explanation
The %D line in the Stochastic RSI is a moving average of the %K line and acts as a signal line. It is slower-moving compared to the %K line and is often used to confirm signals or identify potential reversals when it crosses the %K line.
Stochastic RSI %D Input Settings:
%D Length: Default: 3
What it is: The period used for smoothing the %D line of the Stochastic RSI.
What it does: Smooths out the %D line, making it less sensitive to short-term fluctuations and more reliable for identifying significant market signals.
Example: A length of 3 is often used to provide a smoothed signal line that can help confirm trends or reversals indicated by the %K line.
%D Plot Style, Width, and Color:
What it is: Customization options for the visual representation of the %D line.
What it does: Allows you to adjust the appearance of the %D line on the chart, including line width and color, to match your preferences.
Example: Setting an orange color and a thicker line width for the %D line can help differentiate it from the %K line, making crossover points easier to spot.
%D Fill Color (Below):
What it is: The fill color that appears below the %D line on the chart.
What it does: Adds visual clarity by shading the area below the %D line, making it easier to interpret bearish momentum.
Example: Using a light orange fill color below the %D line can highlight bearish conditions, making it visually easier to identify.
RSI & STOCHASTIC RSI OVERBOUGHT AND OVERSOLD LEVELS
Display of RSI & Stochastic with Overbought & Oversold Levels:
Display of RSI & Stochastic Overbought & Oversold Settings:
RSI & Stochastic Overbought & Oversold Levels Explanation
The Overbought (OB) and Oversold (OS) levels for RSI and Stochastic RSI indicators are key thresholds that help traders identify potential reversal points in the market. These levels are used to determine when an asset is likely overbought or oversold, which can signal a potential trend reversal.
RSI & Stochastic Overbought & Oversold Input Settings:
RSI & Stochastic Level 1 Overbought (OB) & Oversold (OS): Default: OB Level = 170, OS Level = 130
What it is: The first set of thresholds for determining overbought and oversold conditions for both RSI and Stochastic RSI indicators.
What it does: When the RSI or Stochastic RSI crosses above the overbought level, it suggests that the asset might be overbought, potentially signaling a sell opportunity. Conversely, when these indicators drop below the oversold level, it suggests the asset might be oversold, potentially signaling a buy opportunity.
Example: If the RSI crosses above 170, traders might look for signs of a potential trend reversal to the downside, while a cross below 130 might indicate a reversal to the upside.
RSI & Stochastic Level 2 Overbought (OB) & Oversold (OS): Default: OB Level = 180, OS Level = 120
What it is: The second set of thresholds for determining overbought and oversold conditions for both RSI and Stochastic RSI indicators.
What it does: These levels provide an additional set of reference points, allowing traders to differentiate between varying degrees of overbought and oversold conditions, potentially leading to more refined trading decisions.
Example: When the RSI crosses above 180, it might indicate an extreme overbought condition, which could be a stronger signal for a sell, while a cross below 120 might indicate an extreme oversold condition, which could be a stronger signal for a buy.
RSI & Stochastic Overbought (OB) Band Customization:
OB Level 1: Width, Style, and Color:
What it is: Customization options for the visual appearance of the first overbought band on the chart.
What it does: Allows you to set the line width, style (solid, dotted, dashed), and color for the first overbought band, enhancing its visibility on the chart.
Example: A dashed red line with medium width can clearly indicate the first overbought level, helping traders quickly identify when this threshold is crossed.
OB Level 2: Width, Style, and Color:
What it is: Customization options for the visual appearance of the second overbought band on the chart.
What it does: Allows you to set the line width, style, and color for the second overbought band, providing a clear distinction from the first band.
Example: A dashed red line with a slightly thicker width can represent a more significant overbought level, making it easier to differentiate from the first level.
RSI & Stochastic Oversold (OS) Band Customization:
OS Level 1: Width, Style, and Color:
What it is: Customization options for the visual appearance of the first oversold band on the chart.
What it does: Allows you to set the line width, style (solid, dotted, dashed), and color for the first oversold band, making it visually prominent.
Example: A dashed green line with medium width can highlight the first oversold level, helping traders identify potential buying opportunities.
OS Level 2: Width, Style, and Color:
What it is: Customization options for the visual appearance of the second oversold band on the chart.
What it does: Allows you to set the line width, style, and color for the second oversold band, providing an additional visual cue for extreme oversold conditions.
Example: A dashed green line with a thicker width can represent a more significant oversold level, offering a stronger visual cue for potential buying opportunities.
RSI DIVERGENCES
Display of RSI Divergence Labels:
Display of RSI Divergence Settings:
RSI Divergence Lookback Explanation
The RSI Divergence settings allow traders to customize the parameters for detecting divergences between the RSI (Relative Strength Index) and price action. Divergences occur when the price moves in the opposite direction to the RSI, potentially signaling a trend reversal. These settings help refine the accuracy of divergence detection by adjusting the lookback period and range. ( NOTE: This setting only imply to the RSI. This doesn't effect the STOCHASTIC RSI. )
RSI Divergence Lookback Input Settings:
Lookback Left: Default: 10
What it is: The number of bars to look back from the current bar to detect a potential divergence.
What it does: Defines the left-side lookback period for identifying pivot points in the RSI, which are used to spot divergences. A longer lookback period may capture more significant trends but could also miss shorter-term divergences.
Example: A setting of 10 bars means the script will consider pivot points up to 10 bars before the current bar to check for divergence patterns.
Lookback Right: Default: 1
What it is: The number of bars to look forward from the current bar to complete the divergence pattern.
What it does: Defines the right-side lookback period for confirming a potential divergence. This setting helps ensure that the identified divergence is valid by allowing the script to check subsequent bars for confirmation.
Example: A setting of 1 bar means the script will look at the next bar to confirm the divergence pattern, ensuring that the signal is reliable.
Lookback Range Min: Default: 5
What it is: The minimum range of bars required to detect a valid divergence.
What it does: Sets a lower bound on the range of bars considered for divergence detection. A lower minimum range might capture more frequent but possibly less significant divergences.
Example: Setting the minimum range to 5 ensures that only divergences spanning at least 5 bars are considered, filtering out very short-term patterns.
Lookback Range Max: Default: 60
What it is: The maximum range of bars within which a divergence can be detected.
What it does: Sets an upper bound on the range of bars considered for divergence detection. A larger maximum range might capture more significant divergences but could also include less relevant long-term patterns.
Example: Setting the maximum range to 60 bars allows the script to detect divergences over a longer timeframe, capturing more extended divergence patterns that could indicate major trend reversals.
RSI Divergence Explanation
RSI divergences occur when the RSI indicator and price action move in opposite directions, signaling potential trend reversals. This section of the settings allows traders to customize the appearance and detection of both regular and hidden bullish and bearish divergences.
RSI Divergence Input Settings:
R. Bullish Div Label: Default: True
What it is: An option to display labels for regular bullish divergences.
What it does: Enables or disables the visibility of labels that mark regular bullish divergences, where the price makes a lower low while the RSI makes a higher low, indicating a potential upward reversal.
Example: A trader might use this to spot buying opportunities in a downtrend when a bullish divergence suggests the trend may be reversing.
Bullish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of regular bullish divergence labels.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: Selecting a green label color and a distinct line width makes bullish divergences easily recognizable on your chart.
R. Bearish Div Label: Default: True
What it is: An option to display labels for regular bearish divergences.
What it does: Enables or disables the visibility of labels that mark regular bearish divergences, where the price makes a higher high while the RSI makes a lower high, indicating a potential downward reversal.
Example: A trader might use this to spot selling opportunities in an uptrend when a bearish divergence suggests the trend may be reversing.
Bearish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of regular bearish divergence labels.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: Choosing a red label color and a specific line width makes bearish divergences clearly stand out on your chart.
H. Bullish Div Label: Default: False
What it is: An option to display labels for hidden bullish divergences.
What it does: Enables or disables the visibility of labels that mark hidden bullish divergences, where the price makes a higher low while the RSI makes a lower low, indicating potential continuation of an uptrend.
Example: A trader might use this to confirm an existing uptrend when a hidden bullish divergence signals continued buying strength.
Hidden Bullish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of hidden bullish divergence labels.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: A softer green color with a thinner line width might be chosen to subtly indicate hidden bullish divergences, keeping the chart clean while providing useful information.
H. Bearish Div Label: Default: False
What it is: An option to display labels for hidden bearish divergences.
What it does: Enables or disables the visibility of labels that mark hidden bearish divergences, where the price makes a lower high while the RSI makes a higher high, indicating potential continuation of a downtrend.
Example: A trader might use this to confirm an existing downtrend when a hidden bearish divergence signals continued selling pressure.
Hidden Bearish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of hidden bearish divergence labels.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: A muted red color with a thinner line width might be selected to indicate hidden bearish divergences without overwhelming the chart.
Divergence Text Size and Color: Default: S (Small)
What it is: Settings to adjust the size and color of text labels for RSI divergences.
What it does: Allows you to customize the size and color of text labels that display the divergence information on the chart.
Example: Choosing a small text size with a bright white color can make divergence labels easily readable without taking up too much space on the chart.
STOCHASTIC DIVERGENCES
Display of Stochastic RSI Divergence Labels:
Display of Stochastic RSI Divergence Settings:
Stochastic RSI Divergence Explanation
Stochastic RSI divergences occur when the Stochastic RSI indicator and price action move in opposite directions, signaling potential trend reversals. These settings allow traders to customize the detection and visual representation of both regular and hidden bullish and bearish divergences in the Stochastic RSI.
Stochastic RSI Divergence Input Settings:
R. Bullish Div Label: Default: True
What it is: An option to display labels for regular bullish divergences in the Stochastic RSI.
What it does: Enables or disables the visibility of labels that mark regular bullish divergences, where the price makes a lower low while the Stochastic RSI makes a higher low, indicating a potential upward reversal.
Example: A trader might use this to spot buying opportunities in a downtrend when a bullish divergence in the Stochastic RSI suggests the trend may be reversing.
Bullish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of regular bullish divergence labels in the Stochastic RSI.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: Selecting a blue label color and a distinct line width makes bullish divergences in the Stochastic RSI easily recognizable on your chart.
R. Bearish Div Label: Default: True
What it is: An option to display labels for regular bearish divergences in the Stochastic RSI.
What it does: Enables or disables the visibility of labels that mark regular bearish divergences, where the price makes a higher high while the Stochastic RSI makes a lower high, indicating a potential downward reversal.
Example: A trader might use this to spot selling opportunities in an uptrend when a bearish divergence in the Stochastic RSI suggests the trend may be reversing.
Bearish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of regular bearish divergence labels in the Stochastic RSI.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: Choosing an orange label color and a specific line width makes bearish divergences in the Stochastic RSI clearly stand out on your chart.
H. Bullish Div Label: Default: False
What it is: An option to display labels for hidden bullish divergences in the Stochastic RSI.
What it does: Enables or disables the visibility of labels that mark hidden bullish divergences, where the price makes a higher low while the Stochastic RSI makes a lower low, indicating potential continuation of an uptrend.
Example: A trader might use this to confirm an existing uptrend when a hidden bullish divergence in the Stochastic RSI signals continued buying strength.
Hidden Bullish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of hidden bullish divergence labels in the Stochastic RSI.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: A softer blue color with a thinner line width might be chosen to subtly indicate hidden bullish divergences, keeping the chart clean while providing useful information.
H. Bearish Div Label: Default: False
What it is: An option to display labels for hidden bearish divergences in the Stochastic RSI.
What it does: Enables or disables the visibility of labels that mark hidden bearish divergences, where the price makes a lower high while the Stochastic RSI makes a higher high, indicating potential continuation of a downtrend.
Example: A trader might use this to confirm an existing downtrend when a hidden bearish divergence in the Stochastic RSI signals continued selling pressure.
Hidden Bearish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of hidden bearish divergence labels in the Stochastic RSI.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: A muted orange color with a thinner line width might be selected to indicate hidden bearish divergences without overwhelming the chart.
Divergence Text Size and Color: Default: S (Small)
What it is: Settings to adjust the size and color of text labels for Stochastic RSI divergences.
What it does: Allows you to customize the size and color of text labels that display the divergence information on the chart.
Example: Choosing a small text size with a bright white color can make divergence labels easily readable without taking up too much space on the chart.
Alert System:
Custom Alerts for Divergences and Reversals:
What it is: The script includes customizable alert conditions to notify you of detected divergences or potential reversals based on WaveTrend, RSI, and Stochastic RSI.
What it does: Helps you stay informed of key market movements without constantly monitoring the charts, enabling timely decisions.
Example: Setting an alert for regular bearish divergence on the WaveTrend could notify you of a potential sell opportunity as soon as it is detected.
How to Use Alerts:
Set up custom alerts in TradingView based on these conditions to be notified of potential trading opportunities. Alerts are triggered when the indicator detects conditions that match the selected criteria, such as divergences or potential reversals.
By following the detailed guidelines and examples above, you can effectively use and customize this powerful indicator to suit your trading strategy.
For further understanding and customization, refer to the input settings within the script and adjust them to match your trading style and preferences.
How Components Work Together
Synergy and Cross-Validation: The indicator combines multiple layers of analysis to validate trading signals. For example, a WaveTrend buy signal that coincides with a bullish divergence in RSI and positive fast money flow is likely to be more reliable than any single indicator’s signal. This cross-validation reduces the likelihood of false signals and enhances decision-making.
Comprehensive Market Analysis: Each component plays a role in analyzing different aspects of the market. WaveTrend focuses on trend strength, Money Flow indicators assess market sentiment, while RSI and Stochastic RSI offer detailed views of price momentum and potential reversals.
Ideal For
Traders who require a reliable, multifaceted tool for detecting market trends and reversals.
Investors seeking a deeper understanding of market dynamics across different timeframes and conditions, whether in forex, equities, or cryptocurrency markets.
This script is designed to provide a comprehensive tool for technical analysis, combining multiple indicators and divergence detection into one versatile and customizable script. It is especially useful for traders who want to monitor various indicators simultaneously and look for convergence or divergence signals across different technical tools.
Acknowledgements
Special thanks to these amazing creators for inspiration and their creations:
I want to thank these amazing creators for creating there amazing indicators , that inspired me and also gave me a head start by making this indicator! Without their amazing indicators it wouldn't be possible!
vumanchu: VuManChu Cipher B Divergences.
MisterMoTa: RSI + Divergences + Alerts .
DevLucem: Plain Stochastic Divergence.
Note
This indicator is designed to be a powerful tool in your trading arsenal. However , it is essential to backtest and adjust the settings according to your trading strategy before applying it to live trading . If you have any questions or need further assistance, feel free to reach out.
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
Stoch_RSI_ChartEnhanced Stochastic RSI Divergence Indicator with VWAP Filter for Charts
This custom indicator builds upon the classic Stochastic RSI to automatically detect both regular and hidden divergences. It’s designed to help traders spot potential market reversals or continuations using two methods for divergence detection (fractal‑ and pivot‑based) while offering optional VWAP filtering for confirmation.
Key Features
Stoch RSI Calculation
The indicator computes a smoothed Stoch RSI using configurable parameters for RSI length, stochastic length, and smoothing periods. An option to average the K and D lines provides a cleaner momentum view.
Divergence Detection via Fractals & Pivots
Fractal-Based Divergences:
Looks for 4-candle patterns to identify higher-highs or lower-lows in the price that are not confirmed by the oscillator, signaling potential reversals.
Pivot-Based Divergences:
Utilizes TradingView’s built-in pivot functions to find divergence conditions over adjustable pivot ranges.
Regular vs. Hidden Divergences:
Regular Divergence: Occurs when price makes a new extreme (higher high or lower low) while the Stoch RSI fails to follow suit.
Hidden Divergence: Indicates potential trend continuations when the oscillator diverges against the established price trend.
Optional VWAP Filtering
The script includes two optional VWAP filters that work as follows:
VWAP Filter on Regular Divergences:
Only confirms regular divergence signals if the current price satisfies the VWAP condition (e.g., price is above VWAP for bullish signals, below VWAP for bearish signals).
VWAP Filter on Hidden Divergences:
Similarly, hidden divergence signals are validated only when the price meets specific VWAP conditions, adding an extra layer of trend confirmation.
Customizable Alerts and Visual Labels
Easily configure divergence labels (“B” for bullish, “S” for bearish) and enable up to four alert conditions for real‑time notifications when a divergence occurs.
Credits & History:
Log RSI by @fskrypt
Divergence Detection originally by @RicardoSantos (with edits from @JustUncleL)
Further Edits by @NeoButane on August 8, 2018
Latest Edits by @FYMD on June 1, 2024
