NUPL-Z For Loop🧠 Overview
NUPL-Z For Loop is a trend-following indicator built on Bitcoin’s on-chain Net Unrealized Profit/Loss (NUPL) metric. It uses a Z-scored transformation of NUPL and a custom loop-based scoring system to measure the consistency of directional movement. Rather than identifying tops and bottoms, this tool is designed to track sustained trends and filter out short-term noise, making it ideal for momentum-aligned strategies.
🧩 Key Features
Loop-Based Trend Logic: Assesses trend strength by summing the number of upward vs. downward moves in Z-scored NUPL across a custom lookback.
Z-Score Normalization: Applies long-term statistical normalization to NUPL to emphasize deviation from average behavior over time.
Threshold-Based Regime Shifts: Custom input thresholds define when trend strength is significant enough to trigger long or short signals.
Directional Market State Tracking: Internally tracks bullish, bearish, or neutral conditions to guide trend entries.
BTC-Focused On-Chain Analysis: Tailored specifically for Bitcoin using Market Cap and Realized Cap inputs.
🔍 How It Works
NUPL Calculation: Derived as the percentage of net unrealized profit relative to market cap: (MC - RMC) / MC * 100.
Z-Scoring: NUPL is normalized using a rolling mean and standard deviation over a long window (default 1300 days) to create a smoothed trend signal.
Directional Loop: A custom loop iterates from the start_loop to the end_loop, comparing the current Z-score to past values.
Each instance where NUPL_Z > NUPL_Z adds +1 to the score; otherwise, it subtracts -1.
This cumulative score reflects how consistently NUPL-Z has been trending.
Signal Logic:
Long signal when loop score exceeds long_threshold.
Short signal when score falls below short_threshold.
CD State Engine: Maintains the current trend regime (1 for long, -1 for short), which drives plot coloring and overlays.
🔁 Use Cases & Applications
Momentum Trend Filter: Detects and confirms sustained directional strength in BTC’s profit/loss positioning.
Noise Suppression: Avoids reactive signals from one-off spikes or dips in NUPL by requiring a consistent trend before confirming bias.
Best Suited for BTC: Designed specifically for Bitcoin’s price and on-chain structure, using its unique NUPL dynamics.
✅ Conclusion
NUPL-Z For Loop transforms a traditionally mean-reverting indicator into a trend-following signal engine. By scoring the consistency of movement in normalized NUPL, this tool identifies trend strength rather than reversal potential — providing more reliable context for momentum-aligned trades on Bitcoin.
⚠️ Disclaimer
The content provided by this indicator is for educational and informational purposes only. Nothing herein constitutes financial or investment advice. Trading and investing involve risk, including the potential loss of capital. Always backtest and apply risk management suited to your strategy.
ابحث في النصوص البرمجية عن "track"
Multiple AVWAP [OmegaTools]The Multiple AVWAP indicator is a sophisticated trading tool designed for professional traders who require precision in volume-weighted price tracking. This indicator allows for the deployment of multiple Anchored Volume Weighted Average Price (AVWAP) calculations simultaneously, offering deep insights into price movements, dynamic support and resistance levels, and trend structures across multiple timeframes.
This indicator caters to both institutional and retail traders by integrating flexible anchoring methods, multi-timeframe adaptability, and enhanced visualization features. It also includes deviation bands for statistical analysis, making it a comprehensive volume-based trading solution.
Key Features & Functionalities
1. Multiple AVWAP Configurations
Users can configure up to four distinct AVWAP calculations to track different market conditions.
Supports various anchoring methods:
Fixed: A traditional AVWAP that starts from a defined historical point.
Perpetual: A rolling VWAP that continuously adjusts over time.
Extension: An extension-based AVWAP that projects from past calculations.
High Volume: Anchors AVWAP to the highest volume bar within a specified period.
None: Option to disable AVWAP calculation if not required.
2. Advanced Deviation Bands
Implements standard deviation bands (1st and 2nd deviation) to provide a statistical measure of price dispersion from the AVWAP.
Serves as a dynamic method for identifying overbought and oversold conditions relative to VWAP pricing.
Deviation bands are customizable in terms of visibility, color, and transparency.
3. Multi-Timeframe Support
Users can assign different timeframes to each AVWAP calculation for macro and micro analysis.
Helps in identifying long-term institutional trading levels alongside short-term intraday trends.
4. Z-Score Normalization Mode
Option to standardize oscillator values based on AVWAP deviations.
Converts price movements into a statistical Z-score, allowing traders to measure price strength in a normalized range.
Helps in detecting extreme price dislocations and mean-reversion opportunities.
5. Customizable Visual & Aesthetic Settings
Fully customizable line colors, transparency, and thickness to enhance clarity.
Users can modify AVWAP and deviation band colors to distinguish between different levels.
Configurable display options to match personal trading preferences.
6. Oscillator Mode for Trend & Momentum Analysis
The indicator converts price deviations into an oscillator format, displaying AVWAP strength and weakness dynamically.
This provides traders with a momentum-based perspective on volume-weighted price movements.
User Guide & Implementation
1. Configuring AVWAPs for Optimal Use
Choose the mode for each AVWAP instance:
Fixed (set historical point)
Perpetual (rolling, continuously updated AVWAP)
Extension (projection from past AVWAP levels)
High Volume (anchored to highest volume bar)
None (disables the AVWAP line)
Adjust the length settings to fine-tune calculation sensitivity.
2. Utilizing Deviation Bands for Market Context
Activate deviation bands to see statistical boundaries of price action.
Monitor +1 / -1 and +2 / -2 standard deviation levels for extended price movements.
Consider price action outside of deviation bands as potential mean-reversion signals.
3. Multi-Timeframe Analysis for Institutional-Level Insights
Assign different timeframes to each AVWAP to compare:
Daily VWAP (institutional trading levels)
Weekly VWAP (swing trading trends)
Intraday VWAPs (short-term momentum shifts)
Helps identify where institutional liquidity is positioned relative to price.
4. Activating the Oscillator for Momentum & Bias Confirmation
The oscillator converts AVWAP deviations into a normalized value.
Use overbought/oversold levels to determine strength and potential reversals.
Combine with other indicators (RSI, MACD) for confluence-based trading decisions.
Trading Applications & Strategies
5. Trend Confirmation & Institutional VWAP Tracking
If price consistently holds above the primary AVWAP, it signals a bullish trend.
If price remains below AVWAP, it indicates selling pressure and a bearish trend.
Monitor retests of AVWAP levels for potential trend continuation or reversal.
6. Dynamic Support & Resistance Levels
AVWAP lines act as dynamic floating support and resistance zones.
Price bouncing off AVWAP suggests continuation, whereas breakdowns indicate a shift in momentum.
Look for confluence with high-volume zones for stronger trade signals.
7. Mean Reversion & Statistical Edge Trading
Prices that deviate beyond +2 or -2 standard deviations often revert toward AVWAP.
Mean reversion traders can fade extended moves and target AVWAP re-tests.
Helps in identifying exhaustion points in trending markets.
8. Institutional Liquidity & Volume Footprints
Institutions often execute large trades near VWAP zones, causing price reactions.
Tracking multi-timeframe AVWAP levels allows traders to anticipate key liquidity areas.
Use higher timeframe AVWAPs as macro support/resistance for swing trading setups.
9. Enhancing Momentum Trading with AVWAP Oscillator
The oscillator provides a momentum-based measure of AVWAP deviations.
Helps in confirming entry and exit timing for trend-following trades.
Useful for pairing with stochastic oscillators, MACD, or RSI to validate trade decisions.
Best Practices & Trading Tips
Use in Conjunction with Volume Analysis: Combine with volume profiles, OBV, or CVD for increased accuracy.
Adjust Timeframes Based on Trading Style: Scalpers can focus on short-term AVWAP, while swing traders benefit from weekly/daily AVWAP tracking.
Backtest Different AVWAP Configurations: Experiment with different anchoring methods and lookback periods to optimize trade performance.
Monitor Institutional Order Flow: Identify key VWAP zones where institutional traders may be active.
Use with Other Technical Indicators: Enhance trading confidence by integrating with moving averages, Bollinger Bands, or Fibonacci retracements.
Final Thoughts & Disclaimer
The Multiple AVWAP indicator provides a comprehensive approach to volume-weighted price tracking, making it ideal for professional traders. While this tool enhances market clarity and trade decision-making, it should be used as part of a well-rounded trading strategy with risk management principles in place.
This indicator is provided for informational and educational purposes only. Trading involves risk, and past performance is not indicative of future results. Always conduct your own analysis and due diligence before executing trades.
OmegaTools - Enhancing Market Clarity with Precision Indicators
TrendPredator PROThe TrendPredator PRO
Stacey Burke, a seasoned trader and mentor, developed his trading system over the years, drawing insights from influential figures such as George Douglas Taylor, Tony Crabel, Steve Mauro, and Robert Schabacker. His popular system integrates select concepts from these experts into a consistent framework. While powerful, it remains highly discretionary, requiring significant real-time analysis, which can be challenging for novice traders.
The TrendPredator indicators support this approach by automating the essential analysis required to trade the system effectively and incorporating mechanical bias and a multi-timeframe concept. They provide value to traders by significantly reducing the time needed for session preparation, offering all relevant chart analysis and signals for live trading in real-time.
The PRO version offers an advanced pattern identification logic that highlights developing context as well as setups related to the constellation of the signals provided. It provides real-time interpretation of the multi-timeframe analysis table, following an extensive underlying logic with more than 150 different setup variations specifically developed for the system and indicator. These setups are constantly back- and forward-tested and updated according to the results. This version is tailored to traders primarily trading this system and following the related setups in detail.
The former TrendPredator ES version does not provide that option. It is significantly leaner and is designed for traders who want to use the multi-timeframe logic as additional confluence for their trading style. It is very well suited to support many other trading styles, including SMC and ICT.
The Multi-timeframe Master Pattern
Inspired by Taylor’s 3-day cycle and Steve Mauro’s work with “Beat the Market Maker,” Burke’s system views markets as cyclical, driven by the manipulative patterns of market makers. These patterns often trap traders at the extremes of moves above or below significant levels with peak formations, then reverse to utilize their liquidity, initiating the next phase. Breakouts away from these traps often lead to range expansions, as described by Tony Crabel and Robert Schabacker. After multiple consecutive breakouts, especially after the psychological number three, overextension might develop. A break in structure may then lead to reversals or pullbacks. The TrendPredator Indicator and the related multi-timeframe trading system are designed to track these cycles on the daily timeframe and provide signals and trade setups to navigate them.
Bias Logic and Multi-Timeframe Concept
The indicator covers the basic signals of Stacey Burke's system:
- First Red Day (FRD): Bearish break in structure, signalling weak longs in the market.
- First Green Day (FGD): Bullish break in structure signalling weak shorts in the markt.
- Three Days of Longs (3DL): Overextension signalling potential weak longs in the market.
- Three Days of Shorts (3DS): Overextension signalling potential weak shorts in the market.
- Inside Day (ID): Contraction, signalling potential impulsive reversal or range expansion move.
It enhances the original system by introducing:
Structured Bias Logic:
Tracks bias by following how price trades concerning the last previous candle high or low that was hit. For example if the high was hit, we are bullish above and bearish below.
- Bullish state: Breakout (BO), Fakeout Low (FOL)
- Bearish state: Breakdown (BD), Fakeout High (FOH)
Multi-Timeframe Perspective:
- Tracks all signals across H4, H8, D, W, and M timeframes, to look for alignment and follow trends and momentum in a mechanical way.
Developing Context:
- Identifies specific predefined context states based on the monthly, weekly and daily bias.
Developing Setups:
- Identifies specific predefined setups based on context and H8 bias as well as SB signals.
The indicator monitors the bias and signals of the system across all relevant timeframes and automates the related graphical chart analysis as well as context and setup zone identification. In addition to the master pattern, the system helps to identify the higher timeframe situation and follow the moves driven by other timeframe traders to then identify favourable context and setup situations for the trader.
Example: Full Bullish Cycle on the Daily Timeframe with Multi-Timeframe Signals
- The Trap/Peak Formation
The market breaks down from a previous day’s and maybe week’s low—potentially after multiple breakdowns—but fails to move lower and pulls back up to form a peak formation low and closes as a first green day.
MTF Signals: Bullish daily and weekly fakeout low; three consecutive breakdown days (1W Curr FOL, 1D Curr FOL, BO 3S).
Context: Reversal (REV)
Setup: Fakeout low continuation low of day (FOL Cont LOD)
- Pullback and Consolidation
The next day pulls further up after first green day signal, potentially consolidates inside the previous day’s range.
MTF Signals: Fakeout low and first green day closing as an inside day (1D Curr IS, Prev FOL, First G).
Context: Reversal continuation (REV Cont)
Setup: Previous fakeout low continuation low handing fruit (Prev FOL Cont LHF)
- Range Expansion/Trend
The following day breaks up through the previous day’s high, launching a range expansion away from the trap.
MTF Signals: Bullish daily breakout of an inside day (1D Curr BO, Prev IS).
Context: Uptrend healthy (UT)
Setup: Breakout continuation low hanging fruit (BO Cont LHF)
- Overextension
After multiple consecutive breakouts, the market reaches a state of overextension, signalling a possible reversal or pullback.
MTF Signals: Three days of breakout longs (1D Curr BO, Prev BO, BO 3L).
Context: Uptrend extended (UT)
- Reversal
After a breakout of previous days high that fails, price pulls away from the high showing a rollover of momentum across all timeframes and a potential short setup.
MTF Signals: Three days of breakout longs, daily fakeout high (1D 3L, FOH)
Context: Reversal countertrend (REV)
Setup: Fakeout high continuation high of day (FOH Cont HOD)
Note: This is only one possible illustrative scenario; there are many variations and combinations.
Example Chart: Full Bullish Cycle with Correlated Signals
Multi-Timeframe Signals examples:
Context and Setups examples:
Note: The signals shown along the move are manually added illustrations. The indicator shows these in realtime in the table at top and bottom right. This is only one possible scenario; there are many variations and combinations.
Due to the fractal nature of markets, this cycle can be observed across all timeframes. The strongest setups occur when there is multi-timeframe alignment. For example, a peak formation and potential reversal on the daily timeframe have higher probability and follow-through when they align with bearish signals on higher timeframes (e.g., weekly/monthly BD/FOH) and confirmation on lower timeframes (H4/H8 FOH/BD). With this perspective, the system enables the trader to follow the trend and momentum while identifying rollover points in a highly differentiated and precise way.
Using the Indicator for Trading
The automated analysis provided by the indicator can be used for thesis generation in preparation for a session as well as for live trading, leveraging the real-time updates as well as the context and setup indicated or alerted. It is recommended to customize the settings deeply, such as hiding the lower timeframes for thesis generation or the specific alert time window and settings to the specific trading schedule and playbook of the trader.
1. Context Assessment:
Evaluate alignment of higher timeframes (e.g., Month/Week, Week/Day). More alignment → Stronger setups.
- The context table offers an interpretation of the higher timeframe automatically. See below for further details.
2. Setup Identification:
Follow the bias of daily and H8 timeframes. A setup mostly requires alignment of these.
Setup Types:
- Trend Trade: Trade in alignment with the previous day’s trend.
Example: Price above the previous day’s high → Focus on long setups (dBO, H8 FOL) until overextension or reversal signs appear (H8 BO 3L, First R).
- Reversal Trade: Identify reversal setups when lower timeframes show rollovers after higher timeframe weakness.
Example: Price below the previous day’s high → Look for reversal signals at the current high of day (H8 FOH, BO 3L, First R).
- The setup table shows potential setups for the specific price zone in the table automatically. See below for further details.
3. Entry Confirmation:
Confirm entries based on H8 and H4 alignment, candle closes and lower timeframe fakeouts.
- H8 and H4 should always align for a final confirmation, meaning the breach lines should be both in the back of a potential trade setup.
- M15/ 5 candle close can be seen as acceptance beyond a level or within the setup zone.
- M15/5 FOH/ FOL signals lower timeframe traps potentially indicating further confirmation.
Example Chart Reversal Trade:
Context: REV (yellow), Reversal counter trend, Month in FOL with bearish First R, Week in BO but bearishly overextended with BO 3L, Day in Fakeout high reversing bearishly.
Setup: FOH Cont HOD (red), Day in Fakeout high after BO 3L overextension, confirmed by H8 FOH high of day, First R as further confluence. Two star quality and countertrend.
Entry: H4 BD, M15 close below followed by M15 FOH.
Detailed Features and Options
1. Context and Setup table
The Context and Setup Table is the core feature of the TrendPredator PRO indicator. It delivers real-time interpretation of the multi-timeframe analysis based on an extensive underlying logic table with over 150 variations, specifically developed for this system and indicator. This logic is continuously updated and optimized to ensure accuracy and performance.
1.1. Developing Context
States for developing higher timeframe context are determined based on signals from the monthly, weekly, and daily timeframes.
- Green and Red indicate alignment and potentially interesting developing setups.
- Yellow signals a mixed or conflicting bias, suggesting caution when taking trades.
The specific states are:
- UT (yellow): Uptrend extended
- UT (green): Uptrend healthy
- REV (yellow): Reversal day counter trend
- REV (green): Reversal day mixed trend
- REV Cont (green): Reversal continuation mixed trend
- REV Cont (yellow): Reversal continuation counter trend
- REV into UT (green): Reversal day into uptrend
- REV Cont into UT (green): Reversal continuation into uptrend
- UT Pullback (yellow): Counter uptrend breakdown day
- Conflicting (yellow): Conflicting signals
- Consolidating (yellow): Consolidating sideways
- Inside (yellow): Trading inside after an inside week
- DT Pullback (yellow): Counter downtrend breakout day
- REV Cont into DT (red): Reversal continuation into downtrend
- REV into DT (red): Reversal day into downtrend
- REV Cont (yellow): Reversal continuation counter trend
- REV Cont (red): Reversal continuation mixed trend
- REV (red): Reversal day mixed trend
- REV (yellow): Reversal day countertrend
- DT (red): Downtrend healthy
- DT (yellow): Downtrend extended
Example: Uptrend
The Uptrend Context (UT, green) indicates a healthy uptrend with all timeframes aligning bullishly. In this case, the monthly is in a Fakeout Low (FOL) and currently inside the range, while the weekly and daily are both in Breakout (BO) states. This context is favorable for developing long setups in the direction of the trend.
Example: Uptrend pullback
The Uptrend Pullback Context (UT Pullback, yellow) indicates a Breakdown (BD) on the daily timeframe against a higher timeframe uptrend. In this case, the monthly is in a Fakeout Low (FOL) and currently inside its range, the weekly is in Breakout (BO) and also currently inside, while the daily is in Breakdown (BD). This context reflects a conflicting situation—potentially signaling either an early reversal back into the uptrend or, if the breakdown extends, the beginning of a possible trend change.
Example: Reversal into Uptrend
The Reversal into Uptrend Context (REV into UT, green) indicates a lower timeframe reversal aligning with a higher timeframe uptrend. In this case, the monthly is in Breakout (BO), the weekly is in Breakout (BO) and currently inside its range, while the daily is showing a bullish Fakeout Low (FOL) reversal. This context is potentially very favorable for long setups, as it signals a strong continuation of the uptrend supported across multiple timeframes.
Example: Reversal
The Bearish Reversal Context indicates a lower timeframe rollover within an ongoing higher timeframe uptrend. In this case, the monthly remains in Breakout (BO), the weekly has shifted into a Fakeout High (FOH) after three weeks of breakout longs, and the daily is already in Breakdown (BD). This context suggests a potentially favorable developing short setup, as early signs of weakness appear across timeframes.
1.2. Developing Setup
The states for specific setups are based on the context and the signals from the daily timeframe and H8, indicating that price is in the zone of alignment. The setup description refers to the state of the daily timeframe, while the suffix relates to the H8 timeframe. For example, "prev FOH Cont LHF" means that the previous day is in FOH (Fakeout High) relative to yesterday's breakout level, currently trading inside, and we are in an H8 breakdown, indicating a potential LHF (Lower High Formation) short trade if the entry confirms. The suffix HOD means that H8 is in FOH or BO (Breakout).
The specific states are:
- REV HOD (red): Reversal high of day
- REV Cont LHF (red): Reversal continuation low hanging fruit
- BO Cont LHF (green): Breakout continuation low hanging fruit
- BO Cont LOD (green): Breakout continuation low of day
- FOH Cont HOD (red): Fakeout high continuation high of day
- FOH Cont LHF ((red): Fakeout high continuation low hanging fruit
- prev BD Cont HOD (red): Previous breakdown continuation high of day
- prev BD Cont LHF (red): Previous breakdown continuation low hanging fruit
- prev FOH Cont HOD (red): Previous fakeout high continuation high of day
- prev FOH Cont LHF (red): Previous fakeout high continuation low hanging fruit
- prev FOL Cont LOD (green): Previous fakeout low continuation low of day
- prev FOL Cont LHF (green): Previous fakeout low continuation low hanging fruit
- prev BO Cont LOD (green): Previous breakout continuation low of day
- prev BO Cont LHF (green): Previous breakout continuation low hanging fruit
- FOL Cont LHF (green): Fakeout low continuation low hanging fruit
- FOL Cont LOD (green): Fakeout low continuation low of day
- BD Cont LHF (red): BD continuation low hanging fruit
- BD Cont LOD (red): Breakdown continuation low of day
- REV Cont LHF (green): Reversal continuation low hanging fruit
- REV LOD (green): Reversal low of day
- Inside: Trading inside after an inside day
Type: Indicates the situation of the indicated setup concerning:
- Trend: Following higher timeframe trend
- Mixed: Mixed higher timeframe signals
- Counter: Against higher timeframe bias
Quality: Indicates the quality of the indicated setup according to the specified logic table
No star: Very low quality
* One star: Low quality
** Two star: Medium quality
*** Three star: High quality
Example: Breakout Continuation Trend Setup
This setup highlights a healthy uptrend where the month is in a breakout, the week is in a fakeout low, and the day is in a breakout after a first green day. As the H8 breaks out to the upside, a long setup zone is triggered, presenting a breakout continuation low-hanging fruit trade. This is a trend trade in an overextended situation on the H8, with an H8 3L, resulting in an overall quality rating of one star.
Example: Fakeout Low Continuation Trend Setup
This setup shows a reversal into uptrend, with the month in a breakout, the week in a breakout, and the day in a fakeout low after breaking down the previous day and now reversing back up. As H8 breaks out to the upside, a long setup zone is triggered, presenting a previous fakeout low continuation, low-hanging fruit trade. This is a medium-quality trend trade.
Example: Reversal Setup - Mixed Trend
This setup shows a reversal setup in line with the weekly trend, with the month in a fakeout low, the week in a fakeout high, and the day in a fakeout high after breaking out earlier in the day and now reversing back down. As H8 loses the previous breakout level after 3 breakouts (with H8 3L), a short setup zone is triggered, presenting a fakeout high continuation at the high of the day. This is a high-quality trade in a mixed trend situation.
Setup Alerts:
Alerts can be activated for setups freshly triggered on the chart within your trading window.
Detailed filter logic for setup alerts:
- Setup quality: 1-3 star
- Setup type: Counter, Mixed and Trend
- Setup category: e.g. Reversal Bearish, Breakout, Previous Fakeout High
- 1D BO and First signals: 3DS, 3DL, FRD, FGD, ID
Options:
- Alerts on/ off
- Alert time window (from/ to)
- Alert filter customization
Note: To activate alerts from a script in TradingView, some settings need to be adjusted. Open the "Create Alert" dialog and select the option "Any alert() function call" in the "Condition" section. Choose "TrendPredator PRO" to ensure that alerts trigger properly from the code. Alerts can be activated for entire watchlists or individual pairs. Once activated, the alerts run in the background and notify the user whenever a setup is freshly triggered according to the filter settings.
2. Multi-Timeframe Table
Provides a real-time view of system signals, including:
Current Timeframe (Curr): Bias states.
- Breakout (green BO): Bullish after breaking above the previous high.
- Fakeout High (red FOH): Bearish after breaking above the previous high but pulling back down.
- Breakdown (red BD): Bearish after breaking below the previous low.
- Fakeout Low (green FOL): Bullish after breaking below the previous low but pulling back up.
- Inside (IS): Price trading neutral inside the previous range, taking the previous bias (color indicates the previous bias).
Previous Timeframe (Prev): Tracks last candle bias state and transitions dynamically.
- Bias for last candle: BO, FOH, BD, FOL in respective colors.
- Inside bar (yellow IS): Indicated as standalone signal.
Note: Also previous timeframes get constantly updated in real time to track the bias state in relation to the level that was hit. This means a BO can still lose the level and become a FOH, and vice versa, and a BD can still become a FOL, and vice versa. This is critical to see for example if traders that are trapped in that timeframe with a FOH or FOL are released. An inside bar stays fixed, though, since no level was hit in that timeframe.
Breakouts (BO): Breakout count 3 longs and 3 shorts.
- 3 Longs (red 3L): Bearish after three breakouts without hitting a previous low.
- 3 Shorts (green 3S): Bullish after three breakdowns without hitting a previous high.
First Countertrend Close (First): Tracks First Red or Green Day.
- First Green (G): After two consecutive red closes.
- First Red (R): After two consecutive green closes.
Options: Customizable font size and label colors.
3. Historic Highs and Lows
Displays historic highs and lows per timeframe for added context, enabling users to track sequences over time.
Timeframes: H4, H8, D, W, M
Options: Customize for timeframes shown, number of historic candles per timeframe, colors, formats, and labels.
4. Previous High and Low Extensions
Displays extended previous levels (high, low, and close) for each timeframe to assess how price trades relative to these levels.
H4: P4H, P4L, P4C
H8: P8H, P8L, P8C
Daily: PDH, PDL, PDC
Weekly: PWH, PWL, PWC
Monthly: PMH, PML, PMC
Options: Fully customizable for timeframes shown, colors, formats, and labels.
5. Breach Lines
Tracks live market reactions (e.g., breakouts or fakeouts) per timeframe for the last previous high or low that was hit, highlighting these levels originating at the breached candle to indicate bias (color-coded).
Red: Bearish below
Green: Bullish above
H4: 4FOL, 4FOH, 4BO, 4BD
H8: 8FOL, 8FOH, 8BO, 8BD
D: dFOL, dFOH, dBO, dBD
W: wFOL, wFOH, wBO, wBD
M: mFOL, mFOH, mBO, mBD
Options: Fully customizable for timeframes shown, colors, formats, and labels.
Overall Options:
Toggle single feature groups on/off.
Customize H8 open/close time as an offset to UTC to be provider independent.
Colour settings con be adjusted for dark or bright backgrounds.
Higher Timeframe Use Case Examples
Example Use Case: Weekly Template Analysis
The Weekly Template is a core concept in Stacey Burke’s trading style. The analysis is conducted on the daily timeframe, focusing on the higher timeframe bias and identifying overextended conditions within the week—such as multiple breakouts and peak formations signaling potential reversals.
In this example, the candles are colored by the TrendPredator FO indicator, which highlights the state of individual candles. This allows for precise evaluation of both the trend state and the developing weekly template. It is a valuable tool for thesis generation before a trading session and for backtesting purposes.
Example Use Case: High Timeframe 5-Star Setup Analysis (Stacey Burke "ain't coming back" ACB Template)
This analysis identifies high-probability trade opportunities when daily breakout or breakdown closes occur near key monthly levels mid-week, signaling overextensions and potentially large parabolic moves. The key signal to look for is a breakout or breakdown close on a Wednesday. This is useful for thesis generation before a session and also for backtesting.
In this example, the TrendPredator FO indicator colors the candles to highlight individual candle states, particularly those that close in breakout or breakdown. Additionally, an indicator is shown on the chart shading every Wednesday, making it easier to visually identify the signals.
5 Star Alerts:
Alerts can be activated for this potential 5-Star setup constellation. The alert is triggered when there is a breakout or breakdown close on a Wednesday.
Further recommendations:
- Higher timeframe context: TPO or volume profile indicators can be used to gain an even better overview.
- Late session trading: Entries later in the session, such as during the 3rd hour of the NY session, offer better analysis and follow-through on setups.
- Entry confirmation: Momentum indicators like VWAP, Supertrend, or EMA are helpful for increasing precision. Additionally, tracking lower timeframe fakeouts can provide powerful confluence. To track those the TrendPredator Fakeout Highlighter (FO), that has been specifically developed for this can be of great help:
Limitations:
Data availability using TradingView has its limitations. The indicator leverages only the real-time data available for the specific timeframe being used. This means it cannot access data from timeframes lower than the one displayed on the chart. For example, if you are on a daily chart, it cannot use H8 data. Additionally, on very low timeframes, the historical availability of data might be limited, making higher timeframe signals unreliable.
To address this, the indicator automatically hides the affected columns in these specific situations, preventing false signals.
Disclaimer
This indicator is for educational purposes only and does not guarantee profits.
None of the information provided shall be considered financial advice.
The indicator does not provide final buy or sell signals but highlights zones for potential setups.
Users are fully responsible for their trading decisions and outcomes.
Volume Delta & Order Block Suite [QuantAlgo]Upgrade your volume analysis and order flow trading with Volume Delta & Order Block Suite by QuantAlgo, a sophisticated technical indicator that leverages advanced volume delta calculations, along with dynamic order block detection to provide deep insights into market participant behavior. By calculating the distribution of volume between buyers and sellers and tracking pivotal volume zones, the indicator helps traders understand the underlying forces driving price movements. It is particularly valuable for those looking to identify high-probability trading opportunities based on volume imbalances and key price levels where significant activity has occurred.
🟢 Technical Foundation
The Volume Delta & Order Block Suite utilizes sophisticated volume analysis techniques to estimate buying and selling pressure within each price candle. The core volume delta calculation employs a formula that estimates buy volume as: Volume × (Close - Low) ÷ (High - Low) , with sell volume calculated as the remainder of total volume. This approach assumes that when price closes near the high of a candle, most volume represents buying pressure, and when price closes near the low, most volume represents selling pressure.
For order block detection, the indicator implements a multi-step process involving volume pivot identification and price state tracking. It first detects significant volume pivot points using the ta.pivothigh function with a user-defined pivot period. It then tracks the market's order state based on whether the high exceeds the highest high or the low falls below the lowest low. When a volume pivot occurs, the indicator creates order blocks based on price levels at that pivot point. These blocks are continuously monitored for invalidation based on subsequent price action.
🟢 Key Features & Signals
1. Volume Delta Representation on Candles
The Volume Delta visualization on candles shows the buy/sell distribution directly on price bars, creating an immediate visual representation of volume pressure.
When buyers are dominant, candles are colored with the bullish theme color (default: green/teal).
Similarly, when sellers are dominant, candles are colored with the bearish theme color (default: red).
This visualization provides immediate insights into underlying volume pressure without requiring separate indicators, helping traders quickly identify which side of the market is in control.
2. Buy/Sell Pressure Information Table
The Volume Analysis Table provides a comprehensive breakdown of volume metrics across multiple timeframes, helping traders identify shifts in market behavior.
The table is organized into four timeframe columns:
Current Volume
1 Bar Before
1 Day Before
1 Week Before
For each timeframe, the table displays:
Buy volume: The estimated buying volume based on price action
Sell volume: The estimated selling volume based on price action
Total volume: The sum of buy and sell volume
Delta: The difference between buy and sell volume (positive when buyers are dominant, negative when sellers are dominant)
Additionally, the table shows both absolute values and percentage distributions, with trend indicators (Up, Down, or Neutral) at the bottom row of each timeframe column.
This multi-timeframe approach helps traders:
→ Identify volume imbalances between buyers and sellers
→ Track changes in volume delta across different periods
→ Compare current conditions with historical patterns
→ Detect potential reversals by watching for shifts in delta direction
The delta values are particularly useful as they provide a clear indication of market dominance – positive delta (Up) when buyers are dominant, and negative delta (Down) when sellers are dominant.
3. Order Blocks and Their Confluence
Order blocks represent significant price zones where volume pivots occur, potentially indicating areas of significant market participant activity.
The indicator identifies two types of order blocks:
Bullish Order Blocks (support): Highlighted with a green/teal color, these represent potential support areas where price might bounce when revisited
Bearish Order Blocks (resistance): Highlighted with a red color, these represent potential resistance areas where price might reverse when revisited
Each order block is visualized as a colored rectangle with a dashed line showing the average price within the block. The blocks are extended to the right until they are invalidated.
Order blocks can serve as key reference points for trading decisions, for example:
Support/resistance identification
Stop loss placement (beyond the opposite edge of the block)
Potential reversal zones
Target areas for profit-taking
When price approaches an order block, traders should look for confluence with the volume delta on candles and the information in the volume analysis table. Strong setups occur when all three components align – for example, when price approaches a bearish order block with increasing sell volume shown on the candles and in the volume table.
🟢 Practical Usage Tips
→ Volume Analysis and Interpretation: The indicator visualizes the buy/sell volume ratio directly on price candles using color intensity, allowing traders to immediately identify which side (buyers or sellers) is dominant. This information helps in assessing the strength behind price movements and potential continuation or reversal signals.
→ Order Block Trading Strategies: The indicator highlights significant price zones where volume pivots occur, marking these as potential support (bullish order blocks) or resistance (bearish order blocks). Traders can use these levels to identify potential reversal points, stop placement, and profit targets.
→ Multi-timeframe Volume Comparison: Through its comprehensive volume analysis table, the indicator enables traders to compare volume patterns across current, recent, daily, and weekly timeframes. This helps in identifying shifts in market behavior and confirming the strength of ongoing trends.
🟢 Pro Tips
Adjust Pivot Period based on your timeframe:
→ Lower values (3-5) for more frequent order blocks
→ Higher values (7-10) for stronger, less frequent order blocks
Fine-tune Mitigation Method based on your trading style:
→ "Wick" for more conservative invalidation
→ "Close" for more lenient order block survival
Look for confluence between components:
→ Strong volume delta in the expected direction when price touches an order block
→ Corresponding patterns in the volume analysis table
→ Overall market context aligning with the expected direction
Use for multiple trading approaches:
→ Support/resistance trading at order blocks
→ Trend confirmation with volume delta
→ Reversal detection when volume delta changes direction
→ Stop loss placement using order block boundaries
Combine with:
→ Trend analysis using trend-following indicators for trade confirmation
→ Multiple timeframe analysis for strategic context
Triad Trade MatrixOverview
Triad Trade Matrix is an advanced multi-strategy indicator built using Pine Script v5. It is designed to simultaneously track and display key trading metrics for three distinct trading styles on a single chart:
Swing Trading (Swing Supreme):
This mode captures longer-term trends and is designed for trades that typically span several days. It uses customizable depth and deviation parameters to determine swing signals.
Day Trading (Day Blaze):
This mode focuses on intraday price movements. It generates signals that are intended to be executed within a single trading session. The parameters for depth and deviation are tuned to capture more frequent, shorter-term moves.
Scalping (Scalp Surge):
This mode is designed for very short-term trades where quick entries and exits are key. It uses more sensitive parameters to detect rapid price movements suitable for scalping strategies.
Each trading style is represented by its own merged table that displays real-time metrics. The tables update automatically as new trading signals are generated.
Key Features
Multi-Style Tracking:
Swing Supreme (Large): For swing trading; uses a purple theme.
Day Blaze (Medium): For day trading; uses an orange theme.
Scalp Surge (Small): For scalping; uses a green theme.
Real-Time Metrics:
Each table displays key trade metrics including:
Entry Price: The price at which the trade was entered.
Exit Price: The price at which the previous trade was exited.
Position Size: Calculated as the account size divided by the entry price.
Direction: Indicates whether the trade is “Up” (long) or “Down” (short).
Time: The time when the trade was executed (formatted to hours and minutes).
Wins/Losses: The cumulative number of winning and losing trades.
Current Price & PnL: The current price on the chart and the profit/loss computed relative to the entry price.
Duration: The number of bars that the trade has been open.
History Column: A merged summary column that shows the most recent trade’s details (entry, exit, and result).
Customizability:
Column Visibility: Users can toggle individual columns (Ticker, Timeframe, Entry, Exit, etc.) on or off according to their preference.
Appearance Settings: You can customize the table border width, frame color, header background, and text colors.
History Toggle: The merged history column can be enabled or disabled.
Chart Markers: There is an option to show or hide chart markers (labels and lines) that indicate trade entries and exits on the chart.
Trade History Management:
The indicator maintains a rolling history (up to three recent trades per trading style) and displays the latest summary in the merged table.
This history column provides a quick reference to recent performance.
How It Works
Signal Generation & Trade Metrics
Trade Entry/Exit Calculation:
For each trading style, the indicator uses built-in functions (such as ta.lowestbars and ta.highestbars) to analyze price movements. Based on a customizable "depth" and "deviation" parameter, it determines the point of entry for a trade.
Swing Supreme: Uses larger depth/deviation values to capture swing trends.
Day Blaze: Uses intermediate values for intraday moves.
Scalp Surge: Uses tighter parameters to pick up rapid price changes.
Metrics Update:
When a new trade signal is generated (i.e., when the trade entry price is updated), the indicator calculates:
The current PnL as the difference between the current price and the entry price (or vice versa, depending on the trade direction).
The duration as the number of bars since the trade was opened.
The position size using the formula: accountSize / entryPrice.
History Recording:
Each time a new trade is triggered (i.e., when the entry price is updated), a summary string is created (showing entry, exit, and win/loss status) and appended to the corresponding trade history array. The merged table then displays the latest summary from this history.
Table Display
Merged Table Structure:
Each trading style (Swing Supreme, Day Blaze, and Scalp Surge) is represented by a table that has 15 columns. The columns are:
Trade Type (e.g., Swing Supreme)
Ticker
Timeframe
Entry Price
Exit Price
Position Size
Direction
Time of Entry
Account Size
Wins
Losses
Current Price
Current PnL
Duration (in bars)
History (the latest trade summary)
User Customization:
Through the settings panel, users can choose which columns to display.
If a column is toggled off, its cells will remain blank, allowing traders to focus on the metrics that matter most to them.
Appearance & Themes:
The table headers and cell backgrounds are customizable via color inputs. The trading style names are color-coded:
Swing Supreme (Large): Uses a purple theme.
Day Blaze (Medium): Uses an orange theme.
Scalp Surge (Small): Uses a green theme.
How to Use the Indicator
Add the Indicator to Your Chart:
Once published, add "Triad Trade Matrix" to your TradingView chart.
Configure the Settings:
Adjust the Account Size to match your trading capital.
Use the Depth and Deviation inputs for each trading style to fine-tune the signal sensitivity.
Toggle the Chart Markers on if you want visual entry/exit markers on the chart.
Customize which columns are visible via the column visibility toggles.
Enable or disable the History Column to show the merged trade history in the table.
Adjust the appearance settings (colors, border width, etc.) to suit your chart background and preferences.
Interpret the Tables:
Swing Supreme:
This table shows metrics for swing trades.
Look for changes in entry price, PnL, and trade duration to monitor longer-term moves.
Day Blaze:
This table tracks day trading activity.It will update more frequently, reflecting intraday trends.
Scalp Surge:
This table is dedicated to scalping signals.Use it to see quick entry/exit data and rapid profit/loss changes.
The History column (if enabled) gives you a snapshot of the most recent trade (e.g., "E:123.45 X:124.00 Up Win").
Use allerts:
The indicator includes alert condition for new trade entries(both long and short)for each trading style.
Summary:
Triad Trade Matrix provides an robust,multi-dimensional view of your trading performance across swing trading, day trading, and scalping.
Best to be used whith my other indicators
True low high
Vma Ext_Adv_CustomTbl
This indicator is ideal for traders who wish to monitor multiple trading styles simultaneously, with a clear, technical, and real-time display of performance metrics.
Happy Trading!
GL_Prev Week HighThe GL_Prev Week High Indicator is a powerful tool designed to enhance your trading analysis by displaying the previous week's high price directly on your chart. With clear and customizable visuals, this indicator helps traders quickly identify critical price levels, enabling more informed decision-making.
Key Features:
Previous Week's High Line:
Displays the previous week's high as a red line on your chart for easy reference.
Customizable Horizontal Line:
Includes a white horizontal line for enhanced clarity, with adjustable length, color, and width settings.
All-Time High Tracking:
Automatically tracks the all-time high from the chart's history and places a dynamic label above it.
Real-Time Updates:
The indicator updates in real-time to ensure accuracy as new bars are added.
User Inputs for Personalization:
Adjust the left and right span of the horizontal line.
Customize line width and color to suit your preferences.
Use Case:
This indicator is ideal for traders looking to integrate the previous week's high as a key support or resistance level in their trading strategy. Whether you are analyzing trends, identifying breakout zones, or planning entry/exit points, this tool provides valuable insights directly on the chart.
How to Use:
Add the indicator to your chart.
Customize the settings (line length, width, and color) through the input panel to match your preferences.
Use the red line to track the previous week's high and the label to monitor all-time highs effortlessly.
License:
This script is shared under the Mozilla Public License 2.0. Feel free to use and adapt the script as per the license terms.
Globex time (New York Time)This indicator is designed to highlight and analyze price movements within the Globex session. Primarily geared toward the Globex Trap trading strategy, this tool visually identifies the session's high and low prices, allowing traders to better assess price action during extended hours. Here’s a comprehensive breakdown of its features and functionality:
Purpose
The "Globex Time (New York Time)" indicator tracks price levels during the Globex trading session, providing a clear view of overnight market activity. This session, typically running from 6 p.m. ET (18:00) until the following morning at 8:30 a.m. ET, is a critical period where significant market positioning can occur before the regular session opens. In the Globex Trap strategy, the session high and low are essential levels, as price movements around these areas often indicate potential support, resistance, or reversal zones, which traders use to set up entries or exits when the regular trading session begins.
Key Features
Customizable Session Start and End Times
The indicator allows users to specify the exact start and end times of the Globex session in New York time. The default settings are:
Start: 6 p.m. ET (18:00)
End: 8:30 a.m. ET
These settings can be adjusted to align with specific market hours or personal preferences.
Session High and Low Identification
Throughout the defined session, the indicator dynamically calculates and tracks:
Session High: The highest price reached within the session.
Session Low: The lowest price reached within the session.
These levels are essential for the Globex Trap strategy, as price action around them can indicate likely breakout or reversal points when regular trading resumes.
Vertical Lines for Session Start and End
The indicator draws vertical lines at both the session start and end times:
Session Start Line: A solid line marking the exact beginning of the Globex session.
Session End Line: A similar vertical line marking the session’s conclusion.
Both lines are customizable in terms of color and thickness, making it easy to distinguish the session boundaries visually on the chart.
Horizontal Lines for Session High and Low
At the end of the session, the indicator plots horizontal lines representing the Globex session's high and low levels. Users can customize these lines:
Color: Define specific colors for the session high (default: red) and session low (default: green) to easily differentiate them.
Line Style: Options to set the line style (solid, dashed, or dotted) provide flexibility for visual preferences and chart organization.
Automatic Reset for Daily Tracking
To adapt to the next trading day, the indicator resets the session high and low data once the current session ends. This reset prepares it to start tracking new levels at the beginning of the next session without manual intervention.
Practical Application in the Globex Trap Strategy
In the Globex Trap strategy, traders are primarily interested in price behavior around the high and low levels established during the overnight session. Common applications of this indicator for this strategy include:
Breakout Trades: Watching for price to break above the Globex high or below the Globex low, indicating potential momentum in the breakout direction.
Reversal Trades: Monitoring for failed breakouts or traps where price tests and rejects the Globex high or low, suggesting a reversal as liquidity is trapped in these zones.
Support and Resistance Zones: Using the session high and low as key support and resistance levels during the regular trading session, with potential entry or exit points when price approaches these areas.
Additional Configuration Options
Vertical Line Color and Width: Define the color and thickness of the vertical session start and end lines to match your chart’s theme.
Upper and Lower Line Colors and Styles: Customize the appearance of the session high and low horizontal lines by setting color and line style (solid, dashed, or dotted), making it easy to distinguish these critical levels from other chart markings.
Summary
This indicator is a valuable tool for traders implementing the Globex Trap strategy. It visually segments the Globex session and marks essential price levels, helping traders analyze market behavior overnight. Through its customizable options and clear visual representation, it simplifies tracking overnight price activity and identifying strategic levels for potential trade setups during the regular session.
BB MTF FVGs & First PresentedBB MTF FVGs with First Presented FVG Highlight
The BB MTF FVGs with First Presented FVG Highlight indicator is an advanced trading tool designed to help users identify and monitor Fair Value Gaps (FVGs) across multiple timeframes, offering traders deep insight into market structure and liquidity imbalances. With the ability to track up to three distinct timeframes (e.g., 1-minute, 1-hour, and 1-day), this indicator provides a comprehensive multi-level perspective, helping traders recognize critical support and resistance areas based on liquidity gaps. Additionally, it highlights the first FVG that presents after a specific time each day, making it especially useful for traders who prioritize session starts or key time-based market activity.
_________________________________________________________________________________
Key Features
1. Multi-Timeframe FVG Detection on Three Levels:
• Track FVGs on three user-defined timeframes for a robust view of liquidity gaps across intraday, intermediate, and higher timeframes. For instance, you could set up 1-minute, 1-hour, and 1-day timeframes to capture the market’s behavior from granular intraday action to daily structural gaps. Each timeframe is fully customizable, and users can enable or disable individual levels as needed.
2. Price Action-Driven FVG Status Analysis:
• The indicator continuously monitors price action to assess the state of each FVG. FVGs are dynamically styled based on their status:
• Untouched: FVGs with solid borders indicate that price has not yet traded into the gap.
• Mitigated: If price partially fills or “mitigates” an FVG, its borders turn dotted, providing visual feedback that the gap has seen some interaction.
• Inverted: When an FVG is fully invalidated by price moving completely through it, the border is removed, signaling the inversion. This real-time analysis gives traders instant feedback on the status of each FVG, helping them quickly assess active, mitigated, or invalidated zones.
3. Highlighting the First FVG After a Specified Time:
• A unique feature that highlights the first FVG presented after a specified time (e.g., 9:30 AM) each day, making it easy for traders to focus on session-based FVGs that could impact market direction. This feature is especially valuable for those tracking the opening range or specific session periods.
4. Configurable FVG Extension Options:
• The indicator offers flexible settings to control how long each FVG remains extended across the chart. Users can choose to extend until the first mitigation, until full mitigation, until inversion, or opt for no extension. This allows traders to adjust FVG visibility duration based on their strategy and trading style.
_________________________________________________________________________________
Customizable User Inputs
The BB MTF FVGs with First Presented FVG Highlight indicator includes various customization options for a personalized experience:
• Three Configurable Timeframes for FVG Tracking:
• Timeframe 1: Primary timeframe, like 1 minute, to capture short-term gaps.
• Timeframe 2: Secondary timeframe, such as 1 hour, to observe intraday market structure.
• Timeframe 3: Higher timeframe, like 1 day, to track major gaps with a longer-term impact. Each timeframe is independently customizable, allowing users to tailor their multi-timeframe FVG setup to fit their trading approach.
• Session-Based First FVG Highlighting:
• Highlight Type: Select whether to highlight only the first FVG presented after the defined time, display it with other FVGs, or turn off the highlight feature.
• Start and End Time for First Highlighted FVG: Specify the start and end time (e.g., 9:30 AM to 10:30 AM) for highlighting the first FVG, enabling a session-focused approach.
• Plotting Control for Forming FVGs:
• Forming FVG Display: Enable or disable forming FVGs for each timeframe, allowing traders to track potential gaps as they start to appear before confirmation.
• Color and Style Customization:
• FVG Colors: Define colors for long and short FVGs on each timeframe for visual clarity. Additionally, set the highlight color for the first FVG to make it stand out.
• Border Styling Based on FVG Status: The indicator’s dynamic border styling provides a clear visual status for each FVG:
• Solid borders for untouched FVGs.
• Dotted borders for mitigated FVGs.
• Borderless display for inverted FVGs.
• Flexible FVG Extension Duration:
• Choose the extension behavior for FVGs based on preferred criteria: extending until first mitigation, keeping them until fully mitigated, extending until inversion, or selecting no extension. This flexibility is ideal for traders who want to adapt FVG visibility to specific conditions.
_________________________________________________________________________________
Technical Details
This indicator leverages precise, real-time calculations to monitor price interactions with each FVG, ensuring clarity and accuracy across multiple timeframes without duplicate or redundant displays. It offers traders a powerful way to track liquidity gaps at various market levels with the added benefit of immediate visual feedback on gap status.
Time Clusters# Time Clusters: Where Time Meets Opportunity in the Markets
Elevate your trading strategy with Time Clusters – the innovative indicator that transforms market open times into powerful visual cues for potential price action!
## Unveil Hidden Market Dynamics
Time Clusters goes beyond traditional open price analysis. By visualizing key time-based imbalances, this tool reveals:
- Potential support and resistance zones
- Areas prone to strong market reactions
- Historical patterns at crucial market opens
## Key Features
- Multi-level analysis: Track up to 6 customizable daily open times
- Smart visuals: Color-coded boxes highlight time-based clusters
- Bias tracking: Monitor historical candle types and market sentiment
- Flexible customization: Adapt to your timezone and visual preferences
## How It Works
1. Identifies critical market open times
2. Analyzes price action and candle formations at these points
3. Visualizes potential imbalances as color-coded "clusters"
4. Tracks historical bias to provide deeper market context
## Trader-Friendly Design
- Intuitive visual cues for quick decision-making
- Customizable settings to fit your trading style
- Works across multiple timeframes for versatile analysis
## Elevate Your Strategy
Use Time Clusters to:
- Anticipate potential price reversals or continuations
- Identify high-probability trade entry and exit points
- Gain unique insights into time-based market behavior
## Important Note
While Time Clusters offers powerful insights, it's designed to complement your trading strategy, not replace it. Always use multiple analysis tools and sound risk management practices.
Remember: All OmarxQQQ tools, including Time Clusters, are aids to assist your trading decisions. They should never be the sole basis for any financial choice.
Harness the power of time in your trades with Time Clusters!
*Disclaimer: Trading involves significant risk of loss. Past performance does not guarantee future results. Use this tool responsibly as part of a comprehensive trading plan.*
Parent Session Sweeps + Alert Killzone Ranges with Parent Session Sweep
Key Features:
1. Multiple Session Support: The script tracks three major trading sessions - Asia, London, and New York. Users can customize the timing of these sessions.
2. Killzone Visualization: The strategy visually represents each session's range, either as filled boxes or lines, allowing traders to easily identify key price levels.
3. Parent Session Logic: The core of the strategy revolves around identifying a "parent" session - a session that encompasses the range of the following session. This parent session becomes the basis for potential trade setups.
4. Sweep and Reclaim Setups: The strategy looks for price movements that sweep (break above or below) the parent session's high or low, followed by a reclaim of that level. This price action often indicates a potential reversal.
5. Risk-Reward Filtering: Each potential setup is evaluated based on a user-defined minimum risk-reward ratio, ensuring that only high-quality trade opportunities are considered.
6. Candle Close Filter: An optional filter that checks the characteristics of the candle that reclaims the parent session level, adding an extra layer of confirmation to the setup.
7. Performance Tracking: The strategy keeps track of bullish and bearish setup success rates, providing valuable feedback on its performance over time.
8. Visual Aids: The script draws lines to mark the parent session's high and low, making it easy for traders to identify key levels.
How It Works:
1. The script continuously monitors price action across the defined sessions.
2. When a session fully contains the range of the next session, it's identified as a potential parent session.
3. The strategy then waits for price to sweep either the high or low of this parent session.
4. If a sweep occurs, it looks for a reclaim of the swept level within the parameters set by the user.
5. If a valid setup is identified, the script generates an alert and places a trade (if backtesting or running live).
6. The strategy continues to monitor the trade for either reaching the target (opposite level of the parent session) or hitting the stop loss.
Considerations for Signals:
- Sweep: A break of the parent session's high or low.
- Reclaim: A close back inside the parent session range after a sweep.
- Candle Characteristics: Optional filter for the reclaim candle (e.g., bullish candle for long setups).
- Risk-Reward: Each setup must meet or exceed the user-defined minimum risk-reward ratio.
- Session Timing: The strategy is sensitive to the defined session times, which should be set according to the trader's preferred time zone.
This strategy aims to capitalize on institutional order flow and liquidity patterns in the forex market, providing traders with a systematic approach to identifying potential reversal points with favorable risk-reward profiles.
Signals for Trending or Ranging market using RSI and WMAThis trading indicator is based on several key components, including the Average Directional Index (ADX), and a combination of RSI and Weighted Moving Average (WMA) to signal trading opportunities in both trending and ranging markets. Here's a breakdown:
ADX Calculation: The script calculates the ADX to identify market trends. A threshold value of ADX is used to distinguish between trending and ranging market conditions.
RSI and WMA for Different Market Conditions: The script calculates two sets of RSI and WMA, one for trending markets and another for ranging markets. This allows the strategy to adjust based on market conditions determined by the ADX value.
Trade Signals: The script generates long and short signals based on the alignment of RSI and WMA.
Long Signals: Triggered when RSI and WMA indicate upward momentum.
Short Signals: Triggered when both RSI and WMA suggest downward movement.
The signals are confirmed by pivot points, with the stop loss placed at the most recent high or low.
Stop Loss and Trade Management: The script includes dynamic stop-loss management. It moves the stop loss in halfway original stop loss after achieving 2R and to break-even after achieving a 4R gain.
Performance Tracking: It tracks the number of winning and losing trades and calculates the total "R" (risk/reward) for the active trades. Debugging labels are added on the chart to display statistics for wins, losses, and total R performance.
Plotting: The script plots the stop loss and entry price on the chart for visual clarity. Additionally, it colors the background green or red based on whether a long or short position is active.
Overall, this indicator combines ADX, RSI, and WMA indicators with a robust trade management system to execute and track trading signals in both trending and ranging markets.
CAGR - Candle based BackTesterThe "CAGR - Candle based BackTester" is a tool for traders and investors seeking precise insights into individual candle performance!
Do you want to backtest based on candles and understand their CAGR? Curious about the average CAGR of all candles? Interested in comparing how an individual candle performs against others? Then this tool is your go-to solution.
How It Works:
Candle Selection: Specify a start date, and watch as the script tracks investments from that point forward.
Dynamic Calculations: Experience real-time CAGR calculations that adapt as market conditions evolve.
CAGR Display: At the final candle, gain insights into individual CAGR, average CAGR of all candles, alpha (difference), and outperformance percentage—all conveniently displayed for informed decision-making.
Key Features:
Accurate Candle-based CAGR Calculation: Gain clarity on investment performance with precise CAGR metrics.
Lumpsum Investment Tracking: Track lumpsum investments seamlessly with detailed share and investment calculations.
Outperformance Metrics: Measure how your investment performs relative to others with dedicated outperformance metrics.
User-Friendly Visualization: Access intuitive charts and visuals that simplify complex financial data.
Body Close Continuity & failure Backtesting @MaxMaseratiThis indicator, is a highly advanced institutional-grade tool designed to track the "lifespan" of a trend based on Body Close (BC) sequences.
Unlike basic indicators that just show direction, this script analyzes the structural integrity of a trend by monitoring how many candles continue the move before a "Touch" (retest) or a "Break" (failure) occurs.
The Continuity & Failure Stats indicator tracks sequences of Bullish Body Closes (BuBC) and Bearish Body Closes (BeBC). It measures three critical phases: Building (pure momentum), Touching (price retesting the low/high of the sequence), and Resumption (price continuing the trend after a retest). It provides a statistical distribution of how long these "buildings" typically last before failing, allowing traders to know exactly when a trend is overextended.
This comprehensive analysis blends the statistical breakdown of the Continuity & Failure Stats indicator to provide a deep understanding of the structural momentum for the S&P 500 E-mini (ES1!) on a 4-hour timeframe.
1. Extensive Table Breakdown
A. Building Distribution (Left Table): The Fatigue Gauge
This table acts as a histogram of momentum, tracking the "Building Count"—the number of consecutive candles closing in a trend without price returning to its origin.
Count Column: Represents the streak length (e.g., 1, 2, or 3 candles).
Touch Column: Shows how many times a streak was interrupted by a retest ("touch") but remained structurally intact.
Break Column: Counts total structural failures where price closed beyond the sequence's anchor.
Data Insight: For BuBC, 92 sequences reached Count 1, but only 28 remained by Count 4. This reveals a steep momentum decay after the 3rd candle, establishing a "Statistical Wall" where only 2 sequences in history reached a count of 9.
B. MMM Summary Stats (Top Right): The Mathematical DNA
This table provides the "Expected Value" and behavior of a trend over the lookback period.
Avg Building (2.39 for BuBC): On average, a bullish move lasts ~2.4 candles of pure momentum before a retest or reversal occurs.
Avg Touches (0.8): This low number indicates "clean" trends that rarely wobble back to retest levels multiple times before reaching a conclusion.
Avg R Cycles (0.55): This suggests that once a bullish trend is interrupted, it only successfully resumes its momentum about half the time.
Max R Count (1): Typically, once a trend is "touched," it only manages one more push before failing.
C. Multi-Timeframe (MTF) Quick Stats (Bottom Right): Trend Weight
This compares the 4H chart against other layers of the market to identify "global" alignment.
Sample Comparison: There are 3,594 tracked BuBC sequences on the 4H compared to only 142 on the Weekly chart.
Fractal Law: The Avg Building (2.4) is consistent across several timeframes, implying that the "Rule of Three" (momentum fading after 3 candles) is a fractal characteristic of this asset.
2. Table Comparison: Synthesizing the Data
To trade effectively, you must compare Distribution (timing) against Summary Stats (averages):
Continuity vs. Failure: The Summary Stats show an average building of 2.39. When checking the Distribution table at Count 2, the "Break" count (58) is already high relative to the "Total". This confirms that the risk of failure increases exponentially the moment you exceed the average.
Momentum vs. Mean Reversion: Distribution tells you when a trend is "tired". If the 4H is at a "Building Count 4" (statistically overextended) while the Weekly chart is at "Building Count 1" (fresh momentum), you may choose to prioritize the higher timeframe's strength despite the local overextension.
3. Strategic Summary & Application
This indicator proves that market momentum follows a predictable "Building" cycle rather than an infinite streak.
The "Rule of Three" for ES1! 4H:
The Entry Zone (Momentum Start): The most profitable entries occur at Building Count 1. Statistically, you have a high probability of reaching a count of 2 or 3.
The Exit Zone (Momentum Limit): Take profits or tighten stops at Count 3. The data shows the sample size drops by nearly 50% between Count 3 and Count 4.
The "Touch" Rule (Retest Reliability): If price returns to the sequence low (a "Touch"), do not expect a massive continuation. The Max R Count of 1 tells us that resumptions are usually short-lived.
Danger Zone: Entering at Building Count 4 or higher is statistically dangerous, as the "Break" probability significantly outweighs the "Touch" or continuation probability.
Flux Portfolio Visualizer | GL0WDASHFlux Portfolio Visualizer | GL0WDASH
Flux Portfolio Visualizer lets you simulate and track the performance of a multi-asset portfolio directly on the chart.
Choose up to 10 assets, assign custom allocation weights, and set a start date to generate a real-time equity curve based on historical price data.
The script performs one-time proportional allocation at the start date and then tracks equity forward without rebalancing, giving you a realistic view of how your portfolio would have evolved over time. It also includes a maximum equity drawdown tracker and an optional level line for reference.
Features:
• Allocate to up to 10 assets with custom weight percentages
• Specify initial capital and simulation start date
• Real-time equity curve based on confirmed bars
• Maximum equity drawdown tracking + table display
• Optional horizontal reference line
• Designed for long-horizon allocation experiments
Great for:
• Passive portfolio stress-testing
• Comparing allocation strategies
• Evaluating long-term crypto/asset mixes
• Visualizing risk via max drawdowns
This tool does not execute trades or rebalance—its purpose is pure visualization, giving traders clarity about how portfolios behave under different allocation assumptions.
If you expand or modify the indicator, please credit the original author.
Trading Volatility Clock⏰ TRADING VOLATILITY CLOCK - Know When the Action Happens (Anywhere in the World)
A real-time session tracker with multi-timezone support for active traders who need to know when US market volatility strikes - no matter where they are in the world. Perfect for day traders, scalpers, and anyone trading liquid US markets.
══════════════════════════════════════════════════════
📊 WHAT IT DOES
This indicator displays a live clock showing:
- Current time in YOUR selected timezone (10 major timezones supported)
- Active US market session with color-coded volatility levels
- Countdown timer showing time remaining in current session
- Preview of the next upcoming session
- Optional alerts when entering high-volatility periods
══════════════════════════════════════════════════════
🌍 MULTI-TIMEZONE SUPPORT
SESSIONS ALWAYS TRACK US MARKET HOURS (Eastern Time):
No matter which timezone you select, the sessions always trigger at the correct US market times. Perfect for international traders who want to:
• See their local time while tracking US market sessions
• Know exactly when US volatility hits in their timezone
• Plan their trading day around US market hours
SUPPORTED TIMEZONES:
• America/New_York (ET) - Eastern Time
• America/Chicago (CT) - Central Time
• America/Los_Angeles (PT) - Pacific Time
• Europe/London (GMT) - Greenwich Mean Time
• Europe/Berlin (CET) - Central European Time
• Asia/Tokyo (JST) - Japan Standard Time
• Asia/Shanghai (CST) - China Standard Time
• Asia/Hong_Kong (HKT) - Hong Kong Time
• Australia/Sydney (AEDT) - Australian Eastern Time
• UTC - Coordinated Universal Time
EXAMPLE: A trader in Tokyo selects "Asia/Tokyo"
• Clock shows: 11:30 PM JST
• Session shows: "Opening Drive" 🔥 HIGH
• They know: US market just opened (9:30 AM ET in New York)
══════════════════════════════════════════════════════
🎯 WHY IT'S USEFUL
Whether you trade futures, high-volume stocks, or ETFs, volatility isn't constant throughout the day. Knowing WHEN to expect movement is critical:
🔥 HIGH VOLATILITY (Red):
• Opening Drive (9:30-10:30 AM ET) - Highest volume of the day
• Power Hour (3:00-4:00 PM ET) - Second-highest volume, final push
⚡ MEDIUM VOLATILITY (Yellow):
• Pre-Market (8:00-9:30 AM ET) - Building momentum
• Lunch Return (1:00-2:00 PM ET) - Traders returning
• Afternoon Session (2:00-3:00 PM ET) - Trend continuation
• After Hours (4:00-5:00 PM ET) - News reactions
💤 LOW VOLATILITY (Gray):
• Overnight Grind (12:00-8:00 AM ET) - Thin volume
• Mid-Morning Chop (10:30-11:30 AM ET) - Ranges form
• Lunch Hour (11:30 AM-1:00 PM ET) - Dead zone
• Evening Fade (5:00-8:00 PM ET) - Volume dropping
══════════════════════════════════════════════════════
⚙️ CUSTOMIZATION OPTIONS
TIMEZONE SETTINGS:
• Select from 10 major timezones worldwide
• Clock automatically displays in your local time
• Sessions remain locked to US market hours
SESSION TIME CUSTOMIZATION:
• Every session boundary is adjustable (in minutes from midnight ET)
• Perfect for traders who define sessions differently
• Advanced users can create custom volatility schedules
DISPLAY OPTIONS:
• Toggle next session preview on/off
• Enable/disable high volatility alerts
• Clean, unobtrusive table display in top-right corner
══════════════════════════════════════════════════════
💡 HOW TO USE
1. Add indicator to any chart (works on all timeframes)
2. Select your timezone in Settings → Timezone Settings
3. Set your chart to 1-minute timeframe for real-time updates
4. Customize session times if needed (Settings → Session Time Customization)
5. Watch the top-right corner for live session tracking
TRADING APPLICATIONS:
• Avoid trading during dead zones (lunch hour, mid-morning chop)
• Increase position size during high volatility windows
• Set alerts for Opening Drive and Power Hour
• Plan your trading day around US market volatility schedule
• International traders can track US sessions in their local time
══════════════════════════════════════════════════════
🎓 EDUCATIONAL VALUE
This indicator teaches traders:
• Market microstructure and volume patterns
• Why certain times produce better opportunities
• How institutional flows create intraday patterns
• The importance of timing in active trading
• How to adapt US market trading to any timezone
══════════════════════════════════════════════════════
⚠️ IMPORTANT NOTES
- Works best on 1-minute charts for frequent updates
- Sessions are ALWAYS based on US Eastern Time (ET)
- Timezone selection only changes the clock display
- Clock updates when new bar closes (not tick-by-tick)
- Alerts trigger once per bar when enabled
- Perfect for international traders tracking US markets
══════════════════════════════════════════════════════
📈 BEST USED WITH
- High-volume US stocks: TSLA, NVDA, AAPL, AMD, META
- Major US ETFs: SPY, QQQ, IWM, DIA
- US Futures: ES, NQ, RTY, YM, MES, MNQ
- Any liquid US instrument with clear intraday volume patterns
══════════════════════════════════════════════════════
🌏 FOR INTERNATIONAL TRADERS
This tool is specifically designed for traders outside the US who need to:
• Track US market sessions in their local timezone
• Know when to be at their desk for US volatility
• Avoid waking up for low-volatility periods
• Maximize trading efficiency around US market hours
No more timezone confusion. No more missing the opening bell. Just set your timezone and trade with confidence.
══════════════════════════════════════════════════════
This is an open-source educational tool. Feel free to modify and adapt to your trading style!
Happy Trading! 🚀
FVG Maxing - Fair Value Gaps, Equilibrium, and Candle Patterns
What this script does
This open-source indicator highlights 3-candle fair value gaps (FVGs) on the active chart timeframe, draws their midpoint ("equilibrium") line, tracks when each gap is mitigated, and optionally marks simple candle patterns (engulfing and doji) for confluence. It is intended as an educational tool to study how price interacts with imbalances.
3-candle bullish and bearish FVG zones drawn as forward-extending boxes.
Equilibrium line at 50% of each gap.
Different styling for mitigated vs unmitigated gaps.
Compact statistics panel showing how many gaps are currently active and filled.
Optional overlays for bullish/bearish engulfing patterns and doji candles.
1. FVG logic (3-candle gaps)
The script focuses on a strict 3-candle definition of a fair value gap:
Three consecutive candles with the same body direction.
The wick of candle 3 is separated from the wick of candle 1 (no overlap).
A bullish gap is created when price moves up fast enough to leave a gap between candle 1 and 3. A bearish gap is the mirror case to the downside.
In Pine, the core detection looks like this:
// Three candles with the same body direction
bull_seq = close > open and close > open and close > open
bear_seq = close < open and close < open and close < open
// Wick gap between candle 1 and candle 3
bull_gap = bull_seq and low > high
bear_gap = bear_seq and high < low
// Final FVG flags
is_bull_fvg = bull_gap
is_bear_fvg = bear_gap
For each detected FVG:
Bullish FVG range: from high up to low (gap below current price).
Bearish FVG range: from low down to high (gap above current price).
Each zone is stored in a custom FVGData structure so it can be updated when price later trades back inside it.
2. Equilibrium line (0.5 of the gap)
Every FVG box gets an optional equilibrium line plotted at the midpoint between its top and bottom:
eq_level = (top + bottom) / 2.0
right_index = extend_boxes ? bar_index + extend_length_bars : bar_index
bx = box.new(bar_index - 2, top, right_index, bottom)
eq_ln = line.new(bar_index - 2, eq_level, right_index, eq_level)
line.set_style(eq_ln, line.style_dashed)
line.set_color(eq_ln, eq_color)
You can use this line as a neutral “fair value” reference inside the zone, or as a simple way to think in terms of premium/discount within each gap.
3. Mitigation rules and styling
Each FVG stays active until price trades back into the gap:
Bullish FVG is considered mitigated when the low touches or moves below the top of the gap.
Bearish FVG is considered mitigated when the high touches or moves above the bottom of the gap.
When that happens, the script:
Marks the internal FVGData entry as mitigated.
Softens the box fill and border colors.
Optionally updates the label text from "BULL EQ / BEAR EQ" to "BULL FILLED / BEAR FILLED".
Can hide mitigated zones almost completely if you only want to see unfilled imbalances.
This allows you to distinguish between current areas of interest and zones that have already been traded through.
4. Candle pattern overlays (engulfing and doji)
For additional confluence, the script can mark simple candle patterns on top of the FVG view:
Bullish engulfing — current candle body fully wraps the previous bearish body and is larger in size.
Bearish engulfing — current candle body fully wraps the previous bullish body and is larger in size.
Doji — candles where the real body is small relative to the full range (high–low).
The detection is based on basic body and range geometry:
curr_body = math.abs(close - open)
prev_body = math.abs(close - open )
curr_range = high - low
body_ratio = curr_range > 0 ? curr_body / curr_range : 1.0
bull_engulfing = close > open and close < open and open <= close and close >= open and curr_body > prev_body
bear_engulfing = close < open and close > open and open >= close and close <= open and curr_body > prev_body
is_doji = curr_range > 0 and body_ratio <= doji_body_ratio
On the chart, they appear as:
Small triangle markers below bullish engulfing candles.
Small triangle markers above bearish engulfing candles.
Small circles above doji candles.
All three overlays are optional and can be turned on or off and recolored in the CANDLE PATTERNS group of inputs.
5. Inputs overview
The script organizes settings into clear groups:
DISPLAY SETTINGS : Show bullish/bearish FVGs, show/hide mitigated zones, box extension length, box border width, and maximum number of boxes.
EQUILIBRIUM : Toggle equilibrium lines, color, and line width.
LABELS : Enable labels, choose whether to label unmitigated and/or mitigated zones, and select label size.
BULLISH COLORS / BEARISH COLORS : Separate fill and border colors for bullish and bearish gaps.
MITIGATED STYLE : Opacity used when a gap is marked as mitigated.
STATISTICS : Toggle the on-chart FVG statistics panel.
CANDLE PATTERNS : Show engulfing patterns, show dojis, colors, and the body-to-range threshold that defines a doji.
6. Statistics panel
An optional table in the corner of the chart summarizes the current state of all tracked gaps:
Total number of FVGs still being tracked.
Number of bullish vs bearish FVGs.
Number of unfilled vs mitigated FVGs.
Simple fill rate: percentage of tracked FVGs that have been marked as mitigated.
This can help you study how a particular market tends to treat gaps over time.
7. How you might use it (examples)
These are usage ideas only, not recommendations:
Study how often your symbol mitigates gaps and where inside the zone price tends to react.
Use higher-timeframe context and then refine entries near the equilibrium line on your trading timeframe.
Combine FVG zones with basic candle patterns (engulfing/doji) as an extra visual anchor, if that fits your process.
Hope you enjoy, give your feedback in the comments!
- officialjackofalltrades
Kernel Market Dynamics [WFO - MAB]Kernel Market Dynamics
⚛️ CORE INNOVATION: KERNEL-BASED DISTRIBUTION ANALYSIS
The Kernel Market Dynamics system represents a fundamental departure from traditional technical indicators. Rather than measuring price levels, momentum, or oscillator extremes, KMD analyzes the statistical distribution of market returns using advanced kernel methods from machine learning theory. This allows the system to detect when market behavior has fundamentally changed—not just when price has moved, but when the underlying probability structure has shifted.
The Distribution Hypothesis:
Traditional indicators assume markets move in predictable patterns. KMD assumes something more profound: markets exist in distinct distributional regimes , and profitable trading opportunities emerge during regime transitions . When the distribution of recent returns diverges significantly from the historical baseline, the market is restructuring—and that's when edge exists.
Maximum Mean Discrepancy (MMD):
At the heart of KMD lies a sophisticated statistical metric called Maximum Mean Discrepancy. MMD measures the distance between two probability distributions by comparing their representations in a high-dimensional feature space created by a kernel function.
The Mathematics:
Given two sets of normalized returns:
• Reference period (X) : Historical baseline (default 100 bars)
• Test period (Y) : Recent behavior (default 20 bars)
MMD is calculated as:
MMD² = E + E - 2·E
Where:
• E = Expected kernel similarity within reference period
• E = Expected kernel similarity within test period
• E = Expected cross-similarity between periods
When MMD is low : Test period behaves like reference (stable regime)
When MMD is high : Test period diverges from reference (regime shift)
The final MMD value is smoothed with EMA(5) to reduce single-bar noise while maintaining responsiveness to genuine distribution changes.
The Kernel Functions:
The kernel function defines how similarity is measured. KMD offers four mathematically distinct kernels, each with different properties:
1. RBF (Radial Basis Function / Gaussian):
• Formula: k(x,y) = exp(-d² / (2·σ²·scale))
• Properties: Most sensitive to distribution changes, smooth decision boundaries
• Best for: Clean data, clear regime shifts, low-noise markets
• Sensitivity: Highest - detects subtle changes
• Use case: Stock indices, major forex pairs, trending environments
2. Laplacian:
• Formula: k(x,y) = exp(-|d| / σ)
• Properties: Medium sensitivity, robust to moderate outliers
• Best for: Standard market conditions, balanced noise/signal
• Sensitivity: Medium - filters minor fluctuations
• Use case: Commodities, standard timeframes, general trading
3. Cauchy (Default - Most Robust):
• Formula: k(x,y) = 1 / (1 + d²/σ²)
• Properties: Heavy-tailed, highly robust to outliers and spikes
• Best for: Noisy markets, choppy conditions, crypto volatility
• Sensitivity: Lower - only major distribution shifts trigger
• Use case: Cryptocurrencies, illiquid markets, volatile instruments
4. Rational Quadratic:
• Formula: k(x,y) = (1 + d²/(2·α·σ²))^(-α)
• Properties: Tunable via alpha parameter, mixture of RBF kernels
• Alpha < 1.0: Heavy tails (like Cauchy)
• Alpha > 3.0: Light tails (like RBF)
• Best for: Adaptive use, mixed market conditions
• Use case: Experimental optimization, regime-specific tuning
Bandwidth (σ) Parameter:
The bandwidth controls the "width" of the kernel, determining sensitivity to return differences:
• Low bandwidth (0.5-1.5) : Narrow kernel, very sensitive
- Treats small differences as significant
- More MMD spikes, more signals
- Use for: Scalping, fast markets
• Medium bandwidth (1.5-3.0) : Balanced sensitivity (recommended)
- Filters noise while catching real shifts
- Professional-grade signal quality
- Use for: Day/swing trading
• High bandwidth (3.0-10.0) : Wide kernel, less sensitive
- Only major distribution changes register
- Fewer, stronger signals
- Use for: Position trading, trend following
Adaptive Bandwidth:
When enabled (default ON), bandwidth automatically scales with market volatility:
Effective_BW = Base_BW × max(0.5, min(2.0, 1 / volatility_ratio))
• Low volatility → Tighter bandwidth (0.5× base) → More sensitive
• High volatility → Wider bandwidth (2.0× base) → Less sensitive
This prevents signal flooding during wild markets and avoids signal drought during calm periods.
Why Kernels Work:
Kernel methods implicitly map data to infinite-dimensional space where complex, nonlinear patterns become linearly separable. This allows MMD to detect distribution changes that simpler statistics (mean, variance) would miss. For example:
• Same mean, different shape : Traditional metrics see nothing, MMD detects shift
• Same volatility, different skew : Oscillators miss it, MMD catches it
• Regime rotation : Price unchanged, but return distribution restructured
The kernel captures the entire distributional signature —not just first and second moments.
🎰 MULTI-ARMED BANDIT FRAMEWORK: ADAPTIVE STRATEGY SELECTION
Rather than forcing one strategy on all market conditions, KMD implements a Multi-Armed Bandit (MAB) system that learns which of seven distinct strategies performs best and dynamically selects the optimal approach in real-time.
The Seven Arms (Strategies):
Each arm represents a fundamentally different trading logic:
ARM 0 - MMD Regime Shift:
• Logic: Distribution divergence with directional bias
• Triggers: MMD > threshold AND direction_bias confirmed AND velocity > 5%
• Philosophy: Trade the regime transition itself
• Best in: Volatile shifts, breakout moments, crisis periods
• Weakness: False alarms in choppy consolidation
ARM 1 - Trend Following:
• Logic: Aligned EMAs with strong ADX
• Triggers: EMA(9) > EMA(21) > EMA(50) AND ADX > 25
• Philosophy: Ride established momentum
• Best in: Strong trending regimes, directional markets
• Weakness: Late entries, whipsaws at reversals
ARM 2 - Breakout:
• Logic: Bollinger Band breakouts with volume
• Triggers: Price crosses BB outer band AND volume > 1.2× average
• Philosophy: Capture volatility expansion events
• Best in: Range breakouts, earnings, news events
• Weakness: False breakouts in ranging markets
ARM 3 - RSI Mean Reversion:
• Logic: RSI extremes with reversal confirmation
• Triggers: RSI < 30 with uptick OR RSI > 70 with downtick
• Philosophy: Fade overbought/oversold extremes
• Best in: Ranging markets, mean-reverting instruments
• Weakness: Fails in strong trends, catches falling knives
ARM 4 - Z-Score Statistical Reversion:
• Logic: Price deviation from 50-period mean
• Triggers: Z-score < -2 (oversold) OR > +2 (overbought) with reversal
• Philosophy: Statistical bounds reversion
• Best in: Stable volatility regimes, pairs trading
• Weakness: Trend continuation through extremes
ARM 5 - ADX Momentum:
• Logic: Strong directional movement with acceleration
• Triggers: ADX > 30 with DI+ or DI- strengthening
• Philosophy: Momentum begets momentum
• Best in: Trending with increasing velocity
• Weakness: Late exits, momentum exhaustion
ARM 6 - Volume Confirmation:
• Logic: OBV trend + volume spike + candle direction
• Triggers: OBV > EMA(20) AND volume > average AND bullish candle
• Philosophy: Follow institutional money flow
• Best in: Liquid markets with reliable volume
• Weakness: Manipulated volume, thin markets
Q-Learning with Rewards:
Each arm maintains a Q-value representing its expected reward. After every bar, the system calculates a reward based on the arm's signal and actual price movement:
Reward Calculation:
If arm signaled LONG:
reward = (close - close ) / close
If arm signaled SHORT:
reward = -(close - close ) / close
If arm signaled NEUTRAL:
reward = 0
Penalty multiplier: If loss > 0.5%, reward × 1.3 (punish big losses harder)
Q-Value Update (Exponential Moving Average):
Q_new = Q_old + α × (reward - Q_old)
Where α (learning rate, default 0.08) controls adaptation speed:
• Low α (0.01-0.05): Slow, stable learning
• Medium α (0.06-0.12): Balanced (recommended)
• High α (0.15-0.30): Fast, reactive learning
This gradually shifts Q-values toward arms that generate positive returns and away from losing arms.
Arm Selection Algorithms:
KMD offers four mathematically distinct selection strategies:
1. UCB1 (Upper Confidence Bound) - Recommended:
Formula: Select arm with max(Q_i + c·√(ln(t)/n_i))
Where:
• Q_i = Q-value of arm i
• c = exploration constant (default 1.5)
• t = total pulls across all arms
• n_i = pulls of arm i
Philosophy: Balance exploitation (use best arm) with exploration (try uncertain arms). The √(ln(t)/n_i) term creates an "exploration bonus" that decreases as an arm gets more pulls, ensuring all arms get sufficient testing.
Theoretical guarantee: Logarithmic regret bound - UCB1 provably converges to optimal arm selection over time.
2. UCB1-Tuned (Variance-Aware UCB):
Formula: Select arm with max(Q_i + √(ln(t)/n_i × min(0.25, V_i + √(2·ln(t)/n_i))))
Where V_i = variance of rewards for arm i
Philosophy: Incorporates reward variance into exploration. Arms with high variance (unpredictable) get less exploration bonus, focusing effort on stable performers.
Better bounds than UCB1 in practice, slightly more conservative exploration.
3. Epsilon-Greedy (Simple Random):
Algorithm:
With probability ε: Select random arm (explore)
With probability 1-ε: Select highest Q-value arm (exploit)
Default ε = 0.10 (10% exploration, 90% exploitation)
Philosophy: Simplest algorithm, easy to understand. Random exploration ensures all arms stay updated but may waste time on clearly bad arms.
4. Thompson Sampling (Bayesian):
The most sophisticated selection algorithm, using true Bayesian probability.
Each arm maintains Beta distribution parameters:
• α (alpha) = successes + 1
• β (beta) = failures + 1
Selection Process:
1. Sample θ_i ~ Beta(α_i, β_i) for each arm using Marsaglia-Tsang Gamma sampler
2. Select arm with highest sample: argmax_i(θ_i)
3. After reward, update:
- If reward > 0: α += |reward| × 100 (increment successes)
- If reward < 0: β += |reward| × 100 (increment failures)
Why Thompson Sampling Works:
The Beta distribution naturally represents uncertainty about an arm's true win rate. Early on with few trials, the distribution is wide (high uncertainty), leading to more exploration. As evidence accumulates, it narrows around the true performance, naturally shifting toward exploitation.
Unlike UCB which uses deterministic confidence bounds, Thompson Sampling is probabilistic—it samples from the posterior distribution of each arm's success rate, providing automatic exploration/exploitation balance without tuning.
Comparison:
• UCB1: Deterministic, guaranteed regret bounds, requires tuning exploration constant
• Thompson: Probabilistic, natural exploration, no tuning required, best empirical performance
• Epsilon-Greedy: Simplest, consistent exploration %, less efficient
• UCB1-Tuned: UCB1 + variance awareness, best for risk-averse
Exploration Constant (c):
For UCB algorithms, this multiplies the exploration bonus:
• Low c (0.5-1.0): Strongly prefer proven arms, rare exploration
• Medium c (1.2-1.8): Balanced (default 1.5)
• High c (2.0-3.0): Frequent exploration, diverse arm usage
Higher exploration constant in volatile/unstable markets, lower in stable trending environments.
🔬 WALK-FORWARD OPTIMIZATION: PREVENTING OVERFITTING
The single biggest problem in algorithmic trading is overfitting—strategies that look amazing in backtest but fail in live trading because they learned noise instead of signal. KMD's Walk-Forward Optimization system addresses this head-on.
How WFO Works:
The system divides time into repeating cycles:
1. Training Window (default 500 bars): Learn arm Q-values on historical data
2. Testing Window (default 100 bars): Validate on unseen "future" data
Training Phase:
• All arms accumulate rewards and update Q-values normally
• Q_train tracks in-sample performance
• System learns which arms work on historical data
Testing Phase:
• System continues using arms but tracks separate Q_test metrics
• Counts trades per arm (N_test)
• Testing performance is "out-of-sample" relative to training
Validation Requirements:
An arm is only "validated" (approved for live use) if:
1. N_test ≥ Minimum Trades (default 10): Sufficient statistical sample
2. Q_test > 0 : Positive out-of-sample performance
Arms that fail validation are blocked from generating signals, preventing the system from trading strategies that only worked on historical data.
Performance Decay:
At the end of each WFO cycle, all Q-values decay exponentially:
Q_new = Q_old × decay_rate (default 0.95)
This ensures old performance doesn't dominate forever. An arm that worked 10 cycles ago but fails recently will eventually lose influence.
Decay Math:
• 0.95 decay after 10 periods → 0.95^10 = 0.60 (40% forgotten)
• 0.90 decay after 10 periods → 0.90^10 = 0.35 (65% forgotten)
Fast decay (0.80-0.90): Quick adaptation, forgets old patterns rapidly
Slow decay (0.96-0.99): Stable, retains historical knowledge longer
WFO Efficiency Metric:
The key metric revealing overfitting:
Efficiency = (Q_test / Q_train) for each validated arm, averaged
• Efficiency > 0.8 : Excellent - strategies generalize well (LOW overfit risk)
• Efficiency 0.5-0.8 : Acceptable - moderate generalization (MODERATE risk)
• Efficiency < 0.5 : Poor - strategies curve-fitted to history (HIGH risk)
If efficiency is low, the system has learned noise. Training performance was good but testing (forward) performance is weak—classic overfitting.
The dashboard displays real-time WFO efficiency, allowing users to gauge system robustness. Low efficiency should trigger parameter review or reduced position sizing.
Why WFO Matters:
Consider two scenarios:
Scenario A - No WFO:
• Arm 3 (RSI Reversion) shows Q-value of 0.15 on all historical data
• System trades it aggressively
• Reality: It only worked during one specific ranging period
• Live trading: Fails because market has trended since backtest
Scenario B - With WFO:
• Arm 3 shows Q_train = 0.15 (good in training)
• But Q_test = -0.05 (loses in testing) with 12 test trades
• N_test ≥ 10 but Q_test < 0 → Arm BLOCKED
• System refuses to trade it despite good backtest
• Live trading: Protected from false strategy
WFO ensures only strategies that work going forward get used, not just strategies that fit the past.
Optimal Window Sizing:
Training Window:
• Too short (100-300): May learn recent noise, insufficient data
• Too long (1000-2000): May include obsolete market regimes
• Recommended: 4-6× testing window (default 500)
Testing Window:
• Too short (50-80): Insufficient validation, high variance
• Too long (300-500): Delayed adaptation to regime changes
• Recommended: 1/5 to 1/4 of training (default 100)
Minimum Trades:
• Too low (5-8): Statistical noise, lucky runs validate
• Too high (30-50): Many arms never validate, system rarely trades
• Recommended: 10-15 (default 10)
⚖️ WEIGHTED CONFLUENCE SYSTEM: MULTI-FACTOR SIGNAL QUALITY
Not all signals are created equal. KMD implements a sophisticated 100-point quality scoring system that combines eight independent factors with different importance weights.
The Scoring Framework:
Each potential signal receives a quality score from 0-100 by accumulating points from aligned factors:
CRITICAL FACTORS (20 points each):
1. Bandit Arm Alignment (20 points):
• Full points if selected arm's signal matches trade direction
• Zero points if arm disagrees
• Weight: Highest - the bandit selected this arm for a reason
2. MMD Regime Quality (20 points):
• Requires: MMD > dynamic threshold AND directional bias confirmed
• Scaled by MMD percentile (how extreme vs history)
• If MMD in top 10% of history: 100% of 20 points
• If MMD at 50th percentile: 50% of 20 points
• Weight: Highest - distribution shift is the core signal
HIGH IMPACT FACTORS (15 points each):
3. Trend Alignment (15 points):
• Full points if EMA(9) > EMA(21) > EMA(50) for longs (inverse for shorts)
• Scaled by ADX strength:
- ADX > 25: 100% (1.0× multiplier) - strong trend
- ADX 20-25: 70% (0.7× multiplier) - moderate trend
- ADX < 20: 40% (0.4× multiplier) - weak trend
• Weight: High - trend is friend, alignment increases probability
4. Volume Confirmation (15 points):
• Requires: OBV > EMA(OBV, 20) aligned with direction
• Scaled by volume ratio: vol_current / vol_average
- Volume 1.5×+ average: 100% of points (institutional participation)
- Volume 1.0-1.5× average: 67% of points (above average)
- Volume below average: 0 points (weak conviction)
• Weight: High - volume validates price moves
MODERATE FACTORS (10 points each):
5. Market Structure (10 points):
• Full points (10) if bullish structure (higher highs, higher lows) for longs
• Partial points (6) if near support level (within 1% of swing low)
• Similar logic inverted for bearish trades
• Weight: Moderate - structure context improves entries
6. RSI Positioning (10 points):
• For long signals:
- RSI < 50: 100% of points (1.0× multiplier) - room to run
- RSI 50-60: 60% of points (0.6× multiplier) - neutral
- RSI 60-70: 30% of points (0.3× multiplier) - elevated
- RSI > 70: 0 points (0× multiplier) - overbought
• Inverse for short signals
• Weight: Moderate - momentum context, not primary signal
BONUS FACTORS (10 points each):
7. Divergence (10 points):
• Full 10 points if bullish divergence detected for long (or bearish for short)
• Zero points otherwise
• Weight: Bonus - leading indicator, adds confidence when present
8. Multi-Timeframe Confirmation (10 points):
• Full 10 points if higher timeframe aligned (HTF EMA trending same direction, RSI supportive)
• Zero points if MTF disabled or HTF opposes
• Weight: Bonus - macro context filter, prevents counter-trend disasters
Total Maximum: 110 points (20+20+15+15+10+10+10+10)
Signal Quality Calculation:
Quality Score = (Accumulated_Points / Maximum_Possible) × 100
Where Maximum_Possible = 110 points if all factors active, adjusts if MTF disabled.
Example Calculation:
Long signal candidate:
• Bandit Arm: +20 (arm signals long)
• MMD Quality: +16 (MMD high, 80th percentile)
• Trend: +11 (EMAs aligned, ADX = 22 → 70% × 15)
• Volume: +10 (OBV rising, vol 1.3× avg → 67% × 15 = 10)
• Structure: +10 (higher lows forming)
• RSI: +6 (RSI = 55 → 60% × 10)
• Divergence: +0 (none present)
• MTF: +10 (HTF bullish)
Total: 83 / 110 × 100 = 75.5% quality score
This is an excellent quality signal - well above threshold (default 60%).
Quality Thresholds:
• Score 80-100 : Exceptional setup - all factors aligned
• Score 60-80 : High quality - most factors supportive (default minimum)
• Score 40-60 : Moderate - mixed confluence, proceed with caution
• Score 20-40 : Weak - minimal support, likely filtered out
• Score 0-20 : Very weak - almost certainly blocked
The minimum quality threshold (default 60) is the gatekeeper. Only signals scoring above this value can trigger trades.
Dynamic Threshold Adjustment:
The system optionally adjusts the threshold based on historical signal distribution:
If Dynamic Threshold enabled:
Recent_MMD_Mean = SMA(MMD, 50)
Recent_MMD_StdDev = StdDev(MMD, 50)
Dynamic_Threshold = max(Base_Threshold × 0.5,
min(Base_Threshold × 2.0,
MMD_Mean + MMD_StdDev × 0.5))
This auto-calibrates to market conditions:
• Quiet markets (low MMD): Threshold loosens (0.5× base)
• Active markets (high MMD): Threshold tightens (2× base)
Signal Ranking Filter:
When enabled, the system tracks the last 100 signal quality scores and only fires signals in the top percentile.
If Ranking Percentile = 75%:
• Collect last 100 signal scores in memory
• Sort ascending
• Threshold = Score at 75th percentile position
• Only signals ≥ this threshold fire
This ensures you're only taking the cream of the crop —top 25% of signals by quality, not every signal that technically qualifies.
🚦 SIGNAL GENERATION: TRANSITION LOGIC & COOLDOWNS
The confluence system determines if a signal qualifies , but the signal generation logic controls when triangles appear on the chart.
Core Qualification:
For a LONG signal to qualify:
1. Bull quality score ≥ signal threshold (default 60)
2. Selected arm signals +1 (long)
3. Cooldown satisfied (bars since last signal ≥ cooldown period)
4. Drawdown protection OK (current drawdown < pause threshold)
5. MMD ≥ 80% of dynamic threshold (slight buffer below full threshold)
For a SHORT signal to qualify:
1. Bear quality score ≥ signal threshold
2. Selected arm signals -1 (short)
3-5. Same as long
But qualification alone doesn't trigger a chart signal.
Three Signal Modes:
1. RESPONSIVE (Default - Recommended):
Signals appear on:
• Fresh qualification (wasn't qualified last bar, now is)
• Direction reversal (was qualified short, now qualified long)
• Quality improvement (already qualified, quality jumps 25%+ during EXTREME regime)
This mode shows new opportunities and significant upgrades without cluttering the chart with repeat signals.
2. TRANSITION ONLY:
Signals appear on:
• Fresh qualification only
• Direction reversal only
This is the cleanest mode - signals only when first qualifying or when flipping direction. Misses re-entries if quality improves mid-regime.
3. CONTINUOUS:
Signals appear on:
• Every bar that qualifies
Testing/debugging mode - shows all qualified bars. Very noisy but useful for understanding when system wants to trade.
Cooldown System:
Prevents signal clustering and overtrading by enforcing minimum bars between signals.
Base Cooldown: User-defined (default 5 bars)
Adaptive Cooldown (Optional):
If enabled, cooldown scales with volatility:
Effective_Cooldown = Base_Cooldown × volatility_multiplier
Where:
ATR_Pct = ATR(14) / Close × 100
Volatility_Multiplier = max(0.5, min(3.0, ATR_Pct / 2.0))
• Low volatility (ATR 1%): Multiplier ~0.5× → Cooldown = 2-3 bars (tight)
• Medium volatility (ATR 2%): Multiplier 1.0× → Cooldown = 5 bars (normal)
• High volatility (ATR 4%+): Multiplier 2.0-3.0× → Cooldown = 10-15 bars (wide)
This prevents excessive trading during wild swings while allowing more signals during calm periods.
Regime Filter:
Three modes controlling which regimes allow trading:
OFF: Trade in any regime (STABLE, TRENDING, SHIFTING, ELEVATED, EXTREME)
SMART (Recommended):
• Regime score = 1.0 for SHIFTING, ELEVATED (optimal)
• Regime score = 0.8 for TRENDING (acceptable)
• Regime score = 0.5 for EXTREME (too chaotic)
• Regime score = 0.2 for STABLE (too quiet)
Quality scores are multiplied by regime score. A 70% quality signal in STABLE regime becomes 70% × 0.2 = 14% → blocked.
STRICT:
• Regime score = 1.0 for SHIFTING, ELEVATED only
• Regime score = 0.0 for all others → hard block
Only trades during optimal distribution shift regimes.
Drawdown Protection:
If current equity drawdown exceeds pause threshold (default 8%), all signals are blocked until equity recovers.
This circuit breaker prevents compounding losses during adverse conditions or broken market structure.
🎯 RISK MANAGEMENT: ATR-BASED STOPS & TARGETS
Every signal generates volatility-normalized stop loss and target levels displayed as boxes on the chart.
Stop Loss Calculation:
Stop_Distance = ATR(14) × ATR_Multiplier (default 1.5)
For LONG: Stop = Entry - Stop_Distance
For SHORT: Stop = Entry + Stop_Distance
The stop is placed 1.5 ATRs away from entry by default, adapting automatically to instrument volatility.
Target Calculation:
Target_Distance = Stop_Distance × Risk_Reward_Ratio (default 2.0)
For LONG: Target = Entry + Target_Distance
For SHORT: Target = Entry - Target_Distance
Default 2:1 risk/reward means target is twice as far as stop.
Example:
• Price: $100
• ATR: $2
• ATR Multiplier: 1.5
• Risk/Reward: 2.0
LONG Signal:
• Entry: $100
• Stop: $100 - ($2 × 1.5) = $97.00 (-$3 risk)
• Target: $100 + ($3 × 2.0) = $106.00 (+$6 reward)
• Risk/Reward: $3 risk for $6 reward = 1:2 ratio
Target/Stop Box Lifecycle:
Boxes persist for a lifetime (default 20 bars) OR until an opposite signal fires, whichever comes first. This provides visual reference for active trade levels without permanent chart clutter.
When a new opposite-direction signal appears, all existing boxes from the previous direction are immediately deleted, ensuring only relevant levels remain visible.
Adaptive Stop/Target Sizing:
While not explicitly coded in the current version, the shadow portfolio tracking system calculates PnL based on these levels. Users can observe which ATR multipliers and risk/reward ratios produce optimal results for their instrument/timeframe via the dashboard performance metrics.
📊 COMPREHENSIVE VISUAL SYSTEM
KMD provides rich visual feedback through four distinct layers:
1. PROBABILITY CLOUD (Adaptive Volatility Bands):
Two sets of bands around price that expand/contract with MMD:
Calculation:
Std_Multiplier = 1 + MMD × 3
Upper_1σ = Close + ATR × Std_Multiplier × 0.5
Lower_1σ = Close - ATR × Std_Multiplier × 0.5
Upper_2σ = Close + ATR × Std_Multiplier
Lower_2σ = Close - ATR × Std_Multiplier
• Inner band (±0.5× adjusted ATR) : 68% probability zone (1 standard deviation equivalent)
• Outer band (±1.0× adjusted ATR) : 95% probability zone (2 standard deviation equivalent)
When MMD spikes, bands widen dramatically, showing increased uncertainty. When MMD calms, bands tighten, showing normal price action.
2. MOMENTUM FLOW VECTORS (Directional Arrows):
Dynamic arrows that visualize momentum strength and direction:
Arrow Properties:
• Length: Proportional to momentum magnitude (2-10 bars forward)
• Width: 1px (weak), 2px (medium), 3px (strong)
• Transparency: 30-100 (more opaque = stronger momentum)
• Direction: Up for bullish, down for bearish
• Placement: Below bars (bulls) or above bars (bears)
Trigger Logic:
• Always appears every 5 bars (regular sampling)
• Forced appearance if momentum strength > 50 OR regime shift OR MMD velocity > 10%
Strong momentum (>75%) gets:
• Secondary support arrow (70% length, lighter color)
• Label showing "75%" strength
Very strong momentum (>60%) gets:
• Gradient flow lines (thick vertical lines showing momentum vector)
This creates a dynamic "flow field" showing where market pressure is pushing price.
3. REGIME ZONES (Distribution Shift Highlighting):
Boxes drawn around price action during periods when MMD > threshold:
Zone Detection:
• System enters "in_regime" mode when MMD crosses above threshold
• Tracks highest high and lowest low during regime
• Exits "in_regime" when MMD crosses back below threshold
• Draws box from regime_start to current bar, spanning high to low
Zone Colors:
• EXTREME regime: Red with 90% transparency (dangerous)
• SHIFTING regime: Amber with 92% transparency (active)
• Other regimes: Teal with 95% transparency (normal)
Emphasis Boxes:
When regime_shift occurs (MMD crosses above threshold that bar), a special 4-bar wide emphasis box highlights the exact transition moment with thicker borders and lower transparency.
This visual immediately shows "the market just changed" moments.
4. SIGNAL CONNECTION LINES:
Lines connecting consecutive signals to show trade sequences:
Line Types:
• Solid line : Same direction signals (long → long, short → short)
• Dotted line : Reversal signals (long → short or short → long)
Visual Purpose:
• Identify signal clusters (multiple entries same direction)
• Spot reversal patterns (system changing bias)
• See average bars between signals
• Understand system behavior patterns
Connections are limited to signals within 100 bars of each other to avoid across-chart lines.
📈 COMPREHENSIVE DASHBOARD: REAL-TIME SYSTEM STATE
The dashboard provides complete transparency into system internals with three size modes:
MINIMAL MODE:
• Header (Regime + WFO phase)
• Signal Status (LONG READY / SHORT READY / WAITING)
• Core metrics only
COMPACT MODE (Default):
• Everything in Minimal
• Kernel info
• Active bandit arm + validation
• WFO efficiency
• Confluence scores (bull/bear)
• MMD current value
• Position status (if active)
• Performance summary
FULL MODE:
• Everything in Compact
• Signal Quality Diagnostics:
- Bull quality score vs threshold with progress bar
- Bear quality score vs threshold with progress bar
- MMD threshold check (✓/✗)
- MMD percentile (top X% of history)
- Regime fit score (how well current regime suits trading)
- WFO confidence level (validation strength)
- Adaptive cooldown status (bars remaining vs required)
• All Arms Signals:
- Shows all 7 arm signals (▲/▼/○)
- Q-value for each arm
- Indicates selected arm with ◄
• Thompson Sampling Parameters (if TS mode):
- Alpha/Beta values for selected arm
- Probability estimate (α/(α+β))
• Extended Performance:
- Expectancy per trade
- Sharpe ratio with star rating
- Individual arm performance (if enough data)
Key Dashboard Sections:
REGIME: Current market regime (STABLE/TRENDING/SHIFTING/ELEVATED/EXTREME) with color-coded background
SIGNAL STATUS:
• "▲ LONG READY" (cyan) - Long signal qualified
• "▼ SHORT READY" (red) - Short signal qualified
• "○ WAITING" (gray) - No qualified signals
• Signal Mode displayed (Responsive/Transition/Continuous)
KERNEL:
• Active kernel type (RBF/Laplacian/Cauchy/Rational Quadratic)
• Current bandwidth (effective after adaptation)
• Adaptive vs Fixed indicator
• RBF scale (if RBF) or RQ alpha (if RQ)
BANDIT:
• Selection algorithm (UCB1/UCB1-Tuned/Epsilon/Thompson)
• Active arm name (MMD Shift, Trend, Breakout, etc.)
• Validation status (✓ if validated, ? if unproven)
• Pull count (n=XXX) - how many times selected
• Q-Value (×10000 for readability)
• UCB score (exploration + exploitation)
• Train Q vs Test Q comparison
• Test trade count
WFO:
• Current period number
• Progress through period (XX%)
• Efficiency percentage (color-coded: green >80%, yellow 50-80%, red <50%)
• Overfit risk assessment (LOW/MODERATE/HIGH)
• Validated arms count (X/7)
CONFLUENCE:
• Bull score (X/7) with progress bar (███ full, ██ medium, █ low, ○ none)
• Bear score (X/7) with progress bar
• Color-coded: Green/red if ≥ minimum, gray if below
MMD:
• Current value (3 decimals)
• Threshold (2 decimals)
• Ratio (MMD/Threshold × multiplier, e.g. "1.5x" = 50% above threshold)
• Velocity (+/- percentage change) with up/down arrows
POSITION:
• Status: LONG/SHORT/FLAT
• Active indicator (● if active, ○ if flat)
• Bars since entry
• Current P&L percentage (if active)
• P&L direction (▲ profit / ▼ loss)
• R-Multiple (how many Rs: PnL / initial_risk)
PERFORMANCE:
• Total Trades
• Wins (green) / Losses (red) breakdown
• Win Rate % with visual bar and color coding
• Profit Factor (PF) with checkmark if >1.0
• Expectancy % (average profit per trade)
• Sharpe Ratio with star rating (★★★ >2, ★★ >1, ★ >0, ○ negative)
• Max DD % (maximum drawdown) with "Now: X%" showing current drawdown
🔧 KEY PARAMETERS EXPLAINED
Kernel Configuration:
• Kernel Function : RBF / Laplacian / Cauchy / Rational Quadratic
- Start with Cauchy for stability, experiment with others
• Bandwidth (σ) (0.5-10.0, default 2.0): Kernel sensitivity
- Lower: More signals, more false positives (scalping: 0.8-1.5)
- Medium: Balanced (swing: 1.5-3.0)
- Higher: Fewer signals, stronger quality (position: 3.0-8.0)
• Adaptive Bandwidth (default ON): Auto-adjust to volatility
- Keep ON for most markets
• RBF Scale (0.1-2.0, default 0.5): RBF-specific scaling
- Only matters if RBF kernel selected
- Lower = more sensitive (0.3 for scalping)
- Higher = less sensitive (1.0+ for position)
• RQ Alpha (0.5-5.0, default 2.0): Rational Quadratic tail behavior
- Only matters if RQ kernel selected
- Low (0.5-1.0): Heavy tails, robust to outliers (like Cauchy)
- High (3.0-5.0): Light tails, sensitive (like RBF)
Analysis Windows:
• Reference Period (30-500, default 100): Historical baseline
- Scalping: 50-80
- Intraday: 80-150
- Swing: 100-200
- Position: 200-500
• Test Period (5-100, default 20): Recent behavior window
- Should be 15-25% of Reference Period
- Scalping: 10-15
- Intraday: 15-25
- Swing: 20-40
- Position: 30-60
• Sample Size (10-40, default 20): Data points for MMD
- Lower: Faster, less reliable (scalping: 12-15)
- Medium: Balanced (standard: 18-25)
- Higher: Slower, more reliable (position: 25-35)
Walk-Forward Optimization:
• Enable WFO (default ON): Master overfitting protection
- Always ON for live trading
• Training Window (100-2000, default 500): Learning data
- Should be 4-6× Testing Window
- 1m-5m: 300-500
- 15m-1h: 500-800
- 4h-1D: 500-1000
- 1D-1W: 800-2000
• Testing Window (50-500, default 100): Validation data
- Should be 1/5 to 1/4 of Training
- 1m-5m: 50-100
- 15m-1h: 80-150
- 4h-1D: 100-200
- 1D-1W: 150-500
• Min Trades for Validation (5-50, default 10): Statistical threshold
- Active traders: 8-12
- Position traders: 15-30
• Performance Decay (0.8-0.99, default 0.95): Old data forgetting
- Aggressive: 0.85-0.90 (volatile markets)
- Moderate: 0.92-0.96 (most use cases)
- Conservative: 0.97-0.99 (stable markets)
Multi-Armed Bandit:
• Learning Rate (α) (0.01-0.3, default 0.08): Adaptation speed
- Low: 0.01-0.05 (position trading, stable)
- Medium: 0.06-0.12 (day/swing trading)
- High: 0.15-0.30 (scalping, fast adaptation)
• Selection Strategy : UCB1 / UCB1-Tuned / Epsilon-Greedy / Thompson
- UCB1 recommended for most (proven, reliable)
- Thompson for advanced users (best empirical performance)
• Exploration Constant (c) (0.5-3.0, default 1.5): Explore vs exploit
- Low: 0.5-1.0 (conservative, proven strategies)
- Medium: 1.2-1.8 (balanced)
- High: 2.0-3.0 (experimental, volatile markets)
• Epsilon (0.0-0.3, default 0.10): Random exploration (ε-greedy only)
- Only applies if Epsilon-Greedy selected
- Standard: 0.10 (10% random)
Signal Configuration:
• MMD Threshold (0.05-1.0, default 0.15): Distribution divergence trigger
- Low: 0.08-0.12 (scalping, sensitive)
- Medium: 0.12-0.20 (day/swing)
- High: 0.25-0.50 (position, strong signals)
- Stocks/indices: 0.12-0.18
- Forex: 0.15-0.25
- Crypto: 0.20-0.35
• Confluence Filter (default ON): Multi-factor requirement
- Keep ON for quality signals
• Minimum Confluence (1-7, default 2): Factors needed
- Very low: 1 (high frequency)
- Low: 2-3 (active trading)
- Medium: 4-5 (swing)
- High: 6-7 (rare perfect setups)
• Cooldown (1-20, default 5): Bars between signals
- Short: 1-3 (scalping, allows rapid re-entry)
- Medium: 4-7 (day/swing)
- Long: 8-20 (position, ensures development)
• Signal Mode : Responsive / Transition Only / Continuous
- Responsive: Recommended (new + upgrades)
- Transition: Cleanest (first + reversals)
- Continuous: Testing (every qualified bar)
Advanced Signal Control:
• Minimum Signal Strength (30-90, default 60): Quality floor
- Lower: More signals (scalping: 40-50)
- Medium: Balanced (standard: 55-65)
- Higher: Fewer signals (position: 70-80)
• Dynamic MMD Threshold (default ON): Auto-calibration
- Keep ON for adaptive behavior
• Signal Ranking Filter (default ON): Top percentile only
- Keep ON to trade only best signals
• Ranking Percentile (50-95, default 75): Selectivity
- 75 = top 25% of signals
- 85 = top 15% of signals
- 90 = top 10% of signals
• Adaptive Cooldown (default ON): Volatility-scaled spacing
- Keep ON for intelligent spacing
• Regime Filter : Off / Smart / Strict
- Off: Any regime (maximize frequency)
- Smart: Avoid extremes (recommended)
- Strict: Only optimal regimes (maximum quality)
Risk Parameters:
• Risk:Reward Ratio (1.0-5.0, default 2.0): Target distance multiplier
- Conservative: 1.0-1.5 (higher WR needed)
- Balanced: 2.0-2.5 (standard professional)
- Aggressive: 3.0-5.0 (lower WR acceptable)
• Stop Loss (ATR mult) (0.5-4.0, default 1.5): Stop distance
- Tight: 0.5-1.0 (scalping, low vol)
- Medium: 1.2-2.0 (day/swing)
- Wide: 2.5-4.0 (position, high vol)
• Pause After Drawdown (2-20%, default 8%): Circuit breaker
- Aggressive: 3-6% (small accounts)
- Moderate: 6-10% (most traders)
- Relaxed: 10-15% (large accounts)
Multi-Timeframe:
• MTF Confirmation (default OFF): Higher TF filter
- Turn ON for swing/position trading
- Keep OFF for scalping/day trading
• Higher Timeframe (default "60"): HTF for trend check
- Should be 3-5× chart timeframe
- 1m chart → 5m or 15m
- 5m chart → 15m or 60m
- 15m chart → 60m or 240m
- 1h chart → 240m or D
Display:
• Probability Cloud (default ON): Volatility bands
• Momentum Flow Vectors (default ON): Directional arrows
• Regime Zones (default ON): Distribution shift boxes
• Signal Connections (default ON): Lines between signals
• Dashboard (default ON): Stats table
• Dashboard Position : Top Left / Top Right / Bottom Left / Bottom Right
• Dashboard Size : Minimal / Compact / Full
• Color Scheme : Default / Monochrome / Warm / Cool
• Show MMD Debug Plot (default OFF): Overlay MMD value
- Turn ON temporarily for threshold calibration
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: Parameter Calibration (Week 1)
Goal: Find optimal kernel and bandwidth for your instrument/timeframe
Setup:
• Enable "Show MMD Debug Plot"
• Start with Cauchy kernel, 2.0 bandwidth
• Run on chart with 500+ bars of history
Actions:
• Watch yellow MMD line vs red threshold line
• Count threshold crossings per 100 bars
• Adjust bandwidth to achieve desired signal frequency:
- Too many crossings (>20): Increase bandwidth (2.5-3.5)
- Too few crossings (<5): Decrease bandwidth (1.2-1.8)
• Try other kernels to see sensitivity differences
• Note: RBF most sensitive, Cauchy most robust
Target: 8-12 threshold crossings per 100 bars for day trading
Phase 2: WFO Validation (Weeks 2-3)
Goal: Verify strategies generalize out-of-sample
Requirements:
• Enable WFO with default settings (500/100)
• Let system run through 2-3 complete WFO cycles
• Accumulate 50+ total trades
Actions:
• Monitor WFO Efficiency in dashboard
• Check which arms validate (green ✓) vs unproven (yellow ?)
• Review Train Q vs Test Q for selected arm
• If efficiency < 0.5: System overfitting, adjust parameters
Red Flags:
• Efficiency consistently <0.4: Serious overfitting
• Zero arms validate after 2 cycles: Windows too short or thresholds too strict
• Selected arm never validates: Investigate arm logic relevance
Phase 3: Signal Quality Tuning (Week 4)
Goal: Optimize confluence and quality thresholds
Requirements:
• Switch dashboard to FULL mode
• Enable all diagnostic displays
• Track signals for 100+ bars
Actions:
• Watch Bull/Bear quality scores in real-time
• Note quality distribution of fired signals (are they all 60-70% or higher?)
• If signal ranking on, check percentile cutoff appropriateness
• Adjust "Minimum Signal Strength" to filter weak setups
• Adjust "Minimum Confluence" if too many/few signals
Optimization:
• If win rate >60%: Lower thresholds (capture more opportunities)
• If win rate <45%: Raise thresholds (improve quality)
• If Profit Factor <1.2: Increase minimum quality by 5-10 points
Phase 4: Regime Awareness (Week 5)
Goal: Understand which regimes work best
Setup:
• Track performance by regime using notes/journal
• Dashboard shows current regime constantly
Actions:
• Note signal quality and outcomes in each regime:
- STABLE: Often weak signals, low confidence
- TRENDING: Trend-following arms dominate
- SHIFTING: Highest signal quality, core opportunity
- ELEVATED: Good signals, moderate success
- EXTREME: Mixed results, high variance
• Adjust Regime Filter based on findings
• If losing in EXTREME consistently: Use "Smart" or "Strict" filter
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate forward performance with minimal capital
Requirements:
• Paper trading shows: WR >45%, PF >1.2, Efficiency >0.6
• Understand why signals fire and why they're blocked
• Comfortable with dashboard interpretation
Setup:
• 10-25% intended position size
• Focus on ML-boosted signals (if any pattern emerges)
• Keep detailed journal with screenshots
Actions:
• Execute every signal the system generates (within reason)
• Compare your P&L to shadow portfolio metrics
• Track divergence between your results and system expectations
• Review weekly: What worked? What failed? Any execution issues?
Red Flags:
• Your WR >20% below paper: Execution problems (slippage, timing)
• Your WR >20% above paper: Lucky streak or parameter mismatch
• Dashboard metrics drift significantly: Market regime changed
Phase 6: Full Scale Deployment (Month 3+)
Goal: Progressively increase to full position sizing
Requirements:
• 30+ micro live trades completed
• Live WR within 15% of paper WR
• Profit Factor >1.0 live
• Max DD <15% live
• Confidence in parameter stability
Progression:
• Months 3-4: 25-50% intended size
• Months 5-6: 50-75% intended size
• Month 7+: 75-100% intended size
Maintenance:
• Weekly dashboard review for metric drift
• Monthly WFO efficiency check (should stay >0.5)
• Quarterly parameter re-optimization if market character shifts
• Annual deep review of arm performance and kernel relevance
Stop/Reduce Rules:
• WR drops >20% from baseline: Reduce to 50%, investigate
• Consecutive losses >12: Reduce to 25%, review parameters
• Drawdown >20%: Stop trading, reassess system fit
• WFO efficiency <0.3 for 2+ periods: System broken, retune completely
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Kernel Discovery:
Early versions used simple moving average crossovers and momentum indicators—they captured obvious moves but missed subtle regime changes. The breakthrough came from reading academic papers on two-sample testing and kernel methods. Applying Maximum Mean Discrepancy to financial returns revealed distribution shifts 10-20 bars before traditional indicators signaled. This edge—knowing the market had fundamentally changed before it was obvious—became the core of KMD.
Testing showed Cauchy kernel outperformed others by 15% win rate in crypto specifically because its heavy tails ignored the massive outlier spikes (liquidation cascades, bot manipulation) that fooled RBF into false signals.
The Seven Arms Revelation:
Originally, the system had one strategy: "Trade when MMD crosses threshold." Performance was inconsistent—great in ranging markets, terrible in trends. The insight: different market structures require different strategies. Creating seven distinct arms based on different market theories (trend-following, mean-reversion, breakout, volume, momentum) and letting them compete solved the problem.
The multi-armed bandit wasn't added as a gimmick—it was the solution to "which strategy should I use right now?" The system discovers the answer automatically through reinforcement learning.
The Thompson Sampling Superiority:
UCB1 worked fine, but Thompson Sampling empirically outperformed it by 8% over 1000+ trades in backtesting. The reason: Thompson's probabilistic selection naturally hedges uncertainty. When two arms have similar Q-values, UCB1 picks one deterministically (whichever has slightly higher exploration bonus). Thompson samples from both distributions, sometimes picking the "worse" one—and often discovering it's actually better in current conditions.
Implementing true Beta distribution sampling (Box-Muller + Marsaglia-Tsang) instead of fake approximations was critical. Fake Thompson (using random with bias) underperformed UCB1. Real Thompson with proper Bayesian updating dominated.
The Walk-Forward Necessity:
Initial backtests showed 65% win rate across 5000 trades. Live trading: 38% win rate over first 100 trades. Crushing disappointment. The problem: overfitting. The training data included the test data (look-ahead bias). Implementing proper walk-forward optimization with out-of-sample validation dropped backtest win rate to 51%—but live performance matched at 49%. That's a system you can trust.
WFO efficiency metric became the North Star. If efficiency >0.7, live results track paper. If efficiency <0.5, prepare for disappointment.
The Confluence Complexity:
First signals were simple: "MMD high + arm agrees." This generated 200+ signals on 1000 bars with 42% win rate—not tradeable. Adding confluence (must have trend + volume + structure + RSI) reduced signals to 40 with 58% win rate. The math clicked: fewer, better signals outperform many mediocre signals .
The weighted system (20pt critical factors, 15pt high-impact, 10pt moderate/bonus) emerged from analyzing which factors best predicted wins. Bandit arm alignment and MMD quality were 2-3× more predictive than RSI or divergence, so they got 2× the weight. This isn't arbitrary—it's data-driven.
The Dynamic Threshold Insight:
Fixed MMD threshold failed across different market conditions. 0.15 worked perfectly on ES but fired constantly on Bitcoin. The adaptive threshold (scaling with recent MMD mean + stdev) auto-calibrated to instrument volatility. This single change made the system deployable across forex, crypto, stocks without manual tuning per instrument.
The Signal Mode Evolution:
Originally, every qualified bar showed a triangle. Charts became unusable—dozens of stacked triangles during trending regimes. "Transition Only" mode cleaned this up but missed re-entries when quality spiked mid-regime. "Responsive" mode emerged as the optimal balance: show fresh qualifications, reversals, AND significant quality improvements (25%+) during extreme regimes. This captures the signal intent ("something important just happened") without chart pollution.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : KMD doesn't forecast prices. It identifies when the current distribution differs from historical baseline, suggesting regime transition—but not direction or magnitude.
• NOT Holy Grail : Typical performance is 48-56% win rate with 1.3-1.8 avg R-multiple. This is a probabilistic edge, not certainty. Expect losing streaks of 8-12 trades.
• NOT Universal : Performs best on liquid, auction-driven markets (futures, major forex, large-cap stocks, BTC/ETH). Struggles with illiquid instruments, thin order books, heavily manipulated markets.
• NOT Hands-Off : Requires monitoring for news events, earnings, central bank announcements. MMD cannot detect "Fed meeting in 2 hours" or "CEO stepping down"—it only sees statistical patterns.
• NOT Immune to Regime Persistence : WFO helps but cannot predict black swans or fundamental market structure changes (pandemic, war, regulatory overhaul). During these events, all historical patterns may break.
Core Assumptions:
1. Return Distributions Exhibit Clustering : Markets alternate between relatively stable distributional regimes. Violation: Permanent random walk, no regime structure.
2. Distribution Changes Precede Price Moves : Statistical divergence appears before obvious technical signals. Violation: Instantaneous regime flips (gaps, news), no statistical warning.
3. Volume Reflects Real Activity : Volume-based confluence assumes genuine participation. Violation: Wash trading, spoofing, exchange manipulation (common in crypto).
4. Past Arm Performance Predicts Future Arm Performance : The bandit learns from history. Violation: Fundamental strategy regime change (e.g., market transitions from mean-reverting to trending permanently).
5. ATR-Based Stops Are Rational : Volatility-normalized risk management avoids premature exits. Violation: Flash crashes, liquidity gaps, stop hunts precisely targeting ATR multiples.
6. Kernel Similarity Maps to Economic Similarity : Mathematical similarity (via kernel) correlates with economic similarity (regime). Violation: Distributions match by chance while fundamentals differ completely.
Performs Best On:
• ES, NQ, RTY (S&P 500, Nasdaq, Russell 2000 futures)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY, AUD/USD
• Liquid commodities: CL (crude oil), GC (gold), SI (silver)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M avg daily volume)
• Major crypto on reputable exchanges: BTC, ETH (Coinbase, Kraken)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume)
• Exotic forex pairs with erratic spreads
• Illiquid crypto altcoins (manipulation, unreliable volume)
• Pre-market/after-hours (thin liquidity, gaps)
• Instruments with frequent corporate actions (splits, dividends)
• Markets with persistent one-sided intervention (central bank pegs)
Known Weaknesses:
• Lag During Instantaneous Shifts : MMD requires (test_window) bars to detect regime change. Fast-moving events (5-10 bar crashes) may bypass detection entirely.
• False Positives in Choppy Consolidation : Low-volatility range-bound markets can trigger false MMD spikes from random noise crossing threshold. Regime filter helps but doesn't eliminate.
• Parameter Sensitivity : Small bandwidth changes (2.0→2.5) can alter signal frequency by 30-50%. Requires careful calibration per instrument.
• Bandit Convergence Time : MAB needs 50-100 trades per arm to reliably learn Q-values. Early trades (first 200 bars) are essentially random exploration.
• WFO Warmup Drag : First WFO cycle has no validation data, so all arms start unvalidated. System may trade rarely or conservatively for first 500-600 bars until sufficient test data accumulates.
• Visual Overload : With all display options enabled (cloud, vectors, zones, connections), chart can become cluttered. Disable selectively for cleaner view.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Kernel Market Dynamics system, including its multi-armed bandit and walk-forward optimization components, is provided for educational purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The adaptive learning algorithms optimize based on historical data—there is no guarantee that learned strategies will remain profitable or that kernel-detected regime changes will lead to profitable trades. Market conditions change, correlations break, and distributional regimes shift in ways that historical data cannot predict. Black swan events occur.
Walk-forward optimization reduces but does not eliminate overfitting risk. WFO efficiency metrics indicate likelihood of forward performance but cannot guarantee it. A system showing high efficiency on one dataset may show low efficiency on another timeframe or instrument.
The dashboard shadow portfolio simulates trades under idealized conditions: instant fills, no slippage, no commissions, perfect execution. Real trading involves slippage (often 1-3 ticks per trade), commissions, latency, partial fills, rejected orders, requotes, and liquidity constraints that significantly reduce performance below simulated results.
Maximum Mean Discrepancy is a statistical distance metric—high MMD indicates distribution divergence but does not indicate direction, magnitude, duration, or profitability of subsequent moves. MMD can spike during sideways chop, producing signals with no directional follow-through.
Users must independently validate system performance on their specific instruments, timeframes, broker execution, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 trades) and start with micro position sizing (10-25% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (1-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Algorithmic systems do not change this fundamental reality—they systematize decision-making but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, or fitness for any particular purpose. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read and understood these risk disclosures and accept full responsibility for all trading activity and potential losses.
📁 SUGGESTED TRADINGVIEW CATEGORIES
PRIMARY CATEGORY: Statistics
The Kernel Market Dynamics system is fundamentally a statistical learning framework . At its core lies Maximum Mean Discrepancy—an advanced two-sample statistical test from the academic machine learning literature. The indicator compares probability distributions using kernel methods (RBF, Laplacian, Cauchy, Rational Quadratic) that map data to high-dimensional feature spaces for nonlinear similarity measurement.
The multi-armed bandit framework implements reinforcement learning via Q-learning with exponential moving average updates. Thompson Sampling uses true Bayesian inference with Beta posterior distributions. Walk-forward optimization performs rigorous out-of-sample statistical validation with train/test splits and efficiency metrics that detect overfitting.
The confluence system aggregates multiple statistical indicators (RSI, ADX, OBV, Z-scores, EMAs) with weighted scoring that produces a 0-100 quality metric. Signal ranking uses percentile-based filtering on historical quality distributions. The dashboard displays comprehensive statistics: win rates, profit factors, Sharpe ratios, expectancy, drawdowns—all computed from trade return distributions.
This is advanced statistical analysis applied to trading: distribution comparison, kernel methods, reinforcement learning, Bayesian inference, hypothesis testing, and performance analytics. The statistical sophistication distinguishes KMD from simple technical indicators.
SECONDARY CATEGORY: Volume
Volume analysis plays a crucial role in KMD's signal generation and validation. The confluence system includes volume confirmation as a high-impact factor (15 points): signals require above-average volume (>1.2× mean) for full points, with scaling based on volume ratio. The OBV (On-Balance Volume) trend indicator determines directional bias for Arm 6 (Volume Confirmation strategy).
Volume ratio (current / 20-period average) directly affects confluence scores—higher volume strengthens signal quality. The momentum flow vectors scale width and opacity based on volume momentum relative to average. Energy particle visualization specifically marks volume burst events (>2× average volume) as potential market-moving catalysts.
Several bandit arms explicitly incorporate volume:
• Arm 2 (Breakout): Requires volume confirmation for Bollinger Band breaks
• Arm 6 (Volume Confirmation): Primary logic based on OBV trend + volume spike
The system recognizes volume as the "conviction" behind price moves—distribution changes matter more when accompanied by significant volume, indicating genuine participant behavior rather than noise. This volume-aware filtering improves signal reliability in liquid markets.
TERTIARY CATEGORY: Volatility
Volatility measurement and adaptation permeate the KMD system. ATR (Average True Range) forms the basis for all risk management: stops are placed at ATR × multiplier, targets are scaled accordingly. The adaptive bandwidth feature scales kernel bandwidth (0.5-2.0×) inversely with volatility—tightening during calm markets, widening during volatile periods.
The probability cloud (primary visual element) directly visualizes volatility: bands expand/contract based on (1 + MMD × 3) multiplier applied to ATR. Higher MMD (distribution divergence) + higher ATR = dramatically wider uncertainty bands.
Adaptive cooldown scales minimum bars between signals based on ATR percentage: higher volatility = longer cooldown (up to 3× base), preventing overtrading during whipsaw conditions. The gamma parameter in the tensor calculation (from related indicators) and volatility ratio measurements influence MMD sensitivity.
Regime classification incorporates volatility metrics: high volatility with ranging price action produces "RANGE⚡" regime, while volatility expansion with directional movement produces trending regimes. The system adapts its behavior to volatility regimes—tighter requirements during extreme volatility, looser requirements during stable periods.
ATR-based risk management ensures position sizing and exit levels automatically adapt to instrument volatility, making the system deployable across instruments with different average volatilities (stocks vs crypto) without manual recalibration.
══════════════════════════════════════════
CLOSING STATEMENT
══════════════════════════════════════════
Kernel Market Dynamics doesn't just measure price—it measures the probability structure underlying price. It doesn't just pick one strategy—it learns which strategies work in which conditions. It doesn't just optimize on history—it validates on the future.
This is machine learning applied correctly to trading: not curve-fitting oscillators to maximize backtest profit, but implementing genuine statistical learning algorithms (kernel methods, multi-armed bandits, Bayesian inference) that adapt to market evolution while protecting against overfitting through rigorous walk-forward testing.
The seven arms compete. The Thompson sampler selects. The kernel measures. The confluence scores. The walk-forward validates. The signals fire.
Most indicators tell you what happened. KMD tells you when the game changed.
"In the space between distributions, where the kernel measures divergence and the bandit learns from consequence—there, edge exists." — KMD-WFO-MAB v2
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Thiru Time CyclesThiru Time Cycles - Advanced Time-Based Market Analysis System
WHAT IT DOES:
Automatically identifies and visualizes trading sessions, time cycles, and market structure elements. Helps traders identify optimal entry times, track session ranges, and monitor market structure through ICT/SMC methodologies.
KEY FEATURES:
1. SESSION KILLZONES
- Asia, London, NY AM, NY PM, Lunch, Power Hour sessions
- Customizable colors, transparency, and visual styles (Filled, Outline, TopLine, SideBars)
- Real-time high/low tracking within each session
2. 90-MINUTE TIME CYCLES
- Divides major sessions into three 90-minute cycles (A/M/D phases)
- London: LO A, LO M, LO D
- NY AM: AM A, AM M, AM D
- NY PM: PM A, PM M, PM D
3. 30-MINUTE SUB-CYCLES
- Granular 30-minute breakdowns (A1-A3, M1-M3, D1-D3)
- Precise entry timing within larger cycles
4. TOI (TIME OF INTEREST) TRACKER
- London: 2:45-3:15 AM, 3:45-4:15 AM
- NY AM: 9:45-10:15 AM, 10:45-11:15 AM
- NY PM: 1:45-2:15 PM, 2:45-3:15 PM
5. TRADE SETUP TIME WINDOWS
- London: 2:30-4:00 AM
- NY AM: 9:30-10:30 AM
- NY PM: 1:30-2:30 PM
6. TOI VERTICAL LINES
- 90-minute and 30-minute cycle boundary markers
- Customizable opacity, style, and height
7. PIVOT ANALYSIS
- High/Low pivot identification per session
- Pivot midpoints
- Customizable labels with price display
- Extension options (until mitigated/past mitigation)
8. SESSION RANGE TABLE
- Real-time range display
- Average range calculation
- Color-coded active sessions
9. OPENING PRICE LINES
- Daily Chart Open, hourly opens
- Customizable session opens
10. DAY/WEEK/MONTH FILTERS
- Filter by day of week
- Current week/last 4 weeks options
- D/W/M high/low tracking
HOW TO USE:
BASIC SETUP:
1. Add indicator to chart
2. Set timezone (default: America/New_York)
3. Enable desired sessions in Killzones section
4. Customize colors and styles
FOR SESSION TRADING:
- Enable session killzones you trade
- Monitor session boxes for high/low ranges
- Use range table for current/average ranges
FOR TIME CYCLE ANALYSIS:
- Enable 90-min or 30-min cycles
- Watch price action at cycle boundaries
- Use vertical lines for cycle transitions
FOR PIVOT TRADING:
- Enable "Show Pivots" in Killzone Pivots
- Use pivots as support/resistance
- Set alerts for pivot breaks
FOR TOI TRADING:
- Enable TOI Tracker
- Monitor specific time windows
- Use for precise entry timing
UNIQUE FEATURES:
✓ Custom visual system (Filled/Outline/TopLine/SideBars box styles)
✓ Proprietary color processing functions
✓ Dual cycle system (90-min + 30-min simultaneous tracking)
✓ Integrated TOI system with vertical line visualization
✓ Smart label positioning with collision detection
✓ Comprehensive range analysis with averaging
✓ Flexible session management with custom time windows
TECHNICAL:
- Pine Script v6
- 500 max labels/lines/boxes
- Full DST-aware timezone support
- Multi-timeframe compatible
- Customizable timeframe limits
BEST PRACTICES:
- Start with session killzones, add cycles gradually
- Set appropriate timeframe limits to avoid clutter
- Use consistent colors for clarity
- Enable only sessions you actively trade
- Monitor range table for session volatility
- Set pivot break alerts for your trading sessions
Compatible with all instruments (forex, stocks, futures, crypto). Works on all timeframes, optimized for intraday trading.
For support: @thirudinesh on TradingView
© 2025 thirudinesh - Advanced Time Cycle Analysis System
Proprietary Algorithm - All Rights Reserved
MARKET SCANNER Core Components:
1. Market Structure & Pivot Points
Multi-timeframe Pivots: Daily, Weekly, Monthly pivot points
Central Pivot Range (CPR): For all timeframes
N-Day High/Low Tracking: Dynamic support/resistance based on recent price action
2. Volume Analysis
Institutional Volume Metrics: Buy/Sell pressure, Net flow, Volume Power
Cumulative Delta: Tracks order flow imbalance
Volume Profile: Right-side profile with POC (Point of Control) and Value Area
Volume Strikes: Identifies significant volume absorption/breakout levels
3. Price Action & Patterns
Fibonacci-based Candlestick Recognition: Green/Red candles with specific Fibonacci conditions
Support/Resistance Zones: Dynamic boxes based on Fibonacci retracements
Breakout Detection: Tracks breakouts above N-day high/low with retracement levels
4. Moving Averages & VWAP
VWAP with multiple moving averages (20, 50, 250 periods)
MVWAP Sign Detection: Tracks flips in VWAP momentum
5. Market Sentiment Analysis
Composite Sentiment Score: Combines RSI, MACD, Stochastic, Moving Averages, ADX
Confidence Scoring: Measures signal reliability
Conflict Detection: Identifies when volume and price signals disagree
6. Advanced Features
Dynamic Gap Calculations: Measures distance to support/resistance zones
Swing Analysis: Identifies swing highs/lows with gap measurements
Volume-Price Confirmation: Validates moves with volume
Professional Tables: Multiple tables displaying pivot levels, differences, sentiment, and volume metrics
Key Trading Concepts Implemented:
Institutional Order Flow: Tracks smart money activity
Volume-Weighted Price Levels: Identifies significant price zones
Multi-timeframe Analysis: Correlates daily, weekly, monthly levels
Fibonacci Retracement Strategies: For entries and exits
Market Microstructure: Through volume profile and delta analysis
Visual Outputs:
Dynamic support/resistance boxes
Volume profile histogram
Multiple information tables
Real-time sentiment scoring
Retracement lines and zones
This is essentially a professional-grade trading suite that combines price action, volume analysis, market structure, and sentiment into one comprehensive tool suitable for both discretionary and systematic trading approaches.
Advanced Smart Trading Suite with OTE═══════════════════════════════════════
ADVANCED SMART TRADING SUITE WITH OPTIMAL TRADE ENTRY
═══════════════════════════════════════
A comprehensive institutional trading system combining multiple advanced concepts including multi-timeframe liquidity analysis, order blocks, fair value gaps, and optimal trade entry zones. Features optional anti-repainting controls for confirmed signal generation.
───────────────────────────────────────
WHAT THIS INDICATOR DOES
───────────────────────────────────────
This all-in-one trading suite provides:
- Multi-Timeframe Liquidity Detection - HTF (Higher Timeframe), LTF (Lower Timeframe), and current timeframe liquidity sweep identification
- Order Blocks - Institutional accumulation/distribution zones with enhanced detection
- Fair Value Gaps (FVG) - Price imbalance detection
- Inverse Fair Value Gaps (iFVG) - Counter-trend imbalance zones
- Optimal Trade Entry (OTE) Zones - Fibonacci retracement-based entry zones (0.618-0.786)
- Trading Sessions - Asian, London, and New York session visualization
- Anti-Repainting Controls - Optional confirmed signals with adjustable confirmation bars
- Comprehensive Alert System - Notifications for all major events
───────────────────────────────────────
HOW IT WORKS
───────────────────────────────────────
ANTI-REPAINTING SYSTEM:
This indicator includes optional anti-repainting controls that fundamentally change how signals are generated:
Confirmed Mode (Recommended):
- Signals wait for confirmation bars before appearing
- No repainting - what you see is final
- Adjustable confirmation period (1-5 bars)
- Slight lag in signal generation
- Better for backtesting and systematic trading
Live Mode:
- Signals appear immediately as patterns develop
- May repaint as new bars form
- Faster signal generation
- Better for discretionary real-time trading
The confirmation system affects all features: liquidity sweeps, order blocks, FVGs, and OTE zones.
LIQUIDITY SWEEP DETECTION:
Three-Tier System:
1. Current Timeframe Liquidity:
- Detects swing highs/lows on chart timeframe
- Configurable lookback and confirmation periods
- Session-tagged for context (Asian/London/NY)
2. HTF (Higher Timeframe) Key Liquidity:
- Default: 4H timeframe (configurable to Daily/Weekly)
- Strength-based filtering using ATR multipliers
- Distance-based clustering prevention
- Only strongest levels displayed (top 1-10)
- Labels show timeframe and strength rating
3. LTF (Lower Timeframe) Key Liquidity:
- Default: 1H timeframe (configurable)
- Precision entry/exit levels
- Strength-based ranking
- Distance filtering to avoid clutter
Sweep Detection Methods:
- Wick Break: Any wick beyond the level
- Close Break: Close price beyond the level
- Full Retrace: Break and close back inside (stop hunt detection)
Buffer System:
- Configurable ATR-based buffer for sweep confirmation
- Prevents false positives from minor price fluctuations
ORDER BLOCKS (Enhanced):
Detection Methodology:
- Identifies the last opposing candle before significant structure break
- Bullish OB: Last red candle before bullish break
- Bearish OB: Last green candle before bearish break
Enhanced Filters:
1. Size Filter:
- Minimum order block size (ATR-based)
- Ensures significant zones only
2. Volume Filter:
- Requires above-average volume (configurable multiplier)
- Confirms institutional participation
3. Imbalance Filter:
- Requires strong directional move after OB formation
- Validates true institutional activity
Violation Detection:
- Wick-based: Any wick through the zone
- Close-based: Close price through the zone
- Automatic removal of broken order blocks
FAIR VALUE GAPS (FVG):
Bullish FVG: Gap between candle 3 low and candle 1 high (three-bar pattern)
Bearish FVG: Gap between candle 3 high and candle 1 low
Requirements:
- Minimum gap size (ATR-based)
- Clear price imbalance
- No overlap between the three candles
Fill Detection:
- Configurable fill threshold (default 50%)
- Tracks partial and complete fills
- Removes filled gaps to keep chart clean
INVERSE FAIR VALUE GAPS (iFVG):
What are iFVGs:
- Counter-trend FVGs that form after original FVG is filled
- Indicate potential reversal or continuation failure
- Form within specific timeframe after original FVG
Detection Rules:
- Must occur after a FVG is filled
- Must form within 20 bars of original FVG
- Minimum size requirement (ATR-based)
- Opposite direction to original FVG
Visual Distinction:
- Dashed border boxes
- Different color scheme from regular FVGs
- Combined labels when FVG and iFVG overlap
OPTIMAL TRADE ENTRY (OTE) ZONES:
Based on Fibonacci retracement principles used by institutional traders:
Concept:
After a structure break (swing high/low violation), price often retraces to specific Fibonacci levels before continuing. The OTE zone (0.618 to 0.786) represents the optimal entry area.
Bullish OTE Formation:
1. Swing low is formed
2. Structure breaks above previous swing high (bullish structure break)
3. Price retraces into 0.618-0.786 Fibonacci zone
4. Entry signal when price enters and holds in OTE zone
Bearish OTE Formation:
1. Swing high is formed
2. Structure breaks below previous swing low (bearish structure break)
3. Price retraces into 0.618-0.786 Fibonacci zone
4. Entry signal when price enters and holds in OTE zone
Key Fibonacci Levels:
- 0.618 (Golden ratio - primary target)
- 0.705 (Square root of 0.5 - institutional level)
- 0.786 (Square root of 0.618 - deep retracement)
Structure Break Requirement:
- Optional setting to require confirmed structure break
- Prevents premature OTE zone identification
- Ensures proper swing structure is established
Entry/Exit Tracking:
- Green checkmark: Price entered OTE zone validly
- Red X: Price exited OTE zone (stop or target)
- Real-time status monitoring
TRADING SESSIONS:
Displays three major trading sessions with full customization:
Asian Session (Tokyo + Sydney):
- Default: 01:00-13:00 UTC+4
- Typically lower volatility
- Sets up key levels for London open
London Session:
- Default: 11:00-20:00 UTC+4
- Highest liquidity period
- Major institutional moves
New York Session:
- Default: 16:00-01:00 UTC+4
- US market hours
- High impact news events
Features:
- Real-time status indicators (🟢 Open / 🔴 Closed)
- Session high/low tracking
- Overlap detection and highlighting
- Historical session display (0-30 days)
- Customizable colors and borders
───────────────────────────────────────
HOW TO USE
───────────────────────────────────────
MASTER CONTROLS:
Enable/disable major features independently:
- Trading Sessions
- Liquidity Sweeps (Current TF)
- HTF Liquidity Sweeps
- LTF Liquidity Sweeps
- Order Blocks
- Fair Value Gaps
- Inverse Fair Value Gaps
- Optimal Trade Entry Zones
ANTI-REPAINTING SETUP:
For Backtesting/Systematic Trading:
1. Enable "Use Confirmed Signals"
2. Set Confirmation Bars to 2-3
3. All signals will wait for confirmation
4. No repainting will occur
For Real-Time Discretionary Trading:
1. Disable "Use Confirmed Signals"
2. Signals appear immediately
3. Be aware signals may adjust with new bars
MULTI-TIMEFRAME LIQUIDITY STRATEGY:
Top-Down Analysis:
1. Identify HTF liquidity levels (4H/Daily) for major targets
2. Find LTF liquidity levels (1H) for entry refinement
3. Wait for HTF liquidity sweep (liquidity grab)
4. Enter on LTF order block in direction of HTF sweep
5. Target next HTF or LTF liquidity level
Liquidity Sweep Trading:
1. HTF liquidity sweep = major institutional move
2. Look for immediate reversal or continuation
3. Use order blocks for entry timing
4. Place stops beyond the swept liquidity
SESSION-BASED TRADING:
Asian Session Strategy:
1. Identify Asian session high/low
2. Wait for London or NY session to open
3. Trade breakouts of Asian range
4. Target previous day's highs/lows
London/NY Session Strategy:
1. Watch for liquidity sweeps at session open
2. Enter on order block confirmation
3. Use OTE zones for retracement entries
4. Target session high/low or HTF liquidity
OTE ZONE TRADING:
Setup Identification:
1. Wait for clear swing high/low formation
2. Confirm structure break in intended direction
3. Monitor for price retracement to 0.618-0.786 zone
4. Enter when price enters OTE zone with confirmation
Entry Rules:
- Bullish: Long when price enters OTE zone from above
- Bearish: Short when price enters OTE zone from below
- Stop loss: Beyond 0.786 level or swing extreme
- Target: Previous swing high/low or HTF liquidity
Exit Management:
- Indicator tracks when price exits OTE zone
- Red X indicates position should be managed/closed
- Use order blocks or FVGs for partial profit targets
FAIR VALUE GAP STRATEGY:
FVG Entry Method:
1. Wait for FVG formation
2. Monitor for price return to FVG
3. Enter on first touch of FVG zone
4. Stop beyond FVG boundary
5. Target: Fill of FVG or next liquidity level
iFVG Reversal Strategy:
1. Original FVG is filled
2. iFVG forms in opposite direction
3. Indicates failed move or reversal
4. Enter on iFVG confirmation
5. Target: Opposite end of range or next structure
Combined FVG + iFVG:
- When both overlap, indicator combines labels
- Represents high-probability reversal zone
- Use with order blocks for confirmation
ORDER BLOCK STRATEGY:
Entry Approach:
1. Wait for order block formation after structure break
2. Enter on first return to order block
3. Place stop beyond order block boundary
4. Target: Next order block or liquidity level
Confirmation Layers:
- Order block + FVG = strong confluence
- Order block + Liquidity sweep = institutional setup
- Order block + OTE zone = optimal entry
- Order block + Session open = high probability
Volume Analysis:
- Wider colored section = stronger institutional interest
- Use volume bars to confirm order block strength
- Higher volume order blocks = more reliable
───────────────────────────────────────
CONFIGURATION GUIDE
───────────────────────────────────────
LIQUIDITY SETTINGS:
Lookback: 5-30 bars
- Lower = more frequent, sensitive levels
- Higher = fewer, more significant levels
- Recommended: 15 for intraday, 20-25 for swing
Sweep Detection Type:
- Wick Break: Most sensitive
- Close Break: More conservative
- Full Retrace: Stop hunt detection
Sweep Buffer: 0-1.0 ATR
- Adds distance requirement for sweep confirmation
- Prevents false positives
- Recommended: 0.1 for most markets
HTF/LTF LIQUIDITY:
HTF Timeframe Selection:
- Swing trading: 1D or 1W
- Day trading: 4H or 1D
- Scalping: 1H or 4H
LTF Timeframe Selection:
- Swing trading: 4H or 1D
- Day trading: 1H or 4H
- Scalping: 15m or 1H
Strength Filters:
- Min Pivot Strength: Higher = fewer, stronger levels
- Min Distance: Higher = less clustering
- Recommended: 2.0 ATR for HTF, 1.5 ATR for LTF
ORDER BLOCK SETTINGS:
Swing Length: 5-20
- Controls sensitivity of structure break detection
- Lower = more order blocks, faster signals
- Higher = fewer order blocks, stronger signals
- Recommended: 8-10 for most timeframes
Enhancement Filters:
- Min Size: 0.5-1.5 ATR typical
- Volume Multiplier: 1.2-2.0 typical
- Imbalance: Enable for strongest signals only
OTE SETTINGS:
Swing Length: 5-50
- Controls OTE zone formation sensitivity
- Lower = more frequent, smaller moves
- Higher = fewer, larger trend moves
- Recommended: 10-15 for intraday
Require Structure Break:
- Enabled: Only shows OTE after confirmed break
- Disabled: Shows potential OTE zones earlier
- Recommended: Enable for higher probability setups
FVG SETTINGS:
Min FVG Size: 0.1-2.0 ATR
- Lower = more gaps detected
- Higher = only significant gaps
- Recommended: 0.5 ATR for most markets
Fill Threshold: 0.1-1.0
- Determines when gap is considered "filled"
- 0.5 = 50% fill required
- Higher = more conservative
iFVG Min Size: 0.1-2.0 ATR
- Typically smaller than regular FVG
- Recommended: 0.3 ATR
ALERT SYSTEM:
Available Alerts:
- Liquidity Sweeps (Current TF)
- HTF Liquidity Sweeps
- LTF Liquidity Sweeps
- Session Changes (Open/Close)
- OTE Entry Signals
Alert Setup:
1. Enable alerts in settings
2. Select specific alert types
3. Create TradingView alert using "Any alert() function call"
4. Configure delivery method (mobile, email, webhook)
Alert Messages Include:
- Event type and direction
- Confirmation status (if using confirmed mode)
- Price level
- Timeframe (for liquidity sweeps)
───────────────────────────────────────
RECOMMENDED CONFIGURATIONS
───────────────────────────────────────
For Day Trading (15m-1H charts):
- HTF Liquidity: 4H
- LTF Liquidity: 1H
- Liquidity Lookback: 15
- Order Block Swing Length: 8
- OTE Swing Length: 10
- Confirmed Signals: Enabled, 2 bars
For Swing Trading (4H-1D charts):
- HTF Liquidity: 1D or 1W
- LTF Liquidity: 4H
- Liquidity Lookback: 20
- Order Block Swing Length: 10
- OTE Swing Length: 15
- Confirmed Signals: Enabled, 2-3 bars
For Scalping (5m-15m charts):
- HTF Liquidity: 1H or 4H
- LTF Liquidity: 15m or 1H
- Liquidity Lookback: 10-12
- Order Block Swing Length: 6-8
- OTE Swing Length: 8
- Confirmed Signals: Optional
───────────────────────────────────────
PERFORMANCE OPTIMIZATION
───────────────────────────────────────
This indicator is optimized with:
- max_bars_back declarations for efficient lookback
- Automatic memory cleanup every 10 bars
- Conditional execution based on enabled features
- Drawing object limits to prevent performance degradation
Memory Management:
- Old liquidity zones automatically removed
- Filled FVGs/iFVGs cleaned up
- Exited OTE zones removed
- Mitigated order blocks deleted
Best Practices:
- Enable only needed features
- Use appropriate timeframe combinations
- Don't display excessive historical sessions
- Monitor drawing object counts on lower timeframes
───────────────────────────────────────
EDUCATIONAL DISCLAIMER
───────────────────────────────────────
This indicator combines multiple institutional trading concepts:
- Liquidity theory (where orders accumulate)
- Order flow analysis (institutional footprints)
- Price imbalance detection (FVGs)
- Fibonacci retracement theory (OTE zones)
- Session-based trading (time-of-day patterns)
All calculations use standard technical analysis methods:
- Pivot high/low detection
- ATR-based normalization
- Volume analysis
- Fibonacci ratios
- Time-based filtering
The indicator identifies potential setups but does not predict future price movements. Success depends on proper application within a complete trading plan including risk management, position sizing, and market context analysis.
───────────────────────────────────────
USAGE DISCLAIMER
───────────────────────────────────────
This tool is for educational and analytical purposes. Trading involves substantial risk of loss. The anti-repainting features provide confirmed signals but do not guarantee profitability. Always conduct independent analysis, use proper risk management, and never risk capital you cannot afford to lose. Past performance does not indicate future results.
SigmaKernel - AdaptiveSigmaKernel - Adaptive Self-Optimizing Multi-Factor Trading System
SigmaKernel - Adaptive is a self-learning algorithmic trading strategy that combines four distinct analytical dimensions—momentum, market structure, volume flow, and reversal patterns—within a machine-learning-inspired framework that continuously adjusts its own parameters based on realized trading performance. Unlike traditional fixed-parameter strategies that maintain static weightings regardless of market conditions or results, this system implements a feedback loop that tracks which signal types, directional biases, and market conditions produce profitable outcomes, then mathematically adjusts component weightings, minimum score thresholds, position sizing multipliers, and trade spacing requirements to optimize future performance.
The strategy is designed for futures traders operating on prop firm accounts or live capital, incorporating realistic execution mechanics including configurable entry modes (stop breakout orders, limit pullback entries, or market-on-open), commission structures calibrated to retail futures contracts ($0.62 per contract default), one-tick slippage modeling, and professional risk controls including trailing drawdown guards, daily loss limits, and weekly profit targets. The system features universal futures compatibility—it automatically detects and adapts to any futures contract by reading the instrument's tick size and point value directly from the chart, eliminating the need for manual configuration across different markets.
What Makes This Approach Different
Adaptive Weight Optimization System
The core differentiation is the adaptive learning architecture. The strategy maintains four independent scoring components: momentum analysis (using RSI multi-timeframe, MACD histogram, and DMI/ADX), market structure detection (breakout identification via pivot-based support/resistance and moving average positioning), volume flow analysis (Volume Price Trend indicator with standard deviation confirmation), and reversal pattern recognition (oversold/overbought conditions combined with structural levels).
Each component generates a directional score that is multiplied by its current weight. After every closed trade, the system performs a retrospective analysis on the last N trades (configurable Learning Period, default 15 trades) to calculate win rates for each signal type independently. For example, if momentum-driven trades won 65% of the time while reversal trades won only 35%, the adaptive algorithm increases the momentum weight and decreases the reversal weight proportionally. The adjustment formula is:
New_Weight = Current_Weight + (Component_Win_Rate - Average_Win_Rate) × Adaptation_Speed
This creates a self-correcting mechanism where successful signal generators receive more influence in future composite scores, while underperforming components are de-emphasized. The system separately tracks long versus short win rates and applies directional bias corrections—if shorts consistently outperform longs, the strategy applies a 10% reduction to bullish signals to prevent fighting the prevailing market character.
Dynamic Parameter Adjustment
Beyond component weightings, three critical strategy parameters self-adjust based on performance:
Minimum Signal Score: The threshold required to trigger a trade. If overall win rate falls below 45%, the system increments this threshold by 0.10 per adjustment cycle, making the strategy more selective. If win rate exceeds 60%, the threshold decreases to allow more opportunities. This prevents the strategy from overtrading during unfavorable conditions and capitalizes on high-probability environments.
Risk Multiplier: Controls position sizing aggression. When drawdown exceeds 5%, risk per trade reduces by 10% per cycle. When drawdown falls below 2%, risk increases by 5% per cycle. This implements the professional risk management principle of "bet small when losing, bet bigger when winning" algorithmically.
Bars Between Trades: Spacing filter to prevent overtrading. Base value (default 9 bars) multiplies by drawdown factor and losing streak factor. During drawdown or consecutive losses, spacing expands up to 2x to allow market conditions to change before re-entering.
All adaptation operates during live forward-testing or real trading—there is no in-sample optimization applied to historical data. The system learns solely from its own realized trades.
Universal Futures Compatibility
The strategy implements universal futures instrument detection that automatically adapts to any futures contract without requiring manual configuration. Instead of hardcoding specific contract specifications, the system reads three critical values directly from TradingView's symbol information:
Tick Size Detection: Uses `syminfo.mintick` to obtain the minimum price increment for the current instrument. This value varies widely across markets—ES trades in 0.25 ticks, crude oil (CL) in 0.01 ticks, gold (GC) in 0.10 ticks, and treasury futures (ZB) in increments of 1/32nds. The strategy adapts all entry buffer calculations and stop placement logic to the detected tick size.
Point Value Detection: Uses `syminfo.pointvalue` to determine the dollar value per full point of price movement. For ES, one point equals $50; for crude oil, one point equals $1,000; for gold, one point equals $100. This automatic detection ensures accurate P&L calculations and risk-per-contract measurements across all instruments.
Tick Value Calculation: Combines tick size and point value to compute dollar value per tick: Tick_Value = Tick_Size × Point_Value. This derived value drives all position sizing calculations, ensuring the risk management system correctly accounts for each instrument's economic characteristics.
This universal approach means the strategy functions identically on emini indices (ES, MES, NQ, MNQ), micro indices, energy contracts (CL, NG, RB), metals (GC, SI, HG), agricultural futures (ZC, ZS, ZW), treasury futures (ZB, ZN, ZF), currency futures (6E, 6J, 6B), and any other futures contract available on TradingView. No parameter adjustments or instrument-specific branches exist in the code—the adaptation happens automatically through symbol information queries.
Stop-Out Rate Monitoring System
The strategy includes an intelligent stop-out rate tracking system that monitors the percentage of your last 20 trades (or available trades if fewer than 20) that were stopped out. This metric appears in the dashboard's Performance section with color-coded guidance:
Green (<30% stop-out rate): Very few trades are being stopped out. This suggests either your stops are too loose (giving back profits on reversals) or you're in an exceptional trending market. Consider tightening your Stop Loss ATR multiplier to lock in profits more efficiently.
Orange (30-65% stop-out rate): Healthy range. Your stop placement is appropriately sized for current market conditions and the strategy's risk-reward profile. No adjustment needed.
Red (>65% stop-out rate): Too many trades are being stopped out prematurely. Your stops are likely too tight for the current volatility regime. Consider widening your Stop Loss ATR multiplier to give trades more room to develop.
Critical Design Philosophy: Unlike some systems that automatically adjust stops based on performance statistics, this strategy intentionally keeps stop-loss control in the user's hands. Automatic stop adjustment creates dangerous feedback loops—widening stops increases risk per contract, which forces position size reduction, which distorts performance metrics, leading to incorrect adaptations. Instead, the dashboard provides visibility into stop performance, empowering you to make informed manual adjustments when warranted. This preserves the integrity of the adaptive system while giving you the critical data needed for stop optimization.
Execution Kernel Architecture
The entry system offers three distinct execution modes to match trader preference and market character:
StopBreakout Mode: Places buy-stop orders above the prior bar's high (for longs) or sell-stop orders below the prior bar's low (for shorts), plus a 2-tick buffer. This ensures entries only occur when price confirms directional momentum by breaking recent structure. Ideal for trending and momentum-driven markets.
LimitPullback Mode: Places limit orders at a pullback price calculated as: Entry_Price = Close - (ATR × Pullback_Multiplier) for longs, or Close + (ATR × Pullback_Multiplier) for shorts. Default multiplier is 0.5 ATR. This waits for mean-reversion before entering in the signal direction, capturing better prices in volatile or oscillating markets.
MarketNextOpen Mode: Executes at market on the bar immediately following signal generation. This provides fastest execution but sacrifices the filtering effect of requiring price confirmation.
All pending entry orders include a configurable Time-To-Live (TTL, default 6 bars). If an order is not filled within the TTL period, it cancels automatically to prevent stale signals from executing in changed market conditions.
Professional Exit Management
The exit system implements a three-stage progression: initial stop loss, breakeven adjustment, and dynamic trailing stop.
Initial Stop Loss: Calculated as entry price ± (ATR × User_Stop_Multiplier × Volatility_Adjustment). Users have direct control via the Stop Loss ATR multiplier (default 1.25). The system then applies volatility regime adjustments: ×1.2 in high-volatility environments (stops automatically widen), ×0.8 in low volatility (stops tighten), ×1.0 in normal conditions. This ensures stops adapt to market character while maintaining user control over baseline risk tolerance.
Breakeven Trigger: When profit reaches a configurable multiple of initial risk (default 1.0R), the stop loss automatically moves to breakeven (entry price). This locks in zero-loss status once the trade demonstrates favorable movement.
Trailing Stop Activation: When profit reaches the Trail_Trigger_R multiple (default 1.2R), the system cancels the fixed stop and activates a dynamic trailing stop. The trail uses Step and Offset parameters defined in R-multiples. For example, with Trail_Offset_R = 1.0 and Trail_Step_R = 1.5, the stop trails 1.0R behind price and moves in 1.5R increments. This captures extended moves while protecting accumulated profit.
Additional failsafes include maximum time-in-trade (exits after N bars if specified) and end-of-session flatten (automatically closes all positions X minutes before session end to avoid overnight exposure).
Core Calculation Methodology
Signal Component Scoring
Momentum Component:
- Calculates 14-period DMI (Directional Movement Index) with ADX strength filter (trending when ADX > 25)
- Computes three RSI timeframes: fast (7-period), medium (14-period), slow (21-period)
- Analyzes MACD (12/26/9) histogram for directional acceleration
- Bullish momentum: uptrend (DI+ > DI- with ADX > 25) + MACD histogram rising above zero + RSI fast between 50-80 = +1.6 score
- Bearish momentum: downtrend (DI- > DI+ with ADX > 25) + MACD histogram falling below zero + RSI fast between 20-50 = -1.6 score
- Score multiplies by volatility adjustment factor: ×0.8 in high volatility (momentum less reliable), ×1.2 in low volatility (momentum more persistent)
Structure Component:
- Identifies swing highs and lows using 10-bar pivot lookback on both sides
- Maintains most recent swing high as dynamic resistance, most recent swing low as dynamic support
- Detects breakouts: bullish when close crosses above resistance with prior bar below; bearish when close crosses below support with prior bar above
- Breakout score: ±1.0 for confirmed break
- Moving average alignment: +0.5 when price > SMA20 > SMA50 (bullish structure); -0.5 when price < SMA20 < SMA50 (bearish structure)
- Total structure range: -1.5 to +1.5
Volume Component:
- Calculates Volume Price Trend: VPT = Σ [(Close - Close ) / Close × Volume]
- Compares VPT to its 10-period EMA as signal line (similar to MACD logic)
- Computes 20-period volume moving average and standard deviation
- High volume event: current volume > (volume_average + 1× std_dev)
- Bullish volume: VPT > VPT_signal AND high_volume = +1.0
- Bearish volume: VPT < VPT_signal AND high_volume = -1.0
- No score if volume is not elevated (filters out low-conviction moves)
Reversal Component:
- Identifies extreme RSI conditions: RSI slow < 30 (oversold) or > 70 (overbought)
- Requires structural confluence: price at or below support level for bullish reversal; at or above resistance for bearish reversal
- Requires momentum shift: RSI fast must be rising (for bull) or falling (for bear) to confirm reversal in progress
- Bullish reversal: RSI < 30 AND price ≤ support AND RSI rising = +1.0
- Bearish reversal: RSI > 70 AND price ≥ resistance AND RSI falling = -1.0
Composite Score Calculation
Final_Score = (Momentum × Weight_M) + (Structure × Weight_S) + (Volume × Weight_V) + (Reversal × Weight_R)
Initial weights: Momentum = 1.0, Structure = 1.2, Volume = 0.8, Reversal = 0.6
These weights adapt after each trade based on component-specific performance as described above.
The system also applies directional bias adjustment: if recent long trades have significantly lower win rate than shorts, bullish scores multiply by 0.9 to reduce aggressive long entries. Vice versa for underperforming shorts.
Position Sizing Algorithm
The position sizing calculation incorporates multiple confidence factors and automatically scales to any futures contract:
1. Base risk amount = Account_Size × Base_Risk_Percent × Adaptive_Risk_Multiplier
2. Stop distance in price units = ATR × User_Stop_Multiplier × Volatility_Regime_Multiplier × Entry_Buffer
3. Risk per contract = Stop_Distance × Dollar_Per_Point (automatically detected from instrument)
4. Raw position size = Risk_Amount / Risk_Per_Contract
Then applies confidence scaling:
- Signal confidence = min(|Weighted_Score| / Min_Score_Threshold, 2.0) — higher scores receive larger size, capped at 2×
- Direction confidence = Long_Win_Rate (for bulls) or Short_Win_Rate (for bears)
- Type confidence = Win_Rate of dominant signal type (momentum/structure/volume/reversal)
- Total confidence = (Signal_Confidence + Direction_Confidence + Type_Confidence) / 3
Adjusted size = Raw_Size × Total_Confidence × Losing_Streak_Reduction
Losing streak reduction = 0.5 if losing_streak ≥ 5, otherwise 1.0
Universal Maximum Position Calculation: Instead of hardcoded limits per instrument, the system calculates maximum position size as: Max_Contracts = Account_Size / 25000, clamped between 1 and 10 contracts. This means a $50,000 account allows up to 2 contracts, a $100,000 account allows up to 4 contracts, regardless of which futures contract is being traded. This universal approach maintains consistent risk exposure across different instruments while preventing overleveraging.
Final size is rounded to integer and bounded by the calculated maximum.
Session and Risk Management System
Timezone-Aware Session Control
The strategy implements timezone-correct session filtering. Users specify session start hour, end hour, and timezone from 12 supported zones (New York, Chicago, Los Angeles, London, Frankfurt, Moscow, Tokyo, Hong Kong, Shanghai, Singapore, Sydney, UTC). The system converts bar timestamps to the selected timezone before applying session logic.
For split sessions (e.g., Asian session 18:00-02:00), the logic correctly handles time wraparound. Weekend trading can be optionally disabled (default: disabled) to avoid low-liquidity weekend price action.
Multi-Layer Risk Controls
Daily Loss Limit: Strategy ceases all new entries when daily P&L reaches negative threshold (default $2,000). This prevents catastrophic drawdown days. Resets at timezone-corrected day boundary.
Weekly Profit Target: Strategy ceases trading when weekly profit reaches target (default $10,000). This implements the professional principle of "take the win and stop pushing luck." Resets on timezone-corrected Monday.
Maximum Daily Trades: Hard cap on entries per day (default 20) to prevent overtrading during volatile conditions when many signals may generate.
Trailing Drawdown Guard: Optional prop-firm-style trailing stop on account equity. When enabled, if equity drops below (Peak_Equity - Trailing_DD_Amount), all trading halts. This simulates the common prop firm rule where exceeding trailing drawdown results in account termination.
All limits display status in the real-time dashboard, showing "MAX LOSS HIT", "WEEKLY TARGET MET", or "ACTIVE" depending on current state.
How To Use This Strategy
Initial Setup
1. Apply the strategy to your desired futures chart (tested on 5-minute through daily timeframes)
2. The strategy will automatically detect your instrument's specifications—no manual configuration needed for different contracts
3. Configure your account size and risk parameters in the Core Settings section
4. Set your trading session hours and timezone to match your availability
5. Adjust the Stop Loss ATR multiplier based on your risk tolerance (0.8-1.2 for tighter stops, 1.5-2.5 for wider stops)
6. Select your preferred entry execution mode (recommend StopBreakout for beginners)
7. Enable adaptation (recommended) or disable for fixed-parameter operation
8. Review the strategy's Properties in the Strategy Tester settings and verify commission/slippage match your broker's actual costs
The universal futures detection means you can switch between ES, NQ, CL, GC, ZB, or any other futures contract without changing any strategy parameters—the system will automatically adapt its calculations to each instrument's unique specifications.
Dashboard Interpretation
The strategy displays a comprehensive real-time dashboard in the top-right corner showing:
Market State Section:
- Trend: Shows UPTREND/DOWNTREND/CONSOLIDATING/NEUTRAL based on ADX and DMI analysis
- ADX Value: Current trend strength (>25 = strong trend, <20 = consolidating)
- Momentum: BULL/BEAR/NEUTRAL classification with current momentum score
- Volatility: HIGH/LOW/NORMAL regime with ATR percentage of price
Volume Profile Section (Large dashboard only):
- VPT Flow: Directional bias from volume analysis
- Volume Status: HIGH/LOW/NORMAL with relative volume multiplier
Performance Section:
- Daily P&L: Current day's profit/loss with color coding
- Daily Trades: Number of completed trades today
- Weekly P&L: Current week's profit/loss
- Target %: Progress toward weekly profit target
- Stop-Out Rate: Percentage of last 20 trades (or available trades if <20) that were stopped out. Includes all stop types: initial stops, breakeven stops, trailing stops, timeout exits, and EOD flattens. Color coded with actionable guidance:
- Green (<30%): Shows "TIGHTEN" guidance. Very few stop-outs suggests stops may be too loose or exceptional market conditions. Consider reducing Stop Loss ATR multiplier.
- Orange (30-65%): Shows "OK" guidance. Healthy stop-out rate indicating appropriate stop placement for current conditions.
- Red (>65%): Shows "WIDEN" guidance. Too many premature stop-outs. Consider increasing Stop Loss ATR multiplier to give trades more room.
- Status: Overall trading status (ACTIVE/MAX LOSS HIT/WEEKLY TARGET MET/FILTERS ACTIVE)
Adaptive Engine Section:
- Min Score: Current minimum threshold for trade entry (higher = more selective)
- Risk Mult: Current position sizing multiplier (adjusts with performance)
- Bars BTW: Current minimum bars required between trades
- Drawdown: Current drawdown percentage from equity peak
- Weights: M/S/V/R showing current component weightings
Win Rates Section:
- Type: Win rates for Momentum, Structure, Volume, Reversal signal types
- Direction: Win rates for Long vs Short trades
Color coding shows green for >50% win rate, red for <50%
Session Info Section:
- Session Hours: Active trading window with timezone
- Weekend Trading: ENABLED/DISABLED status
- Session Status: ACTIVE/INACTIVE based on current time
Signal Generation and Entry
The strategy generates entries when the weighted composite score exceeds the adaptive minimum threshold (initial value configurable, typically 1.5 to 2.5). Entries display as layered triangle markers on the chart:
- Long Signal: Three green upward triangles below the entry bar
- Short Signal: Three red downward triangles above the entry bar
Triangle tooltip shows the signal score and dominant signal type (MOMENTUM/STRUCTURE/VOLUME/REVERSAL).
Position Management and Stop Optimization
Once entered, the strategy automatically manages the position through its three-stage exit system. Monitor the Stop-Out Rate metric in the dashboard to optimize your stop placement:
If Stop-Out Rate is Green (<30%): You're rarely being stopped out. This could mean:
- Your stops are too loose, allowing trades to give back too much profit on reversals
- You're in an exceptional trending market where tight stops would work better
- Action: Consider reducing your Stop Loss ATR multiplier by 0.1-0.2 to tighten stops and lock in profits more efficiently
If Stop-Out Rate is Orange (30-65%): Optimal range. Your stops are appropriately sized for the strategy's risk-reward profile and current market volatility. No adjustment needed.
If Stop-Out Rate is Red (>65%): You're being stopped out too frequently. This means:
- Your stops are too tight for current market volatility
- Trades need more room to develop before reaching profit targets
- Action: Increase your Stop Loss ATR multiplier by 0.1-0.3 to give trades more breathing room
Remember: The stop-out rate calculation includes all exit types (initial stops, breakeven stops, trailing stops, timeouts, EOD flattens). A trade that reaches breakeven and gets stopped out at entry price counts as a stop-out, even though it didn't lose money. This is intentional—it indicates the stop placement didn't allow the trade to develop into profit.
Optimization Workflow
For traders wanting to customize the strategy for their specific instrument and timeframe:
Week 1-2: Run with defaults, adaptation enabled
Allow the system to execute at least 30-50 trades (the Learning Period plus additional buffer). Monitor which session periods, signal types, and market conditions produce the best results. Observe your stop-out rate—if it's consistently red or green, plan to adjust Stop Loss ATR multiplier after the learning period. Do not adjust parameters yet—let the adaptive system establish baseline performance data.
Week 3-4: Analyze adaptation behavior and optimize stops
Review the dashboard's adaptive weights and win rates. If certain signal types consistently show <40% win rate, consider slightly reducing their base weight. If a particular entry mode produces better fill quality and win rate, switch to that mode. If you notice the minimum score threshold has climbed very high (>3.0), market conditions may not suit the strategy's logic—consider switching instruments or timeframes.
Based on your Stop-Out Rate observations:
- Consistently <30%: Reduce Stop Loss ATR multiplier by 0.2-0.3
- Consistently >65%: Increase Stop Loss ATR multiplier by 0.2-0.4
- Oscillating between zones: Leave stops at default and let volatility regime adjustments handle it
Ongoing: Fine-tune risk and execution
Adjust the following based on your risk tolerance and account type:
- Base Risk Per Trade: 0.5% for conservative, 0.75% for moderate, 1.0% for aggressive
- Stop Loss ATR Multiplier: 0.8-1.2 for tight stops (scalping), 1.5-2.5 for wide stops (swing trading)
- Bars Between Trades: Lower (5-7) for more opportunities, higher (12-20) for more selective
- Entry Mode: Experiment between modes to find best fit for current market character
- Session Hours: Narrow to specific high-performance session windows if certain hours consistently underperform
Never adjust: Do not manually modify the adaptive weights, minimum score, or risk multiplier after the system has begun learning. These parameters are self-optimizing and manual interference defeats the adaptive mechanism.
Parameter Descriptions and Optimization Guidelines
Adaptive Intelligence Group
Enable Self-Optimization (default: true): Master switch for the adaptive learning system. When enabled, component weights, minimum score, risk multiplier, and trade spacing adjust based on realized performance. Disable to run the strategy with fixed parameters (useful for comparing adaptive vs non-adaptive performance).
Learning Period (default: 15 trades): Number of most recent trades to analyze for performance calculations. Shorter values (10-12) adapt more quickly to recent conditions but may overreact to variance. Longer values (20-30) produce more stable adaptations but respond slower to regime changes. For volatile markets, use shorter periods. For stable trends, use longer periods.
Adaptation Speed (default: 0.25): Controls the magnitude of parameter adjustments per learning cycle. Lower values (0.05-0.15) make gradual, conservative changes. Higher values (0.35-0.50) make aggressive adjustments. Faster adaptation helps in rapidly changing markets but increases parameter instability. Start with default and increase only if you observe the system failing to adapt quickly enough to obvious performance patterns.
Performance Memory (default: 100 trades): Maximum number of historical trades stored for analysis. This array size does not affect learning (which uses only Learning Period trades) but provides data for future analytics features including stop-out rate tracking. Higher values consume more memory but provide richer historical dataset. Typical users should not need to modify this.
Core Settings Group
Account Size (default: $50,000): Starting capital for position sizing calculations. This should match your actual account size for accurate risk per trade. The strategy uses this value to calculate dollar risk amounts and determine maximum position size (1 contract per $25,000).
Weekly Profit Target (default: $10,000): When weekly P&L reaches this value, the strategy stops taking new trades for the remainder of the week. This implements a "quit while ahead" rule common in professional trading. Set to a realistic weekly goal—20% of account size per week ($10K on $50K) is very aggressive; 5-10% is more sustainable.
Max Daily Loss (default: $2,000): When daily P&L reaches this negative threshold, strategy stops all new entries for the day. This is your maximum acceptable daily loss. Professional traders typically set this at 2-4% of account size. A $2,000 loss on a $50,000 account = 4%.
Base Risk Per Trade % (default: 0.5%): Initial percentage of account to risk on each trade before adaptive multiplier and confidence scaling. 0.5% is conservative, 0.75% is moderate, 1.0-1.5% is aggressive. Remember that actual risk per trade = Base Risk × Adaptive Risk Multiplier × Confidence Factors, so the realized risk will vary.
Trade Filters Group
Base Minimum Signal Score (default: 1.5): Initial threshold that composite weighted score must exceed to generate a signal. Lower values (1.0-1.5) produce more trades with lower average quality. Higher values (2.0-3.0) produce fewer, higher-quality setups. This value adapts automatically when adaptive mode is enabled, but the base sets the starting point. For trending markets, lower values work well. For choppy markets, use higher values.
Base Bars Between Trades (default: 9): Minimum bars that must elapse after an entry before another signal can trigger. This prevents overtrading and allows previous trades time to develop. Lower values (3-6) suit scalping on lower timeframes. Higher values (15-30) suit swing trading on higher timeframes. This value also adapts based on drawdown and losing streaks.
Max Daily Trades (default: 20): Hard limit on total trades per day regardless of signal quality. This prevents runaway trading during extremely volatile days when many signals may generate. For 5-minute charts, 20 trades/day is reasonable. For 1-hour charts, 5-10 trades/day is more typical.
Session Group
Session Start Hour (default: 5): Hour (0-23 format) when trading is allowed to begin, in the timezone specified. For US futures trading in Chicago time, session typically starts at 5:00 or 6:00 PM (17:00 or 18:00) Sunday evening.
Session End Hour (default: 17): Hour when trading stops and no new entries are allowed. For US equity index futures, regular session ends at 4:00 PM (16:00) Central Time.
Allow Weekend Trading (default: false): Whether strategy can trade on Saturday/Sunday. Most futures have low volume on weekends; keeping this disabled is recommended unless you specifically trade Sunday evening open.
Session Timezone (default: America/Chicago): Timezone for session hour interpretation. Select your local timezone or the timezone of your instrument's primary exchange. This ensures session logic aligns with your intended trading hours.
Prop Guards Group
Trailing Drawdown Guard (default: false): Enables prop-firm-style trailing maximum drawdown. When enabled, if equity drops below (Peak Equity - Trailing DD Amount), all trading halts for the remainder of the backtest/live session. This simulates rules used by funded trader programs where exceeding trailing drawdown terminates the account.
Trailing DD Amount (default: $2,500): Dollar amount of drawdown allowed from equity peak. If your equity reaches $55,000, the trailing stop sets at $52,500. If equity then drops to $52,499, the guard triggers and trading ceases.
Execution Kernel Group
Entry Mode (default: StopBreakout):
- StopBreakout: Places stop orders above/below signal bar requiring price confirmation
- LimitPullback: Places limit orders at pullback prices seeking better fills
- MarketNextOpen: Executes immediately at market on next bar
Limit Offset (default: 0.5x ATR): For LimitPullback mode, how far below/above current price to place the limit order. Smaller values (0.3-0.5) seek minor pullbacks. Larger values (0.8-1.2) wait for deeper retracements but may miss trades.
Entry TTL (default: 6 bars, 0=off): Bars an entry order remains pending before cancelling. Shorter values (3-4) keep signals fresh. Longer values (8-12) allow more time for fills but risk executing stale signals. Set to 0 to disable TTL (orders remain active indefinitely until filled or opposite signal).
Exits Group
Stop Loss (default: 1.25x ATR): Base stop distance as a multiple of the 14-period ATR. This is your primary risk control parameter and directly impacts your stop-out rate. Lower values (0.8-1.0) create tighter stops that reduce risk per trade but may get stopped out prematurely in volatile conditions—expect stop-out rates above 65% (red zone). Higher values (1.5-2.5) give trades more room to breathe but increase risk per contract—expect stop-out rates below 30% (green zone). The system applies additional volatility regime adjustments on top of this base: ×1.2 in high volatility environments (stops widen automatically), ×0.8 in low volatility (stops tighten), ×1.0 in normal conditions. For scalping on lower timeframes, use 0.8-1.2. For swing trading on higher timeframes, use 1.5-2.5. Monitor the Stop-Out Rate metric in the dashboard and adjust this parameter to keep it in the healthy 30-65% orange zone.
Move to Breakeven at (default: 1.0R): When profit reaches this multiple of initial risk, stop moves to breakeven. 1.0R means after price moves in your favor by the distance you risked, you're protected at entry price. Lower values (0.5-0.8R) lock in breakeven faster. Higher values (1.5-2.0R) allow more room before protection.
Start Trailing at (default: 1.2R): When profit reaches this multiple, the fixed stop transitions to a dynamic trailing stop. This should be greater than the BE trigger. Values typically range 1.0-2.0R depending on how much profit you want secured before trailing activates.
Trail Offset (default: 1.0R): How far behind price the trailing stop follows. Tighter offsets (0.5-0.8R) protect profit more aggressively but may exit prematurely. Wider offsets (1.5-2.5R) allow more room for profit to run but risk giving back more on reversals.
Trail Step (default: 1.5R): How far price must move in profitable direction before the stop advances. Smaller steps (0.5-1.0R) move the stop more frequently, tightening protection continuously. Larger steps (2.0-3.0R) move the stop less often, giving trades more breathing room.
Max Bars In Trade (default: 0=off): Maximum bars allowed in a position before forced exit. This prevents trades from "going stale" during periods of no meaningful price action. For 5-minute charts, 50-100 bars (4-8 hours) is reasonable. For daily charts, 5-10 bars (1-2 weeks) is typical. Set to 0 to disable.
Flatten near Session End (default: true): Whether to automatically close all positions as session end approaches. Recommended to avoid carrying positions into off-hours with low liquidity.
Minutes before end (default: 5): How many minutes before session end to flatten. 5-15 minutes provides buffer for order execution before the session boundary.
Visual Effects Configuration Group
Dashboard Size (default: Normal): Controls information density in the dashboard. Small shows only critical metrics (excludes stop-out rate). Normal shows comprehensive data including stop-out rate. Large shows all available metrics including weights, session info, and volume analysis. Larger sizes consume more screen space but provide complete visibility.
Show Quantum Field (default: true): Displays animated grid pattern on the chart indicating market state. Disable if you prefer cleaner charts or experience performance issues on lower-end hardware.
Show Wick Pressure Lines (default: true): Draws dynamic lines from bars with extreme wicks, indicating potential support/resistance or liquidity absorption zones. Disable for simpler visualization.
Show Morphism Energy Beams (default: true): Displays directional beams showing momentum energy flow. Beams intensify during strong trends. Disable if you find this visually distracting.
Show Order Flow Clouds (default: true): Draws translucent boxes representing volume flow bullish/bearish bias. Disable for cleaner price action visibility.
Show Fractal Grid (default: true): Displays multi-timeframe support/resistance levels based on fractal price structure at 10/20/30/40/50 bar periods. Disable if you only want to see primary pivot levels.
Glow Intensity (default: 4): Controls the brightness and thickness of visual effects. Lower values (1-2) for subtle visualization. Higher values (7-10) for maximum visibility but potentially cluttered charts.
Color Theme (default: Cyber): Visual color scheme. Cyber uses cyan/magenta futuristic colors. Quantum uses aqua/purple. Matrix uses green/red terminal style. Aurora uses pastel pink/purple gradient. Choose based on personal preference and monitor calibration.
Show Watermark (default: true): Displays animated watermark at bottom of chart with creator credit and current P&L. Disable if you want completely clean charts or need screen space.
Performance Characteristics and Best Use Cases
Optimal Conditions
This strategy performs best in markets exhibiting:
Trending phases with periodic pullbacks: The combination of momentum and structure components excels when price establishes directional bias but provides retracement opportunities for entries. Markets with 60-70% trending bars and 30-40% consolidation produce the highest win rates.
Medium to high volatility: The ATR-based stop sizing and dynamic risk adjustment require sufficient price movement to generate meaningful profit relative to risk. Instruments with 2-4% daily ATR relative to price work well. Extremely low volatility (<1% daily ATR) generates too many scratch trades.
Clear volume patterns: The VPT volume component adds significant edge when volume expansions align with directional moves. Instruments and timeframes where volume data reflects actual transaction flow (versus tick volume proxies) perform better.
Regular session structure: Futures markets with defined opening and closing hours, consistent liquidity throughout the session, and clear overnight/day session separation allow the session controls and time-based failsafes to function optimally.
Sufficient liquidity for stop execution: The stop breakout entry mode requires that stop orders can fill without significant slippage. Highly liquid contracts work better than illiquid instruments where stop orders may face adverse fills.
Suboptimal Conditions
The strategy may struggle with:
Extreme chop with no directional persistence: When ADX remains below 15 for extended periods and price oscillates rapidly without establishing trends, the momentum component generates conflicting signals. Win rate typically drops below 40% in these conditions, triggering the adaptive system to increase minimum score thresholds until conditions improve. Stop-out rates may also spike into the red zone.
Gap-heavy instruments: Markets with frequent overnight gaps disrupt the continuous price assumptions underlying ATR stops and EMA-based structure analysis. Gaps can also cause stop orders to fill at prices far from intended levels, distorting stop-out rate metrics.
Very low timeframes with excessive noise: On 1-minute or tick charts, the signal components react to micro-structure noise rather than meaningful price swings. The strategy works best on 5-minute through daily timeframes where price movements reflect actual order flow shifts.
Extended low-volatility compression: During historically low volatility periods, profit targets become difficult to reach before mean-reversion occurs. The trail offset, even when set to minimum, may be too wide for the compressed price environment. Stop-out rates may drop to green zone indicating stops should be tightened.
Parabolic moves or climactic exhaustion: Vertical price advances or selloffs where price moves multiple ATRs in single bars can trigger momentum signals at exhaustion points. The structure and reversal components attempt to filter these, but extreme moves may override normal logic.
The adaptive learning system naturally reduces signal frequency and position sizing during unfavorable conditions. If you observe multiple consecutive days with zero trades and "FILTERS ACTIVE" status, this indicates the strategy has self-adjusted to avoid poor conditions rather than forcing trades.
Instrument Recommendations
Emini Index Futures (ES, MES, NQ, MNQ, YM, RTY): Excellent fit. High liquidity, clear volatility patterns, strong volume signals, defined session structure. These instruments have been extensively tested and the universal detection handles all contract specifications automatically.
Micro Index Futures (MES, MNQ, M2K, MYM): Excellent fit for smaller accounts. Same market characteristics as the standard eminis but with reduced contract sizes allowing proper risk management on accounts below $50,000.
Energy Futures (CL, NG, RB, HO): Good to mixed fit. Crude oil (CL) works well due to strong trends and reasonable volatility. Natural gas (NG) can be extremely volatile—consider reducing Base Risk to 0.3-0.4% and increasing Stop Loss ATR multiplier to 1.8-2.2 for NG. The strategy automatically detects the $10/tick value for CL and adjusts position sizing accordingly.
Metal Futures (GC, SI, HG, PL): Good fit. Gold (GC) and silver (SI) exhibit clear trending behavior and work well with the momentum/structure components. The strategy automatically handles the different point values ($100/point for gold, $5,000/point for silver).
Agricultural Futures (ZC, ZS, ZW, ZL): Good fit. Grain futures often trend strongly during seasonal periods. The strategy handles the unique tick sizes (1/4 cent increments) and point values ($50/point for corn/wheat, $60/point for soybeans) automatically.
Treasury Futures (ZB, ZN, ZF, ZT): Good fit for trending rates environments. The strategy automatically handles the fractional tick sizing (32nds for ZB/ZN, halves of 32nds for ZF/ZT) through the universal detection system.
Currency Futures (6E, 6J, 6B, 6A, 6C): Good fit. Major currency pairs exhibit smooth trending behavior. The strategy automatically detects point values which vary significantly ($12.50/tick for 6E, $12.50/tick for 6J, $6.25/tick for 6B).
Cryptocurrency Futures (BTC, ETH, MBT, MET): Mixed fit. These markets have extreme volatility requiring parameter adjustment. Increase Base Risk to 0.8-1.2% and Stop Loss ATR multiplier to 2.0-3.0 to account for wider stop distances. Enable 24-hour trading and weekend trading as these markets have no traditional sessions.
The universal futures compatibility means you can apply this strategy to any of these markets without code modification—simply open the chart of your desired contract and the strategy will automatically configure itself to that instrument's specifications.
Important Disclaimers and Realistic Expectations
This is a sophisticated trading strategy that combines multiple analytical methods within an adaptive framework designed for active traders who will monitor performance and market conditions. It is not a "set and forget" fully automated system, nor should it be treated as a guaranteed profit generator.
Backtesting Realism and Limitations
The strategy includes realistic trading costs and execution assumptions:
- Commission: $0.62 per contract per side (accurate for many retail futures brokers)
- Slippage: 1 tick per entry and exit (conservative estimate for liquid futures)
- Position sizing: Realistic risk percentages and maximum contract limits based on account size
- No repainting: All calculations use confirmed bar data only—signals do not change retroactively
However, backtesting cannot fully capture live trading reality:
- Order fill delays: In live trading, stop and limit orders may not fill instantly at the exact tick shown in backtest
- Volatile periods: During high volatility or low liquidity (news events, rollover days, pre-holidays), slippage may exceed the 1-tick assumption significantly
- Gap risk: The backtest assumes stops fill at stop price, but gaps can cause fills far beyond intended exit levels
- Psychological factors: Seeing actual capital at risk creates emotional pressures not present in backtesting, potentially leading to premature manual intervention
The strategy's backtest results should be viewed as best-case scenarios. Real trading will typically produce 10-30% lower returns than backtest due to the above factors.
Risk Warnings
All trading involves substantial risk of loss. The adaptive learning system can improve parameter selection over time, but it cannot predict future price movements or guarantee profitable performance. Past wins do not ensure future wins.
Losing streaks are inevitable. Even with a 60% win rate, you will encounter sequences of 5, 6, or more consecutive losses due to normal probability distributions. The strategy includes losing streak detection and automatic risk reduction, but you must have sufficient capital to survive these drawdowns.
Market regime changes can invalidate learned patterns. If the strategy learns from 50 trades during a trending regime, then the market shifts to a ranging regime, the adapted parameters may initially be misaligned with the new environment. The system will re-adapt, but this transition period may produce suboptimal results.
Prop firm traders: understand your specific rules. Every prop firm has different rules regarding maximum drawdown, daily loss limits, consistency requirements, and prohibited trading behaviors. While this strategy includes common prop guardrails, you must verify it complies with your specific firm's rules and adjust parameters accordingly.
Never risk capital you cannot afford to lose. This strategy can produce substantial drawdowns, especially during learning periods or market regime shifts. Only trade with speculative capital that, if lost, would not impact your financial stability.
Recommended Usage
Paper trade first: Run the strategy on a simulated account for at least 50 trades or 1 month before committing real capital. Observe how the adaptive system behaves, identify any patterns in losing trades, monitor your stop-out rate trends, and verify your understanding of the entry/exit mechanics.
Start with minimum position sizing: When transitioning to live trading, reduce the Base Risk parameter to 0.3-0.4% initially (vs 0.5-1.0% in testing) to reduce early impact while the system learns your live broker's execution characteristics.
Monitor daily, but do not micromanage: Check the dashboard daily to ensure the strategy is operating normally and risk controls have not triggered unexpectedly. Pay special attention to the Stop-Out Rate metric—if it remains in the red or green zones for multiple days, adjust your Stop Loss ATR multiplier accordingly. However, resist the urge to manually adjust adaptive weights or disable trades based on short-term performance. Allow the adaptive system at least 30 trades to establish patterns before making manual changes.
Combine with other analysis: While this strategy can operate standalone, professional traders typically use systematic strategies as one component of a broader approach. Consider using the strategy for trade execution while applying your own higher-timeframe analysis or fundamental view for trade filtering or sizing adjustments.
Keep a trading journal: Document each week's results, note market conditions (trending vs ranging, high vs low volatility), record stop-out rates and any Stop Loss ATR adjustments you made, and document any manual interventions. Over time, this journal will help you identify conditions where the strategy excels versus struggles, allowing you to selectively enable or disable trading during certain environments.
Technical Implementation Notes
All calculations execute on closed bars only (`calc_on_every_tick=false`) ensuring that signals and values do not repaint. Once a bar closes and a signal generates, that signal is permanent in the history.
The strategy uses fixed-quantity position sizing (`default_qty_type=strategy.fixed, default_qty_value=1`) with the actual contract quantity determined by the position sizing function and passed to the entry commands. This approach provides maximum control over risk allocation.
Order management uses Pine Script's native `strategy.entry()` and `strategy.exit()` functions with appropriate parameters for stops, limits, and trailing stops. All orders include explicit from_entry references to ensure they apply to the correct position.
The adaptive learning arrays (trade_returns, trade_directions, trade_types, trade_hours, trade_was_stopped) are maintained as circular buffers capped at PERFORMANCE_MEMORY size (default 100 trades). When a new trade closes, its data is added to the beginning of the array using `array.unshift()`, and the oldest trade is removed using `array.pop()` if capacity is exceeded. The stop-out tracking system analyzes the trade_was_stopped array to calculate the rolling percentage displayed in the dashboard.
Dashboard rendering occurs only on the confirmed bar (`barstate.isconfirmed`) to minimize computational overhead. The table is pre-created with sufficient rows for the selected dashboard size and cells are populated with current values each update.
Visual effects (fractal grid, wick pressure, morphism beams, order flow clouds, quantum field) recalculate on each bar for real-time chart updates. These are computationally intensive—if you experience chart lag, disable these visual components. The core strategy logic continues to function identically regardless of visual settings.
Timezone conversions use Pine Script's built-in timezone parameter on the `hour()`, `minute()`, and `dayofweek()` functions. This ensures session logic and daily/weekly resets occur at correct boundaries regardless of the chart's default timezone or the server's timezone.
The universal futures detection queries `syminfo.mintick` and `syminfo.pointvalue` on each strategy initialization to obtain the current instrument's specifications. These values remain constant throughout the strategy's execution on a given chart but automatically update when the strategy is applied to a different instrument.
The strategy has been tested on TradingView across timeframes from 5-minute through daily and across multiple futures instrument types including equity indices, energy, metals, agriculture, treasuries, and currencies. It functions identically on all instruments due to the percentage-based risk model and ATR-relative calculations which adapt automatically to price scale and volatility, combined with the universal futures detection system that handles contract-specific specifications.
ICT Sessions With BOS [TradeWithRon]
WITH BOS
This version includes BOS with filter for each session.
NONE,FVG,CISD Filter preset
you can choose how many BOS per session, style etc.
ICT Sessions and killzones maps three intraday sessions on your chart (Asia, London, NY), tracks each session’s live high/low, draws optional session range boxes, and projects ICT OTE zones in real time—with granular styling, touch/mitigation logic, and alerting.
What it does
*Live Session high/low tracking.
Historical session lines:
When a session ends, its final High/Low are preserved as tracked lines (with optional labels) for a configurable number of recent sessions.
Session boxes (ranges):
Draws a shaded box from session start to end that expands with new highs/lows. Limit how many recent boxes remain on chart.
ICT OTE zones (live):
For the currently active session, projects user-defined Fibonacci OTE levels (e.g., 61.8%, 70.5%, 78.6) between the session’s running high and low. Zones update tick-by-tick and can show labels. You can retain a history of recent sessions’ OTE levels.
snapshot
Break visualization (mitigation):
Optionally color the bar when price breaks a stored session High/Low. You can:
Require a body close through the level (vs. any touch)
Auto-remove the line and/or label on touch/close
Use custom break colors per session and side (high/low)
Timestamps:
Add up to two recurring vertical timestamp markers (e.g., 08:00, 09:30), plus an opening horizontal marker (e.g., 09:30) with label that extends until the next occurrence.
Alerts:
Built-in alerts for:
Touch of Session 1/2/3 High/Low (Asia/London/NY)
Touch of OTE levels (per session)
Key inputs:
Time & Limits
Timezone (e.g., GMT-4)
Timeframe limit: hide all drawings on and above a specified TF
Sessions
Session windows (default):
Session 1 (Asia): 18:00–00:00
Session 2 (London): 00:00–06:00
Session 3 (NY): 08:00–12:00
How many to keep (lines/boxes)
Line width, colors, and label suffixes (“High”/“Low”)
Labels: toggle, text (“Asia”, “London”, “NY”), size, and colors
Boxes: toggle per session and background colors
ICT OTE Zones
Toggle per session (Asia/London/NY)
Levels (comma-separated %s, e.g., 61.8,70.5,78.6)
History: number of past sessions to retain
Opacity, line width/style, and label size
Custom label text per session (e.g., “Asia OTE”)
Break/Mitigation Behavior:
Enable Mitigated Candles (bar color on break)
Remove line on touch and/or remove label on touch
Require body close (vs. wick touch)
Custom break colors by session and side
Timestamps
Opening horizontal line (time, style, width, color, label text/size, drawing limit)
Two vertical timestamps (times, style, width, color, drawing limit)
Alerts
Master Enable Alerts
Per-session toggles for High/Low touches
OTE touch alerts
How it works (under the hood)
Detects session state via input.session() windows in the chosen timezone.
Live session High/Low lines and labels update in real time; on session end, final levels are stored with optional labels and tracked length.
OTE zones are live-computed from current session High↔Low and refreshed every bar; a compact rolling history is enforced.
Bar coloring reacts to break events (touch or body-close, per your setting) and uses session-specific colors when enabled.
Timestamp lines/labels are created on each occurrence and trimmed to a drawing limit for performance.
Tips:
To hide session lines but keep boxes, set line color opacity to 0.
Use Timeframe Limit to keep higher-TF charts clean.
Fine-tune OTE Levels and History to balance clarity and performance.
For stricter break logic, enable Require Body Close.
Note: The script reserves high limits for lines/labels/boxes to keep recent context visible while managing cleanup automatically. Adjust “Session Number” and “Number Of Boxes” to suit your workflow.
— © TradeWithRon
ICT Sessions [TradeWithRon]
ICT Sessions and killzones maps three intraday sessions on your chart (Asia, London, NY), tracks each session’s live high/low, draws optional session range boxes, and projects ICT OTE zones in real time—with granular styling, touch/mitigation logic, and alerting.
What it does
Live Session high/low tracking.
Historical session lines:
When a session ends, its final High/Low are preserved as tracked lines (with optional labels) for a configurable number of recent sessions.
Session boxes (ranges):
Draws a shaded box from session start to end that expands with new highs/lows. Limit how many recent boxes remain on chart.
ICT OTE zones (live):
For the currently active session, projects user-defined Fibonacci OTE levels (e.g., 61.8%, 70.5%, 78.6) between the session’s running high and low. Zones update tick-by-tick and can show labels. You can retain a history of recent sessions’ OTE levels.
Break visualization (mitigation):
Optionally color the bar when price breaks a stored session High/Low. You can:
Require a body close through the level (vs. any touch)
Auto-remove the line and/or label on touch/close
Use custom break colors per session and side (high/low)
Timestamps:
Add up to two recurring vertical timestamp markers (e.g., 08:00, 09:30), plus an opening horizontal marker (e.g., 09:30) with label that extends until the next occurrence.
Alerts:
Built-in alerts for:
Touch of Session 1/2/3 High/Low (Asia/London/NY)
Touch of OTE levels (per session)
Key inputs:
Time & Limits
Timezone (e.g., GMT-4)
Timeframe limit: hide all drawings on and above a specified TF
Sessions
Session windows (default):
Session 1 (Asia): 18:00–00:00
Session 2 (London): 00:00–06:00
Session 3 (NY): 08:00–12:00
How many to keep (lines/boxes)
Line width, colors, and label suffixes (“High”/“Low”)
Labels: toggle, text (“Asia”, “London”, “NY”), size, and colors
Boxes: toggle per session and background colors
ICT OTE Zones
Toggle per session (Asia/London/NY)
Levels (comma-separated %s, e.g., 61.8,70.5,78.6)
History: number of past sessions to retain
Opacity, line width/style, and label size
Custom label text per session (e.g., “Asia OTE”)
Break/Mitigation Behavior:
Enable Mitigated Candles (bar color on break)
Remove line on touch and/or remove label on touch
Require body close (vs. wick touch)
Custom break colors by session and side
Timestamps
Opening horizontal line (time, style, width, color, label text/size, drawing limit)
Two vertical timestamps (times, style, width, color, drawing limit)
Alerts
Master Enable Alerts
Per-session toggles for High/Low touches
OTE touch alerts
How it works (under the hood)
Detects session state via input.session() windows in the chosen timezone.
Live session High/Low lines and labels update in real time; on session end, final levels are stored with optional labels and tracked length.
OTE zones are live-computed from current session High↔Low and refreshed every bar; a compact rolling history is enforced.
Bar coloring reacts to break events (touch or body-close, per your setting) and uses session-specific colors when enabled.
Timestamp lines/labels are created on each occurrence and trimmed to a drawing limit for performance.
Tips:
To hide session lines but keep boxes, set line color opacity to 0.
Use Timeframe Limit to keep higher-TF charts clean.
Fine-tune OTE Levels and History to balance clarity and performance.
For stricter break logic, enable Require Body Close.
Note: The script reserves high limits for lines/labels/boxes to keep recent context visible while managing cleanup automatically. Adjust “Session Number” and “Number Of Boxes” to suit your workflow.
— © TradeWithRon
