VCAI Stochastic RSI+VCAI Stoch RSI+ is a cleaned-up Stochastic RSI built with V-Core colours for faster, clearer momentum reads and more reliable OB/OS signals.
What it shows:
Purple %K line → bearish momentum strengthening
Yellow %D line → bullish momentum building and smoothing
Soft purple/yellow background bands → OB/OS exhaustion zones, not just raw 80/20 triggers
Midline at 50 → balance point where momentum shifts between bull- and bear-side control
Optional HTF mode → run Stoch RSI from any timeframe while viewing it on your current chart
How to read it:
Both lines rising out of OS → early bullish shift; pullbacks that hold direction favour continuation
Both lines falling from OB → early bearish shift; bounces into the purple OB zone can become fade setups
Lines stacked and moving together → strong, cleaner momentum
Lines crossing repeatedly → low-conviction, choppy conditions
OB/OS shading highlights exhaustion so you focus on moves with context, not every 80/20 tick
Why it’s different:
Classic Stoch RSI is hyper-sensitive and mostly noise.
VCAI Stoch RSI+ applies V-Core’s colour-driven regime logic, controlled OB/OS shading, and optional HTF smoothing so you see momentum structure instead of clutter — making it easier to judge when momentum is genuinely shifting and when it’s just another wiggle.
Search in scripts for "bear"
Trend Vector Pro v2.0Title: Trend Vector Pro v2.0
👨💻 Developed by: Mohammed Bedaiwi
💡 Strategy Overview & Coherence
Trend Vector Pro (TVPro) is a momentum-based trend & reversal strategy that uses a custom smoothed oscillator, an optional ADX filter, and classic Pivot Points to create a single, coherent trading framework.
Instead of stacking random indicators, TVPro is built around these integrated components:
A custom momentum engine (signal generation)
An optional ADX filter (trend quality control)
Daily Pivot Points (context, targets & S/R)
Swing-based “Golden Bar” trailing stops (trade management)
Optional extended bar detection (overextension alerts)
All parts are designed to work together and are documented below to address originality & usefulness requirements.
🔍 Core Components & Justification
1. Custom Momentum Engine (Main Signal Source)
TVPro’s engine is a custom oscillator derived from the bar midpoint ( hl2 ), similar in spirit to the Awesome Oscillator but adapted and fully integrated into the strategy. It measures velocity and acceleration of price, letting the script distinguish between strong impulses, weakening trends, and pure noise.
2. ADX Filter (Trend Strength Validation – Optional)
Uses Average Directional Index (ADX) as a gatekeeper.
Why this matters: This prevents the strategy from firing signals in choppy, non-trending environments (when ADX is below the threshold) and keeps trades focused on periods of clear directional strength.
3. Classic Pivot Points (Context & Targets)
Calculates Daily Pivot Points ( PP, R1-R3, S1-S3 ) via request.security() using prior session data.
Why this matters: Momentum gives the signal, ADX validates the environment, and Pivots add external structure for risk and target planning. This is a designed interaction, not a random mashup.
🧭 Trend State Logic (5-State Bar Coloring)
The strategy uses the momentum's value + slope to define five states, turning the chart into a visual momentum map:
🟢 STRONG BULL (Bright Green): Momentum accelerating UP. → Strong upside impulse.
🌲 WEAK BULL (Dark Green): Momentum decelerating DOWN (while positive). → Pullback/pause zone.
🔴 STRONG BEAR (Bright Red): Momentum accelerating DOWN. → Strong downside impulse.
🍷 WEAK BEAR (Dark Red): Momentum decelerating UP (while negative). → Rally/short-covering zone.
🔵 NEUTRAL / CHOP (Cyan): Momentum is near zero (based on noise threshold). → Consolidation / low volatility.
🎯 Signal Logic Modes
TVPro provides two selectable entry styles, controlled by input:
Reversals Only (Cleaner Mode – Default): Targets trend flips. Entry triggers when the current state is Bullish (or Bearish) and the previous state was not. This reduces noise and over-trading.
All Strong Pulses (Aggressive Mode): Targets acceleration phases. Entry triggers when the bar turns to STRONG BULL or STRONG BEAR after any other state. This mode produces more trades.
📌 Risk Management Tools
Golden Bars – Trailing Stops: Yellow “Trail” Arrows mark confirmed Swing Highs/Lows. These are used as logical trailing stop levels based on market structure.
Extended Bars: Detects when price closes outside a 2-standard-deviation channel, flagging overextension where a pullback is more likely.
Pivot Points: Used as external targets for Take Profit and structural stop placement.
⚙️ Strategy Defaults (Crucial for Publication Compliance)
To keep backtest results realistic and in line with House Rules, TVPro is published with the following fixed default settings:
Order Size: 5% of equity per trade ( default_qty_value = 5 )
Commission: 0.04% per order ( commission_value = 0.04 )
Slippage: 2 ticks ( slippage = 2 )
Initial Capital: 10,000
📘 How to Trade with Trend Vector Pro
Entry: Take Long when a Long signal appears and confirm the bar is Green (Bull state). Short for Red (Bear state).
Stop Loss: Place the initial SL near the latest swing High/Low, or near a relevant Pivot level.
Trade Management: Follow Golden (Trail) Arrows to trail your stop behind structure.
Exits: Exit when: the trailing stop is hit, Price reaches a major Pivot level, or an opposite signal prints.
🛑 Disclaimer
This script is for educational purposes only and does not constitute financial advice. Past performance does not guarantee future results. Always forward-test and use proper risk management before applying any strategy to live trading.
Trend Vector Pro v2.0Trend Vector Pro v2.0
👨💻 Developed by: Mohammed Bedaiwi
💡 Strategy Overview & Coherence
Trend Vector Pro (TVPro) is a momentum-based trend & reversal strategy that uses a custom smoothed oscillator, an optional ADX filter, and classic Pivot Points to create a single, coherent trading framework.
Instead of stacking random indicators, TVPro is built around these integrated components:
A custom momentum engine (signal generation)
An optional ADX filter (trend quality control)
Daily Pivot Points (context, targets & S/R)
Swing-based “Golden Bar” trailing stops (trade management)
Optional extended bar detection (overextension alerts)
All parts are designed to work together and are documented below to address originality & usefulness requirements.
🔍 Core Components & Justification
1. Custom Momentum Engine (Main Signal Source)
TVPro’s engine is a custom oscillator derived from the bar midpoint ( hl2 ), similar in spirit to the Awesome Oscillator but adapted and fully integrated into the strategy. It measures velocity and acceleration of price, letting the script distinguish between strong impulses, weakening trends, and pure noise.
2. ADX Filter (Trend Strength Validation – Optional)
Uses Average Directional Index (ADX) as a gatekeeper.
Why this matters: This prevents the strategy from firing signals in choppy, non-trending environments (when ADX is below the threshold) and keeps trades focused on periods of clear directional strength.
3. Classic Pivot Points (Context & Targets)
Calculates Daily Pivot Points ( PP, R1-R3, S1-S3 ) via request.security() using prior session data.
Why this matters: Momentum gives the signal, ADX validates the environment, and Pivots add external structure for risk and target planning. This is a designed interaction, not a random mashup.
🧭 Trend State Logic (5-State Bar Coloring)
The strategy uses the momentum's value + slope to define five states, turning the chart into a visual momentum map:
🟢 STRONG BULL (Bright Green): Momentum accelerating UP. → Strong upside impulse.
🌲 WEAK BULL (Dark Green): Momentum decelerating DOWN (while positive). → Pullback/pause zone.
🔴 STRONG BEAR (Bright Red): Momentum accelerating DOWN. → Strong downside impulse.
🍷 WEAK BEAR (Dark Red): Momentum decelerating UP (while negative). → Rally/short-covering zone.
🔵 NEUTRAL / CHOP (Cyan): Momentum is near zero (based on noise threshold). → Consolidation / low volatility.
🎯 Signal Logic Modes
TVPro provides two selectable entry styles, controlled by input:
Reversals Only (Cleaner Mode – Default): Targets trend flips. Entry triggers when the current state is Bullish (or Bearish) and the previous state was not. This reduces noise and over-trading.
All Strong Pulses (Aggressive Mode): Targets acceleration phases. Entry triggers when the bar turns to STRONG BULL or STRONG BEAR after any other state. This mode produces more trades.
📌 Risk Management Tools
🟡 Golden Bars – Trailing Stops: Yellow “Trail” Arrows mark confirmed Swing Highs/Lows. These are used as logical trailing stop levels based on market structure.
Extended Bars: Detects when price closes outside a 2-standard-deviation channel, flagging overextension where a pullback is more likely.
Pivot Points: Used as external targets for Take Profit and structural stop placement.
⚙️ Strategy Defaults (Crucial for Publication Compliance)
To keep backtest results realistic and in line with House Rules, TVPro is published with the following fixed default settings:
Order Size: 5% of equity per trade ( default_qty_value = 5 )
Commission: 0.04% per order ( commission_value = 0.04 )
Slippage: 2 ticks ( slippage = 2 )
Initial Capital: 10,000
📘 How to Trade with Trend Vector Pro
Entry: Take Long when a Long signal appears and confirm the bar is Green (Bull state). Short for Red (Bear state).
Stop Loss: Place the initial SL near the latest swing High/Low, or near a relevant Pivot level.
Trade Management: Follow Golden (Trail) Arrows to trail your stop behind structure.
Exits: Exit when: the trailing stop is hit, Price reaches a major Pivot level, or an opposite signal prints.
🛑 Disclaimer
This script is for educational purposes only and does not constitute financial advice. Past performance does not guarantee future results. Always forward-test and use proper risk management before applying any strategy to live trading.
2026 CHRISTMAS PRESENT CHRISTMAS PRESENT
Overview
The Cash Detector is a comprehensive trading strategy that combines momentum analysis with price action confirmation to identify high-probability entry points. This strategy is designed to capture trend reversals and continuation moves by requiring multiple confirming signals before entry, significantly reducing false signals common in single-indicator systems.
Strategy Background
The strategy is built on the principle of confluence trading requiring multiple technical factors to align before taking a position. It focuses on two critical phases of market rotation:
Q2 Momentum Phase: Uses MACD crossovers to identify shifts in market momentum, signaling when bulls or bears are gaining control.
Q4 Trigger Phase: Employs engulfing candlestick patterns to confirm strong directional pressure and validate the momentum signal with actual price action.
By combining these elements, the strategy filters out weak signals and focuses only on setups where both momentum AND price action agree on direction.
Key Features
Dual Confirmation System: Requires both MACD momentum shift and engulfing candle pattern
RSI Filter: Optional overbought/oversold filter to avoid extreme conditions
Built-in Risk Management: Configurable stop loss and take profit levels
Performance Dashboard: Real-time ROI metrics displayed on chart
Full Backtesting: Strategy mode allows historical performance analysis
Trading Rules
LONG ENTRY BUY
All conditions must occur on the same candle:
1. Momentum Confirmation:
MACD line crosses above signal line bullish crossover
2. Price Action Confirmation:
Bullish engulfing pattern forms:
Current close greater than previous open
Current open less than previous close
Current close greater than current open
3. RSI Filter Optional:
RSI less than 70 not overbought
Visual Signal: Green LONG label appears below the candle
SHORT ENTRY SELL
All conditions must occur on the same candle:
1. Momentum Confirmation:
MACD line crosses below signal line bearish crossover
2. Price Action Confirmation:
Bearish engulfing pattern forms:
Current close less than previous open
Current open greater than previous close
Current close less than current open
3. RSI Filter Optional:
RSI greater than 30 not oversold
Visual Signal: Red SHORT label appears above the candle
Exit Rules
Stop Loss Default 2 percent
Long: Exit if price drops 2 percent below entry
Short: Exit if price rises 2 percent above entry
Take Profit Default 4 percent
Long: Exit if price rises 4 percent above entry
Short: Exit if price drops 4 percent below entry
Input Parameters
Indicator Settings
MACD Fast Length: 12 default
MACD Slow Length: 26 default
RSI Length: 14 default
Risk Management
Use Stop Loss: Enable or disable stop loss
Stop Loss percent: Percentage risk per trade default 2 percent
Use Take Profit: Enable or disable take profit
Take Profit percent: Target profit per trade default 4 percent
Filters
Use RSI Filter: Enable or disable RSI overbought oversold filter
RSI Overbought: Upper threshold default 70
RSI Oversold: Lower threshold default 30
Performance Metrics
The built-in dashboard displays:
Net Profit: Total profit loss in currency and percentage
Total Trades: Number of completed trades
Win Rate: Percentage of profitable trades
Profit Factor: Ratio of gross profit to gross loss
Average Win Loss: Mean profit per winning losing trade
Max Drawdown: Largest peak to trough decline
Best Practices
1. Timeframe Selection: Works on multiple timeframes test on 15min 1H 4H and daily
2. Market Conditions: Most effective in trending markets with clear momentum
3. Risk Reward Ratio: Default 1:2 ratio 2 percent risk 4 percent reward is conservative adjust based on backtesting
4. Combine with Context: Consider overall market trend and support resistance levels
5. Backtest First: Always backtest on your specific instrument and timeframe before live trading
Risk Disclaimer
This strategy is for educational purposes. Past performance does not guarantee future results. Always:
Backtest thoroughly on historical data
Paper trade before using real capital
Use proper position sizing and risk management
Never risk more than you can afford to lose
Customization Tips
Aggressive traders: Reduce stop loss to 1.5 percent increase take profit to 5 percent
Conservative traders: Increase stop loss to 3 percent reduce take profit to 3 percent
Ranging markets: Enable RSI filter to avoid false breakouts
Strong trends: Disable RSI filter to catch all momentum shifts
Technical Details
Indicators Used:
Moving Average Convergence Divergence MACD
Relative Strength Index RSI
Candlestick Pattern Recognition
Strategy Type: Trend following with momentum confirmation
Best Suited For: Stocks Forex Crypto Indices
Version 1.0
Compatible with Pine Script v5
Trinity Ultimate 10 MA Ribbons)I got tired of trying to find a multi MA ribbon that could also color change and allow different types, if it exists then I could not find it... So here it is...
The **Trinity Ultimate 10 MA Ribbon** is a highly customizable, professional-grade moving average ribbon that combines extreme flexibility with beautiful visual feedback. Designed for traders who want full control without sacrificing clarity, it allows you to build a ribbon using up to ten completely independent moving averages — each with its own length, type, color, thickness, and visibility setting — while automatically coloring both the lines and the fills according to bullish or bearish conditions.
### Key Features
- Ten fully independent moving averages that can be mixed and matched exactly as you want.
- Each MA has its own selectable type: EMA (default), SMA, WMA, HMA, RMA, VWMA, or ALMA — perfect for combining fast EMAs with a slow HMA or a classic 200-period SMA.
- Every single MA line automatically changes color in real time: bright green when price is above the MA (bullish) and red when price is below the MA (bearish), making trend strength instantly visible across all timeframes.
- Smart, reactive ribbon fills that appear only between consecutive enabled MAs. Turn any MA on or off and the fills instantly adjust — no gaps, no broken bands, no manual rework.
- Nine layered fills with individually adjustable transparency (default is gradually increasing transparency from the fastest to the slowest MA), creating a smooth, depth-like ribbon effect that looks stunning on any chart background.
- Fill color itself is dynamic: green for bullish candles (close > open) and red for bearish candles, or you can customize both colors to any shade you prefer.
- Full control over every visual element: base colors, line thickness (1–10), lengths, and show/hide toggles for each of the ten MAs.
- Clean and lightweight code that compiles instantly in Pine Script v5 and works on all markets and timeframes without lag.
In short, this is the most flexible and visually informative moving-average ribbon available on TradingView today. Whether you want a classic 9-EMA ribbon, a Guppy-style multiple-timeframe setup, a hybrid EMA/HMA mix, or just three or four key levels, the indicator adapts perfectly while always telling you at a glance where the bulls and bears are in control.
Adaptive Genesis Engine [AGE]ADAPTIVE GENESIS ENGINE (AGE)
Pure Signal Evolution Through Genetic Algorithms
Where Darwin Meets Technical Analysis
🧬 WHAT YOU'RE GETTING - THE PURE INDICATOR
This is a technical analysis indicator - it generates signals, visualizes probability, and shows you the evolutionary process in real-time. This is NOT a strategy with automatic execution - it's a sophisticated signal generation system that you control .
What This Indicator Does:
Generates Long/Short entry signals with probability scores (35-88% range)
Evolves a population of up to 12 competing strategies using genetic algorithms
Validates strategies through walk-forward optimization (train/test cycles)
Visualizes signal quality through premium gradient clouds and confidence halos
Displays comprehensive metrics via enhanced dashboard
Provides alerts for entries and exits
Works on any timeframe, any instrument, any broker
What This Indicator Does NOT Do:
Execute trades automatically
Manage positions or calculate position sizes
Place orders on your behalf
Make trading decisions for you
This is pure signal intelligence. AGE tells you when and how confident it is. You decide whether and how much to trade.
🔬 THE SCIENCE: GENETIC ALGORITHMS MEET TECHNICAL ANALYSIS
What Makes This Different - The Evolutionary Foundation
Most indicators are static - they use the same parameters forever, regardless of market conditions. AGE is alive . It maintains a population of competing strategies that evolve, adapt, and improve through natural selection principles:
Birth: New strategies spawn through crossover breeding (combining DNA from fit parents) plus random mutation for exploration
Life: Each strategy trades virtually via shadow portfolios, accumulating wins/losses, tracking drawdown, and building performance history
Selection: Strategies are ranked by comprehensive fitness scoring (win rate, expectancy, drawdown control, signal efficiency)
Death: Weak strategies are culled periodically, with elite performers (top 2 by default) protected from removal
Evolution: The gene pool continuously improves as successful traits propagate and unsuccessful ones die out
This is not curve-fitting. Each new strategy must prove itself on out-of-sample data through walk-forward validation before being trusted for live signals.
🧪 THE DNA: WHAT EVOLVES
Every strategy carries a 10-gene chromosome controlling how it interprets market data:
Signal Sensitivity Genes
Entropy Sensitivity (0.5-2.0): Weight given to market order/disorder calculations. Low values = conservative, require strong directional clarity. High values = aggressive, act on weaker order signals.
Momentum Sensitivity (0.5-2.0): Weight given to RSI/ROC/MACD composite. Controls responsiveness to momentum shifts vs. mean-reversion setups.
Structure Sensitivity (0.5-2.0): Weight given to support/resistance positioning. Determines how much price location within swing range matters.
Probability Adjustment Genes
Probability Boost (-0.10 to +0.10): Inherent bias toward aggressive (+) or conservative (-) entries. Acts as personality trait - some strategies naturally optimistic, others pessimistic.
Trend Strength Requirement (0.3-0.8): Minimum trend conviction needed before signaling. Higher values = only trades strong trends, lower values = acts in weak/sideways markets.
Volume Filter (0.5-1.5): Strictness of volume confirmation. Higher values = requires strong volume, lower values = volume less important.
Risk Management Genes
ATR Multiplier (1.5-4.0): Base volatility scaling for all price levels. Controls whether strategy uses tight or wide stops/targets relative to ATR.
Stop Multiplier (1.0-2.5): Stop loss tightness. Lower values = aggressive profit protection, higher values = more breathing room.
Target Multiplier (1.5-4.0): Profit target ambition. Lower values = quick scalping exits, higher values = swing trading holds.
Adaptation Gene
Regime Adaptation (0.0-1.0): How much strategy adjusts behavior based on detected market regime (trending/volatile/choppy). Higher values = more reactive to regime changes.
The Magic: AGE doesn't just try random combinations. Through tournament selection and fitness-weighted crossover, successful gene combinations spread through the population while unsuccessful ones fade away. Over 50-100 bars, you'll see the population converge toward genes that work for YOUR instrument and timeframe.
📊 THE SIGNAL ENGINE: THREE-LAYER SYNTHESIS
Before any strategy generates a signal, AGE calculates probability through multi-indicator confluence:
Layer 1 - Market Entropy (Information Theory)
Measures whether price movements exhibit directional order or random walk characteristics:
The Math:
Shannon Entropy = -Σ(p × log(p))
Market Order = 1 - (Entropy / 0.693)
What It Means:
High entropy = choppy, random market → low confidence signals
Low entropy = directional market → high confidence signals
Direction determined by up-move vs down-move dominance over lookback period (default: 20 bars)
Signal Output: -1.0 to +1.0 (bearish order to bullish order)
Layer 2 - Momentum Synthesis
Combines three momentum indicators into single composite score:
Components:
RSI (40% weight): Normalized to -1/+1 scale using (RSI-50)/50
Rate of Change (30% weight): Percentage change over lookback (default: 14 bars), clamped to ±1
MACD Histogram (30% weight): Fast(12) - Slow(26), normalized by ATR
Why This Matters: RSI catches mean-reversion opportunities, ROC catches raw momentum, MACD catches momentum divergence. Weighting favors RSI for reliability while keeping other perspectives.
Signal Output: -1.0 to +1.0 (strong bearish to strong bullish)
Layer 3 - Structure Analysis
Evaluates price position within swing range (default: 50-bar lookback):
Position Classification:
Bottom 20% of range = Support Zone → bullish bounce potential
Top 20% of range = Resistance Zone → bearish rejection potential
Middle 60% = Neutral Zone → breakout/breakdown monitoring
Signal Logic:
At support + bullish candle = +0.7 (strong buy setup)
At resistance + bearish candle = -0.7 (strong sell setup)
Breaking above range highs = +0.5 (breakout confirmation)
Breaking below range lows = -0.5 (breakdown confirmation)
Consolidation within range = ±0.3 (weak directional bias)
Signal Output: -1.0 to +1.0 (bearish structure to bullish structure)
Confluence Voting System
Each layer casts a vote (Long/Short/Neutral). The system requires minimum 2-of-3 agreement (configurable 1-3) before generating a signal:
Examples:
Entropy: Bullish, Momentum: Bullish, Structure: Neutral → Signal generated (2 long votes)
Entropy: Bearish, Momentum: Neutral, Structure: Neutral → No signal (only 1 short vote)
All three bullish → Signal generated with +5% probability bonus
This is the key to quality. Single indicators give too many false signals. Triple confirmation dramatically improves accuracy.
📈 PROBABILITY CALCULATION: HOW CONFIDENCE IS MEASURED
Base Probability:
Raw_Prob = 50% + (Average_Signal_Strength × 25%)
Then AGE applies strategic adjustments:
Trend Alignment:
Signal with trend: +4%
Signal against strong trend: -8%
Weak/no trend: no adjustment
Regime Adaptation:
Trending market (efficiency >50%, moderate vol): +3%
Volatile market (vol ratio >1.5x): -5%
Choppy market (low efficiency): -2%
Volume Confirmation:
Volume > 70% of 20-bar SMA: no change
Volume below threshold: -3%
Volatility State (DVS Ratio):
High vol (>1.8x baseline): -4% (reduce confidence in chaos)
Low vol (<0.7x baseline): -2% (markets can whipsaw in compression)
Moderate elevated vol (1.0-1.3x): +2% (trending conditions emerging)
Confluence Bonus:
All 3 indicators agree: +5%
2 of 3 agree: +2%
Strategy Gene Adjustment:
Probability Boost gene: -10% to +10%
Regime Adaptation gene: scales regime adjustments by 0-100%
Final Probability: Clamped between 35% (minimum) and 88% (maximum)
Why These Ranges?
Below 35% = too uncertain, better not to signal
Above 88% = unrealistic, creates overconfidence
Sweet spot: 65-80% for quality entries
🔄 THE SHADOW PORTFOLIO SYSTEM: HOW STRATEGIES COMPETE
Each active strategy maintains a virtual trading account that executes in parallel with real-time data:
Shadow Trading Mechanics
Entry Logic:
Calculate signal direction, probability, and confluence using strategy's unique DNA
Check if signal meets quality gate:
Probability ≥ configured minimum threshold (default: 65%)
Confluence ≥ configured minimum (default: 2 of 3)
Direction is not zero (must be long or short, not neutral)
Verify signal persistence:
Base requirement: 2 bars (configurable 1-5)
Adapts based on probability: high-prob signals (75%+) enter 1 bar faster, low-prob signals need 1 bar more
Adjusts for regime: trending markets reduce persistence by 1, volatile markets add 1
Apply additional filters:
Trend strength must exceed strategy's requirement gene
Regime filter: if volatile market detected, probability must be 72%+ to override
Volume confirmation required (volume > 70% of average)
If all conditions met for required persistence bars, enter shadow position at current close price
Position Management:
Entry Price: Recorded at close of entry bar
Stop Loss: ATR-based distance = ATR × ATR_Mult (gene) × Stop_Mult (gene) × DVS_Ratio
Take Profit: ATR-based distance = ATR × ATR_Mult (gene) × Target_Mult (gene) × DVS_Ratio
Position: +1 (long) or -1 (short), only one at a time per strategy
Exit Logic:
Check if price hit stop (on low) or target (on high) on current bar
Record trade outcome in R-multiples (profit/loss normalized by ATR)
Update performance metrics:
Total trades counter incremented
Wins counter (if profit > 0)
Cumulative P&L updated
Peak equity tracked (for drawdown calculation)
Maximum drawdown from peak recorded
Enter cooldown period (default: 8 bars, configurable 3-20) before next entry allowed
Reset signal age counter to zero
Walk-Forward Tracking:
During position lifecycle, trades are categorized:
Training Phase (first 250 bars): Trade counted toward training metrics
Testing Phase (next 75 bars): Trade counted toward testing metrics (out-of-sample)
Live Phase (after WFO period): Trade counted toward overall metrics
Why Shadow Portfolios?
No lookahead bias (uses only data available at the bar)
Realistic execution simulation (entry on close, stop/target checks on high/low)
Independent performance tracking for true fitness comparison
Allows safe experimentation without risking capital
Each strategy learns from its own experience
🏆 FITNESS SCORING: HOW STRATEGIES ARE RANKED
Fitness is not just win rate. AGE uses a comprehensive multi-factor scoring system:
Core Metrics (Minimum 3 trades required)
Win Rate (30% of fitness):
WinRate = Wins / TotalTrades
Normalized directly (0.0-1.0 scale)
Total P&L (30% of fitness):
Normalized_PnL = (PnL + 300) / 600
Clamped 0.0-1.0. Assumes P&L range of -300R to +300R for normalization scale.
Expectancy (25% of fitness):
Expectancy = Total_PnL / Total_Trades
Normalized_Expectancy = (Expectancy + 30) / 60
Clamped 0.0-1.0. Rewards consistency of profit per trade.
Drawdown Control (15% of fitness):
Normalized_DD = 1 - (Max_Drawdown / 15)
Clamped 0.0-1.0. Penalizes strategies that suffer large equity retracements from peak.
Sample Size Adjustment
Quality Factor:
<50 trades: 1.0 (full weight, small sample)
50-100 trades: 0.95 (slight penalty for medium sample)
100 trades: 0.85 (larger penalty for large sample)
Why penalize more trades? Prevents strategies from gaming the system by taking hundreds of tiny trades to inflate statistics. Favors quality over quantity.
Bonus Adjustments
Walk-Forward Validation Bonus:
if (WFO_Validated):
Fitness += (WFO_Efficiency - 0.5) × 0.1
Strategies proven on out-of-sample data receive up to +10% fitness boost based on test/train efficiency ratio.
Signal Efficiency Bonus (if diagnostics enabled):
if (Signals_Evaluated > 10):
Pass_Rate = Signals_Passed / Signals_Evaluated
Fitness += (Pass_Rate - 0.1) × 0.05
Rewards strategies that generate high-quality signals passing the quality gate, not just profitable trades.
Final Fitness: Clamped at 0.0 minimum (prevents negative fitness values)
Result: Elite strategies typically achieve 0.50-0.75 fitness. Anything above 0.60 is excellent. Below 0.30 is prime candidate for culling.
🔬 WALK-FORWARD OPTIMIZATION: ANTI-OVERFITTING PROTECTION
This is what separates AGE from curve-fitted garbage indicators.
The Three-Phase Process
Every new strategy undergoes a rigorous validation lifecycle:
Phase 1 - Training Window (First 250 bars, configurable 100-500):
Strategy trades normally via shadow portfolio
All trades count toward training performance metrics
System learns which gene combinations produce profitable patterns
Tracks independently: Training_Trades, Training_Wins, Training_PnL
Phase 2 - Testing Window (Next 75 bars, configurable 30-200):
Strategy continues trading without any parameter changes
Trades now count toward testing performance metrics (separate tracking)
This is out-of-sample data - strategy has never seen these bars during "optimization"
Tracks independently: Testing_Trades, Testing_Wins, Testing_PnL
Phase 3 - Validation Check:
Minimum_Trades = 5 (configurable 3-15)
IF (Train_Trades >= Minimum AND Test_Trades >= Minimum):
WR_Efficiency = Test_WinRate / Train_WinRate
Expectancy_Efficiency = Test_Expectancy / Train_Expectancy
WFO_Efficiency = (WR_Efficiency + Expectancy_Efficiency) / 2
IF (WFO_Efficiency >= 0.55): // configurable 0.3-0.9
Strategy.Validated = TRUE
Strategy receives fitness bonus
ELSE:
Strategy receives 30% fitness penalty
ELSE:
Validation deferred (insufficient trades in one or both periods)
What Validation Means
Validated Strategy (Green "✓ VAL" in dashboard):
Performed at least 55% as well on unseen data compared to training data
Gets fitness bonus: +(efficiency - 0.5) × 0.1
Receives priority during tournament selection for breeding
More likely to be chosen as active trading strategy
Unvalidated Strategy (Orange "○ TRAIN" in dashboard):
Failed to maintain performance on test data (likely curve-fitted to training period)
Receives 30% fitness penalty (0.7x multiplier)
Makes strategy prime candidate for culling
Can still trade but with lower selection probability
Insufficient Data (continues collecting):
Hasn't completed both training and testing periods yet
OR hasn't achieved minimum trade count in both periods
Validation check deferred until requirements met
Why 55% Efficiency Threshold?
If a strategy earned 10R during training but only 5.5R during testing, it still proved an edge exists beyond random luck. Requiring 100% efficiency would be unrealistic - market conditions change between periods. But requiring >50% ensures the strategy didn't completely degrade on fresh data.
The Protection: Strategies that work great on historical data but fail on new data are automatically identified and penalized. This prevents the population from being polluted by overfitted strategies that would fail in live trading.
🌊 DYNAMIC VOLATILITY SCALING (DVS): ADAPTIVE STOP/TARGET PLACEMENT
AGE doesn't use fixed stop distances. It adapts to current volatility conditions in real-time.
Four Volatility Measurement Methods
1. ATR Ratio (Simple Method):
Current_Vol = ATR(14) / Close
Baseline_Vol = SMA(Current_Vol, 100)
Ratio = Current_Vol / Baseline_Vol
Basic comparison of current ATR to 100-bar moving average baseline.
2. Parkinson (High-Low Range Based):
For each bar: HL = log(High / Low)
Parkinson_Vol = sqrt(Σ(HL²) / (4 × Period × log(2)))
More stable than close-to-close volatility. Captures intraday range expansion without overnight gap noise.
3. Garman-Klass (OHLC Based):
HL_Term = 0.5 × ²
CO_Term = (2×log(2) - 1) × ²
GK_Vol = sqrt(Σ(HL_Term - CO_Term) / Period)
Most sophisticated estimator. Incorporates all four price points (open, high, low, close) plus gap information.
4. Ensemble Method (Default - Median of All Three):
Ratio_1 = ATR_Current / ATR_Baseline
Ratio_2 = Parkinson_Current / Parkinson_Baseline
Ratio_3 = GK_Current / GK_Baseline
DVS_Ratio = Median(Ratio_1, Ratio_2, Ratio_3)
Why Ensemble?
Takes median to avoid outliers and false spikes
If ATR jumps but range-based methods stay calm, median prevents overreaction
If one method fails, other two compensate
Most robust approach across different market conditions
Sensitivity Scaling
Scaled_Ratio = (Raw_Ratio) ^ Sensitivity
Sensitivity 0.3: Cube root - heavily dampens volatility impact
Sensitivity 0.5: Square root - moderate dampening
Sensitivity 0.7 (Default): Balanced response to volatility changes
Sensitivity 1.0: Linear - full 1:1 volatility impact
Sensitivity 1.5: Exponential - amplified response to volatility spikes
Safety Clamps: Final DVS Ratio always clamped between 0.5x and 2.5x baseline to prevent extreme position sizing or stop placement errors.
How DVS Affects Shadow Trading
Every strategy's stop and target distances are multiplied by the current DVS ratio:
Stop Loss Distance:
Stop_Distance = ATR × ATR_Mult (gene) × Stop_Mult (gene) × DVS_Ratio
Take Profit Distance:
Target_Distance = ATR × ATR_Mult (gene) × Target_Mult (gene) × DVS_Ratio
Example Scenario:
ATR = 10 points
Strategy's ATR_Mult gene = 2.5
Strategy's Stop_Mult gene = 1.5
Strategy's Target_Mult gene = 2.5
DVS_Ratio = 1.4 (40% above baseline volatility - market heating up)
Stop = 10 × 2.5 × 1.5 × 1.4 = 52.5 points (vs. 37.5 in normal vol)
Target = 10 × 2.5 × 2.5 × 1.4 = 87.5 points (vs. 62.5 in normal vol)
Result:
During volatility spikes: Stops automatically widen to avoid noise-based exits, targets extend for bigger moves
During calm periods: Stops tighten for better risk/reward, targets compress for realistic profit-taking
Strategies adapt risk management to match current market behavior
🧬 THE EVOLUTIONARY CYCLE: SPAWN, COMPETE, CULL
Initialization (Bar 1)
AGE begins with 4 seed strategies (if evolution enabled):
Seed Strategy #0 (Balanced):
All sensitivities at 1.0 (neutral)
Zero probability boost
Moderate trend requirement (0.4)
Standard ATR/stop/target multiples (2.5/1.5/2.5)
Mid-level regime adaptation (0.5)
Seed Strategy #1 (Momentum-Focused):
Lower entropy sensitivity (0.7), higher momentum (1.5)
Slight probability boost (+0.03)
Higher trend requirement (0.5)
Tighter stops (1.3), wider targets (3.0)
Seed Strategy #2 (Entropy-Driven):
Higher entropy sensitivity (1.5), lower momentum (0.8)
Slight probability penalty (-0.02)
More trend tolerant (0.6)
Wider stops (1.8), standard targets (2.5)
Seed Strategy #3 (Structure-Based):
Balanced entropy/momentum (0.8/0.9), high structure (1.4)
Slight probability boost (+0.02)
Lower trend requirement (0.35)
Moderate risk parameters (1.6/2.8)
All seeds start with WFO validation bypassed if WFO is disabled, or must validate if enabled.
Spawning New Strategies
Timing (Adaptive):
Historical phase: Every 30 bars (configurable 10-100)
Live phase: Every 200 bars (configurable 100-500)
Automatically switches to live timing when barstate.isrealtime triggers
Conditions:
Current population < max population limit (default: 8, configurable 4-12)
At least 2 active strategies exist (need parents)
Available slot in population array
Selection Process:
Run tournament selection 3 times with different seeds
Each tournament: randomly sample active strategies, pick highest fitness
Best from 3 tournaments becomes Parent 1
Repeat independently for Parent 2
Ensures fit parents but maintains diversity
Crossover Breeding:
For each of 10 genes:
Parent1_Fitness = fitness
Parent2_Fitness = fitness
Weight1 = Parent1_Fitness / (Parent1_Fitness + Parent2_Fitness)
Gene1 = parent1's value
Gene2 = parent2's value
Child_Gene = Weight1 × Gene1 + (1 - Weight1) × Gene2
Fitness-weighted crossover ensures fitter parent contributes more genetic material.
Mutation:
For each gene in child:
IF (random < mutation_rate):
Gene_Range = GENE_MAX - GENE_MIN
Noise = (random - 0.5) × 2 × mutation_strength × Gene_Range
Mutated_Gene = Clamp(Child_Gene + Noise, GENE_MIN, GENE_MAX)
Historical mutation rate: 20% (aggressive exploration)
Live mutation rate: 8% (conservative stability)
Mutation strength: 12% of gene range (configurable 5-25%)
Initialization of New Strategy:
Unique ID assigned (total_spawned counter)
Parent ID recorded
Generation = max(parent generations) + 1
Birth bar recorded (for age tracking)
All performance metrics zeroed
Shadow portfolio reset
WFO validation flag set to false (must prove itself)
Result: New strategy with hybrid DNA enters population, begins trading in next bar.
Competition (Every Bar)
All active strategies:
Calculate their signal based on unique DNA
Check quality gate with their thresholds
Manage shadow positions (entries/exits)
Update performance metrics
Recalculate fitness score
Track WFO validation progress
Strategies compete indirectly through fitness ranking - no direct interaction.
Culling Weak Strategies
Timing (Adaptive):
Historical phase: Every 60 bars (configurable 20-200, should be 2x spawn interval)
Live phase: Every 400 bars (configurable 200-1000, should be 2x spawn interval)
Minimum Adaptation Score (MAS):
Initial MAS = 0.10
MAS decays: MAS × 0.995 every cull cycle
Minimum MAS = 0.03 (floor)
MAS represents the "survival threshold" - strategies below this fitness level are vulnerable.
Culling Conditions (ALL must be true):
Population > minimum population (default: 3, configurable 2-4)
At least one strategy has fitness < MAS
Strategy's age > culling interval (prevents premature culling of new strategies)
Strategy is not in top N elite (default: 2, configurable 1-3)
Culling Process:
Find worst strategy:
For each active strategy:
IF (age > cull_interval):
Fitness = base_fitness
IF (not WFO_validated AND WFO_enabled):
Fitness × 0.7 // 30% penalty for unvalidated
IF (Fitness < MAS AND Fitness < worst_fitness_found):
worst_strategy = this_strategy
worst_fitness = Fitness
IF (worst_strategy found):
Count elite strategies with fitness > worst_fitness
IF (elite_count >= elite_preservation_count):
Deactivate worst_strategy (set active flag = false)
Increment total_culled counter
Elite Protection:
Even if a strategy's fitness falls below MAS, it survives if fewer than N strategies are better. This prevents culling when population is generally weak.
Result: Weak strategies removed from population, freeing slots for new spawns. Gene pool improves over time.
Selection for Display (Every Bar)
AGE chooses one strategy to display signals:
Best fitness = -1
Selected = none
For each active strategy:
Fitness = base_fitness
IF (WFO_validated):
Fitness × 1.3 // 30% bonus for validated strategies
IF (Fitness > best_fitness):
best_fitness = Fitness
selected_strategy = this_strategy
Display selected strategy's signals on chart
Result: Only the highest-fitness (optionally validated-boosted) strategy's signals appear as chart markers. Other strategies trade invisibly in shadow portfolios.
🎨 PREMIUM VISUALIZATION SYSTEM
AGE includes sophisticated visual feedback that standard indicators lack:
1. Gradient Probability Cloud (Optional, Default: ON)
Multi-layer gradient showing signal buildup 2-3 bars before entry:
Activation Conditions:
Signal persistence > 0 (same directional signal held for multiple bars)
Signal probability ≥ minimum threshold (65% by default)
Signal hasn't yet executed (still in "forming" state)
Visual Construction:
7 gradient layers by default (configurable 3-15)
Each layer is a line-fill pair (top line, bottom line, filled between)
Layer spacing: 0.3 to 1.0 × ATR above/below price
Outer layers = faint, inner layers = bright
Color transitions from base to intense based on layer position
Transparency scales with probability (high prob = more opaque)
Color Selection:
Long signals: Gradient from theme.gradient_bull_mid to theme.gradient_bull_strong
Short signals: Gradient from theme.gradient_bear_mid to theme.gradient_bear_strong
Base transparency: 92%, reduces by up to 8% for high-probability setups
Dynamic Behavior:
Cloud grows/shrinks as signal persistence increases/decreases
Redraws every bar while signal is forming
Disappears when signal executes or invalidates
Performance Note: Computationally expensive due to linefill objects. Disable or reduce layers if chart performance degrades.
2. Population Fitness Ribbon (Optional, Default: ON)
Histogram showing fitness distribution across active strategies:
Activation: Only draws on last bar (barstate.islast) to avoid historical clutter
Visual Construction:
10 histogram layers by default (configurable 5-20)
Plots 50 bars back from current bar
Positioned below price at: lowest_low(100) - 1.5×ATR (doesn't interfere with price action)
Each layer represents a fitness threshold (evenly spaced min to max fitness)
Layer Logic:
For layer_num from 0 to ribbon_layers:
Fitness_threshold = min_fitness + (max_fitness - min_fitness) × (layer / layers)
Count strategies with fitness ≥ threshold
Height = ATR × 0.15 × (count / total_active)
Y_position = base_level + ATR × 0.2 × layer
Color = Gradient from weak to strong based on layer position
Line_width = Scaled by height (taller = thicker)
Visual Feedback:
Tall, bright ribbon = healthy population, many fit strategies at high fitness levels
Short, dim ribbon = weak population, few strategies achieving good fitness
Ribbon compression (layers close together) = population converging to similar fitness
Ribbon spread = diverse fitness range, active selection pressure
Use Case: Quick visual health check without opening dashboard. Ribbon growing upward over time = population improving.
3. Confidence Halo (Optional, Default: ON)
Circular polyline around entry signals showing probability strength:
Activation: Draws when new position opens (shadow_position changes from 0 to ±1)
Visual Construction:
20-segment polyline forming approximate circle
Center: Low - 0.5×ATR (long) or High + 0.5×ATR (short)
Radius: 0.3×ATR (low confidence) to 1.0×ATR (elite confidence)
Scales with: (probability - min_probability) / (1.0 - min_probability)
Color Coding:
Elite (85%+): Cyan (theme.conf_elite), large radius, minimal transparency (40%)
Strong (75-85%): Strong green (theme.conf_strong), medium radius, moderate transparency (50%)
Good (65-75%): Good green (theme.conf_good), smaller radius, more transparent (60%)
Moderate (<65%): Moderate green (theme.conf_moderate), tiny radius, very transparent (70%)
Technical Detail:
Uses chart.point array with index-based positioning
5-bar horizontal spread for circular appearance (±5 bars from entry)
Curved=false (Pine Script polyline limitation)
Fill color matches line color but more transparent (88% vs line's transparency)
Purpose: Instant visual probability assessment. No need to check dashboard - halo size/brightness tells the story.
4. Evolution Event Markers (Optional, Default: ON)
Visual indicators of genetic algorithm activity:
Spawn Markers (Diamond, Cyan):
Plots when total_spawned increases on current bar
Location: bottom of chart (location.bottom)
Color: theme.spawn_marker (cyan/bright blue)
Size: tiny
Indicates new strategy just entered population
Cull Markers (X-Cross, Red):
Plots when total_culled increases on current bar
Location: bottom of chart (location.bottom)
Color: theme.cull_marker (red/pink)
Size: tiny
Indicates weak strategy just removed from population
What It Tells You:
Frequent spawning early = population building, active exploration
Frequent culling early = high selection pressure, weak strategies dying fast
Balanced spawn/cull = healthy evolutionary churn
No markers for long periods = stable population (evolution plateaued or optimal genes found)
5. Entry/Exit Markers
Clear visual signals for selected strategy's trades:
Long Entry (Triangle Up, Green):
Plots when selected strategy opens long position (position changes 0 → +1)
Location: below bar (location.belowbar)
Color: theme.long_primary (green/cyan depending on theme)
Transparency: Scales with probability:
Elite (85%+): 0% (fully opaque)
Strong (75-85%): 10%
Good (65-75%): 20%
Acceptable (55-65%): 35%
Size: small
Short Entry (Triangle Down, Red):
Plots when selected strategy opens short position (position changes 0 → -1)
Location: above bar (location.abovebar)
Color: theme.short_primary (red/pink depending on theme)
Transparency: Same scaling as long entries
Size: small
Exit (X-Cross, Orange):
Plots when selected strategy closes position (position changes ±1 → 0)
Location: absolute (at actual exit price if stop/target lines enabled)
Color: theme.exit_color (orange/yellow depending on theme)
Transparency: 0% (fully opaque)
Size: tiny
Result: Clean, probability-scaled markers that don't clutter chart but convey essential information.
6. Stop Loss & Take Profit Lines (Optional, Default: ON)
Visual representation of shadow portfolio risk levels:
Stop Loss Line:
Plots when selected strategy has active position
Level: shadow_stop value from selected strategy
Color: theme.short_primary with 60% transparency (red/pink, subtle)
Width: 2
Style: plot.style_linebr (breaks when no position)
Take Profit Line:
Plots when selected strategy has active position
Level: shadow_target value from selected strategy
Color: theme.long_primary with 60% transparency (green, subtle)
Width: 2
Style: plot.style_linebr (breaks when no position)
Purpose:
Shows where shadow portfolio would exit for stop/target
Helps visualize strategy's risk/reward ratio
Useful for manual traders to set similar levels
Disable for cleaner chart (recommended for presentations)
7. Dynamic Trend EMA
Gradient-colored trend line that visualizes trend strength:
Calculation:
EMA(close, trend_length) - default 50 period (configurable 20-100)
Slope calculated over 10 bars: (current_ema - ema ) / ema × 100
Color Logic:
Trend_direction:
Slope > 0.1% = Bullish (1)
Slope < -0.1% = Bearish (-1)
Otherwise = Neutral (0)
Trend_strength = abs(slope)
Color = Gradient between:
- Neutral color (gray/purple)
- Strong bullish (bright green) if direction = 1
- Strong bearish (bright red) if direction = -1
Gradient factor = trend_strength (0 to 1+ scale)
Visual Behavior:
Faint gray/purple = weak/no trend (choppy conditions)
Light green/red = emerging trend (low strength)
Bright green/red = strong trend (high conviction)
Color intensity = trend strength magnitude
Transparency: 50% (subtle, doesn't overpower price action)
Purpose: Subconscious awareness of trend state without checking dashboard or indicators.
8. Regime Background Tinting (Subtle)
Ultra-low opacity background color indicating detected market regime:
Regime Detection:
Efficiency = directional_movement / total_range (over trend_length bars)
Vol_ratio = current_volatility / average_volatility
IF (efficiency > 0.5 AND vol_ratio < 1.3):
Regime = Trending (1)
ELSE IF (vol_ratio > 1.5):
Regime = Volatile (2)
ELSE:
Regime = Choppy (0)
Background Colors:
Trending: theme.regime_trending (dark green, 92-93% transparency)
Volatile: theme.regime_volatile (dark red, 93% transparency)
Choppy: No tint (normal background)
Purpose:
Subliminal regime awareness
Helps explain why signals are/aren't generating
Trending = ideal conditions for AGE
Volatile = fewer signals, higher thresholds applied
Choppy = mixed signals, lower confidence
Important: Extremely subtle by design. Not meant to be obvious, just subconscious context.
📊 ENHANCED DASHBOARD
Comprehensive real-time metrics in single organized panel (top-right position):
Dashboard Structure (5 columns × 14 rows)
Header Row:
Column 0: "🧬 AGE PRO" + phase indicator (🔴 LIVE or ⏪ HIST)
Column 1: "POPULATION"
Column 2: "PERFORMANCE"
Column 3: "CURRENT SIGNAL"
Column 4: "ACTIVE STRATEGY"
Column 0: Market State
Regime (📈 TREND / 🌊 CHAOS / ➖ CHOP)
DVS Ratio (current volatility scaling factor, format: #.##)
Trend Direction (▲ BULL / ▼ BEAR / ➖ FLAT with color coding)
Trend Strength (0-100 scale, format: #.##)
Column 1: Population Metrics
Active strategies (count / max_population)
Validated strategies (WFO passed / active total)
Current generation number
Total spawned (all-time strategy births)
Total culled (all-time strategy deaths)
Column 2: Aggregate Performance
Total trades across all active strategies
Aggregate win rate (%) - color-coded:
Green (>55%)
Orange (45-55%)
Red (<45%)
Total P&L in R-multiples - color-coded by positive/negative
Best fitness score in population (format: #.###)
MAS - Minimum Adaptation Score (cull threshold, format: #.###)
Column 3: Current Signal Status
Status indicator:
"▲ LONG" (green) if selected strategy in long position
"▼ SHORT" (red) if selected strategy in short position
"⏳ FORMING" (orange) if signal persisting but not yet executed
"○ WAITING" (gray) if no active signal
Confidence percentage (0-100%, format: #.#%)
Quality assessment:
"🔥 ELITE" (cyan) for 85%+ probability
"✓ STRONG" (bright green) for 75-85%
"○ GOOD" (green) for 65-75%
"- LOW" (dim) for <65%
Confluence score (X/3 format)
Signal age:
"X bars" if signal forming
"IN TRADE" if position active
"---" if no signal
Column 4: Selected Strategy Details
Strategy ID number (#X format)
Validation status:
"✓ VAL" (green) if WFO validated
"○ TRAIN" (orange) if still in training/testing phase
Generation number (GX format)
Personal fitness score (format: #.### with color coding)
Trade count
P&L and win rate (format: #.#R (##%) with color coding)
Color Scheme:
Panel background: theme.panel_bg (dark, low opacity)
Panel headers: theme.panel_header (slightly lighter)
Primary text: theme.text_primary (bright, high contrast)
Secondary text: theme.text_secondary (dim, lower contrast)
Positive metrics: theme.metric_positive (green)
Warning metrics: theme.metric_warning (orange)
Negative metrics: theme.metric_negative (red)
Special markers: theme.validated_marker, theme.spawn_marker
Update Frequency: Only on barstate.islast (current bar) to minimize CPU usage
Purpose:
Quick overview of entire system state
No need to check multiple indicators
Trading decisions informed by population health, regime state, and signal quality
Transparency into what AGE is thinking
🔍 DIAGNOSTICS PANEL (Optional, Default: OFF)
Detailed signal quality tracking for optimization and debugging:
Panel Structure (3 columns × 8 rows)
Position: Bottom-right corner (doesn't interfere with main dashboard)
Header Row:
Column 0: "🔍 DIAGNOSTICS"
Column 1: "COUNT"
Column 2: "%"
Metrics Tracked (for selected strategy only):
Total Evaluated:
Every signal that passed initial calculation (direction ≠ 0)
Represents total opportunities considered
✓ Passed:
Signals that passed quality gate and executed
Green color coding
Percentage of evaluated signals
Rejection Breakdown:
⨯ Probability:
Rejected because probability < minimum threshold
Most common rejection reason typically
⨯ Confluence:
Rejected because confluence < minimum required (e.g., only 1 of 3 indicators agreed)
⨯ Trend:
Rejected because signal opposed strong trend
Indicates counter-trend protection working
⨯ Regime:
Rejected because volatile regime detected and probability wasn't high enough to override
Shows regime filter in action
⨯ Volume:
Rejected because volume < 70% of 20-bar average
Indicates volume confirmation requirement
Color Coding:
Passed count: Green (success metric)
Rejection counts: Red (failure metrics)
Percentages: Gray (neutral, informational)
Performance Cost: Slight CPU overhead for tracking counters. Disable when not actively optimizing settings.
How to Use Diagnostics
Scenario 1: Too Few Signals
Evaluated: 200
Passed: 10 (5%)
⨯ Probability: 120 (60%)
⨯ Confluence: 40 (20%)
⨯ Others: 30 (15%)
Diagnosis: Probability threshold too high for this strategy's DNA.
Solution: Lower min probability from 65% to 60%, or allow strategy more time to evolve better DNA.
Scenario 2: Too Many False Signals
Evaluated: 200
Passed: 80 (40%)
Strategy win rate: 45%
Diagnosis: Quality gate too loose, letting low-quality signals through.
Solution: Raise min probability to 70%, or increase min confluence to 3 (all indicators must agree).
Scenario 3: Regime-Specific Issues
⨯ Regime: 90 (45% of rejections)
Diagnosis: Frequent volatile regime detection blocking otherwise good signals.
Solution: Either accept fewer trades during chaos (recommended), or disable regime filter if you want signals regardless of market state.
Optimization Workflow:
Enable diagnostics
Run 200+ bars
Analyze rejection patterns
Adjust settings based on data
Re-run and compare pass rate
Disable diagnostics when satisfied
⚙️ CONFIGURATION GUIDE
🧬 Evolution Engine Settings
Enable AGE Evolution (Default: ON):
ON: Full genetic algorithm (recommended for best results)
OFF: Uses only 4 seed strategies, no spawning/culling (static population for comparison testing)
Max Population (4-12, Default: 8):
Higher = more diversity, more exploration, slower performance
Lower = faster computation, less exploration, risk of premature convergence
Sweet spot: 6-8 for most use cases
4 = minimum for meaningful evolution
12 = maximum before diminishing returns
Min Population (2-4, Default: 3):
Safety floor - system never culls below this count
Prevents population extinction during harsh selection
Should be at least half of max population
Elite Preservation (1-3, Default: 2):
Top N performers completely immune to culling
Ensures best genes always survive
1 = minimal protection, aggressive selection
2 = balanced (recommended)
3 = conservative, slower gene pool turnover
Historical: Spawn Interval (10-100, Default: 30):
Bars between spawning new strategies during historical data
Lower = faster evolution, more exploration
Higher = slower evolution, more evaluation time per strategy
30 bars = ~1-2 hours on 15min chart
Historical: Cull Interval (20-200, Default: 60):
Bars between culling weak strategies during historical data
Should be 2x spawn interval for balanced churn
Lower = aggressive selection pressure
Higher = patient evaluation
Live: Spawn Interval (100-500, Default: 200):
Bars between spawning during live trading
Much slower than historical for stability
Prevents population chaos during live trading
200 bars = ~1.5 trading days on 15min chart
Live: Cull Interval (200-1000, Default: 400):
Bars between culling during live trading
Should be 2x live spawn interval
Conservative removal during live trading
Historical: Mutation Rate (0.05-0.40, Default: 0.20):
Probability each gene mutates during breeding (20% = 2 out of 10 genes on average)
Higher = more exploration, slower convergence
Lower = more exploitation, faster convergence but risk of local optima
20% balances exploration vs exploitation
Live: Mutation Rate (0.02-0.20, Default: 0.08):
Mutation rate during live trading
Much lower for stability (don't want population to suddenly degrade)
8% = mostly inherits parent genes with small tweaks
Mutation Strength (0.05-0.25, Default: 0.12):
How much genes change when mutated (% of gene's total range)
0.05 = tiny nudges (fine-tuning)
0.12 = moderate jumps (recommended)
0.25 = large leaps (aggressive exploration)
Example: If gene range is 0.5-2.0, 12% strength = ±0.18 possible change
📈 Signal Quality Settings
Min Signal Probability (0.55-0.80, Default: 0.65):
Quality gate threshold - signals below this never generate
0.55-0.60 = More signals, accept lower confidence (higher risk)
0.65 = Institutional-grade balance (recommended)
0.70-0.75 = Fewer but higher-quality signals (conservative)
0.80+ = Very selective, very few signals (ultra-conservative)
Min Confluence Score (1-3, Default: 2):
Required indicator agreement before signal generates
1 = Any single indicator can trigger (not recommended - too many false signals)
2 = Requires 2 of 3 indicators agree (RECOMMENDED for balance)
3 = All 3 must agree (very selective, few signals, high quality)
Base Persistence Bars (1-5, Default: 2):
Base bars signal must persist before entry
System adapts automatically:
High probability signals (75%+) enter 1 bar faster
Low probability signals (<68%) need 1 bar more
Trending regime: -1 bar (faster entries)
Volatile regime: +1 bar (more confirmation)
1 = Immediate entry after quality gate (responsive but prone to whipsaw)
2 = Balanced confirmation (recommended)
3-5 = Patient confirmation (slower but more reliable)
Cooldown After Trade (3-20, Default: 8):
Bars to wait after exit before next entry allowed
Prevents overtrading and revenge trading
3 = Minimal cooldown (active trading)
8 = Balanced (recommended)
15-20 = Conservative (position trading)
Entropy Length (10-50, Default: 20):
Lookback period for market order/disorder calculation
Lower = more responsive to regime changes (noisy)
Higher = more stable regime detection (laggy)
20 = works across most timeframes
Momentum Length (5-30, Default: 14):
Period for RSI/ROC calculations
14 = standard (RSI default)
Lower = more signals, less reliable
Higher = fewer signals, more reliable
Structure Length (20-100, Default: 50):
Lookback for support/resistance swing range
20 = short-term swings (day trading)
50 = medium-term structure (recommended)
100 = major structure (position trading)
Trend EMA Length (20-100, Default: 50):
EMA period for trend detection and direction bias
20 = short-term trend (responsive)
50 = medium-term trend (recommended)
100 = long-term trend (position trading)
ATR Period (5-30, Default: 14):
Period for volatility measurement
14 = standard ATR
Lower = more responsive to vol changes
Higher = smoother vol calculation
📊 Volatility Scaling (DVS) Settings
Enable DVS (Default: ON):
Dynamic volatility scaling for adaptive stop/target placement
Highly recommended to leave ON
OFF only for testing fixed-distance stops
DVS Method (Default: Ensemble):
ATR Ratio: Simple, fast, single-method (good for beginners)
Parkinson: High-low range based (good for intraday)
Garman-Klass: OHLC based (sophisticated, considers gaps)
Ensemble: Median of all three (RECOMMENDED - most robust)
DVS Memory (20-200, Default: 100):
Lookback for baseline volatility comparison
20 = very responsive to vol changes (can overreact)
100 = balanced adaptation (recommended)
200 = slow, stable baseline (minimizes false vol signals)
DVS Sensitivity (0.3-1.5, Default: 0.7):
How much volatility affects scaling (power-law exponent)
0.3 = Conservative, heavily dampens vol impact (cube root)
0.5 = Moderate dampening (square root)
0.7 = Balanced response (recommended)
1.0 = Linear, full 1:1 vol response
1.5 = Aggressive, amplified response (exponential)
🔬 Walk-Forward Optimization Settings
Enable WFO (Default: ON):
Out-of-sample validation to prevent overfitting
Highly recommended to leave ON
OFF only for testing or if you want unvalidated strategies
Training Window (100-500, Default: 250):
Bars for in-sample optimization
100 = fast validation, less data (risky)
250 = balanced (recommended) - about 1-2 months on daily, 1-2 weeks on 15min
500 = patient validation, more data (conservative)
Testing Window (30-200, Default: 75):
Bars for out-of-sample validation
Should be ~30% of training window
30 = minimal test (fast validation)
75 = balanced (recommended)
200 = extensive test (very conservative)
Min Trades for Validation (3-15, Default: 5):
Required trades in BOTH training AND testing periods
3 = minimal sample (risky, fast validation)
5 = balanced (recommended)
10+ = conservative (slow validation, high confidence)
WFO Efficiency Threshold (0.3-0.9, Default: 0.55):
Minimum test/train performance ratio required
0.30 = Very loose (test must be 30% as good as training)
0.55 = Balanced (recommended) - test must be 55% as good
0.70+ = Strict (test must closely match training)
Higher = fewer validated strategies, lower risk of overfitting
🎨 Premium Visuals Settings
Visual Theme:
Neon Genesis: Cyberpunk aesthetic (cyan/magenta/purple)
Carbon Fiber: Industrial look (blue/red/gray)
Quantum Blue: Quantum computing (blue/purple/pink)
Aurora: Northern lights (teal/orange/purple)
⚡ Gradient Probability Cloud (Default: ON):
Multi-layer gradient showing signal buildup
Turn OFF if chart lags or for cleaner look
Cloud Gradient Layers (3-15, Default: 7):
More layers = smoother gradient, more CPU intensive
Fewer layers = faster, blockier appearance
🎗️ Population Fitness Ribbon (Default: ON):
Histogram showing fitness distribution
Turn OFF for cleaner chart
Ribbon Layers (5-20, Default: 10):
More layers = finer fitness detail
Fewer layers = simpler histogram
⭕ Signal Confidence Halo (Default: ON):
Circular indicator around entry signals
Size/brightness scales with probability
Minimal performance cost
🔬 Evolution Event Markers (Default: ON):
Diamond (spawn) and X (cull) markers
Shows genetic algorithm activity
Minimal performance cost
🎯 Stop/Target Lines (Default: ON):
Shows shadow portfolio stop/target levels
Turn OFF for cleaner chart (recommended for screenshots/presentations)
📊 Enhanced Dashboard (Default: ON):
Comprehensive metrics panel
Should stay ON unless you want zero overlays
🔍 Diagnostics Panel (Default: OFF):
Detailed signal rejection tracking
Turn ON when optimizing settings
Turn OFF during normal use (slight performance cost)
📈 USAGE WORKFLOW - HOW TO USE THIS INDICATOR
Phase 1: Initial Setup & Learning
Add AGE to your chart
Recommended timeframes: 15min, 30min, 1H (best signal-to-noise ratio)
Works on: 5min (day trading), 4H (swing trading), Daily (position trading)
Load 1000+ bars for sufficient evolution history
Let the population evolve (100+ bars minimum)
First 50 bars: Random exploration, poor results expected
Bars 50-150: Population converging, fitness improving
Bars 150+: Stable performance, validated strategies emerging
Watch the dashboard metrics
Population should grow toward max capacity
Generation number should advance regularly
Validated strategies counter should increase
Best fitness should trend upward toward 0.50-0.70 range
Observe evolution markers
Diamond markers (cyan) = new strategies spawning
X markers (red) = weak strategies being culled
Frequent early activity = healthy evolution
Activity slowing = population stabilizing
Be patient. Evolution takes time. Don't judge performance before 150+ bars.
Phase 2: Signal Observation
Watch signals form
Gradient cloud builds up 2-3 bars before entry
Cloud brightness = probability strength
Cloud thickness = signal persistence
Check signal quality
Look at confidence halo size when entry marker appears
Large bright halo = elite setup (85%+)
Medium halo = strong setup (75-85%)
Small halo = good setup (65-75%)
Verify market conditions
Check trend EMA color (green = uptrend, red = downtrend, gray = choppy)
Check background tint (green = trending, red = volatile, clear = choppy)
Trending background + aligned signal = ideal conditions
Review dashboard signal status
Current Signal column shows:
Status (Long/Short/Forming/Waiting)
Confidence % (actual probability value)
Quality assessment (Elite/Strong/Good)
Confluence score (2/3 or 3/3 preferred)
Only signals meeting ALL quality gates appear on chart. If you're not seeing signals, population is either still learning or market conditions aren't suitable.
Phase 3: Manual Trading Execution
When Long Signal Fires:
Verify confidence level (dashboard or halo size)
Confirm trend alignment (EMA sloping up, green color)
Check regime (preferably trending or choppy, avoid volatile)
Enter long manually on your broker platform
Set stop loss at displayed stop line level (if lines enabled), or use your own risk management
Set take profit at displayed target line level, or trail manually
Monitor position - exit if X marker appears (signal reversal)
When Short Signal Fires:
Same verification process
Confirm downtrend (EMA sloping down, red color)
Enter short manually
Use displayed stop/target levels or your own
AGE tells you WHEN and HOW CONFIDENT. You decide WHETHER and HOW MUCH.
Phase 4: Set Up Alerts (Never Miss a Signal)
Right-click on indicator name in legend
Select "Add Alert"
Choose condition:
"AGE Long" = Long entry signal fired
"AGE Short" = Short entry signal fired
"AGE Exit" = Position reversal/exit signal
Set notification method:
Sound alert (popup on chart)
Email notification
Webhook to phone/trading platform
Mobile app push notification
Name the alert (e.g., "AGE BTCUSD 15min Long")
Save alert
Recommended: Set alerts for both long and short, enable mobile push notifications. You'll get alerted in real-time even if not watching charts.
Phase 5: Monitor Population Health
Weekly Review:
Check dashboard Population column:
Active count should be near max (6-8 of 8)
Validated count should be >50% of active
Generation should be advancing (1-2 per week typical)
Check dashboard Performance column:
Aggregate win rate should be >50% (target: 55-65%)
Total P&L should be positive (may fluctuate)
Best fitness should be >0.50 (target: 0.55-0.70)
MAS should be declining slowly (normal adaptation)
Check Active Strategy column:
Selected strategy should be validated (✓ VAL)
Personal fitness should match best fitness
Trade count should be accumulating
Win rate should be >50%
Warning Signs:
Zero validated strategies after 300+ bars = settings too strict or market unsuitable
Best fitness stuck <0.30 = population struggling, consider parameter adjustment
No spawning/culling for 200+ bars = evolution stalled (may be optimal or need reset)
Aggregate win rate <45% sustained = system not working on this instrument/timeframe
Health Check Pass:
50%+ strategies validated
Best fitness >0.50
Aggregate win rate >52%
Regular spawn/cull activity
Selected strategy validated
Phase 6: Optimization (If Needed)
Enable Diagnostics Panel (bottom-right) for data-driven tuning:
Problem: Too Few Signals
Evaluated: 200
Passed: 8 (4%)
⨯ Probability: 140 (70%)
Solutions:
Lower min probability: 65% → 60% or 55%
Reduce min confluence: 2 → 1
Lower base persistence: 2 → 1
Increase mutation rate temporarily to explore new genes
Check if regime filter is blocking signals (⨯ Regime high?)
Problem: Too Many False Signals
Evaluated: 200
Passed: 90 (45%)
Win rate: 42%
Solutions:
Raise min probability: 65% → 70% or 75%
Increase min confluence: 2 → 3
Raise base persistence: 2 → 3
Enable WFO if disabled (validates strategies before use)
Check if volume filter is being ignored (⨯ Volume low?)
Problem: Counter-Trend Losses
⨯ Trend: 5 (only 5% rejected)
Losses often occur against trend
Solutions:
System should already filter trend opposition
May need stronger trend requirement
Consider only taking signals aligned with higher timeframe trend
Use longer trend EMA (50 → 100)
Problem: Volatile Market Whipsaws
⨯ Regime: 100 (50% rejected by volatile regime)
Still getting stopped out frequently
Solutions:
System is correctly blocking volatile signals
Losses happening because vol filter isn't strict enough
Consider not trading during volatile periods (respect the regime)
Or disable regime filter and accept higher risk
Optimization Workflow:
Enable diagnostics
Run 200+ bars with current settings
Analyze rejection patterns and win rate
Make ONE change at a time (scientific method)
Re-run 200+ bars and compare results
Keep change if improvement, revert if worse
Disable diagnostics when satisfied
Never change multiple parameters at once - you won't know what worked.
Phase 7: Multi-Instrument Deployment
AGE learns independently on each chart:
Recommended Strategy:
Deploy AGE on 3-5 different instruments
Different asset classes ideal (e.g., ES futures, EURUSD, BTCUSD, SPY, Gold)
Each learns optimal strategies for that instrument's personality
Take signals from all 5 charts
Natural diversification reduces overall risk
Why This Works:
When one market is choppy, others may be trending
Different instruments respond to different news/catalysts
Portfolio-level win rate more stable than single-instrument
Evolution explores different parameter spaces on each chart
Setup:
Same settings across all charts (or customize if preferred)
Set alerts for all
Take every validated signal across all instruments
Position size based on total account (don't overleverage any single signal)
⚠️ REALISTIC EXPECTATIONS - CRITICAL READING
What AGE Can Do
✅ Generate probability-weighted signals using genetic algorithms
✅ Evolve strategies in real-time through natural selection
✅ Validate strategies on out-of-sample data (walk-forward optimization)
✅ Adapt to changing market conditions automatically over time
✅ Provide comprehensive metrics on population health and signal quality
✅ Work on any instrument, any timeframe, any broker
✅ Improve over time as weak strategies are culled and fit strategies breed
What AGE Cannot Do
❌ Win every trade (typical win rate: 55-65% at best)
❌ Predict the future with certainty (markets are probabilistic, not deterministic)
❌ Work perfectly from bar 1 (needs 100-150 bars to learn and stabilize)
❌ Guarantee profits under all market conditions
❌ Replace your trading discipline and risk management
❌ Execute trades automatically (this is an indicator, not a strategy)
❌ Prevent all losses (drawdowns are normal and expected)
❌ Adapt instantly to regime changes (re-learning takes 50-100 bars)
Performance Realities
Typical Performance After Evolution Stabilizes (150+ bars):
Win Rate: 55-65% (excellent for trend-following systems)
Profit Factor: 1.5-2.5 (realistic for validated strategies)
Signal Frequency: 5-15 signals per 100 bars (quality over quantity)
Drawdown Periods: 20-40% of time in equity retracement (normal trading reality)
Max Consecutive Losses: 5-8 losses possible even with 60% win rate (probability says this is normal)
Evolution Timeline:
Bars 0-50: Random exploration, learning phase - poor results expected, don't judge yet
Bars 50-150: Population converging, fitness climbing - results improving
Bars 150-300: Stable performance, most strategies validated - consistent results
Bars 300+: Mature population, optimal genes dominant - best results
Market Condition Dependency:
Trending Markets: AGE excels - clear directional moves, high-probability setups
Choppy Markets: AGE struggles - fewer signals generated, lower win rate
Volatile Markets: AGE cautious - higher rejection rate, wider stops, fewer trades
Market Regime Changes:
When market shifts from trending to choppy overnight
Validated strategies can become temporarily invalidated
AGE will adapt through evolution, but not instantly
Expect 50-100 bar re-learning period after major regime shifts
Fitness may temporarily drop then recover
This is NOT a holy grail. It's a sophisticated signal generator that learns and adapts using genetic algorithms. Your success depends on:
Patience during learning periods (don't abandon after 3 losses)
Proper position sizing (risk 0.5-2% per trade, not 10%)
Following signals consistently (cherry-picking defeats statistical edge)
Not abandoning system prematurely (give it 200+ bars minimum)
Understanding probability (60% win rate means 40% of trades WILL lose)
Respecting market conditions (trending = trade more, choppy = trade less)
Managing emotions (AGE is emotionless, you need to be too)
Expected Drawdowns:
Single-strategy max DD: 10-20% of equity (normal)
Portfolio across multiple instruments: 5-15% (diversification helps)
Losing streaks: 3-5 consecutive losses expected periodically
No indicator eliminates risk. AGE manages risk through:
Quality gates (rejecting low-probability signals)
Confluence requirements (multi-indicator confirmation)
Persistence requirements (no knee-jerk reactions)
Regime awareness (reduced trading in chaos)
Walk-forward validation (preventing overfitting)
But it cannot prevent all losses. That's inherent to trading.
🔧 TECHNICAL SPECIFICATIONS
Platform: TradingView Pine Script v5
Indicator Type: Overlay indicator (plots on price chart)
Execution Type: Signals only - no automatic order placement
Computational Load:
Moderate to High (genetic algorithms + shadow portfolios)
8 strategies × shadow portfolio simulation = significant computation
Premium visuals add additional load (gradient cloud, fitness ribbon)
TradingView Resource Limits (Built-in Caps):
Max Bars Back: 500 (sufficient for WFO and evolution)
Max Labels: 100 (plenty for entry/exit markers)
Max Lines: 150 (adequate for stop/target lines)
Max Boxes: 50 (not heavily used)
Max Polylines: 100 (confidence halos)
Recommended Chart Settings:
Timeframe: 15min to 1H (optimal signal/noise balance)
5min: Works but noisier, more signals
4H/Daily: Works but fewer signals
Bars Loaded: 1000+ (ensures sufficient evolution history)
Replay Mode: Excellent for testing without risk
Performance Optimization Tips:
Disable gradient cloud if chart lags (most CPU intensive visual)
Disable fitness ribbon if still laggy
Reduce cloud layers from 7 to 3
Reduce ribbon layers from 10 to 5
Turn off diagnostics panel unless actively tuning
Close other heavy indicators to free resources
Browser/Platform Compatibility:
Works on all modern browsers (Chrome, Firefox, Safari, Edge)
Mobile app supported (full functionality on phone/tablet)
Desktop app supported (best performance)
Web version supported (may be slower on older computers)
Data Requirements:
Real-time or delayed data both work
No special data feeds required
Works with TradingView's standard data
Historical + live data seamlessly integrated
🎓 THEORETICAL FOUNDATIONS
AGE synthesizes advanced concepts from multiple disciplines:
Evolutionary Computation
Genetic Algorithms (Holland, 1975): Population-based optimization through natural selection metaphor
Tournament Selection: Fitness-based parent selection with diversity preservation
Crossover Operators: Fitness-weighted gene recombination from two parents
Mutation Operators: Random gene perturbation for exploration of new parameter space
Elitism: Preservation of top N performers to prevent loss of best solutions
Adaptive Parameters: Different mutation rates for historical vs. live phases
Technical Analysis
Support/Resistance: Price structure within swing ranges
Trend Following: EMA-based directional bias
Momentum Analysis: RSI, ROC, MACD composite indicators
Volatility Analysis: ATR-based risk scaling
Volume Confirmation: Trade activity validation
Information Theory
Shannon Entropy (1948): Quantification of market order vs. disorder
Signal-to-Noise Ratio: Directional information vs. random walk
Information Content: How much "information" a price move contains
Statistics & Probability
Walk-Forward Analysis: Rolling in-sample/out-of-sample optimization
Out-of-Sample Validation: Testing on unseen data to prevent overfitting
Monte Carlo Principles: Shadow portfolio simulation with realistic execution
Expectancy Theory: Win rate × avg win - loss rate × avg loss
Probability Distributions: Signal confidence quantification
Risk Management
ATR-Based Stops: Volatility-normalized risk per trade
Volatility Regime Detection: Market state classification (trending/choppy/volatile)
Drawdown Control: Peak-to-trough equity measurement
R-Multiple Normalization: Performance measurement in risk units
Machine Learning Concepts
Online Learning: Continuous adaptation as new data arrives
Fitness Functions: Multi-objective optimization (win rate + expectancy + drawdown)
Exploration vs. Exploitation: Balance between trying new strategies and using proven ones
Overfitting Prevention: Walk-forward validation as regularization
Novel Contribution:
AGE is the first TradingView indicator to apply genetic algorithms to real-time indicator parameter optimization while maintaining strict anti-overfitting controls through walk-forward validation.
Most "adaptive" indicators simply recalibrate lookback periods or thresholds. AGE evolves entirely new strategies through competitive selection - it's not parameter tuning, it's Darwinian evolution of trading logic itself.
The combination of:
Genetic algorithm population management
Shadow portfolio simulation for realistic fitness evaluation
Walk-forward validation to prevent overfitting
Multi-indicator confluence for signal quality
Dynamic volatility scaling for adaptive risk
...creates a system that genuinely learns and improves over time while avoiding the curse of curve-fitting that plagues most optimization approaches.
🏗️ DEVELOPMENT NOTES
This project represents months of intensive development, facing significant technical challenges:
Challenge 1: Making Genetics Actually Work
Early versions spawned garbage strategies that polluted the gene pool:
Random gene combinations produced nonsensical parameter sets
Weak strategies survived too long, dragging down population
No clear convergence toward optimal solutions
Solution:
Comprehensive fitness scoring (4 factors: win rate, P&L, expectancy, drawdown)
Elite preservation (top 2 always protected)
Walk-forward validation (unproven strategies penalized 30%)
Tournament selection (fitness-weighted breeding)
Adaptive culling (MAS decay creates increasing selection pressure)
Challenge 2: Balancing Evolution Speed vs. Stability
Too fast = population chaos, no convergence. Too slow = can't adapt to regime changes.
Solution:
Dual-phase timing: Fast evolution during historical (30/60 bar intervals), slow during live (200/400 bar intervals)
Adaptive mutation rates: 20% historical, 8% live
Spawn/cull ratio: Always 2:1 to prevent population collapse
Challenge 3: Shadow Portfolio Accuracy
Needed realistic trade simulation without lookahead bias:
Can't peek at future bars for exits
Must track multiple portfolios simultaneously
Stop/target checks must use bar's high/low correctly
Solution:
Entry on close (realistic)
Exit checks on current bar's high/low (realistic)
Independent position tracking per strategy
Cooldown periods to prevent unrealistic rapid re-entry
ATR-normalized P&L (R-multiples) for fair comparison across volatility regimes
Challenge 4: Pine Script Compilation Limits
Hit TradingView's execution limits multiple times:
Too many array operations
Too many variables
Too complex conditional logic
Solution:
Optimized data structures (single DNA array instead of 8 separate arrays)
Minimal visual overlays (only essential plots)
Efficient fitness calculations (vectorized where possible)
Strategic use of barstate.islast to minimize dashboard updates
Challenge 5: Walk-Forward Implementation
Standard WFO is difficult in Pine Script:
Can't easily "roll forward" through historical data
Can't re-optimize strategies mid-stream
Must work in real-time streaming environment
Solution:
Age-based phase detection (first 250 bars = training, next 75 = testing)
Separate metric tracking for train vs. test
Efficiency calculation at fixed interval (after test period completes)
Validation flag persists for strategy lifetime
Challenge 6: Signal Quality Control
Early versions generated too many signals with poor win rates:
Single indicators produced excessive noise
No trend alignment
No regime awareness
Instant entries on single-bar spikes
Solution:
Three-layer confluence system (entropy + momentum + structure)
Minimum 2-of-3 agreement requirement
Trend alignment checks (penalty for counter-trend)
Regime-based probability adjustments
Persistence requirements (signals must hold multiple bars)
Volume confirmation
Quality gate (probability + confluence thresholds)
The Result
A system that:
Truly evolves (not just parameter sweeps)
Truly validates (out-of-sample testing)
Truly adapts (ongoing competition and breeding)
Stays within TradingView's platform constraints
Provides institutional-quality signals
Maintains transparency (full metrics dashboard)
Development time: 3+ months of iterative refinement
Lines of code: ~1500 (highly optimized)
Test instruments: ES, NQ, EURUSD, BTCUSD, SPY, AAPL
Test timeframes: 5min, 15min, 1H, Daily
🎯 FINAL WORDS
The Adaptive Genesis Engine is not just another indicator - it's a living system that learns, adapts, and improves through the same principles that drive biological evolution. Every bar it observes adds to its experience. Every strategy it spawns explores new parameter combinations. Every strategy it culls removes weakness from the gene pool.
This is evolution in action on your charts.
You're not getting a static formula locked in time. You're getting a system that thinks , that competes , that survives through natural selection. The strongest strategies rise to the top. The weakest die. The gene pool improves generation after generation.
AGE doesn't claim to predict the future - it adapts to whatever the future brings. When markets shift from trending to choppy, from calm to volatile, from bullish to bearish - AGE evolves new strategies suited to the new regime.
Use it on any instrument. Any timeframe. Any market condition. AGE will adapt.
This indicator gives you the pure signal intelligence. How you choose to act on it - position sizing, risk management, execution discipline - that's your responsibility. AGE tells you when and how confident . You decide whether and how much .
Trust the process. Respect the evolution. Let Darwin work.
"In markets, as in nature, it is not the strongest strategies that survive, nor the most intelligent - but those most responsive to change."
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
— Happy Holiday's
Multi-Candle Reversal ConfirmationMulti-Candle Reversal Confirmation (MCRC)
This indicator identifies potential price reversals using a 3-candle confirmation pattern. It filters out noise by requiring a significant prior trend before signaling, helping you catch turning points rather than getting trapped in choppy price action.
How It Works
The indicator uses a three-step process to confirm reversals:
Candle 1 (Rejection) - Detects a rejection candle after a sustained move. This includes hammer/shooting star patterns with long wicks, doji candles showing indecision, or stall candles with unusually small bodies.
Candle 2 (Reversal) - Confirms the candle closes in the opposite direction of the prior trend.
Candle 3 (Confirmation) - Validates the reversal by either continuing in the new direction or breaking the high/low of the previous candle.
Key Features
Requires a significant prior trend before looking for reversals (no signals in choppy, sideways markets)
Uses ATR to measure move significance, adapting to current volatility
Marks rejection candles with small circles for early awareness
Confirmed signals shown as triangles with Bull/Bear labels
Built-in alerts for all signal types
Settings
Wick to Body Ratio - How pronounced the rejection wick must be compared to the candle body (default: 2.0)
Doji Threshold - Maximum body size relative to total range to qualify as a doji (default: 0.1)
Trend Lookback - Number of candles to analyze for prior trend detection (default: 5)
Trend Strength - Percentage of lookback candles required in trend direction (default: 0.6 = 60%)
Minimum Move (ATR multiple) - How large the prior move must be before signaling (default: 1.5)
Show Bullish/Bearish - Toggle each signal type on or off
Visual Signals
Small Circle - Marks potential rejection candles (first candle in the pattern)
Green Triangle (Bull) - Confirmed bullish reversal signal
Red Triangle (Bear) - Confirmed bearish reversal signal
Alerts
Three alert options are available:
Bullish Reversal Confirmed
Bearish Reversal Confirmed
Any Reversal Confirmed
How To Set Up Alerts
Add the indicator to your chart
Right-click on the chart and select "Add Alert" (or press Alt+A)
In the Condition dropdown, select "Multi-Candle Reversal Confirmation"
Choose your preferred alert type
Set notification preferences (popup, email, sound, webhook)
Click "Create"
Tips For Best Results
Combine with key support/resistance levels for higher probability trades
Use higher timeframe trend direction as a filter
Adjust Trend Lookback based on your timeframe (higher for longer timeframes)
Increase Minimum Move ATR in volatile conditions to reduce false signals
Signals appearing near VWAP, moving averages, or prior day levels tend to be more reliable
Note: This indicator is for informational purposes only and should not be used as the sole basis for trading decisions. Always use proper risk management and consider combining with other forms of analysis.
六脉齐发多空策略六脉齐发多空策略
# Six Meridians Unified Long/Short Strategy
## Overview
The "Six Meridians Unified Long/Short Strategy" is a comprehensive quantitative trading strategy built on TradingView Pine Script v6, designed for cross-asset long/short trading (stocks, cryptocurrencies, futures, forex, etc.). It leverages the resonance of **6 classic technical indicators** to filter high-confidence trading signals, reducing false signals caused by single-indicator bias and improving the reliability of entry/exit decisions.
## Core Indicators (6 "Meridians")
The strategy evaluates bullish/bearish trends by calculating 6 key technical indicators, with a "bullish count" system to quantify trend strength:
| Indicator | Calculation Parameters | Bullish Condition | Bearish Condition |
|-------------------------|------------------------------|--------------------------------------------|--------------------------------------------|
| MACD | Fast=12, Slow=26, Signal=9 | MACD line crosses above Signal line | MACD line crosses below Signal line |
| KDJ (Stochastic Oscillator) | Length=14, SmoothK=3, SmoothD=3 | K line > D line | K line < D line |
| RSI (Relative Strength Index) | Short=6, Long=12 | Short-period RSI (6) > Long-period RSI (12) | Short-period RSI (6) < Long-period RSI (12) |
| LWR (Modified Williams %R) | Length=14, Smooth=6 | LWR1 (WMA-smooth) > LWR2 (6-period WMA) | LWR1 < LWR2 |
| BBI (Bollinger Band Index) | EMA(3)+EMA(6)+EMA(12)+EMA(24) /4 | Close price > BBI line | Close price < BBI line |
| MTM (Momentum) | Period=12, MMS=6, MMM=14 | Short momentum line (MMS) > Long momentum line (MMM) | Short momentum line (MMS) < Long momentum line (MMM) |
## Trading Logic
The strategy uses a "count-based" trigger mechanism to execute position management (no pyramiding allowed):
### Long Position Rules
1. **Entry**: Open long position only when all 6 indicators show bullish signals (`bullCount = 6`).
2. **Partial Exit**: Reduce 50% of long position when 4 indicators remain bullish (`bullCount = 4`).
3. **Full Exit**: Close all long positions when ≤3 indicators are bullish (`bullCount ≤ 3`).
### Short Position Rules
1. **Entry**: Open short position only when all 6 indicators show bearish signals (`bearCount = 6`).
2. **Partial Exit**: Cover 50% of short position when 4 indicators remain bearish (`bearCount = 4`).
3. **Full Exit**: Close all short positions when ≤3 indicators are bearish (`bearCount ≤ 3`).
## Strategy Parameters (Risk & Capital Management)
| Parameter | Value | Description |
|--------------------------|----------------|----------------------------------------------|
| Initial Capital | $100,000 | Starting equity for backtesting |
| Default Order Size | $10,000 (cash) | Fixed cash amount per trade (instead of lots) |
| Commission | 0.1% per trade | Realistic transaction cost (percent-based) |
| Margin Requirement | 100% | No leverage (1:1 trading) |
| Pyramiding | 0 | No additional positions on existing trades |
## Key Features
1. **Multi-Indicator Resonance**: Eliminates noise from single-indicator false signals by requiring consensus across 6 diverse technical metrics.
2. **Gradual Position Management**: Partial exit (50%) before full closure to lock in profits and reduce downside risk.
3. **Full Automation**: Automatically executes entry/exit/position adjustment without manual intervention.
4. **Visualization Tools**: Plots BBI line, long/short signal labels, and bullish indicator count for easy strategy monitoring.
5. **Versatility**: Adaptable to multiple timeframes (15min, 1H, 4H, daily) and asset classes.
## Notes
- The strategy is optimized for trend-following markets and may underperform in choppy/range-bound conditions.
- Backtest results should be validated across different market cycles (bull, bear, sideways) before live trading.
- Parameters (e.g., indicator periods, order size) can be adjusted based on specific asset volatility and trading style.
AlphaStrike: Volatility & Pinbar Reversion SystemDescription:
The Concept: Solving the "Context" Problem One of the hardest challenges in trading is identifying whether the market is in a "Trend State" or a "Mean Reversion State." Using trend indicators in a range leads to false breakouts, while using reversal indicators in a strong trend leads to catching falling knives.
This script solves this issue by combining an ATR-based Trend Filter with a conditional Price Action Reversion engine. It does not simply overlay two indicators; it uses a filtering logic to ensure that Reversal signals are only generated when Momentum, Volatility, and Candle Geometry all align at the same time.
How It Works (The Logic) This script functions as a "Hybrid" system with two distinct engines running simultaneously:
1. The Trend Engine (Bias Filter) We use an ATR-based SuperTrend calculation to determine the dominant market direction.
Purpose: This acts as a "No Trade Zone" filter.
Logic: If the Trend Line is Green, the statistical bias is bullish. If Red, the bias is bearish. This helps traders avoid shorting strong uptrends or buying weak downtrends.
2. The Reversal Engine (Signal Generator) This is where the script differentiates itself from standard "Bollinger + RSI" mashups. A signal is NOT generated just because price hits a band. The script requires a specific "Pinbar" candle pattern to validate the move.
The "Blue Dot" (Bullish Reversal) Logic:
Condition A: Price must be below the Lower Bollinger Band (2 Standard Deviations).
Condition B: RSI (14) must be Oversold (< 35).
Condition C (The Filter): The candle must form a Bullish Pinbar. The script calculates the ratio of the lower wick to the body. If the wick is 2x longer than the body, it confirms that buyers actively rejected the lower prices.
The "Orange Dot" (Bearish Reversal) Logic:
Condition A: Price must be above the Upper Bollinger Band.
Condition B: RSI (14) must be Overbought (> 65).
Condition C (The Filter): The candle must form a Bearish Pinbar (long upper wick), indicating buyer exhaustion.
Visual Guide & Usage
Green/Red Line: Use this to trail your Stop Loss or determine trend direction.
Triangles (Breakouts): These marks indicate a shift in volatility where the trend officially flips.
Dots (Reversals): These are high-probability zones for scalps or entering on pullbacks.
Built-In Risk Management To assist with position sizing, a "Smart Risk" table is included in the bottom right corner.
It automatically detects the nearest market structure (Swing Highs/Lows).
It calculates the distance from the current price to that structure.
It displays the suggested position size to maintain a fixed risk percentage (configurable in Settings).
Note: You must input your Account Balance in the settings for this to work.
Settings
Crypto: Default settings (Factor 3.5) are optimized for high-volatility assets like BTC/ETH to reduce noise.
TradFi: For Forex or Stocks, consider lowering the Factor to 3.0.
Disclaimer This tool is designed for educational analysis and risk management assistance. It does not constitute financial advice. Past performance of signals (like those shown on the chart) does not guarantee future results. Always manage your risk.
EMA 20/50/200 - Warning Note Before Cross EMA 20/50/200 - Smart Cross Detection with Customizable Alerts
A clean and minimalistic indicator that tracks three key Exponential Moving Averages (20, 50, and 200) with intelligent near-cross detection and customizable warning system.
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📊 KEY FEATURES
✓ Triple EMA System
• EMA 20 (Red) - Fast/Short-term trend
• EMA 50 (Yellow) - Medium/Intermediate trend
• EMA 200 (Green) - Slow/Long-term trend & major support/resistance
✓ Smart Near-Cross Detection
• Get warned BEFORE crosses happen (not after)
• Adjustable threshold percentage (how close is "close")
• Automatic hiding after cross to prevent false signals
• Configurable lookback period
✓ Dual Warning System
• Price Label: Appears directly on chart near EMAs
• Info Table: Positioned anywhere on your chart
• Both show distance percentage and direction
• Dynamic positioning to avoid blocking candles
✓ Color-Coded Alerts
• GREEN warning = Bullish cross approaching (EMA 20 crossing UP through EMA 50)
• RED warning = Bearish cross approaching (EMA 20 crossing DOWN through EMA 50)
✓ Cross Signal Detection
• Golden Cross (EMA 50 crosses above EMA 200)
• Death Cross (EMA 50 crosses below EMA 200)
• Fast crosses (EMA 20 and EMA 50)
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⚙️ CUSTOMIZATION OPTIONS
Warning Settings:
• Custom warning text for bull/bear signals
• Adjustable opacity for better visibility
• Toggle distance and direction display
• Flexible table positioning (9 positions available)
• 5 text size options
Alert Settings:
• Golden/Death Cross alerts
• Fast cross alerts (20/50)
• Near-cross warnings (before it happens)
• All alerts are non-repainting
Display Options:
• Show/hide each EMA individually
• Toggle all signals on/off
• Adjustable threshold sensitivity
• Dynamic label positioning
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🎯 HOW TO USE
1. ADD TO CHART
Simply add the indicator to any chart and timeframe
2. ADJUST THRESHOLD
Default is 0.5% - increase for less frequent warnings, decrease for earlier warnings
3. SET UP ALERTS
Create alerts for:
• Near-cross warnings (get notified before the cross)
• Actual crosses (when EMA 20 crosses EMA 50)
• Golden/Death crosses (major trend changes)
4. CUSTOMIZE APPEARANCE
• Change warning text to your language
• Adjust opacity for your chart theme
• Position table where it's most convenient
• Choose label size for visibility
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💡 TRADING TIPS
- Use the near-cross warning to prepare entries/exits BEFORE the cross happens
- Green warning = Prepare for potential long position
- Red warning = Prepare for potential short position
- Combine with other indicators for confirmation
- Higher timeframes = more reliable signals
- Warning disappears after cross to avoid confusion
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🔧 TECHNICAL DETAILS
- Pine Script v6
- Non-repainting (all signals confirm on bar close)
- Works on all timeframes
- Works on all instruments (stocks, crypto, forex, futures)
- Lightweight and efficient
- No external data sources required
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📝 SETTINGS GUIDE
Near Cross Settings:
• Threshold %: How close EMAs must be to trigger warning (default 0.5%)
• Lookback Bars: Hide warning for X bars after a cross (default 3)
Warning Note Style:
• Text Size: Tiny to Huge
• Colors: Customize bull/bear warning colors
• Position: Place table anywhere on chart
• Opacity: 0 (solid) to 90 (very transparent)
Price Label:
• Size: Tiny to Large
• Opacity: Control transparency
• Auto-positioning: Moves to avoid blocking candles
Custom Text:
• Bull/Bear warning messages
• Toggle distance display
• Toggle direction display
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⚠️ IMPORTANT NOTES
- Warnings only appear BEFORE crosses, not after
- After a cross happens, warning is hidden for the lookback period
- Adjust threshold if you're getting too many/too few warnings
- This is a trend-following indicator - best used with confirmation
- Always use proper risk management
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Happy Trading! 📈📉
If you find this indicator useful, please give it a boost and leave a comment!
For questions or suggestions, feel free to reach out.
Volatility Risk PremiumTHE INSURANCE PREMIUM OF THE STOCK MARKET
Every day, millions of investors face a fundamental question that has puzzled economists for decades: how much should protection against market crashes cost? The answer lies in a phenomenon called the Volatility Risk Premium, and understanding it may fundamentally change how you interpret market conditions.
Think of the stock market like a neighborhood where homeowners buy insurance against fire. The insurance company charges premiums based on their estimates of fire risk. But here is the interesting part: insurance companies systematically charge more than the actual expected losses. This difference between what people pay and what actually happens is the insurance premium. The same principle operates in financial markets, but instead of fire insurance, investors buy protection against market volatility through options contracts.
The Volatility Risk Premium, or VRP, measures exactly this difference. It represents the gap between what the market expects volatility to be (implied volatility, as reflected in options prices) and what volatility actually turns out to be (realized volatility, calculated from actual price movements). This indicator quantifies that gap and transforms it into actionable intelligence.
THE FOUNDATION
The academic study of volatility risk premiums began gaining serious traction in the early 2000s, though the phenomenon itself had been observed by practitioners for much longer. Three research papers form the backbone of this indicator's methodology.
Peter Carr and Liuren Wu published their seminal work "Variance Risk Premiums" in the Review of Financial Studies in 2009. Their research established that variance risk premiums exist across virtually all asset classes and persist over time. They documented that on average, implied volatility exceeds realized volatility by approximately three to four percentage points annualized. This is not a small number. It means that sellers of volatility insurance have historically collected a substantial premium for bearing this risk.
Tim Bollerslev, George Tauchen, and Hao Zhou extended this research in their 2009 paper "Expected Stock Returns and Variance Risk Premia," also published in the Review of Financial Studies. Their critical contribution was demonstrating that the VRP is a statistically significant predictor of future equity returns. When the VRP is high, meaning investors are paying substantial premiums for protection, future stock returns tend to be positive. When the VRP collapses or turns negative, it often signals that realized volatility has spiked above expectations, typically during market stress periods.
Gurdip Bakshi and Nikunj Kapadia provided additional theoretical grounding in their 2003 paper "Delta-Hedged Gains and the Negative Market Volatility Risk Premium." They demonstrated through careful empirical analysis why volatility sellers are compensated: the risk is not diversifiable and tends to materialize precisely when investors can least afford losses.
HOW THE INDICATOR CALCULATES VOLATILITY
The calculation begins with two separate measurements that must be compared: implied volatility and realized volatility.
For implied volatility, the indicator uses the CBOE Volatility Index, commonly known as the VIX. The VIX represents the market's expectation of 30-day forward volatility on the S&P 500, calculated from a weighted average of out-of-the-money put and call options. It is often called the "fear gauge" because it rises when investors rush to buy protective options.
Realized volatility requires more careful consideration. The indicator offers three distinct calculation methods, each with specific advantages rooted in academic literature.
The Close-to-Close method is the most straightforward approach. It calculates the standard deviation of logarithmic daily returns over a specified lookback period, then annualizes this figure by multiplying by the square root of 252, the approximate number of trading days in a year. This method is intuitive and widely used, but it only captures information from closing prices and ignores intraday price movements.
The Parkinson estimator, developed by Michael Parkinson in 1980, improves efficiency by incorporating high and low prices. The mathematical formula calculates variance as the sum of squared log ratios of daily highs to lows, divided by four times the natural logarithm of two, times the number of observations. This estimator is theoretically about five times more efficient than the close-to-close method because high and low prices contain additional information about the volatility process.
The Garman-Klass estimator, published by Mark Garman and Michael Klass in 1980, goes further by incorporating opening, high, low, and closing prices. The formula combines half the squared log ratio of high to low prices minus a factor involving the log ratio of close to open. This method achieves the minimum variance among estimators using only these four price points, making it particularly valuable for markets where intraday information is meaningful.
THE CORE VRP CALCULATION
Once both volatility measures are obtained, the VRP calculation is straightforward: subtract realized volatility from implied volatility. A positive result means the market is paying a premium for volatility insurance. A negative result means realized volatility has exceeded expectations, typically indicating market stress.
The raw VRP signal receives slight smoothing through an exponential moving average to reduce noise while preserving responsiveness. The default smoothing period of five days balances signal clarity against lag.
INTERPRETING THE REGIMES
The indicator classifies market conditions into five distinct regimes based on VRP levels.
The EXTREME regime occurs when VRP exceeds ten percentage points. This represents an unusual situation where the gap between implied and realized volatility is historically wide. Markets are pricing in significantly more fear than is materializing. Research suggests this often precedes positive equity returns as the premium normalizes.
The HIGH regime, between five and ten percentage points, indicates elevated risk aversion. Investors are paying above-average premiums for protection. This often occurs after market corrections when fear remains elevated but realized volatility has begun subsiding.
The NORMAL regime covers VRP between zero and five percentage points. This represents the long-term average state of markets where implied volatility modestly exceeds realized volatility. The insurance premium is being collected at typical rates.
The LOW regime, between negative two and zero percentage points, suggests either unusual complacency or that realized volatility is catching up to implied volatility. The premium is shrinking, which can precede either calm continuation or increased stress.
The NEGATIVE regime occurs when realized volatility exceeds implied volatility. This is relatively rare and typically indicates active market stress. Options were priced for less volatility than actually occurred, meaning volatility sellers are experiencing losses. Historically, deeply negative VRP readings have often coincided with market bottoms, though timing the reversal remains challenging.
TERM STRUCTURE ANALYSIS
Beyond the basic VRP calculation, sophisticated market participants analyze how volatility behaves across different time horizons. The indicator calculates VRP using both short-term (default ten days) and long-term (default sixty days) realized volatility windows.
Under normal market conditions, short-term realized volatility tends to be lower than long-term realized volatility. This produces what traders call contango in the term structure, analogous to futures markets where later delivery dates trade at premiums. The RV Slope metric quantifies this relationship.
When markets enter stress periods, the term structure often inverts. Short-term realized volatility spikes above long-term realized volatility as markets experience immediate turmoil. This backwardation condition serves as an early warning signal that current volatility is elevated relative to historical norms.
The academic foundation for term structure analysis comes from Scott Mixon's 2007 paper "The Implied Volatility Term Structure" in the Journal of Derivatives, which documented the predictive power of term structure dynamics.
MEAN REVERSION CHARACTERISTICS
One of the most practically useful properties of the VRP is its tendency to mean-revert. Extreme readings, whether high or low, tend to normalize over time. This creates opportunities for systematic trading strategies.
The indicator tracks VRP in statistical terms by calculating its Z-score relative to the trailing one-year distribution. A Z-score above two indicates that current VRP is more than two standard deviations above its mean, a statistically unusual condition. Similarly, a Z-score below negative two indicates VRP is unusually low.
Mean reversion signals trigger when VRP reaches extreme Z-score levels and then shows initial signs of reversal. A buy signal occurs when VRP recovers from oversold conditions (Z-score below negative two and rising), suggesting that the period of elevated realized volatility may be ending. A sell signal occurs when VRP contracts from overbought conditions (Z-score above two and falling), suggesting the fear premium may be excessive and due for normalization.
These signals should not be interpreted as standalone trading recommendations. They indicate probabilistic conditions based on historical patterns. Market context and other factors always matter.
MOMENTUM ANALYSIS
The rate of change in VRP carries its own information content. Rapidly rising VRP suggests fear is building faster than volatility is materializing, often seen in the early stages of corrections before realized volatility catches up. Rapidly falling VRP indicates either calming conditions or rising realized volatility eating into the premium.
The indicator tracks VRP momentum as the difference between current VRP and VRP from a specified number of bars ago. Positive momentum with positive acceleration suggests strengthening risk aversion. Negative momentum with negative acceleration suggests intensifying stress or rapid normalization from elevated levels.
PRACTICAL APPLICATION
For equity investors, the VRP provides context for risk management decisions. High VRP environments historically favor equity exposure because the market is pricing in more pessimism than typically materializes. Low or negative VRP environments suggest either reducing exposure or hedging, as markets may be underpricing risk.
For options traders, understanding VRP is fundamental to strategy selection. Strategies that sell volatility, such as covered calls, cash-secured puts, or iron condors, tend to profit when VRP is elevated and compress toward its mean. Strategies that buy volatility tend to profit when VRP is low and risk materializes.
For systematic traders, VRP provides a regime filter for other strategies. Momentum strategies may benefit from different parameters in high versus low VRP environments. Mean reversion strategies in VRP itself can form the basis of a complete trading system.
LIMITATIONS AND CONSIDERATIONS
No indicator provides perfect foresight, and the VRP is no exception. Several limitations deserve attention.
The VRP measures a relationship between two estimates, each subject to measurement error. The VIX represents expectations that may prove incorrect. Realized volatility calculations depend on the chosen method and lookback period.
Mean reversion tendencies hold over longer time horizons but provide limited guidance for short-term timing. VRP can remain extreme for extended periods, and mean reversion signals can generate losses if the extremity persists or intensifies.
The indicator is calibrated for equity markets, specifically the S&P 500. Application to other asset classes requires recalibration of thresholds and potentially different data sources.
Historical relationships between VRP and subsequent returns, while statistically robust, do not guarantee future performance. Structural changes in markets, options pricing, or investor behavior could alter these dynamics.
STATISTICAL OUTPUTS
The indicator presents comprehensive statistics including current VRP level, implied volatility from VIX, realized volatility from the selected method, current regime classification, number of bars in the current regime, percentile ranking over the lookback period, Z-score relative to recent history, mean VRP over the lookback period, realized volatility term structure slope, VRP momentum, mean reversion signal status, and overall market bias interpretation.
Color coding throughout the indicator provides immediate visual interpretation. Green tones indicate elevated VRP associated with fear and potential opportunity. Red tones indicate compressed or negative VRP associated with complacency or active stress. Neutral tones indicate normal market conditions.
ALERT CONDITIONS
The indicator provides alerts for regime transitions, extreme statistical readings, term structure inversions, mean reversion signals, and momentum shifts. These can be configured through the TradingView alert system for real-time monitoring across multiple timeframes.
REFERENCES
Bakshi, G., and Kapadia, N. (2003). Delta-Hedged Gains and the Negative Market Volatility Risk Premium. Review of Financial Studies, 16(2), 527-566.
Bollerslev, T., Tauchen, G., and Zhou, H. (2009). Expected Stock Returns and Variance Risk Premia. Review of Financial Studies, 22(11), 4463-4492.
Carr, P., and Wu, L. (2009). Variance Risk Premiums. Review of Financial Studies, 22(3), 1311-1341.
Garman, M. B., and Klass, M. J. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business, 53(1), 67-78.
Mixon, S. (2007). The Implied Volatility Term Structure of Stock Index Options. Journal of Empirical Finance, 14(3), 333-354.
Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53(1), 61-65.
🟡 GOLD 4H HUD v12 — Time-Safe Nuclear Edition🟡 GOLD 4H HUD v12 — Time-Safe Nuclear Edition
A full–scale Smart Money Concepts (SMC) analytics engine designed exclusively for XAUUSD on the 4-Hour timeframe.
This script combines market structure, liquidity, displacement, order blocks, imbalance, volume profile, SMT divergence, and institutional behavior modeling into a single unified HUD.
Built with a time-safe architecture, all structural elements (OB/FVG/Sweep) are stored by timestamp to minimize repainting and preserve event integrity.
📌 Core Features (12 Modules + Full HUD)
1 — Market Structure Engine
Automatically detects:
HH / HL / LH / LL
BOS (Break of Structure)
MSS (Market Structure Shift)
CHOCH (Change of Character)
Real swing pivots & trend state
2 — Sweep Engine (Liquidity Grab Detection)
Identifies institutional liquidity grabs:
Break + reclaim of highs/lows
ATR-filtered invalidation
Displacement-backed sweeps
3 — Time-Safe FVG Engine
Detects Bullish/Bearish Fair Value Gaps
ATR-tolerant FVG logic
Automatic right-extension
Auto-delete when filled or invalid
4 — Time-Safe Order Block Engine
Demand & Supply OB detection
Strength classification (Weak vs Strong)
FVG-overlap confirmation
Timestamp-locked (non-repainting)
5 — Volume Profile Engine (HVN / LVN / POC)
Real-time micro-profile:
High Volume Node (HVN)
Low Volume Node (LVN)
Point of Control (POC)
6 — SMT Engine (Gold vs DXY Divergence)
Smart Money Divergence built-in:
Bullish SMT
Bearish SMT
Directional confirmation with zero lag
7 — Displacement Engine
Measures institutional impulse:
Body-based impulse detection
Multi-leg continuation signals
FVG continuation moves
Generates displacement score
8 — Premium / Discount Model
Auto-classifies price into:
Discount (Buy zone)
Premium (Sell zone)
9 — SMC Trend Engine (Score-Based)
Combines 10+ factors:
Structure
FVG
OB power
Displacement
POC positioning
SMT conditions
Outputs:
BULL / BEAR / RANGE
Full scoring system
10 — Institutional Imbalance Model (IMB Engine)
Combines:
PD zones
Sweep direction
Displacement
SMT
OB strength
CHOCH/MSS
A complete institutional bias filter.
11 — Entry Engine (Signal Fusion Model)
Entry conditions fuse:
Sweep
CHOCH
Displacement
OB strength
FVG alignment
SMT confirmation
Also outputs:
Suggested SL/TP
Entry score
12 — Trendline Engine
Auto-draws:
HL → HL bullish trendlines
LH → LH bearish trendlines
+ Full Nuclear HUD
Displays:
Market structure
Trend direction
SMT / CHOCH / MSS
FVG / OB zones
HVN / LVN / POC
Liquidity strength
Entry model
Liquidity Magnet direction
SL/TP map
A complete institutional dashboard in one place.
⚠ Usage Requirement
This script is designed ONLY for the 4H timeframe.
✨ Summary
GOLD 4H HUD v12 — Time-Safe Nuclear Edition
is not just an indicator.
It is a full institutional-grade SMC analysis system, built specifically for Gold.
If you trade XAUUSD on the 4H timeframe —
this is your complete market intelligence HUD
Execution Heatmap v8 — Classic Blocks (Final Logic)This indicator visualizes real-time market context through a structured execution heatmap, representing multiple analytic dimensions in a compact on-chart panel. Designed for traders who rely on confluence-based decision making, it tracks the shifting behavior of price, volume, and structural regimes to help identify momentum shifts, exhaustion points, and directional conviction.
🔶 Overview
The Execution Heatmap v8 consolidates key elements from trend, volume, and momentum analysis into a single panel. Each row represents a core component of the execution model, colored dynamically to reflect bullish, bearish, neutral, or mixed states. The final block produces a BUY, SELL, or SELL-ALERT classification — fully aligned with the internal logic of the GOLDMASTER‑HUD framework.
🔸 Core Logic Components
VWAP Direction: Detects price bias relative to VWAP (overextended, below value, or neutral).
Impulse Engine: Evaluates momentum using RSI and MFI thresholds to determine directional energy.
Volume Surge: Highlights aggressive volume imbalances and determines the dominant side (bull or bear).
Fake Break Detection: Identifies false breakouts at recent swing extremes to flag potential reversals.
Regime Filter: Measures underlying trend structure using dual‑EMA alignment (20/50 EMA).
Pattern Recognition: Detects emerging HL (higher low) or LH (lower high) structures.
Structure Strength: Maps strong vs. weak structural phases based on regime and pattern alignment.
Final Signal Engine: Synthesizes all modules into actionable classifications:
BUY: Price structure supports trend continuation.
SELL‑ALERT: Early weakness or exhaustion detected within a strong up‑trend.
SELL: Confirmed reversal alignment (momentum, VWAP, volume, and structure all bearish).
WAIT: Caution when conditions remain inconclusive.
🟩🟥 Color‑Coded Heat Blocks
Each metric is represented as a colored cell:
Green: Bullish / upward bias
Red: Bearish / downward bias
Yellow: Neutral / weak / mixed
Dark gray: Undefined or transitional
⚙️ Customization
Adjustable panel position (bottom‑right, bottom‑left, top‑right, top‑left).
Non‑intrusive table layout optimized for overlaying on active charts.
Lightweight execution with minimal resource load, ideal for intraday use.
Volume Flow Anatomy [Kodexius]Volume Flow Anatomy is a dynamic, multi-dimensional volume map that reconstructs how buy, sell, and “stealth” activity is distributed across price rather than just across time. Instead of relying on a static, session-based volume profile, it uses an exponentially decaying memory of recent bars to build a constantly evolving “anatomy” of the auction, where each price level carries an adaptive history of order flow.
The script separates buy vs. sell pressure, adds a third “Stealth Flow” dimension for low-volume price movement (ease of movement / divergence), and automatically derives POC, Value Area, imbalances, absorption zones, and classic profile shapes (D, P, b, B). This gives the trader a compact but highly information-dense map on the right side of the chart to read control (buyers vs. sellers), structure (balanced vs. trending vs. double distribution), and key reaction levels (support/resistance born from flow, not just wicks).
🔹 Features
🔸 Dynamic Lookback with Decay
- The script computes an effective lookback N from the Decay Factor and caps it with Max Lookback.
- Higher decay keeps more history; lower decay emphasizes the most recent flow.
- The profile continuously adapts as new bars are printed.
🔸 Price-Bucketed Flow Map
Each bucket accumulates:
- Sell Flow (sell pressure)
- Buy Flow (buy pressure)
- Stealth Flow (low-volume price movement)
- Box width at each bucket is proportional to the relative intensity of that component.
🔸 Stealth Flow (Low-Volume Price Movement)
- Measures close to close movement relative to volume, emphasizing price movement that occurs on comparatively low volume.
- Helps reveal hidden participation, inefficient moves, and areas that may be vulnerable to re-tests or reversions.
🔸 POC & 70% Value Area (VA)
- Identifies the Point of Control (price bucket with the highest total volume) over the effective lookback.
- Builds a 70% Value Area by expanding from POC towards the nearest high volume neighbors until 70% of the total volume is included.
- POC is drawn as a line over the analyzed range; VA is displayed as a shaded band in the profile area.
🔸 Market Profile Shape Detection
Splits the profile vertically into three zones (bottom / middle / top) and compares their volume distribution.
Classifies structure as:
- D-Shape (Balanced)
- P-Shape (Short Covering)
- b-Shape (Long Liquidation)
- B-Shape (Double Distribution)
Displays a shape label with color coded bias for quick auction context interpretation.
🔸 Imbalance Zones & Absorption
Imbalance: detects buckets where Buy Flow or Sell Flow exceeds the opposite side by at least Imbalance Ratio.
Absorption: flags zones with high volume but low price “ease”, where price is not moving much despite significant volume.
Extends these levels into horizontal zones, marking potential support/resistance and trap areas.
Bullish Imbalance Zone :
Bearish Imbalance Zone :
Absorption Zone :
🔸 Range Context & On-Chart Legend
Draws a Range Box covering the dynamically determined lookback (N bars), with a label displaying the effective bar count.
A bottom-right legend summarizes:
- Color keys for Buy / Sell / Stealth
- POC / VA status
- Bullish vs. Bearish dominance percentage
- Profile shape classification
- Imbalance and Absorption conventions
🔹 Calculations
1. Dynamic Lookback & Price Buckets
int N = math.min(int(4 / (1 - decayFactor) - 1), maxHistory)
float priceHigh = ta.highest(high, N)
float priceLow = ta.lowest(low, N)
float bucketSize = (priceHigh - priceLow) / bucketCount
The effective lookback N is derived from the Decay Factor, using the approximation 4 / (1 - decay) to capture roughly 99% of the decayed influence, then capped with maxHistory to control performance. Over that adaptive range, the script finds the highest and lowest prices and divides the band into bucketCount equal slices (bucketSize). Each slice is a price bucket that will accumulate volume-flow information.
2. Exponentially Decayed Volume Allocation
addValue(array profile, float weight, float minPrice, float maxPrice) =>
for j = 0 to bucketCount - 1
float bucketMin = priceLow + j * bucketSize
float bucketMax = bucketMin + bucketSize
float overlapMin = math.max(minPrice, bucketMin)
float overlapMax = math.min(maxPrice, bucketMax)
float overlapRange = overlapMax - overlapMin
if overlapRange > 0
profile.set(j, profile.get(j) * decayFactor + weight * overlapRange)
This function is the core engine of the indicator. For a given price span and intensity, it checks every bucket for overlap, distributes the weight proportionally to the overlapping range, and before adding new value, decays the existing bucket content by decayFactor. This results in an exponentially weighted profile: recent activity dominates, while older levels retain a gradually fading footprint.
3. POC and 70% Value Area
array totalProfile = array.new(bucketCount, 0)
for j = 0 to bucketCount - 1
float total = sellProfile.get(j) + buyProfile.get(j)
totalProfile.set(j, total)
if total > eaMax
eaMax := total
int pocIdx = 0
float pocVal = 0.0
for j = 0 to bucketCount - 1
if totalProfile.get(j) > pocVal
pocVal := totalProfile.get(j)
pocIdx := j
float totalSum = totalProfile.sum()
float targetSum = totalSum * 0.70
int vaLow = pocIdx
int vaHigh = pocIdx
float currentSum = pocVal
while currentSum < targetSum and (vaLow > 0 or vaHigh < bucketCount - 1)
float lowVal = vaLow > 0 ? totalProfile.get(vaLow - 1) : 0.0
float highVal = vaHigh < bucketCount - 1 ? totalProfile.get(vaHigh + 1) : 0.0
First, totalProfile is built as the sum of buy and sell flow per bucket, and eaMax (the maximum total) is tracked for later normalization. The POC bucket (pocIdx) is simply the index with the highest totalProfile value.
To compute the 70% Value Area, the algorithm starts at the POC bucket and expands outward, each step adding either the upper or lower neighbor depending on which has more volume. This continues until the cumulative volume reaches 70% of totalSum. The result is a volume-driven VA, not necessarily symmetric around POC, which more accurately represents where the market has truly traded.
4. Market Profile Shape Classification
float volTopThird = 0.0
float volMidThird = 0.0
float volBotThird = 0.0
int thirdIdx = int(bucketCount / 3)
for j = 0 to bucketCount - 1
float val = totalProfile.get(j)
if j < thirdIdx
volBotThird += val
else if j < thirdIdx * 2
volMidThird += val
else
volTopThird += val
float totalVolShape = totalProfile.sum()
string shapeStr = "D-Shape (Balanced)"
if (volTopThird > totalVolShape * 0.20) and (volBotThird > totalVolShape * 0.20) and (volMidThird < totalVolShape * 0.50)
shapeStr := "B-Shape (Double Dist)"
else
if pocIdx > bucketCount * 0.5 and volTopThird > volBotThird * 1.3
shapeStr := "P-Shape (Short Covering)"
else if pocIdx < bucketCount * 0.5 and volBotThird > volTopThird * 1.3
shapeStr := "b-Shape (Long Liquidation)"
else
shapeStr := "D-Shape (Balanced)"
The profile is split into bottom, middle, and top thirds. The script compares how much volume is concentrated in each and combines that with the relative location of POC. If both extremes are heavy and the middle light, it labels a B-Shape (double distribution). If the POC is high and the top dominates the bottom, it’s a P-Shape (short covering). If the POC is low and the bottom dominates, it’s a b-Shape (long liquidation). Otherwise, it defaults to a D-Shape (balanced). This provides a quick, at-a-glance assessment of auction structure.
5. Imbalances, Absorption & Zones
bool isBuyImb = showImb and sVal > 0 and (bVal / sVal >= imbRatio)
bool isSellImb = showImb and bVal > 0 and (sVal / bVal >= imbRatio)
float volRatio = eaMax > 0 ? tVal / eaMax : 0
float stRatio = esmRange > 0 ? (stVal - esmMin) / esmRange : 1.0
bool isAbsorp = showAbsorp and volRatio > 0.6 and stRatio < 0.25
if showImbZone
if isSellImb
zoneBoxes.push(box.new(bar_index - N + 1, bucketHi, bar_index + 1, bucketLo, ...))
if isBuyImb
zoneBoxes.push(box.new(bar_index - N + 1, bucketHi, bar_index + 1, bucketLo, ...))
if isAbsorp
zoneBoxes.push(box.new(bar_index - N + 1, bucketHi, bar_index + 1, bucketLo, ...))
Imbalances are identified where one side’s volume (buy or sell) exceeds the other by at least Imbalance Ratio. These buckets are marked as buy or sell imbalance zones, indicating aggressive participation from one side.
Absorption is detected by combining a high volume ratio (volRatio) with a low normalized stealth ratio (stRatio). High volume with limited price movement suggests that opposing orders are absorbing flow at that level. Both imbalance and absorption buckets are extended into horizontal zones from the start of the lookback to the current bar, visually emphasizing key support/resistance and liquidity areas.
6. Building Buy, Sell & Stealth Profiles
sellProfile := array.new(bucketCount, 0)
buyProfile := array.new(bucketCount, 0)
stealthProfile := array.new(bucketCount, 0)
Three arrays are used to store Sell Flow, Buy Flow, and Stealth Flow. Bars are processed from oldest to newest so that decay is applied in correct chronological order. For each bar, a volume density (volume / range) is calculated and distributed across the candle range. Bull candles feed buyProfile, bear candles feed sellProfile.
Stealth Flow computes the close-to-close move between consecutive bars, scaled by 1 / (1 + volume). Big moves on low volume produce high stealth values, which are then allocated across the move’s price span into stealthProfile. This yields a three-layer profile per price level: directional volume and stealthy price movement.
Global Liquidity Index LITEGlobal Liquidity Index (GLI LITE) is an indicator that measures global liquidity by combining the balance sheets of major central banks (FED, ECB, PBOC, BOJ) and the M2 money supply of the world’s largest economies (USA, Europe, China, Japan).
Since liquidity directly influences the price of risk assets (BTC, NASDAQ, SPX, etc.), GLI is one of the most important macro signals for identifying market bull/bear regimes.
What the indicator shows:
GLI momentum line (green = liquidity expansion, orange = contraction)
Fast & Slow MA lines that define the liquidity trend
Bull/Bear background coloring
Green → global liquidity is expanding
Red → liquidity is tightening
Correlation between GLI and the asset price (e.g., BTC)
Macro trend panel (Bull / Bear / Neutral)
How to use the indicator:
Bull regime (Fast MA > Slow MA)
Liquidity is expanding and the market has a natural tailwind. Risk assets tend to perform better.
Bear regime (Fast MA < Slow MA)
Liquidity is tightening — higher risk, increased volatility, and more downside pressure.
GLI ↔ Price Correlation
If correlation is high (e.g., > 0.6), GLI can be an excellent leading indicator for price movement.
ATR/ADR MTF Projection ArrayATR/ADR MTF Projection Array
Overview
A powerful predictive tool that projects ATR (Average True Range) and ADR (Average Daily Range) levels as clean support and resistance arrays on your chart. Designed for traders who want to anticipate the high and low of the day using volatility-based projections with multi-timeframe confluence.
This indicator combines traditional ATR analysis with ICT-style ADR methodology, giving you institutional-grade level projections from a single, customizable tool.
Key Features
🎯 Dual Volatility Metrics
ATR Projections — Classic volatility-based levels with full multi-timeframe support
ADR Projections (ICT Style) — Average Daily Range levels using Inner Circle Trader methodology
Enable/disable each independently based on your trading preference
📊 Multi-Timeframe ATR Analysis
Plot ATR levels from up to 3 timeframes simultaneously (Daily, Weekly, Monthly or custom)
Each timeframe displays with distinct styling for easy identification
Perfect for confluence trading across multiple time horizons
⚡ ICT ADR Methodology
NY Midnight calculation mode (ICT standard) or Classic Daily
Key ICT levels built-in:
1/3 ADR (Judas Swing) — Critical manipulation level where fake moves often terminate
1/2 ADR — Mid-range reference
2/3 ADR — Trending day continuation target
100% ADR — Full daily range completion
150% ADR — Extension target for expansion days
Two projection modes: Static (from anchor) or Dynamic (from session high/low)
🔧 Flexible Anchor Points
Previous Close (default)
Daily Open
Weekly Open
Monthly Open
Session Open
📈 Range Completion Tracking
Real-time display of how much of the expected daily range has been consumed
Visual status indicator helps identify when the day's move may be exhausted
How To Use
For Bias Confirmation:
Establish your directional bias using your preferred method (trigger day, market structure, etc.)
Monitor the 1/3 ADR level during London/NY open for potential Judas Swing (manipulation move)
Target 2/3 to 100% ADR for your HOD/LOD objective
For Target Setting:
Use ATR levels as volatility-based profit targets
ADR 100% level often marks session extremes
When Range Used reaches 100%+, expect consolidation or reversal
For Multi-Timeframe Confluence:
Enable Weekly/Monthly ATR levels alongside Daily
Look for clustering of levels across timeframes for high-probability zones
Settings Guide
Master Controls — Toggle ATR/ADR systems and bull/bear levels independently
ATR Settings — Configure period, multiplier, anchor point, and select which timeframes to display
ATR Level Multipliers — Choose which projection levels to show (0.5x, 0.75x, 1.0x, 1.25x, 1.5x)
ADR Settings (ICT Style) — Select calculation mode (NY Midnight recommended), period (5 days is ICT standard), and projection mode
ADR Level Selection — Toggle individual ICT levels (1/3, 1/2, 2/3, 100%, 150%)
Visual Settings — Customize colors, line styles, labels, and info table position
Alerts Included
ATR 1.0x Bull/Bear Cross
ADR 1/3 Judas Swing Zone (Bull/Bear)
ADR 100% Range Completion (Bull/Bear)
BTC Fear & Greed Incremental StrategyIMPORTANT: READ SETUP GUIDE BELOW OR IT WON'T WORK
# BTC Fear & Greed Incremental Strategy — TradeMaster AI (Pure BTC Stack)
## Strategy Overview
This advanced Bitcoin accumulation strategy is designed for long-term hodlers who want to systematically take profits during greed cycles and accumulate during fear periods, while preserving their core BTC position. Unlike traditional strategies that start with cash, this approach begins with a specified BTC allocation, making it perfect for existing Bitcoin holders who want to optimize their stack management.
## Key Features
### 🎯 **Pure BTC Stack Mode**
- Start with any amount of BTC (configurable)
- Strategy manages your existing stack, not new purchases
- Perfect for hodlers who want to optimize without timing markets
### 📊 **Fear & Greed Integration**
- Uses market sentiment data to drive buy/sell decisions
- Configurable thresholds for greed (selling) and fear (buying) triggers
- Automatic validation to ensure proper 0-100 scale data source
### 🐂 **Bull Year Optimization**
- Smart quarterly selling during bull market years (2017, 2021, 2025)
- Q1: 1% sells, Q2: 2% sells, Q3/Q4: 5% sells (configurable)
- **NO SELLING** during non-bull years - pure accumulation mode
- Preserves BTC during early bull phases, maximizes profits at peaks
### 🐻 **Bear Market Intelligence**
- Multi-regime detection: Bull, Early Bear, Deep Bear, Early Bull
- Different buying strategies based on market conditions
- Enhanced buying during deep bear markets with configurable multipliers
- Visual regime backgrounds for easy market condition identification
### 🛡️ **Risk Management**
- Minimum BTC allocation floor (prevents selling entire stack)
- Configurable position sizing for all trades
- Multiple safety checks and validation
### 📈 **Advanced Visualization**
- Clean 0-100 scale with 2 decimal precision
- Three main indicators: BTC Allocation %, Fear & Greed Index, BTC Holdings
- Real-time portfolio tracking with cash position display
- Enhanced info table showing all key metrics
## How to Use
### **Step 1: Setup**
1. Add the strategy to your BTC/USD chart (daily timeframe recommended)
2. **CRITICAL**: In settings, change the "Fear & Greed Source" from "close" to a proper 0-100 Fear & Greed indicator
---------------
I recommend Crypto Fear & Greed Index by TIA_Technology indicator
When selecting source with this indicator, look for "Crypto Fear and Greed Index:Index"
---------------
3. Set your "Starting BTC Quantity" to match your actual holdings
4. Configure your preferred "Start Date" (when you want the strategy to begin)
### **Step 2: Configure Bull Year Logic**
- Enable "Bull Year Logic" (default: enabled)
- Adjust quarterly sell percentages:
- Q1 (Jan-Mar): 1% (conservative early bull)
- Q2 (Apr-Jun): 2% (moderate mid bull)
- Q3/Q4 (Jul-Dec): 5% (aggressive peak targeting)
- Add future bull years to the list as needed
### **Step 3: Fine-tune Thresholds**
- **Greed Threshold**: 80 (sell when F&G > 80)
- **Fear Threshold**: 20 (buy when F&G < 20 in bull markets)
- **Deep Bear Fear Threshold**: 25 (enhanced buying in bear markets)
- Adjust based on your risk tolerance
### **Step 4: Risk Management**
- Set "Minimum BTC Allocation %" (default 20%) - prevents selling entire stack
- Configure sell/buy percentages based on your position size
- Enable bear market filters for enhanced timing
### **Step 5: Monitor Performance**
- **Orange Line**: Your BTC allocation percentage (target: fluctuate between 20-100%)
- **Blue Line**: Actual BTC holdings (should preserve core position)
- **Pink Line**: Fear & Greed Index (drives all decisions)
- **Table**: Real-time portfolio metrics including cash position
## Reading the Indicators
### **BTC Allocation Percentage (Orange Line)**
- **100%**: All portfolio in BTC, no cash available for buying
- **80%**: 80% BTC, 20% cash ready for fear buying
- **20%**: Minimum allocation, maximum cash position
### **Trading Signals**
- **Green Buy Signals**: Appear during fear periods with available cash
- **Red Sell Signals**: Appear during greed periods in bull years only
- **No Signals**: Either allocation limits reached or non-bull year
## Strategy Logic
### **Bull Years (2017, 2021, 2025)**
- Q1: Conservative 1% sells (preserve stack for later)
- Q2: Moderate 2% sells (gradual profit taking)
- Q3/Q4: Aggressive 5% sells (peak targeting)
- Fear buying active (accumulate on dips)
### **Non-Bull Years**
- **Zero selling** - pure accumulation mode
- Enhanced fear buying during bear markets
- Focus on rebuilding stack for next bull cycle
## Important Notes
- **This is not financial advice** - backtest thoroughly before use
- Designed for **long-term holders** (4+ year cycles)
- **Requires proper Fear & Greed data source** - validate in settings
- Best used on **daily timeframe** for major trend following
- **Cash calculations**: Use allocation % and BTC holdings to calculate available cash: `Cash = (Total Portfolio × (1 - Allocation%/100))`
## Risk Disclaimer
This strategy involves active trading and position management. Past performance does not guarantee future results. Always do your own research and never invest more than you can afford to lose. The strategy is designed for educational purposes and long-term Bitcoin accumulation thesis.
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*Developed by Sol_Crypto for the Bitcoin community. Happy stacking! 🚀*
Ultimate RSI [captainua]Ultimate RSI
Overview
This indicator combines multiple RSI calculations with volume analysis, divergence detection, and trend filtering to provide a comprehensive RSI-based trading system. The script calculates RSI using three different periods (6, 14, 24) and applies various smoothing methods to reduce noise while maintaining responsiveness. The combination of these features creates a multi-layered confirmation system that reduces false signals by requiring alignment across multiple indicators and timeframes.
The script includes optimized configuration presets for instant setup: Scalping, Day Trading, Swing Trading, and Position Trading. Simply select a preset to instantly configure all settings for your trading style, or use Custom mode for full manual control. All settings include automatic input validation to prevent configuration errors and ensure optimal performance.
Configuration Presets
The script includes preset configurations optimized for different trading styles, allowing you to instantly configure the indicator for your preferred trading approach. Simply select a preset from the "Configuration Preset" dropdown menu:
- Scalping: Optimized for fast-paced trading with shorter RSI periods (4, 7, 9) and minimal smoothing. Noise reduction is automatically disabled, and momentum confirmation is disabled to allow faster signal generation. Designed for quick entries and exits in volatile markets.
- Day Trading: Balanced configuration for intraday trading with moderate RSI periods (6, 9, 14) and light smoothing. Momentum confirmation is enabled for better signal quality. Ideal for day trading strategies requiring timely but accurate signals.
- Swing Trading: Configured for medium-term positions with standard RSI periods (14, 14, 21) and moderate smoothing. Provides smoother signals suitable for swing trading timeframes. All noise reduction features remain active.
- Position Trading: Optimized for longer-term trades with extended RSI periods (24, 21, 28) and heavier smoothing. Filters are configured for highest-quality signals. Best for position traders holding trades over multiple days or weeks.
- Custom: Full manual control over all settings. All input parameters are available for complete customization. This is the default mode and maintains full backward compatibility with previous versions.
When a preset is selected, it automatically adjusts RSI periods, smoothing lengths, and filter settings to match the trading style. The preset configurations ensure optimal settings are applied instantly, eliminating the need for manual configuration. All settings can still be manually overridden if needed, providing flexibility while maintaining ease of use.
Input Validation and Error Prevention
The script includes comprehensive input validation to prevent configuration errors:
- Cross-Input Validation: Smoothing lengths are automatically validated to ensure they are always less than their corresponding RSI period length. If you set a smoothing length greater than or equal to the RSI length, the script automatically adjusts it to (RSI Length - 1). This prevents logical errors and ensures valid configurations.
- Input Range Validation: All numeric inputs have minimum and maximum value constraints enforced by TradingView's input system, preventing invalid parameter values.
- Smart Defaults: Preset configurations use validated default values that are tested and optimized for each trading style. When switching between presets, all related settings are automatically updated to maintain consistency.
Core Calculations
Multi-Period RSI:
The script calculates RSI using the standard Wilder's RSI formula: RSI = 100 - (100 / (1 + RS)), where RS = Average Gain / Average Loss over the specified period. Three separate RSI calculations run simultaneously:
- RSI(6): Uses 6-period lookback for high sensitivity to recent price changes, useful for scalping and early signal detection
- RSI(14): Standard 14-period RSI for balanced analysis, the most commonly used RSI period
- RSI(24): Longer 24-period RSI for trend confirmation, provides smoother signals with less noise
Each RSI can be smoothed using EMA, SMA, RMA (Wilder's smoothing), WMA, or Zero-Lag smoothing. Zero-Lag smoothing uses the formula: ZL-RSI = RSI + (RSI - RSI ) to reduce lag while maintaining signal quality. You can apply individual smoothing lengths to each RSI period, or use global smoothing where all three RSIs share the same smoothing length.
Dynamic Overbought/Oversold Thresholds:
Static thresholds (default 70/30) are adjusted based on market volatility using ATR. The formula: Dynamic OB = Base OB + (ATR × Volatility Multiplier × Base Percentage / 100), Dynamic OS = Base OS - (ATR × Volatility Multiplier × Base Percentage / 100). This adapts to volatile markets where traditional 70/30 levels may be too restrictive. During high volatility, the dynamic thresholds widen, and during low volatility, they narrow. The thresholds are clamped between 0-100 to remain within RSI bounds. The ATR is cached for performance optimization, updating on confirmed bars and real-time bars.
Adaptive RSI Calculation:
An adaptive RSI adjusts the standard RSI(14) based on current volatility relative to average volatility. The calculation: Adaptive Factor = (Current ATR / SMA of ATR over 20 periods) × Volatility Multiplier. If SMA of ATR is zero (edge case), the adaptive factor defaults to 0. The adaptive RSI = Base RSI × (1 + Adaptive Factor), clamped to 0-100. This makes the indicator more responsive during high volatility periods when traditional RSI may lag. The adaptive RSI is used for signal generation (buy/sell signals) but is not plotted on the chart.
Overbought/Oversold Fill Zones:
The script provides visual fill zones between the RSI line and the threshold lines when RSI is in overbought or oversold territory. The fill logic uses inclusive conditions: fills are shown when RSI is currently in the zone OR was in the zone on the previous bar. This ensures complete coverage of entry and exit boundaries. A minimum gap of 0.1 RSI points is maintained between the RSI plot and threshold line to ensure reliable polygon rendering in TradingView. The fill uses invisible plots at the threshold levels and the RSI value, with the fill color applied between them. You can select which RSI (6, 14, or 24) to use for the fill zones.
Divergence Detection
Regular Divergence:
Bullish divergence: Price makes a lower low (current low < lowest low from previous lookback period) while RSI makes a higher low (current RSI > lowest RSI from previous lookback period). Bearish divergence: Price makes a higher high (current high > highest high from previous lookback period) while RSI makes a lower high (current RSI < highest RSI from previous lookback period). The script compares current price/RSI values to the lowest/highest values from the previous lookback period using ta.lowest() and ta.highest() functions with index to reference the previous period's extreme.
Pivot-Based Divergence:
An enhanced divergence detection method that uses actual pivot points instead of simple lowest/highest comparisons. This provides more accurate divergence detection by identifying significant pivot lows/highs in both price and RSI. The pivot-based method uses a tolerance-based approach with configurable constants: 1% tolerance for price comparisons (priceTolerancePercent = 0.01) and 1.0 RSI point absolute tolerance for RSI comparisons (pivotTolerance = 1.0). Minimum divergence threshold is 1.0 RSI point (minDivergenceThreshold = 1.0). It looks for two recent pivot points and compares them: for bullish divergence, price makes a lower low (at least 1% lower) while RSI makes a higher low (at least 1.0 point higher). This method reduces false divergences by requiring actual pivot points rather than just any low/high within a period. When enabled, pivot-based divergence replaces the traditional method for more accurate signal generation.
Strong Divergence:
Regular divergence is confirmed by an engulfing candle pattern. Bullish engulfing requires: (1) Previous candle is bearish (close < open ), (2) Current candle is bullish (close > open), (3) Current close > previous open, (4) Current open < previous close. Bearish engulfing is the inverse: previous bullish, current bearish, current close < previous open, current open > previous close. Strong divergence signals are marked with visual indicators (🐂 for bullish, 🐻 for bearish) and have separate alert conditions.
Hidden Divergence:
Continuation patterns that signal trend continuation rather than reversal. Bullish hidden divergence: Price makes a higher low (current low > lowest low from previous period) but RSI makes a lower low (current RSI < lowest RSI from previous period). Bearish hidden divergence: Price makes a lower high (current high < highest high from previous period) but RSI makes a higher high (current RSI > highest RSI from previous period). These patterns indicate the trend is likely to continue in the current direction.
Volume Confirmation System
Volume threshold filtering requires current volume to exceed the volume SMA multiplied by the threshold factor. The formula: Volume Confirmed = Volume > (Volume SMA × Threshold). If the threshold is set to 0.1 or lower, volume confirmation is effectively disabled (always returns true). This allows you to use the indicator without volume filtering if desired.
Volume Climax is detected when volume exceeds: Volume SMA + (Volume StdDev × Multiplier). This indicates potential capitulation moments where extreme volume accompanies price movements. Volume Dry-Up is detected when volume falls below: Volume SMA - (Volume StdDev × Multiplier), indicating low participation periods that may produce unreliable signals. The volume SMA is cached for performance, updating on confirmed and real-time bars.
Multi-RSI Synergy
The script generates signals when multiple RSI periods align in overbought or oversold zones. This creates a confirmation system that reduces false signals. In "ALL" mode, all three RSIs (6, 14, 24) must be simultaneously above the overbought threshold OR all three must be below the oversold threshold. In "2-of-3" mode, any two of the three RSIs must align in the same direction. The script counts how many RSIs are in each zone: twoOfThreeOB = ((rsi6OB ? 1 : 0) + (rsi14OB ? 1 : 0) + (rsi24OB ? 1 : 0)) >= 2.
Synergy signals require: (1) Multi-RSI alignment (ALL or 2-of-3), (2) Volume confirmation, (3) Reset condition satisfied (enough bars since last synergy signal), (4) Additional filters passed (RSI50, Trend, ADX, Volume Dry-Up avoidance). Separate reset conditions track buy and sell signals independently. The reset condition uses ta.barssince() to count bars since the last trigger, returning true if the condition never occurred (allowing first signal) or if enough bars have passed.
Regression Forecasting
The script uses historical RSI values to forecast future RSI direction using four methods. The forecast horizon is configurable (1-50 bars ahead). Historical data is collected into an array, and regression coefficients are calculated based on the selected method.
Linear Regression: Calculates the least-squares fit line (y = mx + b) through the last N RSI values. The calculation: meanX = sumX / horizon, meanY = sumY / horizon, denominator = sumX² - horizon × meanX², m = (sumXY - horizon × meanX × meanY) / denominator, b = meanY - m × meanX. The forecast projects this line forward: forecast = b + m × i for i = 1 to horizon.
Polynomial Regression: Fits a quadratic curve (y = ax² + bx + c) to capture non-linear trends. The system of equations is solved using Cramer's rule with a 3×3 determinant. If the determinant is too small (< 0.0001), the system falls back to linear regression. Coefficients are calculated by solving: n×c + sumX×b + sumX²×a = sumY, sumX×c + sumX²×b + sumX³×a = sumXY, sumX²×c + sumX³×b + sumX⁴×a = sumX²Y. Note: Due to the O(n³) computational complexity of polynomial regression, the forecast horizon is automatically limited to a maximum of 20 bars when using polynomial regression to maintain optimal performance. If you set a horizon greater than 20 bars with polynomial regression, it will be automatically capped at 20 bars.
Exponential Smoothing: Applies exponential smoothing with adaptive alpha = 2/(horizon+1). The smoothing iterates from oldest to newest value: smoothed = alpha × series + (1 - alpha) × smoothed. Trend is calculated by comparing current smoothed value to an earlier smoothed value (at 60% of horizon): trend = (smoothed - earlierSmoothed) / (horizon - earlierIdx). Forecast: forecast = base + trend × i.
Moving Average: Uses the difference between short MA (horizon/2) and long MA (horizon) to estimate trend direction. Trend = (maShort - maLong) / (longLen - shortLen). Forecast: forecast = maShort + trend × i.
Confidence bands are calculated using RMSE (Root Mean Squared Error) of historical forecast accuracy. The error calculation compares historical values with forecast values: RMSE = sqrt(sumSquaredError / count). If insufficient data exists, it falls back to calculating standard deviation of recent RSI values. Confidence bands = forecast ± (RMSE × confidenceLevel). All forecast values and confidence bands are clamped to 0-100 to remain within RSI bounds. The regression functions include comprehensive safety checks: horizon validation (must not exceed array size), empty array handling, edge case handling for horizon=1 scenarios, division-by-zero protection, and bounds checking for all array access operations to prevent runtime errors.
Strong Top/Bottom Detection
Strong buy signals require three conditions: (1) RSI is at its lowest point within the bottom period: rsiVal <= ta.lowest(rsiVal, bottomPeriod), (2) RSI is below the oversold threshold minus a buffer: rsiVal < (oversoldThreshold - rsiTopBottomBuffer), where rsiTopBottomBuffer = 2.0 RSI points, (3) The absolute difference between current RSI and the lowest RSI exceeds the threshold value: abs(rsiVal - ta.lowest(rsiVal, bottomPeriod)) > threshold. This indicates a bounce from extreme levels with sufficient distance from the absolute low.
Strong sell signals use the inverse logic: RSI at highest point, above overbought threshold + rsiTopBottomBuffer (2.0 RSI points), and difference from highest exceeds threshold. Both signals also require: volume confirmation, reset condition satisfied (separate reset for buy vs sell), and all additional filters passed (RSI50, Trend, ADX, Volume Dry-Up avoidance).
The reset condition uses separate logic for buy and sell: resetCondBuy checks bars since isRSIAtBottom, resetCondSell checks bars since isRSIAtTop. This ensures buy signals reset based on bottom conditions and sell signals reset based on top conditions, preventing incorrect signal blocking.
Filtering System
RSI(50) Filter: Only allows buy signals when RSI(14) > 50 (bullish momentum) and sell signals when RSI(14) < 50 (bearish momentum). This filter ensures you're buying in uptrends and selling in downtrends from a momentum perspective. The filter is optional and can be disabled. Recommended to enable for noise reduction.
Trend Filter: Uses a long-term EMA (default 200) to determine trend direction. Buy signals require price above EMA, sell signals require price below EMA. The EMA slope is calculated as: emaSlope = ema - ema . Optional EMA slope filter additionally requires the EMA to be rising (slope > 0) for buy signals or falling (slope < 0) for sell signals. This provides stronger trend confirmation by requiring both price position and EMA direction.
ADX Filter: Uses the Directional Movement Index (calculated via ta.dmi()) to measure trend strength. Signals only fire when ADX exceeds the threshold (default 20), indicating a strong trend rather than choppy markets. The ADX calculation uses separate length and smoothing parameters. This filter helps avoid signals during sideways/consolidation periods.
Volume Dry-Up Avoidance: Prevents signals during periods of extremely low volume relative to average. If volume dry-up is detected and the filter is enabled, signals are blocked. This helps avoid unreliable signals that occur during low participation periods.
RSI Momentum Confirmation: Requires RSI to be accelerating in the signal direction before confirming signals. For buy signals, RSI must be consistently rising (recovering from oversold) over the lookback period. For sell signals, RSI must be consistently falling (declining from overbought) over the lookback period. The momentum check verifies that all consecutive changes are in the correct direction AND the cumulative change is significant. This filter ensures signals only fire when RSI momentum aligns with the signal direction, reducing false signals from weak momentum.
Multi-Timeframe Confirmation: Requires higher timeframe RSI to align with the signal direction. For buy signals, current RSI must be below the higher timeframe RSI by at least the confirmation threshold. For sell signals, current RSI must be above the higher timeframe RSI by at least the confirmation threshold. This ensures signals align with the larger trend context, reducing counter-trend trades. The higher timeframe RSI is fetched using request.security() from the selected timeframe.
All filters use the pattern: filterResult = not filterEnabled OR conditionMet. This means if a filter is disabled, it always passes (returns true). Filters can be combined, and all must pass for a signal to fire.
RSI Centerline and Period Crossovers
RSI(50) Centerline Crossovers: Detects when the selected RSI source crosses above or below the 50 centerline. Bullish crossover: ta.crossover(rsiSource, 50), bearish crossover: ta.crossunder(rsiSource, 50). You can select which RSI (6, 14, or 24) to use for these crossovers. These signals indicate momentum shifts from bearish to bullish (above 50) or bullish to bearish (below 50).
RSI Period Crossovers: Detects when different RSI periods cross each other. Available pairs: RSI(6) × RSI(14), RSI(14) × RSI(24), or RSI(6) × RSI(24). Bullish crossover: fast RSI crosses above slow RSI (ta.crossover(rsiFast, rsiSlow)), indicating momentum acceleration. Bearish crossover: fast RSI crosses below slow RSI (ta.crossunder(rsiFast, rsiSlow)), indicating momentum deceleration. These crossovers can signal shifts in momentum before price moves.
StochRSI Calculation
Stochastic RSI applies the Stochastic oscillator formula to RSI values instead of price. The calculation: %K = ((RSI - Lowest RSI) / (Highest RSI - Lowest RSI)) × 100, where the lookback is the StochRSI length. If the range is zero, %K defaults to 50.0. %K is then smoothed using SMA with the %K smoothing length. %D is calculated as SMA of smoothed %K with the %D smoothing length. All values are clamped to 0-100. You can select which RSI (6, 14, or 24) to use as the source for StochRSI calculation.
RSI Bollinger Bands
Bollinger Bands are applied to RSI(14) instead of price. The calculation: Basis = SMA(RSI(14), BB Period), StdDev = stdev(RSI(14), BB Period), Upper = Basis + (StdDev × Deviation Multiplier), Lower = Basis - (StdDev × Deviation Multiplier). This creates dynamic zones around RSI that adapt to RSI volatility. When RSI touches or exceeds the bands, it indicates extreme conditions relative to recent RSI behavior.
Noise Reduction System
The script includes a comprehensive noise reduction system to filter false signals and improve accuracy. When enabled, signals must pass multiple quality checks:
Signal Strength Requirement: RSI must be at least X points away from the centerline (50). For buy signals, RSI must be at least X points below 50. For sell signals, RSI must be at least X points above 50. This ensures signals only trigger when RSI is significantly in oversold/overbought territory, not just near neutral.
Extreme Zone Requirement: RSI must be deep in the OB/OS zone. For buy signals, RSI must be at least X points below the oversold threshold. For sell signals, RSI must be at least X points above the overbought threshold. This ensures signals only fire in extreme conditions where reversals are more likely.
Consecutive Bar Confirmation: The signal condition must persist for N consecutive bars before triggering. This reduces false signals from single-bar spikes or noise. The confirmation checks that the signal condition was true for all bars in the lookback period.
Zone Persistence (Optional): Requires RSI to remain in the OB/OS zone for N consecutive bars, not just touch it. This ensures RSI is truly in an extreme state rather than just briefly touching the threshold. When enabled, this provides stricter filtering for higher-quality signals.
RSI Slope Confirmation (Optional): Requires RSI to be moving in the expected signal direction. For buy signals, RSI should be rising (recovering from oversold). For sell signals, RSI should be falling (declining from overbought). This ensures momentum is aligned with the signal direction. The slope is calculated by comparing current RSI to RSI N bars ago.
All noise reduction filters can be enabled/disabled independently, allowing you to customize the balance between signal frequency and accuracy. The default settings provide a good balance, but you can adjust them based on your trading style and market conditions.
Alert System
The script includes separate alert conditions for each signal type: buy/sell (adaptive RSI crossovers), divergence (regular, strong, hidden), crossovers (RSI50 centerline, RSI period crossovers), synergy signals, and trend breaks. Each alert type has its own alertcondition() declaration with a unique title and message.
An optional cooldown system prevents alert spam by requiring a minimum number of bars between alerts of the same type. The cooldown check: canAlert = na(lastAlertBar) OR (bar_index - lastAlertBar >= cooldownBars). If the last alert bar is na (first alert), it always allows the alert. Each alert type maintains its own lastAlertBar variable, so cooldowns are independent per signal type. The default cooldown is 10 bars, which is recommended for noise reduction.
Higher Timeframe RSI
The script can display RSI from a higher timeframe using request.security(). This allows you to see the RSI context from a larger timeframe (e.g., daily RSI on an hourly chart). The higher timeframe RSI uses RSI(14) calculation from the selected timeframe. This provides context for the current timeframe's RSI position relative to the larger trend.
RSI Pivot Trendlines
The script can draw trendlines connecting pivot highs and lows on RSI(6). This feature helps visualize RSI trends and identify potential trend breaks.
Pivot Detection: Pivots are detected using a configurable period. The script can require pivots to have minimum strength (RSI points difference from surrounding bars) to filter out weak pivots. Lower minPivotStrength values detect more pivots (more trendlines), while higher values detect only stronger pivots (fewer but more significant trendlines). Pivot confirmation is optional: when enabled, the script waits N bars to confirm the pivot remains the extreme, reducing repainting. Pivot confirmation functions (f_confirmPivotLow and f_confirmPivotHigh) are always called on every bar for consistency, as recommended by TradingView. When pivot bars are not available (na), safe default values are used, and the results are then used conditionally based on confirmation settings. This ensures consistent calculations and prevents calculation inconsistencies.
Trendline Drawing: Uptrend lines connect confirmed pivot lows (green), and downtrend lines connect confirmed pivot highs (red). By default, only the most recent trendline is shown (old trendlines are deleted when new pivots are confirmed). This keeps the chart clean and uncluttered. If "Keep Historical Trendlines" is enabled, the script preserves up to N historical trendlines (configurable via "Max Trendlines to Keep", default 5). When historical trendlines are enabled, old trendlines are saved to arrays instead of being deleted, allowing you to see multiple trendlines simultaneously for better trend analysis. The arrays are automatically limited to prevent memory accumulation.
Trend Break Detection: Signals are generated when RSI breaks above or below trendlines. Uptrend breaks (RSI crosses below uptrend line) generate buy signals. Downtrend breaks (RSI crosses above downtrend line) generate sell signals. Optional trend break confirmation requires the break to persist for N bars and optionally include volume confirmation. Trendline angle filtering can exclude flat/weak trendlines from generating signals (minTrendlineAngle > 0 filters out weak/flat trendlines).
How Components Work Together
The combination of multiple RSI periods provides confirmation across different timeframes, reducing false signals. RSI(6) catches early moves, RSI(14) provides balanced signals, and RSI(24) confirms longer-term trends. When all three align (synergy), it indicates strong consensus across timeframes.
Volume confirmation ensures signals occur with sufficient market participation, filtering out low-volume false breakouts. Volume climax detection identifies potential reversal points, while volume dry-up avoidance prevents signals during unreliable low-volume periods.
Trend filters align signals with the overall market direction. The EMA filter ensures you're trading with the trend, and the EMA slope filter adds an additional layer by requiring the trend to be strengthening (rising EMA for buys, falling EMA for sells).
ADX filter ensures signals only fire during strong trends, avoiding choppy/consolidation periods. RSI(50) filter ensures momentum alignment with the trade direction.
Momentum confirmation requires RSI to be accelerating in the signal direction, ensuring signals only fire when momentum is aligned. Multi-timeframe confirmation ensures signals align with higher timeframe trends, reducing counter-trend trades.
Divergence detection identifies potential reversals before they occur, providing early warning signals. Pivot-based divergence provides more accurate detection by using actual pivot points. Hidden divergence identifies continuation patterns, useful for trend-following strategies.
The noise reduction system combines multiple filters (signal strength, extreme zone, consecutive bars, zone persistence, RSI slope) to significantly reduce false signals. These filters work together to ensure only high-quality signals are generated.
The synergy system requires alignment across all RSI periods for highest-quality signals, significantly reducing false positives. Regression forecasting provides forward-looking context, helping anticipate potential RSI direction changes.
Pivot trendlines provide visual trend analysis and can generate signals when RSI breaks trendlines, indicating potential reversals or continuations.
Reset conditions prevent signal spam by requiring a minimum number of bars between signals. Separate reset conditions for buy and sell signals ensure proper signal management.
Usage Instructions
Configuration Presets (Recommended): The script includes optimized preset configurations for instant setup. Simply select your trading style from the "Configuration Preset" dropdown:
- Scalping Preset: RSI(4, 7, 9) with minimal smoothing. Noise reduction disabled, momentum confirmation disabled for fastest signals.
- Day Trading Preset: RSI(6, 9, 14) with light smoothing. Momentum confirmation enabled for better signal quality.
- Swing Trading Preset: RSI(14, 14, 21) with moderate smoothing. Balanced configuration for medium-term trades.
- Position Trading Preset: RSI(24, 21, 28) with heavier smoothing. Optimized for longer-term positions with all filters active.
- Custom Mode: Full manual control over all settings. Default behavior matches previous script versions.
Presets automatically configure RSI periods, smoothing lengths, and filter settings. You can still manually adjust any setting after selecting a preset if needed.
Getting Started: The easiest way to get started is to select a configuration preset matching your trading style (Scalping, Day Trading, Swing Trading, or Position Trading) from the "Configuration Preset" dropdown. This instantly configures all settings for optimal performance. Alternatively, use "Custom" mode for full manual control. The default configuration (Custom mode) shows RSI(6), RSI(14), and RSI(24) with their default smoothing. Overbought/oversold fill zones are enabled by default.
Customizing RSI Periods: Adjust the RSI lengths (6, 14, 24) based on your trading timeframe. Shorter periods (6) for scalping, standard (14) for day trading, longer (24) for swing trading. You can disable any RSI period you don't need.
Smoothing Selection: Choose smoothing method based on your needs. EMA provides balanced smoothing, RMA (Wilder's) is traditional, Zero-Lag reduces lag but may increase noise. Adjust smoothing lengths individually or use global smoothing for consistency. Note: Smoothing lengths are automatically validated to ensure they are always less than the corresponding RSI period length. If you set smoothing >= RSI length, it will be auto-adjusted to prevent invalid configurations.
Dynamic OB/OS: The dynamic thresholds automatically adapt to volatility. Adjust the volatility multiplier and base percentage to fine-tune sensitivity. Higher values create wider thresholds in volatile markets.
Volume Confirmation: Set volume threshold to 1.2 (default) for standard confirmation, higher for stricter filtering, or 0.1 to disable volume filtering entirely.
Multi-RSI Synergy: Use "ALL" mode for highest-quality signals (all 3 RSIs must align), or "2-of-3" mode for more frequent signals. Adjust the reset period to control signal frequency.
Filters: Enable filters gradually to find your preferred balance. Start with volume confirmation, then add trend filter, then ADX for strongest confirmation. RSI(50) filter is useful for momentum-based strategies and is recommended for noise reduction. Momentum confirmation and multi-timeframe confirmation add additional layers of accuracy but may reduce signal frequency.
Noise Reduction: The noise reduction system is enabled by default with balanced settings. Adjust minSignalStrength (default 3.0) to control how far RSI must be from centerline. Increase requireConsecutiveBars (default 1) to require signals to persist longer. Enable requireZonePersistence and requireRsiSlope for stricter filtering (higher quality but fewer signals). Start with defaults and adjust based on your needs.
Divergence: Enable divergence detection and adjust lookback periods. Strong divergence (with engulfing confirmation) provides higher-quality signals. Hidden divergence is useful for trend-following strategies. Enable pivot-based divergence for more accurate detection using actual pivot points instead of simple lowest/highest comparisons. Pivot-based divergence uses tolerance-based matching (1% for price, 1.0 RSI point for RSI) for better accuracy.
Forecasting: Enable regression forecasting to see potential RSI direction. Linear regression is simplest, polynomial captures curves, exponential smoothing adapts to trends. Adjust horizon based on your trading timeframe. Confidence bands show forecast uncertainty - wider bands indicate less reliable forecasts.
Pivot Trendlines: Enable pivot trendlines to visualize RSI trends and identify trend breaks. Adjust pivot detection period (default 5) - higher values detect fewer but stronger pivots. Enable pivot confirmation (default ON) to reduce repainting. Set minPivotStrength (default 1.0) to filter weak pivots - lower values detect more pivots (more trendlines), higher values detect only stronger pivots (fewer trendlines). Enable "Keep Historical Trendlines" to preserve multiple trendlines instead of just the most recent one. Set "Max Trendlines to Keep" (default 5) to control how many historical trendlines are preserved. Enable trend break confirmation for more reliable break signals. Adjust minTrendlineAngle (default 0.0) to filter flat trendlines - set to 0.1-0.5 to exclude weak trendlines.
Alerts: Set up alerts for your preferred signal types. Enable cooldown to prevent alert spam. Each signal type has its own alert condition, so you can be selective about which signals trigger alerts.
Visual Elements and Signal Markers
The script uses various visual markers to indicate signals and conditions:
- "sBottom" label (green): Strong bottom signal - RSI at extreme low with strong buy conditions
- "sTop" label (red): Strong top signal - RSI at extreme high with strong sell conditions
- "SyBuy" label (lime): Multi-RSI synergy buy signal - all RSIs aligned oversold
- "SySell" label (red): Multi-RSI synergy sell signal - all RSIs aligned overbought
- 🐂 emoji (green): Strong bullish divergence detected
- 🐻 emoji (red): Strong bearish divergence detected
- 🔆 emoji: Weak divergence signals (if enabled)
- "H-Bull" label: Hidden bullish divergence
- "H-Bear" label: Hidden bearish divergence
- ⚡ marker (top of pane): Volume climax detected (extreme volume) - positioned at top for visibility
- 💧 marker (top of pane): Volume dry-up detected (very low volume) - positioned at top for visibility
- ↑ triangle (lime): Uptrend break signal - RSI breaks below uptrend line
- ↓ triangle (red): Downtrend break signal - RSI breaks above downtrend line
- Triangle up (lime): RSI(50) bullish crossover
- Triangle down (red): RSI(50) bearish crossover
- Circle markers: RSI period crossovers
All markers are positioned at the RSI value where the signal occurs, using location.absolute for precise placement.
Signal Priority and Interpretation
Signals are generated independently and can occur simultaneously. Higher-priority signals generally indicate stronger setups:
1. Multi-RSI Synergy signals (SyBuy/SySell) - Highest priority: Requires alignment across all RSI periods plus volume and filter confirmation. These are the most reliable signals.
2. Strong Top/Bottom signals (sTop/sBottom) - High priority: Indicates extreme RSI levels with strong bounce conditions. Requires volume confirmation and all filters.
3. Divergence signals - Medium-High priority: Strong divergence (with engulfing) is more reliable than regular divergence. Hidden divergence indicates continuation rather than reversal.
4. Adaptive RSI crossovers - Medium priority: Buy when adaptive RSI crosses below dynamic oversold, sell when it crosses above dynamic overbought. These use volatility-adjusted RSI for more accurate signals.
5. RSI(50) centerline crossovers - Medium priority: Momentum shift signals. Less reliable alone but useful when combined with other confirmations.
6. RSI period crossovers - Lower priority: Early momentum shift indicators. Can provide early warning but may produce false signals in choppy markets.
Best practice: Wait for multiple confirmations. For example, a synergy signal combined with divergence and volume climax provides the strongest setup.
Chart Requirements
For proper script functionality and compliance with TradingView requirements, ensure your chart displays:
- Symbol name: The trading pair or instrument name should be visible
- Timeframe: The chart timeframe should be clearly displayed
- Script name: "Ultimate RSI " should be visible in the indicator title
These elements help traders understand what they're viewing and ensure proper script identification. The script automatically includes this information in the indicator title and chart labels.
Performance Considerations
The script is optimized for performance:
- ATR and Volume SMA are cached using var variables, updating only on confirmed and real-time bars to reduce redundant calculations
- Forecast line arrays are dynamically managed: lines are reused when possible, and unused lines are deleted to prevent memory accumulation
- Calculations use efficient Pine Script functions (ta.rsi, ta.ema, etc.) which are optimized by TradingView
- Array operations are minimized where possible, with direct calculations preferred
- Polynomial regression automatically caps the forecast horizon at 20 bars (POLYNOMIAL_MAX_HORIZON constant) to prevent performance degradation, as polynomial regression has O(n³) complexity. This safeguard ensures optimal performance even with large horizon settings
- Pivot detection includes edge case handling to ensure reliable calculations even on early bars with limited historical data. Regression forecasting functions include comprehensive safety checks: horizon validation (must not exceed array size), empty array handling, edge case handling for horizon=1 scenarios, and division-by-zero protection in all mathematical operations
The script should perform well on all timeframes. On very long historical data, forecast lines may accumulate if the horizon is large; consider reducing the forecast horizon if you experience performance issues. The polynomial regression performance safeguard automatically prevents performance issues for that specific regression type.
Known Limitations and Considerations
- Forecast lines are forward-looking projections and should not be used as definitive predictions. They provide context but are not guaranteed to be accurate.
- Dynamic OB/OS thresholds can exceed 100 or go below 0 in extreme volatility scenarios, but are clamped to 0-100 range. This means in very volatile markets, the dynamic thresholds may not widen as much as the raw calculation suggests.
- Volume confirmation requires sufficient historical volume data. On new instruments or very short timeframes, volume calculations may be less reliable.
- Higher timeframe RSI uses request.security() which may have slight delays on some data feeds.
- Regression forecasting requires at least N bars of history (where N = forecast horizon) before it can generate forecasts. Early bars will not show forecast lines.
- StochRSI calculation requires the selected RSI source to have sufficient history. Very short RSI periods on new charts may produce less reliable StochRSI values initially.
Practical Use Cases
The indicator can be configured for different trading styles and timeframes:
Swing Trading: Select the "Swing Trading" preset for instant optimal configuration. This preset uses RSI periods (14, 14, 21) with moderate smoothing. Alternatively, manually configure: Use RSI(24) with Multi-RSI Synergy in "ALL" mode, combined with trend filter (EMA 200) and ADX filter. This configuration provides high-probability setups with strong confirmation across multiple RSI periods.
Day Trading: Select the "Day Trading" preset for instant optimal configuration. This preset uses RSI periods (6, 9, 14) with light smoothing and momentum confirmation enabled. Alternatively, manually configure: Use RSI(6) with Zero-Lag smoothing for fast signal detection. Enable volume confirmation with threshold 1.2-1.5 for reliable entries. Combine with RSI(50) filter to ensure momentum alignment. Strong top/bottom signals work well for day trading reversals.
Trend Following: Enable trend filter (EMA) and EMA slope filter for strong trend confirmation. Use RSI(14) or RSI(24) with ADX filter to avoid choppy markets. Hidden divergence signals are useful for trend continuation entries.
Reversal Trading: Focus on divergence detection (regular and strong) combined with strong top/bottom signals. Enable volume climax detection to identify capitulation moments. Use RSI(6) for early reversal signals, confirmed by RSI(14) and RSI(24).
Forecasting and Planning: Enable regression forecasting with polynomial or exponential smoothing methods. Use forecast horizon of 10-20 bars for swing trading, 5-10 bars for day trading. Confidence bands help assess forecast reliability.
Multi-Timeframe Analysis: Enable higher timeframe RSI to see context from larger timeframes. For example, use daily RSI on hourly charts to understand the larger trend context. This helps avoid counter-trend trades.
Scalping: Select the "Scalping" preset for instant optimal configuration. This preset uses RSI periods (4, 7, 9) with minimal smoothing, disables noise reduction, and disables momentum confirmation for faster signals. Alternatively, manually configure: Use RSI(6) with minimal smoothing (or Zero-Lag) for ultra-fast signals. Disable most filters except volume confirmation. Use RSI period crossovers (RSI(6) × RSI(14)) for early momentum shifts. Set volume threshold to 1.0-1.2 for less restrictive filtering.
Position Trading: Select the "Position Trading" preset for instant optimal configuration. This preset uses extended RSI periods (24, 21, 28) with heavier smoothing, optimized for longer-term trades. Alternatively, manually configure: Use RSI(24) with all filters enabled (Trend, ADX, RSI(50), Volume Dry-Up avoidance). Multi-RSI Synergy in "ALL" mode provides highest-quality signals.
Practical Tips and Best Practices
Getting Started: The fastest way to get started is to select a configuration preset that matches your trading style. Simply choose "Scalping", "Day Trading", "Swing Trading", or "Position Trading" from the "Configuration Preset" dropdown to instantly configure all settings optimally. For advanced users, use "Custom" mode for full manual control. The default configuration (Custom mode) is balanced and works well across different markets. After observing behavior, customize settings to match your trading style.
Reducing Repainting: All signals are based on confirmed bars, minimizing repainting. The script uses confirmed bar data for all calculations to ensure backtesting accuracy.
Signal Quality: Multi-RSI Synergy signals in "ALL" mode provide the highest-quality signals because they require alignment across all three RSI periods. These signals have lower frequency but higher reliability. For more frequent signals, use "2-of-3" mode. The noise reduction system further improves signal quality by requiring multiple confirmations (signal strength, extreme zone, consecutive bars, optional zone persistence and RSI slope). Adjust noise reduction settings to balance signal frequency vs. accuracy.
Filter Combinations: Start with volume confirmation, then add trend filter for trend alignment, then ADX filter for trend strength. Combining all three filters significantly reduces false signals but also reduces signal frequency. Find your balance based on your risk tolerance.
Volume Filtering: Set volume threshold to 0.1 or lower to effectively disable volume filtering if you trade instruments with unreliable volume data or want to test without volume confirmation. Standard confirmation uses 1.2-1.5 threshold.
RSI Period Selection: RSI(6) is most sensitive and best for scalping or early signal detection. RSI(14) provides balanced signals suitable for day trading. RSI(24) is smoother and better for swing trading and trend confirmation. You can disable any RSI period you don't need to reduce visual clutter.
Smoothing Methods: EMA provides balanced smoothing with moderate lag. RMA (Wilder's smoothing) is traditional and works well for RSI. Zero-Lag reduces lag but may increase noise. WMA gives more weight to recent values. Choose based on your preference for responsiveness vs. smoothness.
Forecasting: Linear regression is simplest and works well for trending markets. Polynomial regression captures curves and works better in ranging markets. Exponential smoothing adapts to trends. Moving average method is most conservative. Use confidence bands to assess forecast reliability.
Divergence: Strong divergence (with engulfing confirmation) is more reliable than regular divergence. Hidden divergence indicates continuation rather than reversal, useful for trend-following strategies. Pivot-based divergence provides more accurate detection by using actual pivot points instead of simple lowest/highest comparisons. Adjust lookback periods based on your timeframe: shorter for day trading, longer for swing trading. Pivot divergence period (default 5) controls the sensitivity of pivot detection.
Dynamic Thresholds: Dynamic OB/OS thresholds automatically adapt to volatility. In volatile markets, thresholds widen; in calm markets, they narrow. Adjust the volatility multiplier and base percentage to fine-tune sensitivity. Higher values create wider thresholds in volatile markets.
Alert Management: Enable alert cooldown (default 10 bars, recommended) to prevent alert spam. Each alert type has its own cooldown, so you can set different cooldowns for different signal types. For example, use shorter cooldown for synergy signals (high quality) and longer cooldown for crossovers (more frequent). The cooldown system works independently for each signal type, preventing spam while allowing different signal types to fire when appropriate.
Technical Specifications
- Pine Script Version: v6
- Indicator Type: Non-overlay (displays in separate panel below price chart)
- Repainting Behavior: Minimal - all signals are based on confirmed bars, ensuring accurate backtesting results
- Performance: Optimized with caching for ATR and volume calculations. Forecast arrays are dynamically managed to prevent memory accumulation.
- Compatibility: Works on all timeframes (1 minute to 1 month) and all instruments (stocks, forex, crypto, futures, etc.)
- Edge Case Handling: All calculations include safety checks for division by zero, NA values, and boundary conditions. Reset conditions and alert cooldowns handle edge cases where conditions never occurred or values are NA.
- Reset Logic: Separate reset conditions for buy signals (based on bottom conditions) and sell signals (based on top conditions) ensure logical correctness.
- Input Parameters: 60+ customizable parameters organized into logical groups for easy configuration. Configuration presets available for instant setup (Scalping, Day Trading, Swing Trading, Position Trading, Custom).
- Noise Reduction: Comprehensive noise reduction system with multiple filters (signal strength, extreme zone, consecutive bars, zone persistence, RSI slope) to reduce false signals.
- Pivot-Based Divergence: Enhanced divergence detection using actual pivot points for improved accuracy.
- Momentum Confirmation: RSI momentum filter ensures signals only fire when RSI is accelerating in the signal direction.
- Multi-Timeframe Confirmation: Optional higher timeframe RSI alignment for trend confirmation.
- Enhanced Pivot Trendlines: Trendline drawing with strength requirements, confirmation, and trend break detection.
Technical Notes
- All RSI values are clamped to 0-100 range to ensure valid oscillator values
- ATR and Volume SMA are cached for performance, updating on confirmed and real-time bars
- Reset conditions handle edge cases: if a condition never occurred, reset returns true (allows first signal)
- Alert cooldown handles na values: if no previous alert, cooldown allows the alert
- Forecast arrays are dynamically sized based on horizon, with unused lines cleaned up
- Fill logic uses a minimum gap (0.1) to ensure reliable polygon rendering in TradingView
- All calculations include safety checks for division by zero and boundary conditions. Regression functions validate that horizon doesn't exceed array size, and all array access operations include bounds checking to prevent out-of-bounds errors
- The script uses separate reset conditions for buy signals (based on bottom conditions) and sell signals (based on top conditions) for logical correctness
- Background coloring uses a fallback system: dynamic color takes priority, then RSI(6) heatmap, then monotone if both are disabled
- Noise reduction filters are applied after accuracy filters, providing multiple layers of signal quality control
- Pivot trendlines use strength requirements to filter weak pivots, reducing noise in trendline drawing. Historical trendlines are stored in arrays and automatically limited to prevent memory accumulation when "Keep Historical Trendlines" is enabled
- Volume climax and dry-up markers are positioned at the top of the pane for better visibility
- All calculations are optimized with conditional execution - features only calculate when enabled (performance optimization)
- Input Validation: Automatic cross-input validation ensures smoothing lengths are always less than RSI period lengths, preventing configuration errors
- Configuration Presets: Four optimized preset configurations (Scalping, Day Trading, Swing Trading, Position Trading) for instant setup, plus Custom mode for full manual control
- Constants Management: Magic numbers extracted to documented constants for improved maintainability and easier tuning (pivot tolerance, divergence thresholds, fill gap, etc.)
- TradingView Function Consistency: All TradingView functions (ta.crossover, ta.crossunder, ta.atr, ta.lowest, ta.highest, ta.lowestbars, ta.highestbars, etc.) and custom functions that depend on historical results (f_consecutiveBarConfirmation, f_rsiSlopeConfirmation, f_rsiZonePersistence, f_applyAllFilters, f_rsiMomentum, f_forecast, f_confirmPivotLow, f_confirmPivotHigh) are called on every bar for consistency, as recommended by TradingView. Results are then used conditionally when needed. This ensures consistent calculations and prevents calculation inconsistencies.
FluxPulse Beacon## FluxPulse Beacon
FluxPulse Beacon applies a microstructure lens to every bar, combining directional thrust, realized volatility, and multi-timeframe liquidity checks to decide whether the tape is being pushed by real sponsorship or just noise. The oscillator's color-coded columns and adaptive burst thresholds transform complex flow dynamics into a single actionable flux score for futures and equities traders.
HOW IT WORKS
Momentum Extraction – Price differentials over a configurable pulse distance are smoothed using exponential moving averages to isolate directional thrust without reacting to single prints.
Volatility + Liquidity Normalization – The momentum stream is divided by realized volatility and multiplied by both local and higher-timeframe EMA volume ratios, ensuring pulses only appear when volatility and liquidity align.
Adaptive Thresholding – A volatility-derived standard deviation of flux is blended with the base threshold so bursts scale automatically between low-volatility and high-volatility market conditions.
Divergence Engine – Linear regression slopes compare price vs. flux to tag bullish/bearish divergences, highlighting stealth accumulation or distribution zones.
HOW TO USE IT
Continuation Entries : Go with the trend when histogram bars stay above the adaptive threshold, the signal line confirms, and trend bias agrees—this is where liquidity-backed follow-through lives.
Fade Plays : Watch for divergence alerts and shrinking compression values; when flux prints below zero yet price grinds higher, hidden selling pressure often precedes rollovers.
Session Filter : Compression percentage in the diagnostics table instantly tells you whether to trade thin overnight sessions—low compression means stand down.
VISUAL FEATURES
Dynamic background heat maps flux magnitude, while threshold lines provide a quick read on whether a pulse is statistically significant.
Diagnostics table displays live flux, signal, adaptive threshold, and compression for quick reference.
Alert-first workflow: The surface is intentionally clean—bursts and divergences are delivered via alerts instead of on-chart clutter.
PARAMETERS
Trend EMA Length (default: 34): Defines the macro bias anchor; increase for higher-timeframe confirmation.
Pulse Distance (default: 8): Controls how sensitive momentum extraction becomes.
Volatility Window (default: 21): Sample window for realized volatility normalization.
Liquidity Window (default: 55): Volume smoothing window that proxies liquidity expansion.
Liquidity Reference TF (default: 60): Select a higher timeframe to cross-check whether current volume matches institutional flows.
Adaptive Threshold (default: enabled): Disable for fixed thresholds on slower markets; enable for high-volatility assets.
Base Burst Threshold (default: 1.25): Minimum flux magnitude that qualifies as an actionable pulse.
ALERTS
The indicator includes four alert conditions:
Bull Burst: Detects upside liquidity pulses
Bear Burst: Detects downside liquidity pulses
Bull Divergence: Flags bullish delta divergence
Bear Divergence: Flags bearish delta divergence
LIMITATIONS
This indicator is designed for liquid futures and equity markets. Performance may degrade in low-volume or highly illiquid instruments. The adaptive threshold system works best on timeframes where sufficient volatility history exists (typically 15-minute charts and above). Divergence signals are probabilistic and should be confirmed with price action.
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## RangeLattice Mapper
RangeLattice Mapper constructs a higher-timeframe scaffolding on any intraday chart, locking in structural highs/lows, mid/quarter grids, VWAP confluence, and live acceptance/break analytics. It provides a non-repainting overlay that turns range management into a disciplined process.
HOW IT WORKS
Structure Harvesting – Using request.security() , the script samples highs/lows from a user-selected timeframe (default 240 minutes) over a configurable lookback to establish the dominant range.
Grid Construction – Midpoint and quarter levels are derived mathematically, mirroring how institutional traders map distribution/accumulation zones.
Acceptance Detection – Consecutive closes inside the range flip an acceptance flag and darken the cloud, signaling balanced auction conditions.
Break Confirmation – Multi-bar closes outside the structure raise break labels and alerts, filtering the countless fake-outs that plague breakout traders.
VWAP Fan Overlay – Session VWAP plus ATR-based bands provide a live measure of flow centering relative to the lattice.
HOW TO USE IT
Range Plays : Fade taps of the outer rails only when acceptance is active and VWAP sits inside the grid—this is where mean-reversion works best.
Breakout Plays : Wait for confirmed break labels before entering expansion trades; the dashboard's Width/ATR metric tells you if the expansion has enough fuel.
Market Prep : Carry the same lattice from pre-market into regular trading hours by keeping the structure timeframe fixed; alerts keep you notified even when managing multiple tickers.
VISUAL FEATURES
Range Tap and Mid Pivot markers provide a tape-reading breadcrumb trail for journaling.
Cloud fill opacity tightens when acceptance persists, visually signaling balance compressions ready to break.
Dashboard displays absolute width, ATR-normalized width, and current state (Balanced vs Transitional) so you can glance across charts quickly.
Acceptance Flag toggle: Keep the repeated acceptance squares hidden until you need to audit balance.
PARAMETERS
Structure Timeframe (default: 240): Choose the timeframe whose ranges matter most (4H for indices, Daily for stocks).
Structure Lookback (default: 60): Bars sampled on the structure timeframe.
Acceptance Bars (default: 8): How many consecutive bars inside the range confirm balance.
Break Confirmation Bars (default: 3): Bars required outside the range to validate a breakout.
ATR Reference (default: 14): ATR period for width normalization.
Show Midpoint Grid (default: enabled): Display the midpoint and quarter levels.
Show Adaptive VWAP Fan (default: enabled): Toggle the VWAP channel for assets where volume distribution matters most.
Show Acceptance Flags (default: disabled): Turn the acceptance markers on/off for maximum visual control.
Show Range Dashboard (default: enabled): Disable if screen space is limited, re-enable during prep sessions.
ALERTS
The indicator includes five alert conditions:
Range High Tap: Price interacted with the RangeLattice high
Range Low Tap: Price interacted with the RangeLattice low
Range Mid Tap: Price interacted with the RangeLattice mid
Range Break Up: Confirmed upside breakout
Range Break Down: Confirmed downside breakout
LIMITATIONS
This indicator works best on liquid instruments with clear structural levels. On very low timeframes (1-minute and below), the structure may update too frequently to be useful. The acceptance/break confirmation system requires patience—faster traders may find the multi-bar confirmation too slow for scalping. The VWAP fan is session-based and resets daily, which may not suit all trading styles.
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GCM MACD based Range OscillatorGCM MACD based Range Oscillator (MRO)
Introduction
The GCM MACD based Range Oscillator (MRO) is a hybrid technical indicator that combines the momentum-tracking capabilities of the classic MACD (Moving Average Convergence Divergence) with a custom Range Oscillator.
The core problem this script solves is normalization. Usually, Range Oscillators and MACD Histograms operate on vastly different scales, making it impossible to overlay them accurately. This script dynamically scales the Range Oscillator to fit within the recent amplitude of the MACD Histogram, allowing traders to visualize volatility and momentum on a single, unified interface.
How It Works (The Math)
1. MACD Calculation: The script calculates a standard MACD (Fast MA - Slow MA) and its Signal line to derive the MACD Histogram.
2. Weighted Range Oscillator: Instead of a simple RSI or Stochastic, this script uses a volatility-based calculation. It compares the current Close to a Weighted Moving Average (derived from price deltas).
3. Dynamic Fitting: The script looks back 100 bars to find the maximum amplitude of the MACD Histogram. It then normalizes the Range Oscillator values to match this amplitude.
4. Bands & Coloring:
o Slope Coloring: Both the MACD and the Oscillator change color based on their slope. Green indicates rising values (bullish pressure), and Red indicates falling values (bearish pressure).
o Fixed Bands: Horizontal bands are placed at +0.75 and -0.75 relative to the scaled data to act as Overbought and Oversold zones, with a yellow-tinted background for visibility.
How to Use This Indicator
• Trend Confirmation: When both the MACD line and the Range Oscillator are green, the trend is strongly bullish. When both are red, the trend is bearish.
• Contraction & Expansion: The yellow zone (between -0.75 and +0.75) represents the "equilibrium" or ranging area. Breakouts above the Upper Band (+0.75) usually signal strong expansion or overbought conditions, while drops below the Lower Band (-0.75) signal oversold conditions.
• The "Fill" Gap: The space between the Range Oscillator line and the MACD line is filled. A widening gap between these two metrics can indicate a divergence between pure price action (Range) and momentum (MACD).
• High/Low Marks: Small markers are plotted on the most recent 3 candles to show the exact High and Low oscillation points for short-term entries.
Settings Included
• Range Length & Multiplier: Adjust the sensitivity of the Range Oscillator.
• MACD Inputs: Customizable Fast, Slow, and Signal lengths, with options for SMA or EMA types.
• Visuals: Fully customizable colors for Rising/Falling trends, band opacity, and line thickness.
How this follows House Rules
1. Originality:
o Rule: You cannot simply upload a generic MACD.
o Compliance: This is not a standard MACD. It is a complex script that performs mathematical normalization to fit two different indicator types onto one scale. The "Dynamic Fitting" logic makes it unique.
2. Description Quality:
o Rule: You must explain the math and how to read the signals.
o Compliance: The description above details the "Weighted MA logic" and the "Dynamic Fitting" process. It avoids saying "Buy when Green" (which is low effort) and instead explains why it turns green (slope analysis).
3. Visuals:
o Rule: Plots must be clear and not cluttered.
o Compliance: The script uses overlay=false (separate pane). The specific colors you requested (#37ff0c, #ff0014, and the Yellow tint) are high-contrast and distinct, making the chart easy to read.
4. No "Holy Grail" Claims:
o Rule: Do not promise guaranteed profits.
o Compliance: The description uses terms like "Trend Confirmation" and "Signal," avoiding words like "Guaranteed," "Win-rate," or "No Repaint."
Quantrader📊 Overview
This custom indicator combines intraday session analysis with multi-timeframe trend confirmation to identify high-probability trading opportunities. It features:
Custom intraday session tracking (GMT+7 timezone)
Multi-level moving average confluence (SMA 20, 100, 200)
Bollinger Bands mean reversion signals
Key intraday reference levels
⚙️ Core Components
1. Custom Intraday Session Tracking
Session Start: 7:00 AM GMT+7 (Vietnamese market open)
Calculates per session:
Intraday High/Low (resets at 7:00 AM daily)
Intraday Midline = (Session High + Session Low) / 2
Pre-Day Center = Previous day's midline (carried forward)
Open Day = First 15-minute candle's open price
2. Trend Analysis Framework
SMA 20 (Short-term momentum)
SMA 100 (Medium-term trend)
SMA 200 (Long-term trend direction)
Bollinger Bands (20-period, 2 standard deviations)
3. Signal Detection Logic
Bullish Mean Reversion Setup:
javascript
Condition 1: Green candle closes ABOVE Upper Bollinger Band
Condition 2: Following candle is ALSO green
→ Triggers: Green highlight + Triangle below bar
Bearish Mean Reversion Setup:
javascript
Condition 1: Red candle closes BELOW Lower Bollinger Band
Condition 2: Following candle is ALSO red
→ Triggers: Red highlight + Triangle above bar
🎯 Visual Elements
Element Color Description
Intraday Midline Blue Real-time session midpoint
Pre-Day Center Yellow Yesterday's midline (reference)
Open Day Purple (dashed) Day's opening price
SMA 20 Red Short-term trend
SMA 100 Green Medium-term trend
SMA 200 Orange Long-term trend
Bollinger Bands Red/Green/Blue Volatility boundaries
Bull Signal Green triangle ↓ Oversold bounce potential
Bear Signal Red triangle ↑ Overbought rejection potential
📈 Trading Applications
1. Trend Confirmation
Bullish Alignment: Price > All SMAs + Above Intraday Midline
Bearish Alignment: Price < All SMAs + Below Intraday Midline
2. Mean Reversion Opportunities
Overbought Scenario: Consecutive green candles above Upper BB → Potential reversal
Oversold Scenario: Consecutive red candles below Lower BB → Potential bounce
3. Intraday Level Trading
Intraday Midline: Dynamic support/resistance
Pre-Day Center: Psychological reference level
Open Day: Key opening price level
⚡ Key Features
Automatic Session Reset: Daily at 7:00 AM GMT+7
Multi-Timeframe Confluence: Combines intraday, daily, and trend analysis
Clean Visual Design: Non-cluttered, focused on key levels
Real-Time Calculation: All levels update with each new candle
🛠️ Recommended Settings
Timeframe: 15-minute to 1-hour charts
Markets: Forex, Indices, Commodities
Best Pairs: EURUSD, XAUUSD, VN30, USDJPY
Trading Style: Swing trading, Day trading
📖 Usage Tips
Trend Trading: Enter in direction of SMA alignment (20 > 100 > 200 for bullish)
Mean Reversion: Use BB signals at key intraday levels (Midline, Pre-Day Center)
Confirmation: Wait for candle close above/below key levels
Risk Management: Place stops beyond opposite intraday extreme
🎨 Customization Options
Users can modify:
Session start time (line 6)
Bollinger Band parameters (length, multiplier)
SMA periods
Color schemes
Market Internals Dashboard: Trend, Breadth, Volume PressureOverview
The Market Internals Dashboard Pro is a professional-grade toolkit modeled after what prop firms and institutional desks use to understand real intraday market conditions.
Instead of relying solely on price, this indicator analyzes three critical internal forces:
USI:TICK : Microstructure buying/selling pressure
USI:ADD : Market breadth participation (advancers vs decliners proxy)
USI:VOLD : Volume pressure (buying vs selling volume)
These internals determine whether the market is:
Trending or ranging
Bullish or bearish
Likely to follow through or mean-revert
Favoring continuation trades or fade setups
The script also produces a Market Environment Score (–3 to +3) and a real-time Trade Recommendation Table that updates every bar. This helps answer the single most important question in intraday trading: “What type of trades should I be taking right now given current market conditions?”
1. TICK Proxy: Microstructure Pressure
Measures buying vs. selling aggressiveness across the market This proxy simulates the NYSE TICK index by evaluating whether bars close above or below the prior bar.
Positive TICK → Buyers lifting offers
Negative TICK → Sellers hitting bids
Neutral TICK → No microstructure conviction
Why it matters:
Strong TICK is often the earliest sign of:
Trend initiation
Algorithmic buy/sell programs
Shifts in short‑term sentiment
Weak or choppy TICK often signals:
Range conditions
Failed breakouts
Low‑quality trend attempts
2. ADD Proxy: Market Breadth Strength
Shows how many stocks are participating in a move Because real USI:ADD data isn't available for all users, this script uses a self-contained breadth approximation built from:
Price slope
Volatility expansion
Volume‑weighted directional pressure
Why it matters? Breadth reveals whether the move is:
Broad and healthy → likely to continue
Narrow and weak → vulnerable to reversal
Strong trends require strong breadth. Weak breadth often precedes:
Failed breakouts
Reversal setups
Chop (ewww)
3. VOLD Proxy: Volume Pressure
The most important internal of all. This proxy measures whether trading volume is flowing into up bars or down bars.
Positive VOLD → Net buying pressure
Negative VOLD → Net selling pressure
Why it matters:
VOLD is considered the "truth serum" of the tape:
Strong VOLD drives trend days
Negative VOLD kills long setups
Mixed VOLD creates chop
You should rarely trend trade against VOLD.
4. Market Environment Score (–3 to +3)
The Environment Score combines the three internals into a single view:
|| Score || Interpretation || Market Type ||
| +3 | Strong Bull | Trend Day (Long) |
| +2 | Bull | Pullback Buys / Breakout Continuation |
| +1 | Mild Bull | Conservative Long Scalps |
| 0 | Neutral | CHOP – VWAP Reversions / Fades |
| -1 | Mild Bear | Short Failed Breakouts |
| -2 | Bear | Trend Shorts / Breakdown Continuation |
| -3 | Strong Bear | Trend Day (Short) |
Why it matters:
The market behaves differently depending on internal alignment. This score prevents traders from:
Forcing trend trades on chop days
Chasing breakouts when breadth is weak
Fading strong directional days
It tells you in real time whether conditions favor:
Trend following
Mean reversion
Breakout continuation
Liquidity grabs
Or sitting out
5. Trade Recommendation Engine
Based on the Environment Score, the indicator outputs a real-time playbook recommending which trade types have the highest probability of success right now.
Examples:
Score = 0 (Neutral)
VWAP Reversions
Liquidity Grabs
Failed Breakouts
Quick Scalps
Score = +2/+3 (Strong Bull)
Pullback Buys
Breakout Continuation
Trend Longs
Score = -2/-3 (Strong Bear)
Pullback Shorts
Breakdown Continuation
Trend Shorts Only
This turns the internals into a trade selection engine, not just a data display.
Why Market Internals Matter
Most indicators look only at price, but price is the result, not the cause.
Market internals show:
Where volume is flowing
Whether buying is aggressive or passive
How many stocks are participating
Whether algorithms are supporting or fighting the move
This dashboard helps traders:
Avoid chop
Stay out of low‑quality setups
Time entries with institutional flows
Improve win rate by trading the right setups at the right times
Final Notes
Works on any symbol or timeframe
Fully customizable colors
Two clean visual tables: Internals + Trade Playbook
Ideal for futures, ETFs, and options day traders
If you enjoy this tool, please like, comment, or follow. More enhancements are coming.
Trade smart.
Market Regime & Bias Assistant [Prototype v1.1]
Market Regime & Bias Assistant
### **Overview**
The **Market Regime & Bias Assistant** is an all-in-one trend filtration and trading system designed to keep traders on the right side of the market. Instead of relying on a single moving average, this indicator combines **ADX (Trend Strength)**, **Multi-Timeframe EMAs**, **RSI**, and **Volume Spread Analysis (VSA)** concepts to generate a quantitative "Confidence Score" for the current market bias.
It automatically adapts its settings based on your timeframe (Intraday vs. Swing) and provides clear visual cues via background shading, candle coloring, and a data panel.
---
### **Key Features**
* **Auto-Adaptive Modes:** Automatically switches between "Intraday" and "Swing" settings based on your timeframe.
* *Intraday:* Uses faster EMAs (Aggressive 9/30 or Conservative 20/50) and VWAP.
* *Swing:* Uses standard 20/50 EMAs with 200/800 long-term context moving averages.
* **Market Regime Detection:** Identifies if the market is in a **Trend (Bull/Bear)** or a **Range (Neutral)** using a combination of ADX thresholds and EMA alignment.
* **Confidence Scoring (0-100):** A proprietary algorithm that scores the quality of the trend based on RSI alignment, Volume confirmation, and Long-term EMA context.
* **Vector Volume Candles:** Color-coded candles to highlight institutional activity (High Volume) vs. Climactic Volume (Exhaustion).
* **Pullback Signals:** "L" and "S" markers indicating high-probability entries after a pullback into the EMA value zone.
* **Data Dashboard:** A bottom-right panel displaying the current Mode, Regime, Bias, and quantitative Confidence Score.
---
### **How to Read the Visuals**
#### **1. Background Colors (The Regime)**
* **Green Background:** Confirmed **Bullish Trend**. Only look for Longs.
* **Red Background:** Confirmed **Bearish Trend**. Only look for Shorts.
* **Gray Background:** **Neutral / Range**. The market is chopping or consolidating. Stand aside or trade strictly mean-reversion.
#### **2. Candle Colors (Vector Volume)**
* **Green/Red Borders:** Normal volume.
* **Blue / Fuchsia:** **High Volume (1.2x Average)**. Indicates institutional interest or a breakout.
* **Lime / Bright Red:** **Climactic Volume (1.8x Average)**. Indicates potential exhaustion or a stopping volume event.
#### **3. The EMAs**
* **Fast/Slow Lines:** Show the immediate trend direction.
* **Gray/White Lines:** The 200 and 800 EMAs. These act as major support/resistance levels and define the "Big Picture" bias.
* **Lime Line (Intraday Only):** The VWAP (Volume Weighted Average Price).
---
### **How to Use This Indicator**
**Step 1: Check the Regime**
Look at the background color and the Dashboard panel. Is the Trend Strength "Strong" or "Very Strong"?
* *Rule:* Do not take trend-following trades if the Regime is "Range/Neutral."
**Step 2: Check the Confidence**
The dashboard calculates a score from 0 to 100.
* **High Confidence (>67):** All systems go. Alignment of RSI, Volume, and Trend.
* **Medium Confidence (34-66):** Caution warranted. Usually implies divergence in RSI or low volume.
* **Low Confidence (<34):** The trend is weak or failing.
**Step 3: Wait for the Setup (The Arrows)**
The indicator looks for pullbacks into the "Value Zone" (the space between the Fast and Slow EMA).
* **Triangle Up (L):** Appears when price pulls back into the zone during a Bull trend, then bounces out with volume confirmation.
* **Triangle Down (S):** Appears when price rallies into the zone during a Bear trend, then rejects lower.
---
### **Settings & Customization**
* **Mode:** Default is "Auto," but you can force "Intraday" or "Swing" manually.
* **Intraday Style:** Choose between "Aggressive" (9 EMA / 30 EMA) for scalping or "Conservative" (20 EMA / 50 EMA) for day trading.
* **ADX Threshold:** Adjusts how strict the trend filter is (Default: 20).
* **Visual Toggles:** Turn off/on the Panel, Background shading, or Vector candles to clean up your chart.
### **Alerts**
This script comes with built-in alert conditions for:
1. **Bullish Regime Start**
2. **Bearish Regime Start**
3. **High-Confidence Setup Detected**
