Quantum Reversal Detector [JOAT]

Quantum Reversal Detector [JOAT] - Multi-Factor Reversal Probability Analysis

Introduction and Purpose

Quantum Reversal Detector is an open-source overlay indicator that combines multiple reversal detection methods into a unified probability-based framework. The core problem this indicator addresses is the unreliability of single-factor reversal signals. A price touching support means nothing without momentum confirmation; an RSI oversold reading means nothing without price structure context.

This indicator solves that by requiring multiple independent factors to align before generating reversal signals, then expressing the result as a probability score rather than a binary signal.

Why These Components Work Together

The indicator combines five analytical approaches, each addressing a different aspect of reversal detection:

1. RSI Extremes - Identifies momentum exhaustion (overbought/oversold)
2. MACD Crossovers - Confirms momentum direction change
3. Support/Resistance Proximity - Ensures price is at a significant level
4. Multi-Depth Momentum - Analyzes momentum across multiple timeframes
5. Statistical Probability - Quantifies reversal likelihood using Bayesian updating

These components are not randomly combined. Each filter catches reversals that others miss:

• RSI catches momentum exhaustion but misses structural reversals
  
• MACD catches momentum shifts but lags price action
  
• S/R proximity catches structural levels but ignores momentum
  
• Multi-depth momentum catches divergences across timeframes
  
• Probability scoring combines all factors into actionable confidence levels
  

How the Detection System Works

Step 1: Pattern Detection
The indicator first identifies potential reversal conditions:

// Check if price is at support/resistance
float lowestLow = ta.lowest(low, period)
float highestHigh = ta.highest(high, period)
bool atSupport = low <= lowestLow * 1.002
bool atResistance = high >= highestHigh * 0.998

// Check RSI conditions
float rsi = ta.rsi(close, 14)
bool oversold = rsi < 30
bool overbought = rsi > 70

// Check MACD crossover
float macd = ta.ema(close, 12) - ta.ema(close, 26)
float signal = ta.ema(macd, 9)
bool macdBullish = ta.crossover(macd, signal)
bool macdBearish = ta.crossunder(macd, signal)

// Combine for reversal detection
if atSupport and oversold and macdBullish
    bullishReversal := true

Step 2: Multi-Depth Momentum Analysis
The indicator calculates momentum across multiple periods to detect divergences:

calculateQuantumMomentum(series float price, simple int period, simple int depth) =>
    float totalMomentum = 0.0
    for i = 0 to depth - 1
        int currentPeriod = period * (i + 1)
        float momentum = ta.roc(price, currentPeriod)
        totalMomentum += momentum
    totalMomentum / depth

This creates a composite momentum reading that smooths out noise while preserving genuine momentum shifts.

Step 3: Bayesian Probability Calculation
The indicator uses Bayesian updating to calculate reversal probability:

bayesianProbability(series float priorProb, series float likelihood, series float evidence) =>
    float posterior = evidence > 0 ? (likelihood * priorProb) / evidence : priorProb
    math.min(math.max(posterior, 0.0), 1.0)

The prior probability starts at 50% and updates based on:

• RSI extreme readings increase likelihood
  
• MACD crossovers increase likelihood
  
• S/R proximity increases likelihood
  
• Momentum divergence increases likelihood
  

Step 4: Confidence Intervals
Using Monte Carlo simulation concepts, the indicator estimates price distribution:

monteCarloSimulation(series float price, series float volatility, simple int iterations) =>
    float sumPrice = 0.0
    float sumSqDiff = 0.0
    for i = 0 to iterations - 1
        float randomFactor = (i % 10 - 5) / 10.0
        float simulatedPrice = price + volatility * randomFactor
        sumPrice += simulatedPrice
    float avgPrice = sumPrice / iterations
    // Calculate standard deviation for confidence intervals

This provides 95% and 99% confidence bands around the current price.

Signal Classification

Signals are classified by confirmation level:

• Confirmed Reversal: Pattern detected for N consecutive bars (default 3)
  
• High Probability: Confirmed + Bayesian probability > 70%
  
• Ultra High Probability: High probability + PDF above average
  

Dashboard Information

The dashboard displays:

• Bayesian Probability - Updated reversal probability (0-100%)
  
• Quantum Momentum - Multi-depth momentum average
  
• RSI - Current RSI value with overbought/oversold status
  
• Volatility - Current ATR as percentage of price
  
• Reversal Signal - BULLISH, BEARISH, or NONE
  
• Divergence - Momentum divergence detection
  
• MACD - Current MACD histogram value
  
• S/R Zone - AT SUPPORT, AT RESISTANCE, or NEUTRAL
  
• 95% Confidence - Price range with 95% probability
  
• Bull/Bear Targets - ATR-based reversal targets
  

Visual Elements

• Quantum Bands - ATR-based upper and lower channels
  
• Probability Field - Circle layers showing probability distribution
  
• Confidence Bands - 95% and 99% confidence interval circles
  
• Reversal Labels - REV markers at confirmed reversals
  
• High Probability Markers - Star diamonds at high probability setups
  
• Reversal Zones - Boxes around confirmed reversal areas
  
• Divergence Markers - Triangles at momentum divergences
  

How to Use This Indicator

For Reversal Trading:
1. Wait for Bayesian Probability to exceed 70%
2. Confirm price is at S/R zone (dashboard shows AT SUPPORT or AT RESISTANCE)
3. Check that RSI is in extreme territory (oversold for longs, overbought for shorts)
4. Enter when REV label appears with high probability marker

For Risk Management:
1. Use the 95% confidence band as a stop-loss reference
2. Use Bull/Bear Targets for take-profit levels
3. Higher probability readings warrant larger position sizes

For Filtering False Signals:
1. Increase Confirmation Bars to require more consecutive signals
2. Only trade when probability exceeds 70%
3. Require divergence confirmation for highest conviction

Input Parameters

• Reversal Period (21) - Lookback for S/R and momentum calculations
  
• Quantum Depth (5) - Number of momentum layers for multi-depth analysis
  
• Confirmation Bars (3) - Consecutive bars required for confirmation
  
• Detection Sensitivity (1.2) - Band width and target multiplier
  
• Bayesian Probability (true) - Enable probability calculation
  
• Monte Carlo Simulation (true) - Enable confidence interval calculation
  
• Normal Distribution (true) - Enable PDF calculation
  
• Confidence Intervals (true) - Enable confidence bands
  

Timeframe Recommendations

• 1H-4H: Best for swing trading reversals
  
• Daily: Fewer but more significant reversal signals
  
• 15m-30m: More signals, requires higher probability threshold
  

Limitations

• Statistical concepts are simplified implementations for Pine Script
  
• Monte Carlo uses deterministic pseudo-random factors, not true randomness
  
• Bayesian probability uses simplified prior/likelihood model
  
• Reversal detection does not guarantee actual reversals will occur
  
• Confirmation bars add lag to signal generation
  

Open-Source and Disclaimer

This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. The source code is fully visible and can be studied to understand how each component works.

This indicator does not constitute financial advice. Reversal detection is probabilistic, not predictive. The probability scores represent statistical likelihood based on historical patterns, not guaranteed outcomes. Past performance does not guarantee future results. Always use proper risk management, position sizing, and stop-losses.

- Made with passion by officialjackofalltrades