OPEN-SOURCE SCRIPT
Updated EUR/USD Multi-Layer Statistical Regression Strategy
Strategy Overview
This advanced EUR/USD trading system employs a triple-layer linear regression framework with statistical validation and ensemble weighting. It combines short, medium, and long-term regression analyses to generate high-confidence directional signals while enforcing strict risk controls.
Core Components
Multi-Layer Regression Engine:
Parallel regression analysis across 3 customizable timeframes (short/medium/long)
Projects future price values using prediction horizons
Statistical significance filters (R-squared, correlation, slope thresholds)
Signal Validation System:
Lookback validation tests historical prediction accuracy
Ensemble weighting of layer signals (adjustable influence per timeframe)
Confidence scoring combining statistical strength, layer agreement, and validation accuracy
Risk Management:
Position sizing scaled by signal confidence (1%-100% of equity)
Daily loss circuit breaker (halts trading at user-defined threshold)
Forex-tailored execution (pip slippage, percentage-based commissions)
Visual Intelligence:
Real-time regression line plots (3 layered colors)
Projection markers for short-term forecasts
Background coloring for market bias indication
Comprehensive statistics dashboard (R-squared metrics, validation scores, P&L)
Key Parameters
Category Settings
Regression Short/Med/Long lengths (20/50/100 bars)
Statistics Min R² (0.65), Correlation (0.7), Slope (0.0001)
Validation 30-bar lookback, 10-bar projection
Risk Controls 50% position size, 12% daily loss limit, 75% confidence threshold
Trading Logic
Entries require:
Ensemble score > |0.5|
Confidence > threshold
Short & medium-term significance
Active daily loss limit not breached
Exits triggered by:
Opposite high-confidence signals
Daily loss limit violation (emergency exit)
The strategy blends quantitative finance techniques with practical trading safeguards, featuring a self-optimizing design where signal quality directly impacts position sizing. The visual dashboard provides real-time feedback on model performance and market conditions.
This advanced EUR/USD trading system employs a triple-layer linear regression framework with statistical validation and ensemble weighting. It combines short, medium, and long-term regression analyses to generate high-confidence directional signals while enforcing strict risk controls.
Core Components
Multi-Layer Regression Engine:
Parallel regression analysis across 3 customizable timeframes (short/medium/long)
Projects future price values using prediction horizons
Statistical significance filters (R-squared, correlation, slope thresholds)
Signal Validation System:
Lookback validation tests historical prediction accuracy
Ensemble weighting of layer signals (adjustable influence per timeframe)
Confidence scoring combining statistical strength, layer agreement, and validation accuracy
Risk Management:
Position sizing scaled by signal confidence (1%-100% of equity)
Daily loss circuit breaker (halts trading at user-defined threshold)
Forex-tailored execution (pip slippage, percentage-based commissions)
Visual Intelligence:
Real-time regression line plots (3 layered colors)
Projection markers for short-term forecasts
Background coloring for market bias indication
Comprehensive statistics dashboard (R-squared metrics, validation scores, P&L)
Key Parameters
Category Settings
Regression Short/Med/Long lengths (20/50/100 bars)
Statistics Min R² (0.65), Correlation (0.7), Slope (0.0001)
Validation 30-bar lookback, 10-bar projection
Risk Controls 50% position size, 12% daily loss limit, 75% confidence threshold
Trading Logic
Entries require:
Ensemble score > |0.5|
Confidence > threshold
Short & medium-term significance
Active daily loss limit not breached
Exits triggered by:
Opposite high-confidence signals
Daily loss limit violation (emergency exit)
The strategy blends quantitative finance techniques with practical trading safeguards, featuring a self-optimizing design where signal quality directly impacts position sizing. The visual dashboard provides real-time feedback on model performance and market conditions.
Release Notes
EUR/USD Multi-Layer Statistical Regression Strategy v2📊 Strategy Overview
This advanced algorithmic trading strategy employs multi-layer statistical regression analysis combined with adaptive machine learning techniques to identify high-probability trading opportunities in the EUR/USD forex pair. The strategy uses three distinct regression layers operating on different timeframes to create a robust ensemble prediction system.
🔬 Core Methodology
Multi-Layer Regression System
Short-Term Layer (20 periods): Captures immediate price momentum and micro-trends
Medium-Term Layer (50 periods): Identifies intermediate trend direction and strength
Long-Term Layer (100 periods): Provides macro trend context and stability
Statistical Validation Framework
R-Squared Analysis: Ensures regression quality with adaptive thresholds (default ≥0.45)
Correlation Testing: Validates signal reliability with minimum correlation requirements (≥0.5)
Slope Significance: Filters out statistically insignificant price movements
Adaptive Thresholds: Dynamically adjusts validation criteria based on historical performance
🧠 Advanced Features
Dynamic Performance-Based Weighting
The strategy continuously evaluates each regression layer's performance and adjusts their influence weights accordingly:
Base weights: Short 40%, Medium 35%, Long 25%
Dynamic adjustment based on recent statistical quality scores
Real-time weight normalization for optimal ensemble performance
Lookback Validation System
Validates prediction accuracy using historical performance data
Tracks layer-specific reliability metrics
Implements fallback logic for maintaining signal generation during low-confidence periods
Adaptive Statistical Thresholds
Monitors rolling 100-period performance windows
Automatically adjusts R² thresholds based on market conditions
Maintains signal quality while adapting to changing market volatility
💡 Signal Generation Logic
Enhanced Ensemble Scoring
The strategy generates signals through a sophisticated multi-layer approach:
Primary Signals: Generated when all statistical criteria are met
Fallback Signals: Activated with relaxed criteria to maintain trading activity
Quality Weighting: Even "insignificant" signals contribute based on quality scores
Confidence Calculation: Combines agreement scores, statistical quality, and validation metrics
Entry Conditions
Long Entry: Ensemble score >0.3, confidence >65%, multiple layer agreement
Short Entry: Ensemble score <-0.3, confidence >65%, multiple layer agreement
Initialization: Relaxed conditions for first position establishment
🛡️ Risk Management
Position Sizing
Base Size: 50% of equity (configurable 10-100%)
Confidence Multiplier: Dynamic sizing based on signal confidence
Maximum Position: Limited by available equity and risk parameters
Daily Loss Protection
Maximum Daily Loss: 12% of equity (configurable 5-25%)
Emergency Exit: Automatic position closure when daily limit is reached
Halt Trading: Temporary suspension during high-loss periods
Statistical Risk Controls
Minimum Data Requirements: Ensures adequate sample sizes for reliable statistics
Division by Zero Protection: Robust error handling for edge cases
NA Value Management: Proper handling of missing or invalid data points
📈 Performance Optimization
Real-Time Monitoring Dashboard
The strategy includes a comprehensive statistics table displaying:
Net profit and performance metrics
R² values for each regression layer with quality indicators
Dynamic weight allocations
Signal strengths and ensemble scores
Confidence levels and reliability metrics
Adaptive threshold values
Visual Indicators
Regression Lines: Color-coded based on statistical significance
Background Coloring: Visual confidence and trend indication
Signal Markers: Clear entry point identification
Debug Plots: Additional data for strategy analysis
⚙️ Customizable Parameters
Regression Configuration
Layer Lengths: Adjustable periods for each regression layer
Statistical Thresholds: Customizable R², correlation, and slope requirements
Prediction Horizon: Configurable look-forward period (5-20 bars)
Risk Parameters
Position Sizing: Flexible equity percentage allocation
Daily Loss Limits: Configurable maximum daily drawdown
Confidence Thresholds: Adjustable minimum confidence for trade execution
Adaptation Settings
Adaptive Mode: Toggle for dynamic threshold adjustment
Lookback Periods: Customizable validation and adaptation windows
Weight Dynamics: Option for performance-based weight adjustment
🎯 Strategy Applications
Suitable For
Forex Traders: Specifically optimized for EUR/USD pair
Systematic Traders: Fully automated execution with minimal intervention
Risk-Conscious Investors: Multiple layers of risk management
Data-Driven Approaches: Statistical validation and performance tracking
Market Conditions
Trending Markets: Long-term layer provides trend context
Ranging Markets: Short-term layer captures oscillations
Volatile Periods: Adaptive thresholds adjust to changing conditions
Low Volatility: Fallback logic maintains trading activity
📋 Implementation Notes
Backtesting Configuration
Initial Capital: $100,000 USD
Commission: 0.02% per trade
Slippage: 1 pip
Currency: USD base
Pyramiding: Single position allowed
Performance Expectations
The strategy aims to achieve consistent returns through:
Statistical edge identification
Adaptive parameter optimization
Robust risk management
Multi-timeframe analysis convergence
⚠️ Important Disclaimers
Past Performance: No guarantee of future results
Market Risk: All trading involves risk of capital loss
Parameter Sensitivity: Results may vary with different settings
Market Conditions: Performance may vary across different market environments
This strategy represents an advanced implementation of statistical regression analysis for forex trading. Users should thoroughly backtest and paper trade before live implementation. Always trade responsibly and never risk more than you can afford to lose.
Open-source script
In true TradingView spirit, the creator of this script has made it open-source, so that traders can review and verify its functionality. Kudos to the author! While you can use it for free, remember that republishing the code is subject to our House Rules.
Disclaimer
The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.
Open-source script
In true TradingView spirit, the creator of this script has made it open-source, so that traders can review and verify its functionality. Kudos to the author! While you can use it for free, remember that republishing the code is subject to our House Rules.
Disclaimer
The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.