Adaptive Machine Learning Trading System [PhenLabs]📊Adaptive ML Trading System
Version: PineScript™v6
📌Description
The Adaptive ML Trading System is a sophisticated machine learning indicator that combines ensemble modeling with advanced technical analysis. This system uses XGBoost, Random Forest, and Neural Network algorithms to generate high-confidence trading signals while incorporating robust risk management features. Traders benefit from objective, data-driven decision-making that adapts to changing market conditions.
🚀Points of Innovation
• Machine Learning Ensemble - Three integrated models (XGBoost, Random Forest, Neural Network)
• Confidence-Based Trading - Only executes trades when ML confidence exceeds threshold
• Dynamic Risk Management - ATR-based stop loss and max drawdown protection
• Adaptive Position Sizing - Volatility-adjusted position sizing with confidence weighting
• Real-Time Performance Metrics - Live tracking of win rate, Sharpe ratio, and performance
• Multi-Timeframe Feature Analysis - Adaptive lookback periods for different market regimes
🔧Core Components
• ML Ensemble Engine - Weighted combination of XGBoost, Random Forest, and Neural Network outputs
• Feature Normalization System - Advanced preprocessing with custom tanh/sigmoid activation
• Risk Management Module - Dynamic position sizing and drawdown protection
• Performance Dashboard - Real-time metrics and risk status monitoring
• Alert System - Comprehensive alert conditions for entries, exits, and risk events
🔥Key Features
• High-confidence ML signals with customizable confidence thresholds
• Multiple trading modes (Conservative, Balanced, Aggressive) for different risk profiles
• Integrated stop loss and risk management with ATR-based calculations
• Real-time performance metrics including win rate and Sharpe ratio
• Comprehensive alert system with entry, exit, and risk management notifications
• Visual confidence bands and threshold indicators for easy signal interpretation
🎨Visualization
• ML Signal Line - Primary signal output ranging from -1 to +1
• Confidence Bands - Visual representation of model confidence levels
• Threshold Lines - Customizable buy/sell threshold levels
• Position Histogram - Current market position visualization
• Performance Tables - Real-time metrics display in customizable positions
📖Usage Guidelines
Model Configuration
• Confidence Threshold: Default 0.55, Range 0.5-0.95 - Minimum confidence for signals
• Model Sensitivity: Default 0.9, Range 0.1-2.0 - Adjusts signal sensitivity
• Ensemble Mode: Conservative/Balanced/Aggressive - Trading style preference
• Signal Threshold: Default 0.55, Range 0.3-0.9 - ML signal threshold for entries
Risk Management
• Position Size %: Default 10%, Range 1-50% - Portfolio percentage per trade
• Max Drawdown %: Default 15%, Range 5-30% - Maximum allowed drawdown
• Stop Loss ATR: Default 2.0, Range 0.5-5.0 - Stop loss in ATR multiples
• Dynamic Sizing: Default true - Volatility-based position adjustment
Display Settings
• Show Signals: Default true - Display entry/exit signals
• Show Threshold Signals: Default true - Display ±0.6 threshold crosses
• Show Confidence Bands: Default true - Display ML confidence levels
• Performance Dashboard: Default true - Show metrics table
✅Best Use Cases
• Swing trading with 1-5 day holding periods
• Trend-following strategies in established trends
• Volatility breakout trading during high-confidence periods
• Risk-adjusted position sizing for portfolio management
• Multi-timeframe confirmation for existing strategies
⚠️Limitations
• Requires sufficient historical data for accurate ML predictions
• May experience low confidence periods in choppy markets
• Performance varies across different asset classes and timeframes
• Not suitable for very short-term scalping strategies
• Requires understanding of basic risk management principles
💡What Makes This Unique
• True machine learning ensemble with multiple model types
• Confidence-based trading rather than simple signal generation
• Integrated risk management with dynamic position sizing
• Real-time performance tracking and metrics
• Adaptive parameters that adjust to market conditions
🔬How It Works
Feature Calculation: Computes 20+ technical features from price/volume data
Feature Normalization: Applies custom normalization for ML compatibility
Ensemble Prediction: Combines XGBoost, Random Forest, and Neural Network outputs
Signal Generation: Produces confidence-weighted trading signals
Risk Management: Applies position sizing and stop loss rules
Execution: Generates alerts and visual signals based on thresholds
💡Note:
This indicator works best on daily and 4-hour timeframes for most assets. Ensure you understand the risk management settings before live trading. The system includes automatic risk-off modes that halt trading during excessive drawdown periods.
Ensemblelearning
Volume Predictor [PhenLabs]📊 Volume Predictor
Version: PineScript™ v6
📌 Description
The Volume Predictor is an advanced technical indicator that leverages machine learning and statistical modeling techniques to forecast future trading volume. This innovative tool analyzes historical volume patterns to predict volume levels for upcoming bars, providing traders with valuable insights into potential market activity. By combining multiple prediction algorithms with pattern recognition techniques, the indicator delivers forward-looking volume projections that can enhance trading strategies and market analysis.
🚀 Points of Innovation:
Machine learning pattern recognition using Lorentzian distance metrics
Multi-algorithm prediction framework with algorithm selection
Ensemble learning approach combining multiple prediction methods
Real-time accuracy metrics with visual performance dashboard
Dynamic volume normalization for consistent scale representation
Forward-looking visualization with configurable prediction horizon
🔧 Core Components
Pattern Recognition Engine : Identifies similar historical volume patterns using Lorentzian distance metrics
Multi-Algorithm Framework : Offers five distinct prediction methods with configurable parameters
Volume Normalization : Converts raw volume to percentage scale for consistent analysis
Accuracy Tracking : Continuously evaluates prediction performance against actual outcomes
Advanced Visualization : Displays actual vs. predicted volume with configurable future bar projections
Interactive Dashboard : Shows real-time performance metrics and prediction accuracy
🔥 Key Features
The indicator provides comprehensive volume analysis through:
Multiple Prediction Methods : Choose from Lorentzian, KNN Pattern, Ensemble, EMA, or Linear Regression algorithms
Pattern Matching : Identifies similar historical volume patterns to project future volume
Adaptive Predictions : Generates volume forecasts for multiple bars into the future
Performance Tracking : Calculates and displays real-time prediction accuracy metrics
Normalized Scale : Presents volume as a percentage of historical maximums for consistent analysis
Customizable Visualization : Configure how predictions and actual volumes are displayed
Interactive Dashboard : View algorithm performance metrics in a customizable information panel
🎨 Visualization
Actual Volume Columns : Color-coded green/red bars showing current normalized volume
Prediction Columns : Semi-transparent blue columns representing predicted volume levels
Future Bar Projections : Forward-looking volume predictions with configurable transparency
Prediction Dots : Optional white dots highlighting future prediction points
Reference Lines : Visual guides showing the normalized volume scale
Performance Dashboard : Customizable panel displaying prediction method and accuracy metrics
📖 Usage Guidelines
History Lookback Period
Default: 20
Range: 5-100
This setting determines how many historical bars are analyzed for pattern matching. A longer period provides more historical data for pattern recognition but may reduce responsiveness to recent changes. A shorter period emphasizes recent market behavior but might miss longer-term patterns.
🧠 Prediction Method
Algorithm
Default: Lorentzian
Options: Lorentzian, KNN Pattern, Ensemble, EMA, Linear Regression
Selects the algorithm used for volume prediction:
Lorentzian: Uses Lorentzian distance metrics for pattern recognition, offering excellent noise resistance
KNN Pattern: Traditional K-Nearest Neighbors approach for historical pattern matching
Ensemble: Combines multiple methods with weighted averaging for robust predictions
EMA: Simple exponential moving average projection for trend-following predictions
Linear Regression: Projects future values based on linear trend analysis
Pattern Length
Default: 5
Range: 3-10
Defines the number of bars in each pattern for machine learning methods. Shorter patterns increase sensitivity to recent changes, while longer patterns may identify more complex structures but require more historical data.
Neighbors Count
Default: 3
Range: 1-5
Sets the K value (number of nearest neighbors) used in KNN and Lorentzian methods. Higher values produce smoother predictions by averaging more historical patterns, while lower values may capture more specific patterns but could be more susceptible to noise.
Prediction Horizon
Default: 5
Range: 1-10
Determines how many future bars to predict. Longer horizons provide more forward-looking information but typically decrease accuracy as the prediction window extends.
📊 Display Settings
Display Mode
Default: Overlay
Options: Overlay, Prediction Only
Controls how volume information is displayed:
Overlay: Shows both actual volume and predictions on the same chart
Prediction Only: Displays only the predictions without actual volume
Show Prediction Dots
Default: false
When enabled, adds white dots to future predictions for improved visibility and clarity.
Future Bar Transparency (%)
Default: 70
Range: 0-90
Controls the transparency of future prediction bars. Higher values make future bars more transparent, while lower values make them more visible.
📱 Dashboard Settings
Show Dashboard
Default: true
Toggles display of the prediction accuracy dashboard. When enabled, shows real-time accuracy metrics.
Dashboard Location
Default: Bottom Right
Options: Top Left, Top Right, Bottom Left, Bottom Right
Determines where the dashboard appears on the chart.
Dashboard Text Size
Default: Normal
Options: Small, Normal, Large
Controls the size of text in the dashboard for various display sizes.
Dashboard Style
Default: Solid
Options: Solid, Transparent
Sets the visual style of the dashboard background.
Understanding Accuracy Metrics
The dashboard provides key performance metrics to evaluate prediction quality:
Average Error
Shows the average difference between predicted and actual values
Positive values indicate the prediction tends to be higher than actual volume
Negative values indicate the prediction tends to be lower than actual volume
Values closer to zero indicate better prediction accuracy
Accuracy Percentage
A measure of how close predictions are to actual outcomes
Higher percentages (>70%) indicate excellent prediction quality
Moderate percentages (50-70%) indicate acceptable predictions
Lower percentages (<50%) suggest weaker prediction reliability
The accuracy metrics are color-coded for quick assessment:
Green: Strong prediction performance
Orange: Moderate prediction performance
Red: Weaker prediction performance
✅ Best Use Cases
Anticipate upcoming volume spikes or drops
Identify potential volume divergences from price action
Plan entries and exits around expected volume changes
Filter trading signals based on predicted volume support
Optimize position sizing by forecasting market participation
Prepare for potential volatility changes signaled by volume predictions
Enhance technical pattern analysis with volume projection context
⚠️ Limitations
Volume predictions become less accurate over longer time horizons
Performance varies based on market conditions and asset characteristics
Works best on liquid assets with consistent volume patterns
Requires sufficient historical data for pattern recognition
Sudden market events can disrupt prediction accuracy
Volume spikes may be muted in predictions due to normalization
💡 What Makes This Unique
Machine Learning Approach : Applies Lorentzian distance metrics for robust pattern matching
Algorithm Selection : Offers multiple prediction methods to suit different market conditions
Real-time Accuracy Tracking : Provides continuous feedback on prediction performance
Forward Projection : Visualizes multiple future bars with configurable display options
Normalized Scale : Presents volume as a percentage of maximum volume for consistent analysis
Interactive Dashboard : Displays key metrics with customizable appearance and placement
🔬 How It Works
The Volume Predictor processes market data through five main steps:
1. Volume Normalization:
Converts raw volume to percentage of maximum volume in lookback period
Creates consistent scale representation across different timeframes and assets
Stores historical normalized volumes for pattern analysis
2. Pattern Detection:
Identifies similar volume patterns in historical data
Uses Lorentzian distance metrics for robust similarity measurement
Determines strength of pattern match for prediction weighting
3. Algorithm Processing:
Applies selected prediction algorithm to historical patterns
For KNN/Lorentzian: Finds K nearest neighbors and calculates weighted prediction
For Ensemble: Combines multiple methods with optimized weighting
For EMA/Linear Regression: Projects trends based on statistical models
4. Accuracy Calculation:
Compares previous predictions to actual outcomes
Calculates average error and prediction accuracy
Updates performance metrics in real-time
5. Visualization:
Displays normalized actual volume with color-coding
Shows current and future volume predictions
Presents performance metrics through interactive dashboard
💡 Note:
The Volume Predictor performs optimally on liquid assets with established volume patterns. It’s most effective when used in conjunction with price action analysis and other technical indicators. The multi-algorithm approach allows adaptation to different market conditions by switching prediction methods. Pay special attention to the accuracy metrics when evaluating prediction reliability, as sudden market changes can temporarily reduce prediction quality. The normalized percentage scale makes the indicator consistent across different assets and timeframes, providing a standardized approach to volume analysis.
