Volume Predictor [PhenLabs]

📊 Volume Predictor [PhenLabs]
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 SettingsDisplay 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 SettingsShow 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.