TODO.md

CryptoWildfire: ML Integration Roadmap

Overview

This document outlines the roadmap for integrating machine learning prediction models with our existing enhanced components:

1. Enhanced TechnicalChart - With dynamic support/resistance levels, moving averages (7/21-day SMA), volume overlay, and indicator toggles
2. DynamicStopLoss - ATR-based volatility adjustment for intelligent risk management

Expected Performance Targets

Metric	Target	Current Baseline
Price Direction Accuracy	68-72%	55-60%
Confidence Interval Width	<2.5%	N/A
Risk-Adjusted Return	+15% CAGR	+8% CAGR
Prediction Lag	<300ms	450ms

Implementation Phases

Phase 1: ML Training Infrastructure (Days 1-7)

• Historical Data Collection Pipeline

  • Create src/training/data_collector.py
  • Implement data storage (Parquet/HDF5)
  • Build data quality validators

• Feature Engineering Pipeline

  • Connect to TechnicalChart component for technical indicators:

    # Key features to implement
    features['sma7_cross'] = technical['sma7'] / technical['sma21'] - 1  # From your 7/21-day SMA
    features['volume_trend'] = technical['volume'] / technical['volume'].rolling(7).mean()

  • Integrate DynamicStopLoss for volatility metrics:

    # Risk-based features
    features['atr_percent'] = risk['atr14'] / market_data['close']  # Your ATR implementation
    features['stop_distance'] = (market_data['close'] - risk['stop_level']) / market_data['close']

  • Create market context features

• Target Creation & Dataset Assembly

  • Build return calculator for prediction targets
  • Develop dataset assembler with proper splits

Phase 2: Model Training & Evaluation (Days 8-14)

• Model Architecture

  • Implement quantile regression models (q05, q50, q95) for prediction uncertainty:

    # Three models for proper confidence intervals
    self.q50_model = LGBMRegressor(objective='quantile', alpha=0.5)   # Median prediction
    self.q05_model = LGBMRegressor(objective='quantile', alpha=0.05)  # Lower bound (5th percentile)
    self.q95_model = LGBMRegressor(objective='quantile', alpha=0.95)  # Upper bound (95th percentile)

  • Build model registry for version tracking
  • Implement progressive weighting system (25% → 75% maximum influence)

• Training Pipeline

  • Create training script with logging
  • Implement time-series cross-validation
  • Build hyperparameter optimization

• Model Evaluation

  • Create backtesting framework
  • Implement performance dashboard

Phase 3: Prediction API Integration (Days 15-21)

• Prediction Service

  • Create prediction service
  • Implement confidence calculation
  • Add model monitoring:

    # src/ml/monitoring.py
    class ModelMonitor:
        def check_drift(self, current_data, reference_data):
            """Detect feature distribution shifts"""
            return ks_2samp(current_data, reference_data).pvalue < 0.01

• Progressive Weighting System

  • Build model weighting (25% initial → performance-based)
  • Implement prediction explainer

• API Endpoints

  • Create prediction endpoints
  • Integrate with main API
  • Update API documentation

Phase 4: Frontend Integration (Days 22-28)

• Frontend Data Models

  • Create prediction TypeScript interfaces
  • Implement prediction service client

• UI Components

  • Create prediction visualization with confidence bands:

    <TechnicalChart
      predictionBands={{
        upper: prediction.q95,
        lower: prediction.q05,
        median: prediction.q50
      }}
    />

  • Implement confidence meter with reasoning:

    <ConfidenceMeter 
      score={prediction.confidence} 
      reasons={[
        "High ATR volatility reduces confidence",
        "Strong SMA crossover increases confidence"
      ]}
    />

  • Build AnalysisCard enhancements
  • Implement settings controls

• Advanced Integration

  • Create real-time update system with WebSocket streaming
  • Implement user preference storage
  • Build user feedback loop for model improvement:

    # src/analytics/feedback.py
    class PredictionFeedback:
        def log_user_action(self, prediction_id, action):
            """Track if users acted on predictions"""
            self.db.log({
                'prediction_id': prediction_id,
                'user_action': action,
                'outcome': self._calculate_outcome(prediction_id)
            })

Phase 5: Testing & Deployment (Days 29-35)

• Testing

  • Implement unit tests
  • Add integration tests
  • Create performance tests

• Documentation

  • Create developer documentation
  • Build user guide

• Deployment & Monitoring

  • Create deployment pipeline
  • Implement monitoring

Phase 6: Post-Launch Optimization (Days 36-42)

• Feedback Mechanisms

  • Create feedback collection
  • Implement performance analytics

• Continuous Learning

  • Build model update pipeline
  • Implement A/B testing

• Enhancement Planning

  • Create feature prioritization framework
  • Implement system review

Integration Points with Existing Components

Enhanced TechnicalChart Integration

Our ML models will leverage the following features from the existing TechnicalChart component:

1. Dynamic Support/Resistance Levels - Used as key feature inputs
2. Moving Averages (7-day and 21-day SMA) - Used for trend detection
3. Volume Overlay - Used for liquidity analysis
4. Indicator Toggles - Used to control feature importance

DynamicStopLoss Integration

The following aspects of our DynamicStopLoss component will be connected to the ML pipeline:

1. ATR-based Volatility Adjustment - Used for prediction confidence scoring
2. Trailing Stop Logic - Used for profit target recommendations
3. Position Sizing - Used for risk-optimized allocation
4. Correlation Risk Analysis - Used for portfolio-aware predictions

Initial ML Influence

• Starting ML model influence: 25% (conservative approach)
• Will increase based on performance metrics
• Gradual progression to max 75% influence

Next Steps

1. Begin Phase 1 implementation
2. Review progress weekly
3. Adjust timeline as needed based on development velocity