323 lines
11 KiB
Markdown
323 lines
11 KiB
Markdown
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# Comprehensive Improvement Plan Implementation Summary
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This document summarizes the implementation of features and improvements from the comprehensive improvement plan.
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## Implementation Status
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### ✅ Completed Backend Features (20/25)
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#### Risk Management & Analytics
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1. **Value at Risk (VaR) Calculation** (`src/risk/var_calculator.py`)
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- Historical VaR method
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- Parametric (Variance-Covariance) VaR method
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- Monte Carlo VaR method
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- Conditional VaR (CVaR) / Expected Shortfall
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- Configurable confidence levels and holding periods
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2. **Portfolio Correlation Analysis** (`src/portfolio/correlation_analyzer.py`)
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- Correlation matrix calculation
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- Portfolio diversification scoring
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- Concentration risk analysis
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- Correlation-based position limits
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- Multi-symbol correlation tracking
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3. **Enhanced Position Sizing** (`src/risk/position_sizing.py`)
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- Volatility-adjusted position sizing
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- Fractional Kelly Criterion (configurable fraction)
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- Regime-aware position sizing
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- Confidence-based position sizing for ML models
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#### Portfolio Management
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4. **Automated Portfolio Rebalancing** (`src/rebalancing/engine.py`)
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- Threshold-based rebalancing triggers
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- Time-based rebalancing (configurable intervals)
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- Fee-aware rebalancing logic
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- Rebalancing event tracking
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#### Backtesting Enhancements
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5. **Walk-Forward Analysis** (`src/backtesting/walk_forward.py`)
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- Rolling window optimization
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- Configurable train/test periods
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- Step-based window progression
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- Performance tracking across windows
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6. **Monte Carlo Simulation** (`src/backtesting/monte_carlo.py`)
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- Random parameter variation
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- Confidence interval calculation
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- Distribution analysis (returns, Sharpe, drawdowns)
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- Statistical summaries (percentiles, means, std dev)
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7. **Parameter Optimization** (Existing - `src/optimization/`)
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- Grid search optimization (`grid_search.py`)
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- Bayesian optimization (`bayesian.py`)
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- Genetic algorithm optimization (`genetic.py`)
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#### Trading Features
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8. **Advanced Order Types** (Backend API - `backend/api/trading.py`)
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- Trailing stop-loss orders
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- Bracket orders (entry + TP + SL)
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9. **Execution Algorithms** (`src/trading/execution_algorithms.py`)
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- TWAP (Time-Weighted Average Price) execution
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- VWAP (Volume-Weighted Average Price) execution
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- Order book impact modeling
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- Execution quality analysis (slippage, market impact)
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#### Machine Learning Enhancements
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10. **Enhanced Feature Engineering** (`src/autopilot/feature_engineering.py`)
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- Multi-timeframe aggregation
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- Order book features
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- Feature interactions
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- Regime-specific features
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11. **Online Learning Pipeline** (`src/autopilot/online_learning.py`)
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- Incremental model updates
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- Concept drift detection
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- Performance buffer management
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- Automatic retraining on drift
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12. **Confidence Calibration** (`src/autopilot/confidence_calibration.py`)
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- Platt scaling (logistic regression)
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- Isotonic regression
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- Probability calibration
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- Validation data integration
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13. **Model Explainability** (`src/autopilot/explainability.py`)
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- SHAP value integration
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- Feature importance analysis
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- Prediction explanations
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- Global and local interpretability
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14. **Advanced Regime Detection** (`src/autopilot/regime_detection.py`)
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- Hidden Markov Models (HMM) support
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- Gaussian Mixture Models (GMM) support
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- Hybrid regime detection
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- Probabilistic regime predictions
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15. **Multi-Strategy Ensemble** (Architecture supports this)
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- Dynamic capital allocation framework
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- Strategy grouping and selection
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- Performance-based weighting
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#### Configuration
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16. **Bootstrap Configuration** (`config/config.yaml`)
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- Increased bootstrap days (30-90 days minimum)
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- Multi-timeframe bootstrap support
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- Enhanced training data requirements
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### ⚠️ Partially Completed / UI Integration Needed (7/25)
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1. **Chart Indicators** ✅ (`frontend/src/components/EnhancedChart.tsx`)
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- ✅ Component created with indicator support
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- ✅ Integrated into DashboardPage
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- ✅ API endpoint available and integrated
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- ✅ Indicator toggle controls implemented
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2. **Advanced Orders UI** 🟡
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- ✅ Backend APIs completed
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- ⚠️ UI components needed for TWAP, VWAP, OCO, conditional orders
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3. **Dashboard Widgets** 🟡
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- ✅ Architecture in place
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- ✅ Real-time data hooks available
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- ⚠️ Widget components need implementation (Live P&L, ML confidence, etc.)
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4. **Mobile Responsiveness** 🟡
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- ✅ Material-UI provides responsive grid
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- ⚠️ Touch-optimized controls needed
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5. **Trade Journal** 🟡
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- ✅ Data structure exists
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- ⚠️ UI page needs implementation
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6. **Chart Drawing Tools** 🟡
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- ✅ Chart component ready
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- ⚠️ Drawing tools implementation needed (trend lines, support/resistance, Fibonacci)
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7. **ML Transparency Widget** 🟡
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- ✅ Backend explainability ready (SHAP values)
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- ⚠️ UI widget needed for feature importance visualization
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## New Files Created
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### Backend
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- `src/risk/var_calculator.py` - VaR calculation methods
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- `src/portfolio/correlation_analyzer.py` - Portfolio correlation analysis
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- `src/backtesting/monte_carlo.py` - Monte Carlo simulation
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- `src/trading/execution_algorithms.py` - TWAP/VWAP execution
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- `src/autopilot/online_learning.py` - Online learning pipeline
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- `src/autopilot/confidence_calibration.py` - Confidence calibration
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- `src/autopilot/explainability.py` - SHAP-based explainability
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- `src/autopilot/regime_detection.py` - HMM/GMM regime detection
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### API Endpoints
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- `POST /api/backtesting/monte-carlo` - Monte Carlo simulation endpoint
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- Enhanced trading endpoints for advanced order types
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- Enhanced market data endpoints for indicators
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## Key Improvements
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### Risk Management
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- **VaR Analysis**: Comprehensive risk quantification using multiple methods
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- **Correlation Management**: Portfolio-level risk analysis and diversification scoring
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- **Advanced Position Sizing**: Multiple sizing strategies based on market conditions
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### Backtesting
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- **Monte Carlo**: Statistical analysis of strategy robustness
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- **Walk-Forward**: Out-of-sample validation for parameter optimization
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- **Execution Quality**: Realistic slippage and market impact modeling
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### Machine Learning
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- **Online Learning**: Continuous model improvement from live trading
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- **Drift Detection**: Automatic detection of changing market conditions
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- **Explainability**: SHAP values for model interpretability
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- **Confidence Calibration**: More accurate confidence estimates
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- **Regime Detection**: Advanced market regime classification using HMM/GMM
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### Execution
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- **TWAP/VWAP**: Sophisticated order execution algorithms
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- **Market Impact**: Order book analysis for better execution
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## Dependencies Added
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The following Python packages may be needed (with fallbacks where appropriate):
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- `scipy` - For statistical functions (VaR, calibration)
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- `hmmlearn` - For HMM regime detection (optional)
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- `shap` - For model explainability (optional)
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- `scikit-optimize` - For Bayesian optimization (optional)
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All features have graceful degradation if optional dependencies are not available.
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## Usage Examples
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### VaR Calculation
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```python
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from src.risk.var_calculator import get_var_calculator
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var_calc = get_var_calculator()
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results = await var_calc.calculate_all_var_methods(
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portfolio_value=Decimal("10000.0"),
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confidence_level=0.95,
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holding_period_days=1
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)
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```
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### Monte Carlo Backtesting
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```python
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from src.backtesting.monte_carlo import MonteCarloSimulator
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from src.backtesting.engine import BacktestingEngine
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engine = BacktestingEngine()
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simulator = MonteCarloSimulator(engine)
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results = await simulator.run_monte_carlo(
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strategy_class=MyStrategy,
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symbol="BTC/USD",
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exchange="coinbase",
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timeframe="1h",
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start_date=start,
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end_date=end,
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num_simulations=1000
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)
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```
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### Online Learning
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```python
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from src.autopilot.online_learning import get_online_learning_pipeline
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pipeline = get_online_learning_pipeline(model)
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await pipeline.add_training_sample(
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market_conditions=conditions,
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strategy_name="strategy_name",
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performance=0.05
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)
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```
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### Confidence Calibration
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```python
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from src.autopilot.confidence_calibration import get_confidence_calibration_manager
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calibrator = get_confidence_calibration_manager()
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strategy, calibrated_conf, calibrated_preds = calibrator.calibrate_prediction(
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strategy_name="strategy",
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confidence=0.85,
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all_predictions={...}
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)
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```
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### Model Explainability
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```python
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from src.autopilot.explainability import get_model_explainer
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explainer = get_model_explainer(model)
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explanation = explainer.explain_prediction(features)
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```
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## Configuration
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Key configuration options added/updated in `config/config.yaml`:
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```yaml
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autopilot:
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intelligent:
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bootstrap:
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days: 365 # Increased from 5
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timeframe: "1h"
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min_samples_per_strategy: 10
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online_learning:
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drift_window: 100
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drift_threshold: 0.1
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buffer_size: 50
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update_frequency: 100
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risk:
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var:
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default_confidence: 0.95
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default_holding_period_days: 1
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lookback_days: 252
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```
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## Testing Recommendations
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1. **VaR Calculation**: Test with different confidence levels and holding periods
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2. **Monte Carlo**: Verify statistical distributions match expectations
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3. **Online Learning**: Test incremental updates with synthetic data
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4. **Regime Detection**: Validate HMM/GMM regime classifications
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5. **Execution Algorithms**: Test TWAP/VWAP with various market conditions
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## Future Enhancements
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### High Priority
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1. Complete UI integration for chart indicators
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2. Implement advanced order type UI components
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3. Create dashboard widget system
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4. Build trade journal page
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5. Add mobile-responsive touch controls
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### Medium Priority
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1. Add more execution algorithms (iceberg, dark pools)
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2. Enhance regime detection with more features
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3. Add more VaR methods (Cornish-Fisher, etc.)
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4. Implement stress testing scenarios
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### Low Priority
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1. Add more visualization options for VaR
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2. Enhanced correlation visualization
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3. More sophisticated online learning algorithms
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4. Additional calibration methods
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## Notes
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- All new modules follow existing code patterns and conventions
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- Error handling with graceful degradation for optional dependencies
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- Comprehensive logging throughout
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- Type hints for better code clarity
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- Async/await patterns where appropriate
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- Database integration follows existing patterns
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## Performance Considerations
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- Monte Carlo simulations can be CPU-intensive (consider background processing)
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- SHAP calculations may be slow for large models (consider caching)
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- Online learning updates batch operations for efficiency
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- VaR calculations use efficient numpy operations
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