foxhunt/docs/archive/backtesting/BACKTESTING_ML_QUICK_REFERENCE.md

# Backtesting Service ML Integration - Quick Reference

## Key Files

| File | Purpose | Lines | Status |
|------|---------|-------|--------|
| `services/backtesting_service/src/ml_strategy_engine.rs` | ML strategy framework | 540 | ✅ READY |
| `services/backtesting_service/src/performance.rs` | Performance metrics | 665 | ✅ READY |
| `services/backtesting_service/src/strategy_engine.rs` | Strategy execution | 723 | ✅ READY |
| `services/backtesting_service/tests/ml_strategy_backtest_test.rs` | ML tests | 508 | ✅ 8/8 PASSING |
| `services/backtesting_service/tests/ml_backtest_integration_test.rs` | Integration tests | 293 | ❌ 4/4 FAILING |
| `services/backtesting_service/tests/report_generation.rs` | Report tests | 473 | ✅ 12/12 PASSING |

## Feature Extraction

### ML Feature Extractor (7 features)

```rust
// Location: ml_strategy_engine.rs lines 62-173
// Lookback: 20 periods (configurable)
// Output: 7 normalized features [-1, 1]

Features:
1. Price momentum (returns)
2. Short-term MA ratio (5-period)
3. Price volatility (9-bar std dev)
4. Volume ratio
5. Volume MA ratio (5-period)
6. Hour-of-day (normalized)
7. Day-of-week (normalized)
```

### Unified Feature Extractor (16 features)

```rust
// Location: data crate (reused in backtesting)
// From strategy_engine.rs line 310

Features:
- 5 OHLCV (Open, High, Low, Close, Volume)
- 10+ technical indicators:
  - RSI, MACD, Bollinger Bands
  - ATR, EMA, SMA
  - + more
```

## Performance Metrics (20+)

```rust
// Location: performance.rs lines 12-58

Returns:
- total_return, annualized_return, profit_factor

Risk:
- sharpe_ratio, sortino_ratio, calmar_ratio
- max_drawdown, volatility
- var_95 (Value at Risk)
- expected_shortfall (CVaR)

Trade Stats:
- total_trades, winning_trades, losing_trades
- win_rate, avg_win, avg_loss
- largest_win, largest_loss
```

## ML Strategy Components

### MLPoweredStrategy

```rust
// Lines 176-304
pub struct MLPoweredStrategy {
    name: String,
    strategy: Arc<SharedMLStrategy>,
    feature_extractor: MLFeatureExtractor,
    model_performance: HashMap<String, MLModelPerformance>,
    confidence_based_sizing: bool,
    min_confidence_threshold: f64,
}

Key methods:
- get_ensemble_prediction() → Vec<MLPrediction>
- calculate_ensemble_vote() → (f64, f64)
- validate_predictions() → tracks accuracy
- get_performance_summary() → HashMap<String, MLModelPerformance>
```

### MLStrategyEngine

```rust
// Lines 389-539
pub struct MLStrategyEngine {
    base_engine: StrategyEngine,
    ml_strategies: HashMap<String, MLPoweredStrategy>,
    global_model_performance: HashMap<String, MLModelPerformance>,
}

Key methods:
- execute_ml_backtest() → (Vec<BacktestTrade>, HashMap<performance>)
- get_global_model_performance() → HashMap
- generate_performance_report() → String
```

## Ensemble Voting

```rust
// Lines 247-266
// Weighted by confidence, normalized

let weighted_prediction = predictions.iter()
    .map(|p| p.prediction_value * p.confidence)
    .sum::<f64>() / total_confidence;

let average_confidence = predictions.iter()
    .map(|p| p.confidence).sum::<f64>() / predictions.len() as f64;
```

## Confidence Filtering

```rust
// Default: 0.6 (60% threshold)
// Located: ml_strategy_engine.rs line 211

if confidence >= min_confidence_threshold {
    // Generate trade signal
} else {
    // Skip this prediction
}
```

## Available Strategies

### Rule-Based (Implemented)

```
1. moving_average_crossover
2. buy_and_hold
3. news_aware_strategy
```

### ML (Framework Ready, No Trained Models)

```
1. ml_momentum (20-period lookback)
2. ml_ensemble (50-period lookback + voting)
```

## Test Coverage

### Passing Tests (20/24)

**ML Strategy Tests** (8/8 - PASSING)
- Prediction generation
- Ensemble voting
- Trade generation
- Confidence filtering
- Multi-symbol execution
- Performance metrics
- Feature extraction
- Performance tracking

**Report Generation Tests** (12/12 - PASSING)
- Save/load results
- Metrics aggregation
- Drawdown identification
- Equity curve generation
- Export formats
- Concurrent operations

### Failing Tests (4/4 - TDD RED PHASE)

**ML Integration Tests** (0/4 - NOT IMPLEMENTED)
- Full backtest execution
- ML vs rule-based comparison
- Confidence threshold impact
- Target metrics validation

## Data Flow

```
Real DBN Files
    ↓
DbnDataSource.load_ohlcv_bars()
    ↓
MarketData (OHLCV)
    ↓
MLFeatureExtractor (7 features)
    ↓
SharedMLStrategy (ensemble predictions)
    ↓
Confidence Filtering (threshold: 0.6)
    ↓
Ensemble Vote (weighted)
    ↓
Trade Signals (Buy/Sell with sizing)
    ↓
Portfolio Execution (commission + slippage)
    ↓
BacktestTrade (filled trades)
    ↓
PerformanceAnalyzer (20+ metrics)
    ↓
Results (JSON export)
```

## Critical Gaps

| Component | Status | Impact |
|-----------|--------|--------|
| Model Loading | ❌ NOT IMPLEMENTED | Cannot load trained checkpoints |
| Inference Engine | ⚠️ PARTIAL | Using simulator, not real models |
| Batch Predictions | ❌ NOT IMPLEMENTED | Sequential only (~100 bars/sec) |
| Model Registry | ❌ NOT IMPLEMENTED | Hard-coded strategies only |
| Strategy Comparison | ⚠️ SKELETON | Test structure, no implementation |

## To Use Trained ML Models

**Required** (1-2 weeks):
1. Implement checkpoint loader
2. Connect inference engine
3. Add batch prediction support
4. Write integration tests

**Current**: Cannot use trained models yet. Framework ready, missing model loading.

## Real Data Available

- ES.FUT (E-mini S&P 500) - 1m OHLCV
- NQ.FUT (Nasdaq futures) - 1m OHLCV
- ZN.FUT (10-year Treasury) - 1d OHLCV
- 6E.FUT (Euro FX) - 1d OHLCV
- CL.FUT (Crude Oil) - available

## Configuration

**Performance Config** (defaults):
```rust
equity_curve_resolution: 1000
risk_free_rate: 0.02 (2% annual)
```

**Strategy Config** (defaults):
```rust
commission_rate: 0.001 (0.1%)
slippage_rate: 0.0005 (0.05%)
```

## Performance Targets (from CLAUDE.md)

- Win Rate: >55%
- Sharpe Ratio: >1.5
- Max Drawdown: <20%

## Next Steps

1. Load MAMBA-2/DQN/PPO/TFT checkpoints
2. Implement model inference wrapper
3. Connect to ML strategy engine
4. Test with real backtests
5. Optimize batch predictions

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**See**: `/home/jgrusewski/Work/foxhunt/ML_BACKTESTING_INTEGRATION_ANALYSIS.md` for full analysis