foxhunt/docs/codebase-cleanup/AGENT6_QUICK_SUMMARY.md

# Agent 6: Walk-Forward Validation - Quick Summary

## ✅ Research Complete

**Task**: Replace fixed 80/20 train/val split with walk-forward validation in DQN hyperopt

---

## 🎯 Key Findings

### 1. Existing Implementation Found
- **File**: `ml/src/backtesting/barrier_backtest.rs`
- **Algorithm**: Rolling temporal windows with train/test split per window
- **Features**: Embargo period, aggregated metrics, stability score

### 2. Three 80/20 Split Locations
**File**: `ml/src/trainers/dqn/trainer.rs`
1. Line 2376: `load_training_data_from_parquet()`
2. Line 2660: `train_with_normalization()`
3. Line 2777: `load_training_data()` (DBN loading)

All three use same pattern:
```rust
let split_idx = (data.len() * 80) / 100;
let train_data = data[..split_idx].to_vec();
let val_data = data[split_idx..].to_vec();
```

---

## 📋 Implementation Plan

### Phase 1: Create New Module (30 min)
**File**: `ml/src/hyperopt/walk_forward.rs`

```rust
pub struct WalkForwardValidator {
    num_folds: usize,           // Default: 5
    train_ratio: f64,           // Default: 0.8
    embargo_pct: f64,           // Default: 0.01 (1% gap)
}

impl WalkForwardValidator {
    pub fn split<T: Clone>(&self, data: &[T])
        -> Result<Vec<(Vec<T>, Vec<T>)>, MLError> {
        // Split into N folds
        // Each fold: [train][embargo][val]
        // Return Vec of (train, val) tuples
    }
}
```

### Phase 2: Modify DQN Trainer (45 min)
**File**: `ml/src/trainers/dqn/trainer.rs`

1. Add fields to `DQNTrainer`:
   - `enable_walk_forward: bool`
   - `walk_forward_folds: usize`
   - `walk_forward_embargo_pct: f64`

2. Add method:
   ```rust
   pub fn with_walk_forward(mut self, num_folds: usize, embargo_pct: f64) -> Self
   ```

3. Modify 3 functions to check `if self.enable_walk_forward`:
   - `load_training_data_from_parquet()` (line 2376)
   - `train_with_normalization()` (line 2660)
   - `load_training_data()` (line 2777)

### Phase 3: Enable in Hyperopt (15 min)
**File**: `ml/src/hyperopt/adapters/dqn.rs`

Enable by default in `DQNTrainer::new()`:
```rust
let mut trainer = Self::with_buffer_max(...)?;
trainer = trainer.with_walk_forward(5, 0.01); // 5 folds, 1% embargo
```

---

## 🔧 Files to Modify

### New Files (1)
- `ml/src/hyperopt/walk_forward.rs`

### Modified Files (3)
- `ml/src/hyperopt/mod.rs` (add `pub mod walk_forward;`)
- `ml/src/trainers/dqn/trainer.rs` (3 functions)
- `ml/src/hyperopt/adapters/dqn.rs` (enable by default)

---

## ✨ Benefits

1. **Anti-Overfitting**: Validation always AFTER training (no temporal leakage)
2. **Embargo Period**: 1% gap prevents information leakage at boundaries
3. **Robustness**: 5 folds instead of 1 split
4. **Stability Metric**: Variance of Sharpe across folds
5. **Production Alignment**: Mirrors live trading (always predicting future)

---

## 🚀 Compilation Check

```bash
# After Phase 1
cargo check --package ml --lib

# After Phase 2
cargo test --package ml walk_forward

# After Phase 3
cargo test --package ml dqn::trainer
```

---

## 📊 Expected Results

### Before (Fixed 80/20)
```
Training: 80% of data
Validation: 20% of data
Temporal Leakage: Possible
Robustness: Single split
```

### After (Walk-Forward)
```
Training: Fold 0 (first 80% of fold 1)
Embargo: 1% gap
Validation: Fold 0 (remaining 19% of fold 1)
Temporal Leakage: Prevented
Robustness: 5-fold rotation available
```

---

## 🎯 Next Agent Task

**Agent 7**: Implement walk-forward validation based on analysis

**Priority**: Create `walk_forward.rs` module first (smallest, testable unit)

**References**: See full analysis in `AGENT6_WALK_FORWARD_VALIDATION_ANALYSIS.md`