# Agent G9: TFT 225-Feature Update - Quick Reference

**Status**: ✅ COMPLETE  
**Date**: 2025-10-18

---

## What Changed

### 1. Training Script (`ml/examples/train_tft_dbn.rs`)
- ✅ Added `FeatureConfig::wave_d()` initialization
- ✅ Updated `convert_to_tft_data()` to accept feature config
- ✅ Changed historical features from 50 → 225 per timestep
- ✅ Updated array shape: `(lookback_window, 50)` → `(lookback_window, 225)`
- ✅ Added Wave D features (indices 201-224, 24 features)

### 2. Trainer Config (`ml/src/trainers/tft.rs`)
- ✅ Updated `num_unknown_features: 50` → `225`
- ✅ Updated checkpoint naming: `tft_epoch_{}` → `tft_225_epoch_{}`

---

## Feature Breakdown (225 Total)

| Range | Category | Count | Description |
|-------|----------|-------|-------------|
| 0-200 | Wave C | 201 | OHLCV, technicals, microstructure, statistical |
| 201-210 | CUSUM | 10 | Structural break detection |
| 211-215 | ADX | 5 | Directional indicators |
| 216-220 | Transitions | 5 | Regime transition probabilities |
| 221-224 | Adaptive | 4 | Position sizing, stop-loss, Sharpe |

---

## Run Commands

```bash
# Compile check
cargo check -p ml --example train_tft_dbn

# Test compilation
cargo test -p ml --example train_tft_dbn --no-run

# Training (20 epochs, default)
cargo run -p ml --example train_tft_dbn --release

# Custom training
cargo run -p ml --example train_tft_dbn --release -- \
  --epochs 50 \
  --batch-size 32 \
  --lookback 60 \
  --horizon 10
```

---

## Validation Checklist

- ✅ Zero compilation errors
- ✅ Feature count: 225 (verified via `feature_config.feature_count()`)
- ✅ Historical features shape: `[60, 225]`
- ✅ Checkpoint naming: `tft_225_epoch_{epoch}.safetensors`
- ✅ Tests updated: `test_convert_to_tft_format()`

---

## Expected Output

```
Configuration:
  • Feature count: 225 (Wave D: Wave C 201 + Wave D 24)
  • Hidden dimension: 256
  • Lookback window: 60
  • Forecast horizon: 10
```

---

## Files Modified

1. `ml/examples/train_tft_dbn.rs` (~150 lines)
2. `ml/src/trainers/tft.rs` (2 lines)

---

## Next Steps

1. **Dry run**: `cargo run -p ml --example train_tft_dbn --release -- --epochs 1`
2. **Full training**: 50 epochs on ES.FUT data
3. **Multi-symbol**: Train on ES.FUT, NQ.FUT, 6E.FUT, ZN.FUT
4. **Replace proxies**: Integrate real regime features (Agents D13-D16)

---

## Key Metrics

- **Compilation**: ✅ 0 errors, 66 warnings (non-critical)
- **Input dimension**: 225 features (4.5x from 50)
- **Expected inference**: ~3.5ms (target: <5ms)
- **Expected GPU memory**: ~180MB training (440MB total budget)

---

## Notes

⚠️ Wave D features (201-224) are currently **proxy features** derived from OHLCV. They will be replaced with actual regime detection features from Agents D13-D16 in Phase 4.

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**Report**: `AGENT_G9_TFT_225_FEATURES_IMPLEMENTATION_REPORT.md`