86afdb714d
- G15: Ring buffer memory optimization (2.87 GB reduction target) - G16: Memory validation (identified gaps in initial implementation) - G17: Complete memory optimization (fixed RingBuffer design, lazy allocation) - G18: Performance benchmarks (12% faster average, zero regression) - G19: Profiling validation (5μs P50 latency, 99.6% fewer allocations) Production readiness: 92% Test coverage: 34/36 tests passing (94.4%) Memory savings: 66% reduction (2.87 GB for 100K symbols) Performance: 5-40% improvement across all benchmarks Modified files: - ml/src/features/normalization.rs (RingBuffer implementation) - ml/src/features/pipeline.rs (lazy bars allocation) - ml/src/features/volume_features.rs (lazy allocation) - adaptive-strategy/src/ensemble/weight_optimizer.rs (regime Sharpe) - ml/src/tft/mod.rs (225-feature support)
111 lines
2.8 KiB
Markdown
111 lines
2.8 KiB
Markdown
# Agent G9: TFT 225-Feature Update - Quick Reference
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**Status**: ✅ COMPLETE
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**Date**: 2025-10-18
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---
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## What Changed
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### 1. Training Script (`ml/examples/train_tft_dbn.rs`)
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- ✅ Added `FeatureConfig::wave_d()` initialization
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- ✅ Updated `convert_to_tft_data()` to accept feature config
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- ✅ Changed historical features from 50 → 225 per timestep
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- ✅ Updated array shape: `(lookback_window, 50)` → `(lookback_window, 225)`
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- ✅ Added Wave D features (indices 201-224, 24 features)
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### 2. Trainer Config (`ml/src/trainers/tft.rs`)
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- ✅ Updated `num_unknown_features: 50` → `225`
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- ✅ Updated checkpoint naming: `tft_epoch_{}` → `tft_225_epoch_{}`
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---
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## Feature Breakdown (225 Total)
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| Range | Category | Count | Description |
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|-------|----------|-------|-------------|
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| 0-200 | Wave C | 201 | OHLCV, technicals, microstructure, statistical |
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| 201-210 | CUSUM | 10 | Structural break detection |
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| 211-215 | ADX | 5 | Directional indicators |
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| 216-220 | Transitions | 5 | Regime transition probabilities |
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| 221-224 | Adaptive | 4 | Position sizing, stop-loss, Sharpe |
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---
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## Run Commands
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```bash
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# Compile check
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cargo check -p ml --example train_tft_dbn
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# Test compilation
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cargo test -p ml --example train_tft_dbn --no-run
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# Training (20 epochs, default)
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cargo run -p ml --example train_tft_dbn --release
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# Custom training
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cargo run -p ml --example train_tft_dbn --release -- \
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--epochs 50 \
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--batch-size 32 \
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--lookback 60 \
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--horizon 10
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```
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---
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## Validation Checklist
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- ✅ Zero compilation errors
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- ✅ Feature count: 225 (verified via `feature_config.feature_count()`)
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- ✅ Historical features shape: `[60, 225]`
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- ✅ Checkpoint naming: `tft_225_epoch_{epoch}.safetensors`
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- ✅ Tests updated: `test_convert_to_tft_format()`
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---
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## Expected Output
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```
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Configuration:
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• Feature count: 225 (Wave D: Wave C 201 + Wave D 24)
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• Hidden dimension: 256
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• Lookback window: 60
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• Forecast horizon: 10
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```
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---
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## Files Modified
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1. `ml/examples/train_tft_dbn.rs` (~150 lines)
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2. `ml/src/trainers/tft.rs` (2 lines)
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---
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## Next Steps
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1. **Dry run**: `cargo run -p ml --example train_tft_dbn --release -- --epochs 1`
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2. **Full training**: 50 epochs on ES.FUT data
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3. **Multi-symbol**: Train on ES.FUT, NQ.FUT, 6E.FUT, ZN.FUT
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4. **Replace proxies**: Integrate real regime features (Agents D13-D16)
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---
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## Key Metrics
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- **Compilation**: ✅ 0 errors, 66 warnings (non-critical)
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- **Input dimension**: 225 features (4.5x from 50)
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- **Expected inference**: ~3.5ms (target: <5ms)
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- **Expected GPU memory**: ~180MB training (440MB total budget)
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---
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## Notes
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⚠️ 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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---
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**Report**: `AGENT_G9_TFT_225_FEATURES_IMPLEMENTATION_REPORT.md`
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