rckrdmrd
75c4d07690
Hierarchical ML Pipeline for trading predictions:
- Level 0: Attention Models (volatility/flow classification)
- Level 1: Base Models (XGBoost per symbol/timeframe)
- Level 2: Metamodels (XGBoost Stacking + Neural Gating)
Key components:
- src/pipelines/hierarchical_pipeline.py - Main prediction pipeline
- src/models/ - All ML model classes
- src/training/ - Training utilities
- src/api/ - FastAPI endpoints
- scripts/ - Training and evaluation scripts
- config/ - YAML configurations
Note: Trained models (*.joblib, *.pt) are gitignored.
Regenerate with training scripts.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
135 lines
3.8 KiB
Markdown
135 lines
3.8 KiB
Markdown
# Attention Score Model Training Report
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**Generated:** 2026-01-07 03:39:38
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## Overview
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The attention model learns to identify high-flow market moments using volume, volatility, and money flow indicators - WITHOUT hardcoding specific trading hours or sessions.
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## Configuration
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- **Symbols:** GBPUSD
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- **Timeframes:** 5m, 15m
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- **Training Data Cutoff:** 2024-12-31
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- **Training Years:** 5.0
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- **Holdout Years:** 1.0
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### Model Parameters
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| Parameter | Value |
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|-----------|-------|
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| Factor Window | 200 |
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| Horizon Bars | 3 |
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| Low Flow Threshold | 1.0 |
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| High Flow Threshold | 2.0 |
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### Features Used (9 total)
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| Feature | Description |
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|---------|-------------|
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| volume_ratio | volume / rolling_median(volume, 20) |
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| volume_z | z-score of volume over 20 periods |
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| ATR | Average True Range (14 periods) |
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| ATR_ratio | ATR / rolling_median(ATR, 50) |
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| CMF | Chaikin Money Flow (20 periods) |
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| MFI | Money Flow Index (14 periods) |
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| OBV_delta | diff(OBV) / rolling_std(OBV, 20) |
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| BB_width | (BB_upper - BB_lower) / close |
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| displacement | (close - open) / ATR |
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## Training Results
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| Model | Symbol | TF | Reg MAE | Reg R2 | Clf Acc | Clf F1 | N Train | High Flow % |
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|-------|--------|-----|---------|--------|---------|--------|---------|-------------|
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| GBPUSD_5m_attention | GBPUSD | 5m | 0.6262 | 0.1596 | 59.08% | 56.12% | 310727 | 24.3% |
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| GBPUSD_15m_attention | GBPUSD | 15m | 0.6953 | 0.2534 | 60.20% | 56.62% | 104434 | 35.7% |
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## Class Distribution (Holdout Set)
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| Model | Low Flow | Medium Flow | High Flow |
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|-------|----------|-------------|-----------|
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| GBPUSD_5m_attention | 6238 (8.4%) | 49712 (67.3%) | 17951 (24.3%) |
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| GBPUSD_15m_attention | 686 (2.8%) | 15199 (61.5%) | 8830 (35.7%) |
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## Feature Importance
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### GBPUSD_5m_attention
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| Rank | Feature | Combined Importance |
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|------|---------|--------------------|
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| 1 | ATR | 0.3542 |
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| 2 | ATR_ratio | 0.1580 |
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| 3 | BB_width | 0.1348 |
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| 4 | CMF | 0.0814 |
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| 5 | MFI | 0.0610 |
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| 6 | volume_ratio | 0.0604 |
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| 7 | volume_z | 0.0552 |
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| 8 | OBV_delta | 0.0499 |
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| 9 | displacement | 0.0450 |
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### GBPUSD_15m_attention
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| Rank | Feature | Combined Importance |
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|------|---------|--------------------|
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| 1 | ATR_ratio | 0.3374 |
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| 2 | ATR | 0.2368 |
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| 3 | volume_z | 0.1040 |
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| 4 | volume_ratio | 0.0950 |
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| 5 | BB_width | 0.0617 |
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| 6 | MFI | 0.0460 |
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| 7 | CMF | 0.0437 |
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| 8 | displacement | 0.0383 |
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| 9 | OBV_delta | 0.0370 |
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## Interpretation
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### Attention Score (Regression)
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- **< 1.0**: Low flow period - below average market movement expected
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- **1.0 - 2.0**: Medium flow period - average market conditions
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- **> 2.0**: High flow period - above average movement expected (best trading opportunities)
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### Flow Class (Classification)
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- **0 (low_flow)**: move_multiplier < 1.0
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- **1 (medium_flow)**: 1.0 <= move_multiplier < 2.0
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- **2 (high_flow)**: move_multiplier >= 2.0
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## Trading Recommendations
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1. **Filter by attention_score**: Only trade when attention_score > 1.0
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2. **Adjust position sizing**: Increase size when attention_score > 2.0
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3. **Combine with base models**: Use attention_score as feature #51 in prediction models
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4. **Time-agnostic**: The model identifies flow without hardcoded sessions
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## Usage Example
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```python
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from training.attention_trainer import AttentionModelTrainer
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# Load trained models
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trainer = AttentionModelTrainer.load('models/attention/')
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# Get attention score for new OHLCV data
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attention = trainer.get_attention_score(df_ohlcv, 'XAUUSD', '5m')
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# Filter trades
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mask_trade = attention > 1.0 # Only trade in medium/high flow
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# Or use as feature in base models
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df['attention_score'] = attention
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```
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## Files Generated
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- `models/attention/{symbol}_{timeframe}_attention/` - Model directories
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- `models/attention/trainer_metadata.joblib` - Trainer configuration
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- `models/attention/training_summary.csv` - Summary metrics
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---
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*Report generated by Attention Model Training Pipeline*
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