# Attention Score Model Training Report

**Generated:** 2026-01-25 06:00:49

## Overview

The attention model learns to identify high-flow market moments using volume, volatility, and money flow indicators - WITHOUT hardcoding specific trading hours or sessions.

## Configuration

- **Symbols:** XAUUSD
- **Timeframes:** 5m
- **Training Data Cutoff:** 2026-01-20
- **Training Years:** 1.0
- **Holdout Years:** 0.1

### Model Parameters

| Parameter | Value |
|-----------|-------|
| Factor Window | 200 |
| Horizon Bars | 3 |
| Low Flow Threshold | 1.0 |
| High Flow Threshold | 2.0 |

### Features Used (9 total)

| Feature | Description |
|---------|-------------|
| volume_ratio | volume / rolling_median(volume, 20) |
| volume_z | z-score of volume over 20 periods |
| ATR | Average True Range (14 periods) |
| ATR_ratio | ATR / rolling_median(ATR, 50) |
| CMF | Chaikin Money Flow (20 periods) |
| MFI | Money Flow Index (14 periods) |
| OBV_delta | diff(OBV) / rolling_std(OBV, 20) |
| BB_width | (BB_upper - BB_lower) / close |
| displacement | (close - open) / ATR |

## Training Results

| Model | Symbol | TF | Reg MAE | Reg R2 | Clf Acc | Clf F1 | N Train | High Flow % |
|-------|--------|-----|---------|--------|---------|--------|---------|-------------|
| XAUUSD_5m_attention | XAUUSD | 5m | 0.8237 | 0.2248 | 58.35% | 56.10% | 52983 | 40.2% |


## Class Distribution (Holdout Set)

| Model | Low Flow | Medium Flow | High Flow |
|-------|----------|-------------|-----------|
| XAUUSD_5m_attention | 62 (0.9%) | 3890 (58.8%) | 2661 (40.2%) |


## Feature Importance

### XAUUSD_5m_attention

| Rank | Feature | Combined Importance |
|------|---------|--------------------|
| 1 | ATR_ratio | 0.4268 |
| 2 | ATR | 0.1115 |
| 3 | displacement | 0.0801 |
| 4 | BB_width | 0.0801 |
| 5 | volume_ratio | 0.0776 |
| 6 | CMF | 0.0637 |
| 7 | volume_z | 0.0592 |
| 8 | MFI | 0.0524 |
| 9 | OBV_delta | 0.0486 |



## Interpretation

### Attention Score (Regression)

- **< 1.0**: Low flow period - below average market movement expected
- **1.0 - 2.0**: Medium flow period - average market conditions
- **> 2.0**: High flow period - above average movement expected (best trading opportunities)

### Flow Class (Classification)

- **0 (low_flow)**: move_multiplier < 1.0
- **1 (medium_flow)**: 1.0 <= move_multiplier < 2.0
- **2 (high_flow)**: move_multiplier >= 2.0

## Trading Recommendations

1. **Filter by attention_score**: Only trade when attention_score > 1.0
2. **Adjust position sizing**: Increase size when attention_score > 2.0
3. **Combine with base models**: Use attention_score as feature #51 in prediction models
4. **Time-agnostic**: The model identifies flow without hardcoded sessions

## Usage Example

```python
from training.attention_trainer import AttentionModelTrainer

# Load trained models
trainer = AttentionModelTrainer.load('models/attention/')

# Get attention score for new OHLCV data
attention = trainer.get_attention_score(df_ohlcv, 'XAUUSD', '5m')

# Filter trades
mask_trade = attention > 1.0  # Only trade in medium/high flow

# Or use as feature in base models
df['attention_score'] = attention
```

## Files Generated

- `models/attention/{symbol}_{timeframe}_attention/` - Model directories
- `models/attention/trainer_metadata.joblib` - Trainer configuration
- `models/attention/training_summary.csv` - Summary metrics

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*Report generated by Attention Model Training Pipeline*
