# Attention Score Model Training Report

**Generated:** 2026-01-25 06:09:11

## 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, EURUSD, BTCUSD, GBPUSD, USDJPY, AUDUSD
- **Timeframes:** 5m, 15m
- **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% |
| XAUUSD_15m_attention | XAUUSD | 15m | 0.8585 | 0.1183 | 54.28% | 51.34% | 17744 | 41.0% |
| EURUSD_5m_attention | EURUSD | 5m | 0.4217 | 0.2472 | 63.32% | 57.12% | 55849 | 10.5% |
| EURUSD_15m_attention | EURUSD | 15m | 0.5014 | 0.1783 | 61.45% | 55.96% | 18577 | 15.0% |
| BTCUSD_5m_attention | BTCUSD | 5m | 0.5886 | 0.2960 | 62.27% | 55.79% | 79155 | 12.8% |
| BTCUSD_15m_attention | BTCUSD | 15m | 0.6936 | 0.1764 | 60.25% | 56.22% | 26330 | 17.7% |
| GBPUSD_5m_attention | GBPUSD | 5m | 0.5024 | 0.2676 | 59.39% | 56.40% | 55618 | 23.5% |
| GBPUSD_15m_attention | GBPUSD | 15m | 0.5831 | 0.2139 | 59.53% | 56.19% | 18550 | 24.6% |
| USDJPY_5m_attention | USDJPY | 5m | 0.6536 | 0.1863 | 58.56% | 56.72% | 55687 | 26.2% |
| USDJPY_15m_attention | USDJPY | 15m | 0.7212 | 0.0821 | 53.98% | 50.89% | 18567 | 26.6% |
| AUDUSD_5m_attention | AUDUSD | 5m | 0.3715 | -0.2385 | 77.20% | 70.93% | 55315 | 1.9% |
| AUDUSD_15m_attention | AUDUSD | 15m | 0.4988 | -0.1155 | 69.43% | 62.51% | 18387 | 4.6% |


## Class Distribution (Holdout Set)

| Model | Low Flow | Medium Flow | High Flow |
|-------|----------|-------------|-----------|
| XAUUSD_5m_attention | 62 (0.9%) | 3890 (58.8%) | 2661 (40.2%) |
| XAUUSD_15m_attention | 0 (0.0%) | 1312 (59.0%) | 912 (41.0%) |
| EURUSD_5m_attention | 229 (3.3%) | 5964 (86.2%) | 727 (10.5%) |
| EURUSD_15m_attention | 60 (2.6%) | 1908 (82.4%) | 347 (15.0%) |
| BTCUSD_5m_attention | 113 (1.1%) | 9053 (86.1%) | 1347 (12.8%) |
| BTCUSD_15m_attention | 287 (8.2%) | 2597 (74.1%) | 621 (17.7%) |
| GBPUSD_5m_attention | 296 (4.3%) | 4985 (72.2%) | 1621 (23.5%) |
| GBPUSD_15m_attention | 97 (4.2%) | 1648 (71.2%) | 568 (24.6%) |
| USDJPY_5m_attention | 443 (6.4%) | 4661 (67.4%) | 1810 (26.2%) |
| USDJPY_15m_attention | 63 (2.7%) | 1636 (70.7%) | 615 (26.6%) |
| AUDUSD_5m_attention | 231 (3.3%) | 6580 (94.8%) | 130 (1.9%) |
| AUDUSD_15m_attention | 30 (1.3%) | 2181 (94.1%) | 106 (4.6%) |


## 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 |

### XAUUSD_15m_attention

| Rank | Feature | Combined Importance |
|------|---------|--------------------|
| 1 | ATR_ratio | 0.2016 |
| 2 | volume_ratio | 0.1666 |
| 3 | displacement | 0.1320 |
| 4 | volume_z | 0.0976 |
| 5 | BB_width | 0.0921 |
| 6 | ATR | 0.0830 |
| 7 | MFI | 0.0786 |
| 8 | OBV_delta | 0.0763 |
| 9 | CMF | 0.0722 |

### EURUSD_5m_attention

| Rank | Feature | Combined Importance |
|------|---------|--------------------|
| 1 | ATR | 0.3272 |
| 2 | ATR_ratio | 0.2003 |
| 3 | BB_width | 0.1141 |
| 4 | volume_z | 0.0970 |
| 5 | volume_ratio | 0.0849 |
| 6 | CMF | 0.0468 |
| 7 | displacement | 0.0468 |
| 8 | MFI | 0.0438 |
| 9 | OBV_delta | 0.0392 |

### EURUSD_15m_attention

| Rank | Feature | Combined Importance |
|------|---------|--------------------|
| 1 | ATR_ratio | 0.2958 |
| 2 | volume_z | 0.1386 |
| 3 | volume_ratio | 0.1346 |
| 4 | ATR | 0.1167 |
| 5 | BB_width | 0.0719 |
| 6 | MFI | 0.0636 |
| 7 | CMF | 0.0615 |
| 8 | displacement | 0.0598 |
| 9 | OBV_delta | 0.0574 |

### BTCUSD_5m_attention

| Rank | Feature | Combined Importance |
|------|---------|--------------------|
| 1 | ATR_ratio | 0.3239 |
| 2 | BB_width | 0.1286 |
| 3 | volume_ratio | 0.1037 |
| 4 | volume_z | 0.0852 |
| 5 | ATR | 0.0816 |
| 6 | CMF | 0.0793 |
| 7 | displacement | 0.0701 |
| 8 | MFI | 0.0661 |
| 9 | OBV_delta | 0.0616 |

### BTCUSD_15m_attention

| Rank | Feature | Combined Importance |
|------|---------|--------------------|
| 1 | ATR_ratio | 0.3038 |
| 2 | volume_ratio | 0.1570 |
| 3 | BB_width | 0.0998 |
| 4 | ATR | 0.0983 |
| 5 | volume_z | 0.0901 |
| 6 | displacement | 0.0679 |
| 7 | CMF | 0.0664 |
| 8 | OBV_delta | 0.0597 |
| 9 | MFI | 0.0569 |

### GBPUSD_5m_attention

| Rank | Feature | Combined Importance |
|------|---------|--------------------|
| 1 | ATR | 0.3587 |
| 2 | ATR_ratio | 0.1753 |
| 3 | volume_ratio | 0.1055 |
| 4 | BB_width | 0.0981 |
| 5 | volume_z | 0.0852 |
| 6 | displacement | 0.0514 |
| 7 | CMF | 0.0474 |
| 8 | OBV_delta | 0.0419 |
| 9 | MFI | 0.0365 |

### GBPUSD_15m_attention

| Rank | Feature | Combined Importance |
|------|---------|--------------------|
| 1 | ATR_ratio | 0.3105 |
| 2 | volume_z | 0.1476 |
| 3 | volume_ratio | 0.1287 |
| 4 | ATR | 0.1145 |
| 5 | BB_width | 0.0708 |
| 6 | MFI | 0.0605 |
| 7 | CMF | 0.0590 |
| 8 | OBV_delta | 0.0587 |
| 9 | displacement | 0.0499 |

### USDJPY_5m_attention

| Rank | Feature | Combined Importance |
|------|---------|--------------------|
| 1 | ATR_ratio | 0.3854 |
| 2 | ATR | 0.1623 |
| 3 | volume_z | 0.1100 |
| 4 | volume_ratio | 0.0971 |
| 5 | BB_width | 0.0901 |
| 6 | displacement | 0.0479 |
| 7 | OBV_delta | 0.0365 |
| 8 | MFI | 0.0359 |
| 9 | CMF | 0.0349 |

### USDJPY_15m_attention

| Rank | Feature | Combined Importance |
|------|---------|--------------------|
| 1 | volume_ratio | 0.2208 |
| 2 | volume_z | 0.2110 |
| 3 | ATR_ratio | 0.1173 |
| 4 | BB_width | 0.0934 |
| 5 | displacement | 0.0857 |
| 6 | ATR | 0.0829 |
| 7 | CMF | 0.0666 |
| 8 | OBV_delta | 0.0638 |
| 9 | MFI | 0.0585 |

### AUDUSD_5m_attention

| Rank | Feature | Combined Importance |
|------|---------|--------------------|
| 1 | ATR | 0.2316 |
| 2 | volume_ratio | 0.1677 |
| 3 | ATR_ratio | 0.1320 |
| 4 | volume_z | 0.1139 |
| 5 | MFI | 0.0923 |
| 6 | BB_width | 0.0796 |
| 7 | displacement | 0.0718 |
| 8 | CMF | 0.0717 |
| 9 | OBV_delta | 0.0394 |

### AUDUSD_15m_attention

| Rank | Feature | Combined Importance |
|------|---------|--------------------|
| 1 | volume_z | 0.1874 |
| 2 | volume_ratio | 0.1795 |
| 3 | BB_width | 0.1206 |
| 4 | ATR_ratio | 0.1140 |
| 5 | ATR | 0.0936 |
| 6 | CMF | 0.0923 |
| 7 | MFI | 0.0819 |
| 8 | displacement | 0.0779 |
| 9 | OBV_delta | 0.0529 |



## 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*