trading-platform-ml-engine-v2/models/ATTENTION_TRAINING_REPORT_20260106_234655.md

Attention Score Model Training Report

Generated: 2026-01-06 23:46:55

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
• Timeframes: 5m, 15m
• Training Data Cutoff: 2024-03-01
• Training Years: 5.0
• Holdout Years: 1.0

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	0.8528	0.1914	61.44%	57.96%	288386	23.1%
XAUUSD_15m_attention	XAUUSD	15m	0.8564	0.1250	59.39%	54.70%	96801	25.8%
EURUSD_5m_attention	EURUSD	5m	0.6678	0.1569	54.07%	49.84%	312891	34.3%
EURUSD_15m_attention	EURUSD	15m	0.6405	0.2193	60.70%	57.20%	104659	36.3%

Class Distribution (Holdout Set)

Model	Low Flow	Medium Flow	High Flow
XAUUSD_5m_attention	265 (0.4%)	53705 (76.5%)	16238 (23.1%)
XAUUSD_15m_attention	0 (0.0%)	17566 (74.2%)	6106 (25.8%)
EURUSD_5m_attention	2380 (3.2%)	46893 (62.5%)	25781 (34.3%)
EURUSD_15m_attention	443 (1.8%)	15629 (62.0%)	9143 (36.3%)

Feature Importance

XAUUSD_5m_attention

Rank	Feature	Combined Importance
1	ATR_ratio	0.4240
2	BB_width	0.1601
3	ATR	0.1229
4	CMF	0.1164
5	volume_ratio	0.0639
6	volume_z	0.0399
7	displacement	0.0331
8	OBV_delta	0.0213
9	MFI	0.0184

XAUUSD_15m_attention

Rank	Feature	Combined Importance
1	ATR_ratio	0.3364
2	volume_ratio	0.1779
3	BB_width	0.1414
4	volume_z	0.1034
5	displacement	0.0743
6	ATR	0.0651
7	OBV_delta	0.0441
8	CMF	0.0331
9	MFI	0.0243

EURUSD_5m_attention

Rank	Feature	Combined Importance
1	ATR_ratio	0.3577
2	BB_width	0.2217
3	ATR	0.1566
4	volume_ratio	0.0765
5	CMF	0.0569
6	volume_z	0.0536
7	displacement	0.0315
8	OBV_delta	0.0264
9	MFI	0.0191

EURUSD_15m_attention

Rank	Feature	Combined Importance
1	ATR_ratio	0.5007
2	volume_ratio	0.1497
3	volume_z	0.1129
4	ATR	0.0990
5	BB_width	0.0396
6	displacement	0.0284
7	CMF	0.0254
8	OBV_delta	0.0245
9	MFI	0.0198

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

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