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trading-platform-ml-engine-v2/models/ATTENTION_TRAINING_REPORT_20260125_060911.md
Adrian Flores Cortes dcfe83bb44 feat: Update data ingestion and add training reports 
Scripts:
- Update ingest_ohlcv_polygon.py for improved data processing

Reports:
- Add attention model training reports (2x)
- Add standard training reports (2x)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-30 12:24:53 -06:00

8.7 KiB
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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

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

Report generated by Attention Model Training Pipeline