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

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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

Filter by attention_score: Only trade when attention_score > 1.0
Adjust position sizing: Increase size when attention_score > 2.0
Combine with base models: Use attention_score as feature #51 in prediction models
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

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