ffee1900f9
- DATABASE_INVENTORY: Added data_status section with 469K+ bars loaded - ML_INVENTORY: Documented MySQL deprecation and PostgreSQL migration - 6 tickers loaded with 365 days of 5-minute data from Polygon API Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
757 lines
21 KiB
YAML
757 lines
21 KiB
YAML
# ML_INVENTORY.yml - Inventario de Componentes ML Engine
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# Trading Platform Trading Platform
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# Ultima actualizacion: 2026-01-25
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metadata:
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version: "2.2.0"
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last_updated: "2026-01-25"
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epic: "OQI-006"
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description: "Inventario de modelos, features y servicios del ML Engine"
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changelog:
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- version: "2.2.0"
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date: "2026-01-25"
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changes:
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- "Database migration: MySQL deprecated, now using PostgreSQL exclusively"
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- "Added src/data/database.py module for PostgreSQL access"
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- "Created .env with PostgreSQL credentials"
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- "Updated config/database.yaml to mark MySQL as deprecated"
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- "Data loaded from Polygon API into local PostgreSQL (6 tickers, 365 days)"
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- version: "2.1.0"
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date: "2026-01-07"
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changes:
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- "Added models ML-008 to ML-018 (previously undocumented)"
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- "Added SVC-ML-005 HierarchicalPredictorService"
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- "Alignment validation completed"
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- version: "2.0.0"
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date: "2026-01-07"
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changes:
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- "Added AttentionScoreModel (ML-005)"
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- "Added SymbolTimeframeModel with attention (ML-006)"
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- "Added AssetMetamodel (ML-007 - planned)"
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- "Added attention features (FA-001 to FA-009)"
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- version: "1.0.0"
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date: "2025-12-05"
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changes:
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- "Initial inventory creation"
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# ============================================
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# MODELOS DE MACHINE LEARNING
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# ============================================
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models:
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- id: "ML-001"
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name: "PricePredictor"
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description: "Modelo de predicción de dirección de precio"
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type: "classification"
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framework: "PyTorch"
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input_features: 45
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output: "bullish/bearish/neutral"
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confidence_range: "0.0-1.0"
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horizons:
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- "1h"
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- "4h"
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- "1d"
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symbols_supported:
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- "stocks_us"
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- "crypto_major"
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training_frequency: "weekly"
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accuracy_target: "65%"
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related_rf: "RF-ML-001"
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status: "planned"
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- id: "ML-002"
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name: "TrendDetector"
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description: "Detector de tendencias y cambios de tendencia"
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type: "classification"
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framework: "PyTorch"
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input_features: 30
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output: "uptrend/downtrend/ranging"
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horizons:
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- "4h"
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- "1d"
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- "1w"
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related_rf: "RF-ML-002"
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status: "planned"
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- id: "ML-003"
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name: "VolatilityPredictor"
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description: "Predictor de volatilidad futura"
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type: "regression"
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framework: "PyTorch"
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input_features: 25
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output: "volatility_percent"
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related_rf: "RF-ML-003"
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status: "planned"
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- id: "ML-004"
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name: "SentimentAnalyzer"
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description: "Análisis de sentimiento de noticias"
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type: "classification"
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framework: "Transformers"
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model_base: "FinBERT"
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output: "positive/negative/neutral"
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related_rf: "RF-ML-004"
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status: "planned"
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- id: "ML-005"
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name: "AttentionScoreModel"
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description: "Modelo de atencion que aprende CUANDO prestar atencion al mercado (Nivel 0 de arquitectura jerarquica)"
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type: "dual (regression + classification)"
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framework: "XGBoost"
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input_features: 9
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features:
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- "volume_ratio"
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- "volume_z"
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- "ATR"
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- "ATR_ratio"
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- "CMF"
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- "MFI"
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- "OBV_delta"
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- "BB_width"
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- "displacement"
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output:
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regression: "attention_score (0-3)"
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classification: "flow_class (0=low, 1=medium, 2=high)"
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target: "move_multiplier = future_range / rolling_median(range)"
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symbols_supported:
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- "XAUUSD"
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- "EURUSD"
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- "BTCUSD"
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- "GBPUSD"
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- "USDJPY"
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timeframes:
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- "5m"
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- "15m"
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training_frequency: "weekly"
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metrics:
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r2_regression: "0.12-0.22"
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classification_accuracy: "54-61%"
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related_et: "ET-ML-007"
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files:
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model: "src/models/attention_score_model.py"
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trainer: "src/training/attention_trainer.py"
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script: "scripts/train_attention_model.py"
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status: "implemented"
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implementation_date: "2026-01-06"
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- id: "ML-006"
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name: "SymbolTimeframeModel"
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description: "Modelo base de prediccion de rango con attention features (Nivel 1 de arquitectura jerarquica)"
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type: "regression"
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framework: "XGBoost"
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input_features: 52
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features_breakdown:
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base_features: 50
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attention_features: 2
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attention_features:
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- "attention_score"
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- "attention_class"
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output:
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- "delta_high (multiplos de factor)"
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- "delta_low (multiplos de factor)"
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symbols_supported:
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- "XAUUSD"
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- "EURUSD"
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- "BTCUSD"
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- "GBPUSD"
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- "USDJPY"
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timeframes:
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- "5m"
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- "15m"
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training_frequency: "weekly"
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uses_attention: true
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related_et: "ET-ML-007"
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files:
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trainer: "src/training/symbol_timeframe_trainer.py"
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script: "scripts/train_symbol_timeframe_models.py"
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status: "implemented"
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implementation_date: "2026-01-06"
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- id: "ML-007"
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name: "AssetMetamodel"
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description: "Metamodelo por activo que sintetiza predicciones de 5m y 15m (Nivel 2 de arquitectura jerarquica)"
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type: "dual (regression + classification)"
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framework: "XGBoost"
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input_features: 10
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features:
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predictions:
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- "pred_high_5m"
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- "pred_low_5m"
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- "pred_high_15m"
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- "pred_low_15m"
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attention:
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- "attention_5m"
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- "attention_15m"
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- "attention_class_5m"
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- "attention_class_15m"
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context:
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- "ATR_ratio"
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- "volume_z"
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output:
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- "delta_high_final"
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- "delta_low_final"
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- "confidence (binary + probability)"
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symbols_trained:
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- "XAUUSD"
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- "EURUSD"
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- "GBPUSD"
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- "USDJPY"
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- "BTCUSD"
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symbols_pending: []
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training_frequency: "weekly"
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uses_oos_predictions: true
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oos_period: "2024-06-01 to 2025-12-31"
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metrics:
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XAUUSD:
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samples: 18749
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mae_high: 2.0818
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mae_low: 2.2241
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r2_high: 0.0674
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r2_low: 0.1150
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confidence_accuracy: "90.01%"
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improvement_vs_avg: "+1.9%"
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EURUSD:
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samples: 19505
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mae_high: 0.0005
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mae_low: 0.0004
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r2_high: -0.0417
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r2_low: -0.0043
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confidence_accuracy: "86.26%"
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improvement_vs_avg: "+3.0%"
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GBPUSD:
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samples: 17412
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confidence_accuracy: "93.0%"
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status: "trained"
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USDJPY:
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samples: 16547
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confidence_accuracy: "93.6%"
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status: "trained"
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BTCUSD:
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samples: 23233
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mae_high: 150.58
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mae_low: 175.84
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r2_high: 0.163
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r2_low: 0.035
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confidence_accuracy: "87.3%"
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improvement_vs_avg: "+5.3%"
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status: "trained"
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backtest:
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strategy: "aggressive_filter"
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win_rate: "46.8%"
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expectancy: "+0.0700"
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profit_factor: 1.17
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related_et: "ET-ML-007"
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files:
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model: "src/models/asset_metamodel.py"
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trainer: "src/training/metamodel_trainer.py"
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script: "scripts/train_metamodels.py"
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saved_models:
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- "models/metamodels/XAUUSD/"
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- "models/metamodels/EURUSD/"
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- "models/metamodels/GBPUSD/"
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- "models/metamodels/USDJPY/"
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- "models/metamodels/BTCUSD/"
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status: "implemented"
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implementation_date: "2026-01-07"
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- id: "ML-008"
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name: "RangePredictor"
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description: "Legacy range prediction model"
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type: "regression"
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framework: "XGBoost"
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file: "src/models/range_predictor.py"
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status: "implemented"
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- id: "ML-009"
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name: "RangePredictorV2"
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description: "Multi-timeframe range prediction model"
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type: "regression"
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framework: "XGBoost"
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file: "src/models/range_predictor_v2.py"
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status: "implemented"
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- id: "ML-010"
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name: "RangePredictorFactor"
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description: "Factor-based range prediction model"
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type: "regression"
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framework: "XGBoost"
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file: "src/models/range_predictor_factor.py"
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status: "implemented"
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- id: "ML-011"
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name: "EnhancedRangePredictor"
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description: "Enhanced range predictor with context"
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type: "regression"
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framework: "XGBoost"
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file: "src/models/enhanced_range_predictor.py"
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status: "implemented"
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- id: "ML-012"
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name: "AMDDetectorML"
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description: "AMD phases ML detector"
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type: "classification"
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framework: "XGBoost"
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file: "src/models/amd_detector_ml.py"
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status: "implemented"
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- id: "ML-013"
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name: "ICTSMCDetector"
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description: "ICT/SMC patterns detector"
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type: "classification"
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framework: "XGBoost"
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file: "src/models/ict_smc_detector.py"
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status: "implemented"
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- id: "ML-014"
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name: "MovementMagnitudePredictor"
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description: "Movement USD prediction model"
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type: "regression"
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framework: "XGBoost"
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file: "src/models/movement_magnitude_predictor.py"
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status: "implemented"
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- id: "ML-015"
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name: "TPSLClassifier"
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description: "TP/SL probability classifier"
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type: "classification"
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framework: "XGBoost"
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file: "src/models/tp_sl_classifier.py"
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status: "implemented"
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- id: "ML-016"
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name: "SignalGenerator"
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description: "Trading signals generator"
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type: "classification"
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framework: "XGBoost"
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file: "src/models/signal_generator.py"
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status: "implemented"
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- id: "ML-017"
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name: "DualHorizonEnsemble"
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description: "Multi-horizon ensemble model"
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type: "ensemble"
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framework: "XGBoost"
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file: "src/models/dual_horizon_ensemble.py"
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status: "implemented"
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- id: "ML-018"
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name: "NeuralGatingMetamodel"
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description: "Neural gating metamodel"
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type: "ensemble"
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framework: "PyTorch"
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file: "src/models/neural_gating_metamodel.py"
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status: "implemented"
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# ============================================
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# FEATURES ENGINEERING
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# ============================================
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features:
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technical:
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- id: "FT-001"
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name: "rsi_14"
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description: "Relative Strength Index 14 períodos"
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type: "float"
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range: "0-100"
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- id: "FT-002"
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name: "macd_signal"
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description: "MACD Signal Line"
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type: "float"
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- id: "FT-003"
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name: "macd_histogram"
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description: "MACD Histogram"
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type: "float"
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- id: "FT-004"
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name: "bb_position"
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description: "Posición relativa en Bollinger Bands"
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type: "float"
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range: "0-1"
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- id: "FT-005"
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name: "sma_20_50_cross"
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description: "Cruce SMA 20/50"
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type: "int"
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values: "-1/0/1"
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- id: "FT-006"
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name: "atr_14"
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description: "Average True Range 14 períodos"
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type: "float"
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- id: "FT-007"
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name: "volume_ratio"
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description: "Ratio volumen actual vs promedio"
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type: "float"
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- id: "FT-008"
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name: "price_momentum"
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description: "Momentum de precio (ROC)"
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type: "float"
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market_structure:
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- id: "FM-001"
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name: "support_distance"
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description: "Distancia al soporte más cercano"
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type: "float"
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- id: "FM-002"
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name: "resistance_distance"
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description: "Distancia a la resistencia más cercana"
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type: "float"
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- id: "FM-003"
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name: "trend_strength"
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description: "Fuerza de la tendencia (ADX)"
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type: "float"
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range: "0-100"
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sentiment:
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- id: "FS-001"
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name: "news_sentiment"
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description: "Sentimiento de noticias recientes"
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type: "float"
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range: "-1 to 1"
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- id: "FS-002"
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name: "social_sentiment"
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description: "Sentimiento de redes sociales"
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type: "float"
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range: "-1 to 1"
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- id: "FS-003"
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name: "fear_greed_index"
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description: "Indice de miedo y codicia (crypto)"
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type: "int"
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range: "0-100"
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attention:
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- id: "FA-001"
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name: "volume_ratio"
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description: "Ratio de volumen actual vs mediana movil"
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type: "float"
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calculation: "volume / rolling_median(volume, 20)"
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used_by: ["ML-005"]
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- id: "FA-002"
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name: "volume_z"
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description: "Z-score del volumen"
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type: "float"
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calculation: "(volume - rolling_mean) / rolling_std"
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window: 20
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used_by: ["ML-005"]
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- id: "FA-003"
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name: "ATR_ratio"
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description: "Ratio de ATR vs mediana movil - FEATURE MAS IMPORTANTE"
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type: "float"
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calculation: "ATR / rolling_median(ATR, 50)"
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importance: "34-50%"
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used_by: ["ML-005"]
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- id: "FA-004"
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name: "CMF"
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description: "Chaikin Money Flow - flujo de dinero"
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type: "float"
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range: "-1 to 1"
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used_by: ["ML-005"]
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- id: "FA-005"
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name: "MFI"
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description: "Money Flow Index"
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type: "float"
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range: "0-100"
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used_by: ["ML-005"]
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- id: "FA-006"
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name: "OBV_delta"
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description: "Cambio en On-Balance Volume normalizado"
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type: "float"
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calculation: "diff(OBV) / rolling_std(OBV, 20)"
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used_by: ["ML-005"]
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- id: "FA-007"
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name: "BB_width"
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description: "Ancho de Bollinger Bands normalizado"
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type: "float"
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calculation: "(BB_upper - BB_lower) / close"
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used_by: ["ML-005"]
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- id: "FA-008"
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name: "displacement"
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description: "Desplazamiento de precio normalizado por ATR"
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type: "float"
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calculation: "(close - open) / ATR"
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used_by: ["ML-005"]
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- id: "FA-009"
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name: "attention_score"
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description: "Score de atencion generado por modelo ML-005"
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type: "float"
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range: "0-3"
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output_of: "ML-005"
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used_by: ["ML-006", "ML-007"]
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- id: "FA-010"
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name: "attention_class"
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description: "Clasificacion de flujo generada por modelo ML-005"
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type: "int"
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values: "0=low_flow, 1=medium_flow, 2=high_flow"
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output_of: "ML-005"
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used_by: ["ML-006", "ML-007"]
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# ============================================
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# SERVICIOS ML
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# ============================================
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services:
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- id: "SVC-ML-001"
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name: "MLPredictionService"
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description: "Servicio principal de predicciones"
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framework: "FastAPI"
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endpoints:
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- path: "/predict/{symbol}"
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method: "GET"
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description: "Obtener predicción para símbolo"
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- path: "/predict/batch"
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method: "POST"
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description: "Predicciones en batch"
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related_et: "ET-ML-001"
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- id: "SVC-ML-002"
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name: "FeatureEngineering"
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description: "Cálculo y cache de features"
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framework: "Python"
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dependencies:
|
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- "pandas"
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- "numpy"
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- "ta-lib"
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related_et: "ET-ML-002"
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|
|
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- id: "SVC-ML-003"
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name: "ModelTrainer"
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|
description: "Entrenamiento y actualización de modelos"
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framework: "PyTorch"
|
|
schedule: "weekly"
|
|
related_et: "ET-ML-003"
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|
|
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- id: "SVC-ML-004"
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name: "ModelRegistry"
|
|
description: "Registro y versionado de modelos"
|
|
framework: "MLflow"
|
|
storage: "S3"
|
|
related_et: "ET-ML-004"
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|
|
|
- id: "SVC-ML-005"
|
|
name: "HierarchicalPredictorService"
|
|
description: "Servicio de predicción jerárquica de 3 niveles"
|
|
framework: "Python"
|
|
file: "src/services/hierarchical_predictor.py"
|
|
related_et: "ET-ML-007"
|
|
|
|
# ============================================
|
|
# PIPELINES
|
|
# ============================================
|
|
pipelines:
|
|
- id: "PIP-001"
|
|
name: "RealTimePrediction"
|
|
description: "Pipeline de predicción en tiempo real"
|
|
steps:
|
|
- "fetch_market_data"
|
|
- "calculate_features"
|
|
- "normalize_features"
|
|
- "run_inference"
|
|
- "post_process"
|
|
- "cache_result"
|
|
latency_target: "< 500ms"
|
|
|
|
- id: "PIP-002"
|
|
name: "DailyRetrain"
|
|
description: "Pipeline de reentrenamiento diario"
|
|
steps:
|
|
- "fetch_training_data"
|
|
- "feature_engineering"
|
|
- "train_model"
|
|
- "evaluate_model"
|
|
- "register_if_improved"
|
|
schedule: "daily"
|
|
|
|
- id: "PIP-003"
|
|
name: "BatchPrediction"
|
|
description: "Pipeline de predicción en batch"
|
|
steps:
|
|
- "fetch_symbols_list"
|
|
- "parallel_feature_calc"
|
|
- "batch_inference"
|
|
- "store_results"
|
|
schedule: "every_4h"
|
|
|
|
# ============================================
|
|
# CONFIGURACIÓN
|
|
# ============================================
|
|
config:
|
|
inference:
|
|
cache_ttl: 60 # segundos
|
|
batch_size: 100
|
|
timeout: 5000 # ms
|
|
|
|
training:
|
|
train_test_split: 0.8
|
|
validation_split: 0.1
|
|
epochs: 100
|
|
early_stopping_patience: 10
|
|
|
|
features:
|
|
lookback_periods:
|
|
short: 20
|
|
medium: 50
|
|
long: 200
|
|
normalization: "min_max"
|
|
|
|
# ============================================
|
|
# MÉTRICAS Y MONITOREO
|
|
# ============================================
|
|
metrics:
|
|
model_performance:
|
|
- name: "accuracy"
|
|
target: "> 0.65"
|
|
- name: "precision"
|
|
target: "> 0.60"
|
|
- name: "recall"
|
|
target: "> 0.60"
|
|
- name: "f1_score"
|
|
target: "> 0.60"
|
|
|
|
service_health:
|
|
- name: "latency_p99"
|
|
target: "< 1000ms"
|
|
- name: "availability"
|
|
target: "> 99.5%"
|
|
- name: "error_rate"
|
|
target: "< 1%"
|
|
|
|
# ============================================
|
|
# INTEGRACIÓN CON TRADINGAGENT
|
|
# ============================================
|
|
tradingagent_integration:
|
|
description: "Migración del ML Engine existente de TradingAgent"
|
|
source_repo: "tradingagent"
|
|
components_to_migrate:
|
|
- name: "PredictionEngine"
|
|
source: "tradingagent/ml/prediction_engine.py"
|
|
target: "apps/ml-engine/src/prediction/"
|
|
status: "planned"
|
|
|
|
- name: "FeatureCalculator"
|
|
source: "tradingagent/ml/features.py"
|
|
target: "apps/ml-engine/src/features/"
|
|
status: "planned"
|
|
|
|
- name: "ModelLoader"
|
|
source: "tradingagent/ml/model_loader.py"
|
|
target: "apps/ml-engine/src/models/"
|
|
status: "planned"
|
|
|
|
api_compatibility:
|
|
description: "Mantener compatibilidad con API existente"
|
|
endpoints_to_preserve:
|
|
- "/api/v1/predict"
|
|
- "/api/v1/signals"
|
|
- "/api/v1/features"
|
|
|
|
# ============================================
|
|
# NOTAS DE COMPATIBILIDAD DE FEATURES
|
|
# ============================================
|
|
feature_compatibility:
|
|
description: "Documentación de compatibilidad entre modelos con diferentes números de features"
|
|
last_updated: "2026-01-07"
|
|
|
|
models_feature_count:
|
|
GBPUSD:
|
|
feature_count: 50
|
|
uses_attention: false
|
|
note: "Entrenado con use_attention_features=False"
|
|
status: "trained"
|
|
training_date: "2026-01-07"
|
|
EURUSD:
|
|
feature_count: 52
|
|
uses_attention: true
|
|
note: "Entrenado con attention_score y attention_class"
|
|
status: "trained"
|
|
training_date: "2026-01-06"
|
|
XAUUSD:
|
|
feature_count: 52
|
|
uses_attention: true
|
|
note: "Entrenado con attention_score y attention_class"
|
|
status: "trained"
|
|
training_date: "2026-01-06"
|
|
USDJPY:
|
|
feature_count: 50
|
|
uses_attention: false
|
|
note: "Attention models trained, base models without attention features"
|
|
status: "trained"
|
|
training_date: "2026-01-07"
|
|
backtest_results:
|
|
period: "2024-09-01 to 2024-12-31"
|
|
win_rate: "39.2%"
|
|
expectancy: "-0.0544"
|
|
confidence_accuracy: "93.6%"
|
|
BTCUSD:
|
|
feature_count: 50
|
|
uses_attention: false
|
|
note: "ACTUALIZADO - Datos de Polygon API (2024-2025)"
|
|
status: "trained"
|
|
training_date: "2026-01-07"
|
|
data_source:
|
|
provider: "Polygon.io API"
|
|
available_range: "2015-03-22 to 2025-12-31"
|
|
new_data_range: "2024-01-07 to 2025-12-31"
|
|
new_records: 215699
|
|
total_records: 367500
|
|
model_metrics:
|
|
attention_5m:
|
|
r2: 0.223
|
|
accuracy: "62.3%"
|
|
attention_15m:
|
|
r2: 0.169
|
|
accuracy: "59.9%"
|
|
metamodel:
|
|
confidence_accuracy: "87.3%"
|
|
improvement_over_avg: "5.3%"
|
|
backtest_results:
|
|
period: "2025-09-01 to 2025-12-31"
|
|
best_strategy: "aggressive_filter"
|
|
trades: 2524
|
|
win_rate: "46.8%"
|
|
expectancy: "+0.0700"
|
|
profit_factor: 1.17
|
|
total_profit_r: "+176.71"
|
|
status: "PROFITABLE"
|
|
|
|
pipeline_handling:
|
|
description: "El pipeline maneja automáticamente la diferencia de features"
|
|
mechanism: "_prepare_features_for_base_model() excluye attention_score y attention_class"
|
|
files:
|
|
- "src/pipelines/hierarchical_pipeline.py:402-408"
|
|
- "src/training/metamodel_trainer.py:343-349"
|
|
|
|
known_issues_resolved:
|
|
- id: "FIX-001"
|
|
date: "2026-01-07"
|
|
issue: "Feature shape mismatch, expected: 50, got 52"
|
|
cause: "Caché de Python contenía código sin el fix de exclusión"
|
|
resolution: "Limpieza de __pycache__ y *.pyc"
|
|
status: "RESOLVED"
|
|
|
|
# ============================================
|
|
# REFERENCIAS
|
|
# ============================================
|
|
references:
|
|
requirements:
|
|
- "docs/02-definicion-modulos/OQI-006-ml-signals/requerimientos/"
|
|
specifications:
|
|
- "docs/02-definicion-modulos/OQI-006-ml-signals/especificaciones/"
|
|
traceability:
|
|
- "docs/02-definicion-modulos/OQI-006-ml-signals/implementacion/TRACEABILITY.yml"
|
|
fix_documentation:
|
|
- "docs/99-analisis/PLAN-IMPLEMENTACION-FASES.md#fase-8"
|