rckrdmrd
ea1879f4ad
- Configure workspace Git repository with comprehensive .gitignore - Add Odoo as submodule for ERP reference code - Include documentation: SETUP.md, GIT-STRUCTURE.md - Add gitignore templates for projects (backend, frontend, database) - Structure supports independent repos per project/subproject level Workspace includes: - core/ - Reusable patterns, modules, orchestration system - projects/ - Active projects (erp-suite, gamilit, trading-platform, etc.) - knowledge-base/ - Reference code and patterns (includes Odoo submodule) - devtools/ - Development tools and templates - customers/ - Client implementations template 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
452 lines
14 KiB
YAML
452 lines
14 KiB
YAML
# TRACEABILITY.yml - OQI-006 Señales ML
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# Mapeo de requerimientos a implementación
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version: "1.0.0"
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epic: OQI-006
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name: "Señales ML y Predicciones"
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updated: "2025-12-05"
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status: pending
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# Resumen de trazabilidad
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summary:
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total_requirements: 5
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total_specs: 5
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total_user_stories: 7
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total_files_to_implement: 40
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test_coverage: "TBD"
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story_points: 40
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# Mapeo de Requerimientos Funcionales
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requirements:
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RF-ML-001:
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name: "Predicciones de Precio"
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status: pending
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specs:
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- ET-ML-001
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- ET-ML-002
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user_stories:
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- US-ML-001
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implementation:
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ml_engine:
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- path: ml-engine/services/prediction_service.py
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description: "Servicio de predicciones"
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methods:
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- get_prediction
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- get_predictions_batch
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- get_prediction_history
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- invalidate_cache
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- path: ml-engine/models/lstm_predictor.py
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description: "Modelo LSTM de predicción"
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methods:
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- predict
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- train
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- evaluate
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- path: ml-engine/models/transformer_predictor.py
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description: "Modelo Transformer de predicción"
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methods:
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- predict
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- train
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- evaluate
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backend:
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- path: apps/backend/src/modules/ml/services/ml-proxy.service.ts
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description: "Proxy al ML Engine"
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methods:
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- getPrediction
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- getPredictionsBatch
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- path: apps/backend/src/modules/ml/controllers/ml.controller.ts
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methods:
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- getPrediction
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- path: apps/backend/src/modules/ml/ml.routes.ts
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routes:
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- "GET /ml/predictions/:symbol"
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- "POST /ml/predictions/batch"
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frontend:
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- path: apps/frontend/src/modules/trading/components/MLOverlay.tsx
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description: "Overlay de predicciones en chart"
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- path: apps/frontend/src/modules/trading/components/PredictionPanel.tsx
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description: "Panel de predicciones"
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database:
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- path: apps/database/schemas/06_ml_schema.sql
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tables:
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- predictions
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- prediction_accuracy
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enums:
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- prediction_type_enum
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- prediction_horizon_enum
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tests:
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- path: ml-engine/tests/test_prediction_service.py
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status: pending
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RF-ML-002:
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name: "Señales de Trading"
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status: pending
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specs:
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- ET-ML-001
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- ET-ML-004
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user_stories:
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- US-ML-002
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- US-ML-006
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- US-ML-007
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implementation:
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ml_engine:
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- path: ml-engine/services/signal_service.py
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description: "Servicio de señales"
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methods:
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- generate_signal
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- get_active_signals
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- get_signal_history
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- evaluate_signal
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- path: ml-engine/strategies/momentum_strategy.py
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description: "Estrategia de momentum ML"
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- path: ml-engine/strategies/mean_reversion_strategy.py
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description: "Estrategia de reversión a la media"
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- path: ml-engine/strategies/amd_strategy.py
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description: "Estrategia AMD (Accumulation/Manipulation/Distribution)"
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backend:
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- path: apps/backend/src/modules/ml/services/signal.service.ts
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description: "Servicio de señales"
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methods:
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- getSignals
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- getActiveSignals
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- getSignalHistory
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- subscribeToSignals
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- path: apps/backend/src/modules/ml/controllers/ml.controller.ts
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methods:
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- getSignals
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- getSignalById
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- path: apps/backend/src/modules/ml/ml.routes.ts
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routes:
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- "GET /ml/signals"
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- "GET /ml/signals/:id"
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- "GET /ml/signals/history"
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frontend:
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- path: apps/frontend/src/modules/trading/pages/Signals.tsx
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description: "Página de señales"
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- path: apps/frontend/src/modules/trading/components/SignalCard.tsx
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description: "Tarjeta de señal"
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- path: apps/frontend/src/modules/trading/components/SignalList.tsx
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description: "Lista de señales"
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- path: apps/frontend/src/modules/trading/components/SignalOverlay.tsx
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description: "Overlay de señales en chart"
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database:
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- path: apps/database/schemas/06_ml_schema.sql
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tables:
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- signals
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- signal_outcomes
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enums:
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- signal_type_enum
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- signal_strength_enum
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- signal_status_enum
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tests:
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- path: ml-engine/tests/test_signal_service.py
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status: pending
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RF-ML-003:
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name: "Indicadores ML"
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status: pending
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specs:
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- ET-ML-003
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user_stories: []
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implementation:
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ml_engine:
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- path: ml-engine/indicators/ml_trend_indicator.py
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description: "Indicador de tendencia ML"
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- path: ml-engine/indicators/ml_volatility_indicator.py
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description: "Indicador de volatilidad ML"
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- path: ml-engine/indicators/amd_zone_indicator.py
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description: "Indicador de zonas AMD"
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- path: ml-engine/indicators/smart_money_indicator.py
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description: "Indicador de Smart Money"
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backend:
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- path: apps/backend/src/modules/ml/services/ml-indicator.service.ts
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description: "Servicio de indicadores ML"
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methods:
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- getMLIndicators
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- getIndicatorData
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- path: apps/backend/src/modules/ml/ml.routes.ts
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routes:
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- "GET /ml/indicators/:symbol"
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frontend:
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- path: apps/frontend/src/modules/trading/components/AMDZoneIndicator.tsx
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description: "Indicador de zonas AMD"
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- path: apps/frontend/src/modules/trading/components/MLTrendIndicator.tsx
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description: "Indicador de tendencia ML"
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database:
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- path: apps/database/schemas/06_ml_schema.sql
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tables:
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- ml_indicators
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- amd_zones
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tests:
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- path: ml-engine/tests/test_ml_indicators.py
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status: pending
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RF-ML-004:
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name: "Entrenamiento de Modelos"
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status: pending
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specs:
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- ET-ML-002
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user_stories: []
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implementation:
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ml_engine:
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- path: ml-engine/training/train_lstm.py
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description: "Script de entrenamiento LSTM"
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- path: ml-engine/training/train_transformer.py
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description: "Script de entrenamiento Transformer"
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- path: ml-engine/training/hyperparameter_tuning.py
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description: "Optimización de hiperparámetros"
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- path: ml-engine/training/model_registry.py
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description: "Registro de modelos"
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- path: ml-engine/data/feature_engineering.py
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description: "Ingeniería de features"
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- path: ml-engine/data/data_pipeline.py
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description: "Pipeline de datos"
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database:
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- path: apps/database/schemas/06_ml_schema.sql
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tables:
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- ml_models
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- model_versions
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- training_runs
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- feature_sets
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enums:
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- model_type_enum
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- model_status_enum
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tests:
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- path: ml-engine/tests/test_training.py
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status: pending
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RF-ML-005:
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name: "Notificaciones de Señales"
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status: pending
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specs:
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- ET-ML-005
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user_stories:
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- US-ML-004
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implementation:
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backend:
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- path: apps/backend/src/modules/ml/services/notification.service.ts
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description: "Servicio de notificaciones ML"
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methods:
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- notifySignal
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- subscribeToAlerts
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- unsubscribeFromAlerts
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- getNotificationPreferences
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- path: apps/backend/src/modules/ml/controllers/ml.controller.ts
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methods:
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- setNotificationPreferences
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- getNotificationPreferences
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- path: apps/backend/src/modules/ml/ml.routes.ts
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routes:
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- "GET /ml/notifications/preferences"
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- "PUT /ml/notifications/preferences"
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- "POST /ml/notifications/subscribe"
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frontend:
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- path: apps/frontend/src/modules/settings/components/SignalNotifications.tsx
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description: "Configuración de notificaciones de señales"
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database:
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- path: apps/database/schemas/06_ml_schema.sql
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tables:
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- signal_subscriptions
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- signal_notifications
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external:
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- service: Firebase Cloud Messaging
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usage: "Push notifications"
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- service: Twilio
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usage: "SMS notifications"
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tests:
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- path: apps/backend/tests/ml/notification.service.test.ts
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status: pending
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# Modelos ML
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ml_models:
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- name: LSTM Price Predictor
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type: lstm
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input: OHLCV + technical indicators
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output: Price prediction (1h, 4h, 1d)
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accuracy_target: ">60%"
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- name: Transformer Trend
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type: transformer
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input: Market data + sentiment
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output: Trend direction
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accuracy_target: ">55%"
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- name: AMD Zone Detector
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type: cnn
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input: Price action patterns
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output: AMD zones
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accuracy_target: ">65%"
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- name: Signal Generator
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type: ensemble
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input: All model outputs
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output: Trading signals
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accuracy_target: ">50% win rate"
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# Feature Engineering
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features:
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technical:
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- RSI (14, 21)
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- MACD
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- Bollinger Bands
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- ATR
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- VWAP
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- Volume Profile
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pattern:
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- Candlestick patterns
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- Chart patterns
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- Support/Resistance levels
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market:
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- Market sentiment
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- VIX
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- Correlation matrix
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custom:
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- AMD zones
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- Smart Money indicators
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- Order flow
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# Mapeo de archivos de configuración
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config_files:
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ml_engine:
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- path: ml-engine/config/model_config.yaml
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description: "Configuración de modelos"
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- path: ml-engine/config/training_config.yaml
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description: "Configuración de entrenamiento"
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- path: ml-engine/config/inference_config.yaml
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description: "Configuración de inferencia"
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backend:
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- path: apps/backend/src/modules/ml/ml.module.ts
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description: "Módulo ML"
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- path: apps/backend/src/modules/ml/types/ml.types.ts
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description: "Tipos TypeScript"
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- path: apps/backend/src/config/ml.config.ts
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description: "Configuración de conexión ML"
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frontend:
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- path: apps/frontend/src/modules/trading/stores/ml.store.ts
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description: "Store de ML Zustand"
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- path: apps/frontend/src/modules/trading/hooks/useMLSignals.ts
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description: "Hook de señales ML"
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# Dependencias externas
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external_dependencies:
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python:
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- name: torch
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version: "^2.1.0"
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usage: "Deep learning framework"
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- name: tensorflow
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version: "^2.15.0"
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usage: "Alternative DL framework"
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- name: scikit-learn
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version: "^1.3.0"
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usage: "ML utilities"
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- name: pandas
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version: "^2.1.0"
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usage: "Data manipulation"
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- name: numpy
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version: "^1.26.0"
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usage: "Numerical computing"
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- name: fastapi
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version: "^0.104.0"
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usage: "ML API server"
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- name: ta-lib
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version: "^0.4.28"
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usage: "Technical analysis"
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npm_backend:
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- name: axios
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version: "^1.6.0"
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usage: "HTTP client for ML Engine"
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npm_frontend:
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- name: lightweight-charts
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version: "^4.1.0"
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usage: "ML overlays en charts"
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# APIs
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api_endpoints:
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ml_engine:
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base_url: "http://localhost:8000/api/v1"
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endpoints:
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- method: GET
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path: /predict/:symbol
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description: "Obtener predicción"
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- method: POST
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path: /predict/batch
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description: "Predicciones en batch"
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- method: GET
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path: /signals
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description: "Señales activas"
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- method: GET
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path: /indicators/:symbol
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description: "Indicadores ML"
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- method: POST
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path: /train
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description: "Iniciar entrenamiento"
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auth: admin
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backend:
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base_path: /api/v1/ml
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endpoints:
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- method: GET
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path: /predictions/:symbol
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requirement: RF-ML-001
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auth: true
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- method: GET
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path: /signals
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requirement: RF-ML-002
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auth: true
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- method: GET
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path: /signals/:id
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requirement: RF-ML-002
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auth: true
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- method: GET
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path: /indicators/:symbol
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requirement: RF-ML-003
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auth: true
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- method: GET
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path: /accuracy
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requirement: RF-ML-001
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auth: true
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- method: PUT
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path: /notifications/preferences
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requirement: RF-ML-005
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auth: true
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# Integración con TradingAgent existente
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tradingagent_integration:
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source: "/home/isem/workspace-old/UbuntuML/TradingAgent"
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components_to_migrate:
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- path: backend/market_data/
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target: ml-engine/data/
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status: pending
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- path: backend/ml_models/
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target: ml-engine/models/
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status: pending
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- path: backend/strategies/
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target: ml-engine/strategies/
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status: pending
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- path: backend/backtest/
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target: ml-engine/backtest/
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status: pending
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new_components:
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- AMD zone detection
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- Smart Money indicators
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- Signal aggregation
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- Real-time inference API
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# Dependencias de épicas
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dependencies:
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blocks:
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- epic: OQI-004
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reason: "Agentes de inversión usan señales ML"
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- epic: OQI-007
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reason: "LLM Agent consulta señales ML"
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blocked_by:
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- epic: OQI-001
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reason: "Requiere autenticación"
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- epic: OQI-003
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reason: "Requiere datos de trading/charts"
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# Notas y decisiones
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notes:
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- "ML Engine como microservicio Python separado"
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- "Modelos entrenados offline, inferencia en tiempo real"
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- "Cache de predicciones con TTL de 5 minutos"
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- "Señales generadas cada 15 minutos"
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- "Accuracy tracking para mejora continua"
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- "A/B testing de estrategias"
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- "Migrar componentes de TradingAgent gradualmente"
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