rckrdmrd
a7cca885f0
Changes include: - Updated architecture documentation - Enhanced module definitions (OQI-001 to OQI-008) - ML integration documentation updates - Trading strategies documentation - Orchestration and inventory updates - Docker configuration updates 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
369 lines
9.7 KiB
Markdown
369 lines
9.7 KiB
Markdown
---
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id: "PLAN-LLM-TRADING-INTEGRATION-2026"
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title: "Plan de Desarrollo - LLM Trading Integration 2026"
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type: "Development Plan"
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project: "trading-platform"
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version: "1.0.0"
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created_date: "2026-01-04"
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updated_date: "2026-01-04"
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author: "Orquestador Agent - OrbiQuant IA"
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status: "Active"
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---
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# Plan de Desarrollo: LLM Trading Integration 2026
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**Fecha:** 2026-01-04
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**Epica:** OQI-010 - LLM Trading Integration
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**Estado:** Planificacion Activa
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**Story Points Total:** 89 SP
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---
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## Resumen Ejecutivo
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Este plan detalla la implementacion de la integracion avanzada del LLM para trading autonomo, incluyendo fine-tuning, MCP servers, gestion de riesgo y API de predicciones.
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### Hardware Disponible
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| Recurso | Especificacion |
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|---------|----------------|
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| GPU | NVIDIA 16GB VRAM |
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| Modelo Base | chatgpt-oss / Llama 3 8B |
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| Quantizacion | Q5_K_M (balance calidad/VRAM) |
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| Fine-tuning | LoRA (r=16, alpha=32) |
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### Entregables Principales
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1. **MCP Binance Connector** - Puerto 3606
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2. **LLM Fine-tuned** con estrategias AMD/ICT/SMC
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3. **Risk Management Service** integrado
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4. **API de Predicciones** para frontend
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5. **Sistema de Tracking** de predicciones
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---
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## Fase 1: Infraestructura (Semanas 1-2)
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### Objetivo
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Establecer la infraestructura base para los nuevos componentes.
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### Tareas
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#### 1.1 MCP Binance Connector
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- [ ] Crear estructura del proyecto `apps/mcp-binance-connector/`
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- [ ] Implementar tools de market data (get_ticker, get_klines, get_orderbook)
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- [ ] Implementar tools de account (get_account, get_positions)
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- [ ] Implementar tools de orders (create_order, cancel_order)
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- [ ] Implementar tools de futures (positions, leverage)
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- [ ] Configurar Docker y puerto 3606
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- [ ] Tests unitarios y de integracion
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**Responsable:** Backend Developer
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**Story Points:** 8
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#### 1.2 DDL PostgreSQL
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- [ ] Crear tabla `ml.llm_predictions`
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- [ ] Crear tabla `ml.prediction_outcomes`
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- [ ] Crear tabla `ml.llm_decisions`
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- [ ] Crear tabla `ml.risk_events`
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- [ ] Crear funcion `ml.calculate_prediction_accuracy()`
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- [ ] Crear indices de performance
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**Responsable:** Database Developer
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**Story Points:** 5
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#### 1.3 Pipeline de Fine-Tuning
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- [ ] Configurar ambiente de entrenamiento
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- [ ] Crear script de generacion de dataset
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- [ ] Implementar dataset de estrategias AMD
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- [ ] Implementar dataset de conceptos ICT/SMC
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- [ ] Implementar dataset de risk management
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- [ ] Crear script de entrenamiento LoRA
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- [ ] Crear script de merge y conversion a GGUF
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**Responsable:** ML Specialist
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**Story Points:** 8
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### Entregables Fase 1
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- MCP Binance Connector funcionando en testnet
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- Tablas de PostgreSQL creadas
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- Pipeline de fine-tuning configurado
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---
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## Fase 2: Core LLM Features (Semanas 3-5)
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### Objetivo
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Implementar las funcionalidades core del LLM trading agent.
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### Tareas
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#### 2.1 Fine-Tuning del Modelo
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- [ ] Generar dataset completo (>20,000 ejemplos)
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- [ ] Ejecutar entrenamiento LoRA (3 epochs)
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- [ ] Evaluar modelo con dataset de validacion
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- [ ] Optimizar hiperparametros si es necesario
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- [ ] Convertir a GGUF y cargar en Ollama
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- [ ] Validar respuestas del modelo fine-tuned
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**Responsable:** ML Specialist
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**Story Points:** 8
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#### 2.2 Risk Management Service
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- [ ] Implementar RiskManager class
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- [ ] Implementar position sizing calculator
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- [ ] Implementar drawdown monitor
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- [ ] Implementar circuit breaker
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- [ ] Implementar exposure tracker
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- [ ] Integrar con LLM decision flow
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- [ ] Tests unitarios
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**Responsable:** Backend Developer
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**Story Points:** 8
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#### 2.3 ML Analyzer Service
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- [ ] Implementar MLAnalyzer class
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- [ ] Integracion con ML Engine (AMD, Range, ICT)
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- [ ] Calculo de confluence score
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- [ ] Generacion de explicaciones en lenguaje natural
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- [ ] Cache de predicciones en Redis
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**Responsable:** ML Specialist
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**Story Points:** 5
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#### 2.4 Risk Validation Pre-Trade
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- [ ] Implementar validacion antes de execute_trade
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- [ ] Verificar position size limits
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- [ ] Verificar daily drawdown
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- [ ] Verificar exposure total
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- [ ] Verificar trades diarios
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- [ ] Responder con razon si rechazado
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**Responsable:** Backend Developer
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**Story Points:** 5
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### Entregables Fase 2
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- Modelo LLM fine-tuned funcionando
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- Risk Manager integrado
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- ML Analyzer con confluence score
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---
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## Fase 3: API e Integracion (Semanas 6-7)
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### Objetivo
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Exponer APIs para frontend y completar integracion entre servicios.
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### Tareas
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#### 3.1 API de Predicciones REST
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- [ ] Endpoint POST /api/v1/predictions/analyze
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- [ ] Endpoint GET /api/v1/predictions/history/{symbol}
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- [ ] Endpoint GET /api/v1/predictions/accuracy/{symbol}
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- [ ] Endpoint GET /api/v1/predictions/active-signals
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- [ ] Documentacion OpenAPI
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**Responsable:** Backend Developer
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**Story Points:** 5
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#### 3.2 WebSocket Predicciones
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- [ ] Implementar PredictionWebSocketManager
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- [ ] Endpoint WS /ws/predictions/{symbol}
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- [ ] Broadcast de predicciones cada 5s
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- [ ] Manejo de conexiones/desconexiones
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- [ ] Rate limiting
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**Responsable:** Backend Developer
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**Story Points:** 5
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#### 3.3 MCP Orchestrator
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- [ ] Implementar MCPOrchestrator class
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- [ ] Metodo call_tool(server, tool, params)
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- [ ] Metodo get_combined_portfolio()
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- [ ] Metodo execute_trade_on_best_venue()
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- [ ] Integracion con LLM decision flow
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**Responsable:** Backend Developer
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**Story Points:** 5
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#### 3.4 Ejecucion de Trades MT4/Binance
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- [ ] Integracion completa con MCP MT4
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- [ ] Integracion completa con MCP Binance
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- [ ] Seleccion automatica de venue
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- [ ] Logging de trades ejecutados
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- [ ] Persistencia de decisiones
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**Responsable:** Backend Developer
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**Story Points:** 8
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### Entregables Fase 3
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- API REST de predicciones funcionando
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- WebSocket real-time
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- Ejecucion de trades via MCP
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---
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## Fase 4: Tracking y Optimizacion (Semanas 8-9)
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### Objetivo
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Implementar tracking de outcomes y optimizar el sistema.
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### Tareas
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#### 4.1 Tracking de Outcomes
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- [ ] Servicio de monitoreo de predicciones
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- [ ] Deteccion automatica de outcomes
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- [ ] Calculo de accuracy por simbolo
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- [ ] Calculo de profit factor
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- [ ] Persistencia en prediction_outcomes
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**Responsable:** Backend Developer
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**Story Points:** 5
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#### 4.2 Metricas de Accuracy
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- [ ] Dashboard de accuracy por modelo
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- [ ] Grafico de accuracy temporal
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- [ ] Filtros por simbolo/timeframe
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- [ ] Export de datos
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**Responsable:** Frontend Developer
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**Story Points:** 5
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#### 4.3 Circuit Breaker Automatico
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- [ ] Deteccion de daily drawdown limit
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- [ ] Deteccion de perdidas consecutivas
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- [ ] Pausa automatica de trading
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- [ ] Alertas a usuario
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- [ ] Resume manual requerido
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**Responsable:** Backend Developer
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**Story Points:** 3
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#### 4.4 Fine-Tuning con Datos de Produccion
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- [ ] Recolectar decisiones correctas
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- [ ] Generar nuevos ejemplos de training
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- [ ] Re-entrenar modelo
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- [ ] A/B testing de versiones
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- [ ] Rollout gradual
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**Responsable:** ML Specialist
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**Story Points:** 8
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### Entregables Fase 4
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- Sistema de tracking funcionando
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- Dashboard de accuracy
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- Circuit breaker automatico
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---
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## Fase 5: Testing y Deployment (Semana 10)
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### Objetivo
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Validar el sistema completo y desplegar a produccion.
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### Tareas
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#### 5.1 Tests de Integracion
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- [ ] Tests E2E del flujo completo
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- [ ] Tests de MCP Binance con testnet
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- [ ] Tests de risk management
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- [ ] Tests de persistence
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- [ ] Coverage > 70%
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**Responsable:** Testing
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**Story Points:** 3
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#### 5.2 Backtesting de Decisiones
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- [ ] Ejecutar backtesting con datos historicos
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- [ ] Validar que risk limits se respetan
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- [ ] Comparar accuracy real vs backtesting
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- [ ] Documentar resultados
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**Responsable:** ML Specialist
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**Story Points:** 5
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#### 5.3 Documentacion Final
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- [ ] Actualizar README de cada componente
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- [ ] Documentar APIs con OpenAPI
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- [ ] Crear guia de operaciones
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- [ ] Actualizar AGENTS.md
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**Responsable:** Tech Writer
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**Story Points:** 3
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#### 5.4 Deployment
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- [ ] Build de imagenes Docker
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- [ ] Configurar docker-compose.llm-advanced.yaml
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- [ ] Deploy a staging
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- [ ] Smoke tests
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- [ ] Deploy a produccion
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- [ ] Monitoreo inicial
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**Responsable:** DevOps
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**Story Points:** 5
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### Entregables Fase 5
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- Sistema completo testeado
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- Documentacion actualizada
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- Deploy a produccion
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---
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## Resumen de Story Points por Fase
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| Fase | Descripcion | SP | Duracion |
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|------|-------------|-----|----------|
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| Fase 1 | Infraestructura | 21 | 2 semanas |
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| Fase 2 | Core LLM | 26 | 2-3 semanas |
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| Fase 3 | API e Integracion | 23 | 2 semanas |
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| Fase 4 | Tracking y Optimizacion | 21 | 2 semanas |
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| Fase 5 | Testing y Deployment | 16 | 1 semana |
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| **Total** | | **107** | **9-10 semanas** |
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---
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## Riesgos y Mitigaciones
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| Riesgo | Probabilidad | Impacto | Mitigacion |
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|--------|--------------|---------|------------|
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| VRAM insuficiente | Media | Alto | LoRA + quantizacion Q5_K_M |
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| Latencia alta | Media | Alto | Cache, batch processing |
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| Errores del LLM | Alta | Critico | Risk limits, paper trading |
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| Rate limits Binance | Media | Medio | Rate limiter, caching |
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| Fine-tuning overfitting | Media | Alto | Validacion, early stopping |
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---
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## Metricas de Exito
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| Metrica | Target | Medicion |
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|---------|--------|----------|
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| Direction Accuracy | >65% | Semanal |
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| Response Time | <5s | Continuo |
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| Risk Adherence | 100% | Continuo |
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| System Uptime | >99% | Continuo |
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| Fine-tuning Perplexity | <3.0 | Post-training |
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---
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## Siguiente Paso Inmediato
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1. **Crear MCP Binance Connector** - Estructura base del proyecto
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2. **Ejecutar DDL** - Crear nuevas tablas en PostgreSQL
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3. **Preparar Dataset** - Iniciar recoleccion de ejemplos de training
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---
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## Referencias
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- [Epica OQI-010](../docs/02-definicion-modulos/OQI-010-llm-trading-integration/README.md)
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- [Integracion LLM Fine-Tuning](../docs/01-arquitectura/INTEGRACION-LLM-FINE-TUNING.md)
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- [MCP Binance Spec](../docs/01-arquitectura/MCP-BINANCE-CONNECTOR-SPEC.md)
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- [Plan ML-LLM-Trading Original](./PLAN-ML-LLM-TRADING.md)
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---
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**Documento Generado:** 2026-01-04
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**Autor:** Orquestador Agent - OrbiQuant IA
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**Version:** 1.0.0
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