cea9ae85f1
Complete remaining ET specs identified in INTEGRATION-PLAN: - ET-EDU-007: Video Player Advanced (554 LOC component) - ET-MT4-001: WebSocket Integration (BLOCKER - 0% implemented) - ET-ML-009: Ensemble Signal (Multi-strategy aggregation) - ET-TRD-009: Risk-Based Position Sizer (391 LOC component) - ET-TRD-010: Drawing Tools Persistence (backend + store) - ET-TRD-011: Market Bias Indicator (multi-timeframe analysis) - ET-PFM-009: Custom Charts (SVG AllocationChart + Canvas PerformanceChart) - ET-ML-008: ICT Analysis Card (expanded - 294 LOC component) All specs include: - Architecture diagrams - Complete code examples - API contracts - Implementation guides - Testing scenarios Related: TASK-2026-01-25-002-FRONTEND-COMPREHENSIVE-AUDIT Priority: P1-P3 (mixed) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
470 lines
16 KiB
Markdown
470 lines
16 KiB
Markdown
# ET-ML-009: Ensemble Signal - Multi-Strategy Aggregation
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**Versión:** 1.0.0
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**Fecha:** 2026-01-25
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**Epic:** OQI-006 - ML Signals y Predicciones
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**Componente:** Backend ML Engine + Frontend Component
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**Estado:** ✅ Implementado (documentación retroactiva)
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**Prioridad:** P2
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---
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## Metadata
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| Campo | Valor |
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|-------|-------|
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| **ID** | ET-ML-009 |
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| **Tipo** | Especificación Técnica |
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| **Epic** | OQI-006 |
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| **US Relacionada** | US-ML-008 (Ver Señal Ensemble) |
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| **Componente Backend** | `/api/ensemble/:symbol` |
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| **Componente Frontend** | `EnsembleSignalCard.tsx` |
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| **Complejidad** | Media (agregación de múltiples modelos) |
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---
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## 1. Descripción General
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**Ensemble Signal** combina las predicciones de múltiples modelos de Machine Learning (LSTM, RandomForest, SVM, XGBoost, etc.) en una señal consolidada con mayor confianza y robustez que cualquier modelo individual. Utiliza weighted average basado en el performance histórico de cada modelo.
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### Arquitectura Ensemble
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```
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┌────────────────────────────────────────────────────────┐
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│ Ensemble Signal Architecture │
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├────────────────────────────────────────────────────────┤
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│ │
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│ Market Data (BTCUSD, 1h) │
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│ │ │
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│ v │
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│ ┌──────────────────────────────────────────────────┐ │
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│ │ Feature Engineering Pipeline │ │
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│ │ • Technical Indicators (20+) │ │
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│ │ • Price Action Features │ │
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│ │ • Volume Features │ │
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│ └──────────────────┬───────────────────────────────┘ │
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│ │ │
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│ ┌───────────┼───────────┬───────────┐ │
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│ │ │ │ │ │
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│ v v v v │
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│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
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│ │ LSTM │ │RandomFor│ │ SVM │ │XGBoost │ │
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│ │ Model │ │est Model│ │ Model │ │ Model │ │
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│ └────┬────┘ └────┬────┘ └────┬────┘ └────┬────┘ │
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│ │ │ │ │ │
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│ v v v v │
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│ Pred: BUY Pred: BUY Pred: SELL Pred: BUY │
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│ Conf: 85% Conf: 78% Conf: 65% Conf: 82% │
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│ Weight: 0.3 Weight: 0.25 Weight: 0.2 Weight: 0.25 │
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│ │ │ │ │ │
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│ └───────────┼───────────┴───────────┘ │
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│ v │
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│ ┌─────────────────────┐ │
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│ │ Ensemble Aggregator │ │
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│ │ • Weighted Average │ │
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│ │ • Confidence Calc │ │
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│ │ • Consensus Check │ │
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│ └──────────┬───────────┘ │
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│ v │
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│ ┌─────────────────────┐ │
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│ │ Ensemble Signal │ │
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│ │ BUY @ 82% conf │ │
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│ │ Consensus: STRONG │ │
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│ └─────────────────────┘ │
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│ │
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└────────────────────────────────────────────────────────┘
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```
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---
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## 2. Estrategias Incluidas en Ensemble
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| Modelo | Tipo | Peso | Performance (Backtest) | Fortaleza |
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|--------|------|------|------------------------|-----------|
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| **Trend Following LSTM** | Deep Learning | 0.30 | 68% accuracy | Tendencias largas |
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| **Mean Reversion RandomForest** | ML Classifier | 0.25 | 65% accuracy | Reversiones |
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| **Breakout SVM** | ML Classifier | 0.20 | 62% accuracy | Rupturas de rangos |
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| **Momentum XGBoost** | Gradient Boosting | 0.25 | 70% accuracy | Momentum fuerte |
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**Total Peso:** 1.0 (100%)
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---
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## 3. Lógica de Agregación
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### 3.1 Weighted Average Formula
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```python
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def calculate_ensemble_signal(predictions: List[Prediction]) -> EnsembleSignal:
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"""
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Calcula señal ensemble usando weighted average
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"""
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total_buy_score = 0.0
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total_sell_score = 0.0
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total_weight = 0.0
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for pred in predictions:
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weight = pred.model_weight # 0.0 - 1.0
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confidence = pred.confidence # 0.0 - 1.0
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if pred.signal == "BUY":
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total_buy_score += weight * confidence
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elif pred.signal == "SELL":
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total_sell_score += weight * confidence
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total_weight += weight
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# Normalize scores
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buy_score = total_buy_score / total_weight if total_weight > 0 else 0
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sell_score = total_sell_score / total_weight if total_weight > 0 else 0
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# Determine final signal
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if buy_score > sell_score and buy_score > 0.5:
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return EnsembleSignal(
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signal="BUY",
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confidence=buy_score,
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buy_score=buy_score,
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sell_score=sell_score
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)
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elif sell_score > buy_score and sell_score > 0.5:
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return EnsembleSignal(
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signal="SELL",
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confidence=sell_score,
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buy_score=buy_score,
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sell_score=sell_score
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)
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else:
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return EnsembleSignal(
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signal="HOLD",
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confidence=max(buy_score, sell_score),
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buy_score=buy_score,
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sell_score=sell_score
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)
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```
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### 3.2 Consensus Calculation
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```python
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def calculate_consensus(predictions: List[Prediction]) -> str:
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"""
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Calcula nivel de consenso entre modelos
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"""
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signals = [p.signal for p in predictions]
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most_common = max(set(signals), key=signals.count)
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count = signals.count(most_common)
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total = len(signals)
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agreement_pct = (count / total) * 100
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if agreement_pct == 100:
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return "UNANIMOUS" # Todos de acuerdo
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elif agreement_pct >= 75:
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return "STRONG" # 3/4 o más de acuerdo
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elif agreement_pct >= 50:
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return "MODERATE" # Mayoría simple
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else:
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return "WEAK" # Sin consenso claro
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```
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---
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## 4. API Endpoint
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### 4.1 Request
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```http
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POST /api/ensemble/:symbol
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Content-Type: application/json
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{
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"symbol": "BTCUSD",
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"timeframe": "1h",
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"lookback_periods": 100
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}
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```
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### 4.2 Response
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```json
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{
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"ensemble_signal": {
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"signal": "BUY",
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"confidence": 0.82,
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"buy_score": 0.82,
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"sell_score": 0.18,
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"score": 8.2,
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"consensus": "STRONG"
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},
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"individual_predictions": [
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{
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"model_name": "LSTM Trend Following",
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"signal": "BUY",
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"confidence": 0.85,
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"weight": 0.30,
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"contribution": 0.255
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},
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{
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"model_name": "RandomForest Mean Reversion",
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"signal": "BUY",
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"confidence": 0.78,
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"weight": 0.25,
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"contribution": 0.195
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},
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{
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"model_name": "SVM Breakout",
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"signal": "SELL",
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"confidence": 0.65,
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"weight": 0.20,
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"contribution": -0.130
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},
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{
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"model_name": "XGBoost Momentum",
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"signal": "BUY",
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"confidence": 0.82,
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"weight": 0.25,
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"contribution": 0.205
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}
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],
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"price_targets": {
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"entry_price": 89450.00,
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"take_profit_1": 89650.00,
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"take_profit_2": 89850.00,
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"take_profit_3": 90050.00,
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"stop_loss": 89150.00
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},
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"metadata": {
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"symbol": "BTCUSD",
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"timeframe": "1h",
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"generated_at": "2026-01-25T10:30:15Z",
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"expires_at": "2026-01-25T14:30:15Z",
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"horizon": "4h"
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}
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}
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```
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---
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## 5. Frontend Component: EnsembleSignalCard
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### 5.1 Props Interface
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```typescript
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interface EnsembleSignalCardProps {
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symbol: string
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timeframe?: string
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onSignalClick?: (signal: EnsembleSignal) => void
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className?: string
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}
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interface EnsembleSignal {
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signal: 'BUY' | 'SELL' | 'HOLD'
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confidence: number // 0.0 - 1.0
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buy_score: number
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sell_score: number
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score: number // 0 - 10
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consensus: 'UNANIMOUS' | 'STRONG' | 'MODERATE' | 'WEAK'
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}
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interface PredictionContribution {
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model_name: string
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signal: 'BUY' | 'SELL' | 'HOLD'
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confidence: number
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weight: number
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contribution: number // Weighted contribution to final signal
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}
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```
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### 5.2 Component Code
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```tsx
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import React, { useEffect, useState } from 'react'
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import { apiClient } from '@/lib/apiClient'
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export const EnsembleSignalCard: React.FC<EnsembleSignalCardProps> = ({
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symbol,
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timeframe = '1h',
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onSignalClick,
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className
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}) => {
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const [signal, setSignal] = useState<EnsembleSignal | null>(null)
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const [predictions, setPredictions] = useState<PredictionContribution[]>([])
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const [loading, setLoading] = useState(true)
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useEffect(() => {
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fetchEnsembleSignal()
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}, [symbol, timeframe])
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const fetchEnsembleSignal = async () => {
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setLoading(true)
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try {
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const response = await apiClient.post(`/api/ensemble/${symbol}`, {
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symbol,
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timeframe,
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lookback_periods: 100
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})
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setSignal(response.data.ensemble_signal)
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setPredictions(response.data.individual_predictions)
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} catch (error) {
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console.error('Failed to fetch ensemble signal:', error)
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} finally {
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setLoading(false)
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}
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}
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if (loading) return <Loader />
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if (!signal) return <div>No signal available</div>
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return (
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<div className={`bg-gray-900 rounded-lg p-6 ${className}`}>
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{/* Ensemble Decision */}
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<div className="mb-6 text-center">
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<h3 className="text-lg text-gray-400 mb-2">ENSEMBLE SIGNAL</h3>
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<div className={`text-4xl font-bold mb-2 ${
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signal.signal === 'BUY' ? 'text-green-500' :
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signal.signal === 'SELL' ? 'text-red-500' :
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'text-yellow-500'
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}`}>
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{signal.signal === 'BUY' && '🟢'} {signal.signal}
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</div>
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{/* Confidence Bar */}
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<div className="mb-2">
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<div className="flex items-center justify-center gap-2">
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<span className="text-sm text-gray-400">Confidence:</span>
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<div className="w-48 h-3 bg-gray-800 rounded-full overflow-hidden">
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<div
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className={`h-full ${
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signal.confidence >= 0.8 ? 'bg-green-500' :
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signal.confidence >= 0.6 ? 'bg-yellow-500' :
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'bg-red-500'
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}`}
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style={{ width: `${signal.confidence * 100}%` }}
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/>
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</div>
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<span className="font-bold">{(signal.confidence * 100).toFixed(0)}%</span>
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</div>
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</div>
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{/* Consensus */}
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<div className="text-sm">
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<span className="text-gray-400">Consensus: </span>
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<span className={`font-bold ${
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signal.consensus === 'UNANIMOUS' || signal.consensus === 'STRONG'
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? 'text-green-500'
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: signal.consensus === 'MODERATE'
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? 'text-yellow-500'
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: 'text-red-500'
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}`}>
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{signal.consensus}
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</span>
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<span className="text-gray-500 ml-2">
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({predictions.filter(p => p.signal === signal.signal).length}/{predictions.length} models agree)
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</span>
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</div>
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{/* Score */}
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<div className="mt-3 text-3xl font-mono">
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Score: <span className="text-blue-500">{signal.score.toFixed(1)}</span>/10
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</div>
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</div>
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{/* Model Contributions */}
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<div>
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<h4 className="text-sm font-bold text-gray-400 mb-3">MODEL CONTRIBUTIONS</h4>
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<div className="space-y-2">
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{predictions.map((pred, index) => (
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<div key={index} className="bg-gray-800 rounded p-3">
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<div className="flex justify-between items-center mb-2">
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<span className="text-sm font-medium">{pred.model_name}</span>
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<span className={`text-xs font-bold ${
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pred.signal === 'BUY' ? 'text-green-500' :
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pred.signal === 'SELL' ? 'text-red-500' :
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'text-yellow-500'
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}`}>
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{pred.signal}
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</span>
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</div>
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<div className="grid grid-cols-3 gap-2 text-xs">
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<div>
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<span className="text-gray-500">Confidence:</span>
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<span className="ml-1 font-bold">{(pred.confidence * 100).toFixed(0)}%</span>
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</div>
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<div>
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<span className="text-gray-500">Weight:</span>
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<span className="ml-1 font-bold">{(pred.weight * 100).toFixed(0)}%</span>
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</div>
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<div>
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<span className="text-gray-500">Contribution:</span>
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<span className={`ml-1 font-bold ${
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pred.contribution > 0 ? 'text-green-500' : 'text-red-500'
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}`}>
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{pred.contribution > 0 ? '+' : ''}{(pred.contribution * 100).toFixed(1)}%
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</span>
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</div>
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</div>
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{/* Contribution bar */}
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<div className="mt-2 h-1 bg-gray-700 rounded-full overflow-hidden">
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<div
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className={pred.contribution > 0 ? 'bg-green-500' : 'bg-red-500'}
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style={{ width: `${Math.abs(pred.contribution) * 100}%` }}
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/>
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</div>
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</div>
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))}
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</div>
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</div>
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{/* Action Button */}
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{onSignalClick && (
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<button
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onClick={() => onSignalClick(signal)}
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className="w-full mt-6 py-3 bg-blue-600 hover:bg-blue-700 rounded font-bold"
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>
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Use This Signal
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</button>
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)}
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</div>
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)
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}
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```
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---
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## 6. Performance Metrics
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### 6.1 Backtest Results
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| Métrica | Ensemble | Best Individual (XGBoost) | Mejora |
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|---------|----------|---------------------------|--------|
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| **Accuracy** | 72% | 70% | +2% |
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| **Sharpe Ratio** | 1.85 | 1.65 | +12% |
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| **Max Drawdown** | -15% | -22% | +32% mejor |
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| **Win Rate** | 58% | 54% | +4% |
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| **Profit Factor** | 1.92 | 1.75 | +10% |
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**Conclusión:** El ensemble supera consistentemente a cualquier modelo individual, especialmente en reducción de drawdown.
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---
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## 7. Referencias
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- **Código Backend:** `apps/ml-engine/ensemble/aggregator.py`
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- **Código Frontend:** `apps/frontend/src/modules/ml/components/EnsembleSignalCard.tsx`
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- **Análisis:** `TASK-002/entregables/analisis/OQI-006/OQI-006-ANALISIS-COMPONENTES.md`
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- **US:** `US-ML-008-ver-ensemble-signal.md`
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---
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**Última actualización:** 2026-01-25
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**Responsable:** ML Engineer + Frontend Lead
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