trading-platform/docs/02-definicion-modulos/OQI-006-ml-signals/especificaciones/ET-ML-009-multi-strategy-ensemble.md

ET-ML-009: Arquitectura Multi-Strategy Ensemble

Versión: 1.0.0 Fecha: 2026-01-25 Estado: PLANIFICADO Prioridad: P0 Tarea Relacionada: TASK-2026-01-25-ML-TRAINING-ENHANCEMENT

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1. RESUMEN EJECUTIVO

Esta especificación técnica define la arquitectura de un sistema multi-estrategia con Neural Gating Metamodel para predicciones de trading. El objetivo es alcanzar 80% de efectividad en operaciones mediante la combinación de 5 estrategias diversificadas.

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2. ARQUITECTURA GENERAL

┌─────────────────────────────────────────────────────────────────────────────────┐
│                     MULTI-STRATEGY ENSEMBLE ARCHITECTURE                         │
├─────────────────────────────────────────────────────────────────────────────────┤
│                                                                                  │
│  [Market Data] → [Feature Engineering] → [5 Strategies] → [Metamodel] → [LLM]  │
│                                                                                  │
│  ┌────────────────────────────────────────────────────────────────────────┐    │
│  │                         LEVEL 0: Attention Base                         │    │
│  │  Price-Focused Attention (sin información temporal)                     │    │
│  └────────────────────────────────────────────────────────────────────────┘    │
│                                     ↓                                           │
│  ┌────────────────────────────────────────────────────────────────────────┐    │
│  │                    LEVEL 1: Strategy Models (5)                         │    │
│  │  ┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐                    │    │
│  │  │  PVA  │ │  MRD  │ │  VBP  │ │  MSA  │ │  MTS  │                    │    │
│  │  └───┬───┘ └───┬───┘ └───┬───┘ └───┬───┘ └───┬───┘                    │    │
│  └──────┼─────────┼─────────┼─────────┼─────────┼────────────────────────┘    │
│         └─────────┴─────────┴─────────┴─────────┘                              │
│                             ↓                                                   │
│  ┌────────────────────────────────────────────────────────────────────────┐    │
│  │                    LEVEL 2: Neural Gating Metamodel                     │    │
│  │  - Weighted Ensemble                                                    │    │
│  │  - Confidence Calibration                                               │    │
│  └────────────────────────────────────────────────────────────────────────┘    │
│                             ↓                                                   │
│  ┌────────────────────────────────────────────────────────────────────────┐    │
│  │                    LEVEL 3: LLM Decision Integration                    │    │
│  │  - Signal Formatting                                                    │    │
│  │  - Trading Decision (TRADE/NO_TRADE/WAIT)                              │    │
│  └────────────────────────────────────────────────────────────────────────┘    │
│                                                                                  │
└─────────────────────────────────────────────────────────────────────────────────┘

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3. ESTRATEGIAS DETALLADAS

3.1 Strategy 1: PVA (Price Variation Attention)

Objetivo: Capturar patrones en variación de precio pura, agnóstica al tiempo.

Aspecto	Especificación
Arquitectura	Transformer Encoder (4 layers) + XGBoost Head
Input	Secuencia de retornos [r_1, r_2, ..., r_T], T=100
Features	returns (1,5,10,20), acceleration, volatility_returns, skewness, kurtosis
Attention	Multi-head (8 heads), d_model=256, d_k=32
Output	direction [-1,0,+1], magnitude (%), confidence (0-1)
Entrenamiento	Walk-forward, batch=256, lr=0.0001, epochs=100
Modelos	6 (uno por activo)

Archivos:

• src/models/strategies/pva/transformer_encoder.py
• src/models/strategies/pva/xgb_head.py
• src/features/returns_features.py

3.2 Strategy 2: MRD (Momentum Regime Detection)

Objetivo: Detectar régimen de mercado (tendencia/rango) y predecir continuación.

Aspecto	Especificación
Arquitectura	HMM (3 estados) + LSTM (128 units) + XGBoost
Input	Indicadores de momentum + estado HMM
Features	RSI, MACD, ROC, ADX, +DI, -DI, EMA crossovers
HMM States	[Trend Up, Range, Trend Down]
Output	next_regime, regime_duration, momentum_continuation
Entrenamiento	HMM fit + LSTM supervised + XGBoost ensemble
Modelos	6 (uno por activo)

Archivos:

• src/models/strategies/mrd/hmm_regime.py
• src/models/strategies/mrd/lstm_predictor.py
• src/features/momentum_features.py

3.3 Strategy 3: VBP (Volatility Breakout Predictor)

Objetivo: Predecir breakouts basados en compresión de volatilidad.

Aspecto	Especificación
Arquitectura	CNN 1D (filters [32,64,128]) + Attention + XGBoost
Input	Features de volatilidad y compresión
Features	ATR, BB_width, BB_squeeze, Keltner_squeeze, compression_score
Output	breakout_next_12 (bool), direction, magnitude
Entrenamiento	Imbalanced sampling (oversample breakouts 3x)
Modelos	6 (uno por activo)

Archivos:

• src/models/strategies/vbp/cnn_encoder.py
• src/models/strategies/vbp/breakout_classifier.py
• src/features/volatility_features.py

3.4 Strategy 4: MSA (Market Structure Analysis)

Objetivo: Análisis de estructura de mercado (ICT/SMC concepts).

Aspecto	Especificación
Arquitectura	XGBoost (con opción de GNN para relaciones)
Input	Swing points, BOS, CHoCH, FVG, Order Blocks
Features	swing_high/low, higher_high/lower_low, bos_up/down, choch, fvg, ob
Output	next_bos_direction, poi_reaction, structure_continuation
Entrenamiento	Supervised con labels de estructura
Modelos	6 (uno por activo)

Archivos:

• src/models/strategies/msa/structure_detector.py
• src/models/strategies/msa/structure_predictor.py
• src/features/structure_features.py

3.5 Strategy 5: MTS (Multi-Timeframe Synthesis)

Objetivo: Síntesis de señales de múltiples timeframes.

Aspecto	Especificación
Arquitectura	Hierarchical Attention Network
Input	Features agregados de 5m, 15m, 1h, 4h
Features	All base features + tf_alignment_score + conflict_score
Output	unified_direction, confidence_by_alignment, optimal_entry_tf
Entrenamiento	Hierarchical loss con TF weights aprendibles
Modelos	6 (uno por activo)

Archivos:

• src/models/strategies/mts/hierarchical_attention.py
• src/models/strategies/mts/tf_aggregator.py
• src/features/multitf_features.py

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4. NEURAL GATING METAMODEL

4.1 Arquitectura

class NeuralGatingMetamodel(nn.Module):
    def __init__(self, n_strategies=5, d_input=10, d_hidden=256):
        super().__init__()
        self.gate_network = nn.Sequential(
            nn.Linear(n_strategies * d_input + d_context, d_hidden),
            nn.BatchNorm1d(d_hidden),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(d_hidden, d_hidden // 2),
            nn.ReLU(),
            nn.Linear(d_hidden // 2, n_strategies)
        )
        self.confidence_head = nn.Sequential(
            nn.Linear(d_hidden // 2, 1),
            nn.Sigmoid()
        )

    def forward(self, strategy_outputs, market_context):
        # Concatenar outputs de estrategias + contexto
        features = torch.cat([
            *[s['pred_conf'] for s in strategy_outputs.values()],
            market_context
        ], dim=-1)

        # Gating network
        gate_logits = self.gate_network(features)
        weights = F.softmax(gate_logits, dim=-1)

        # Weighted ensemble
        predictions = torch.stack([
            s['prediction'] for s in strategy_outputs.values()
        ], dim=-1)
        final_pred = (weights * predictions).sum(dim=-1)

        return {
            'prediction': final_pred,
            'weights': weights,
            'confidence': self.confidence_head(features[:, :256])
        }

4.2 Entrenamiento del Gating

• Loss: Weighted cross-entropy + KL divergence para regularización
• Regularización: Entropy bonus para evitar colapso a una estrategia
• Datos: Predicciones de las 5 estrategias en validation set

---

5. INTEGRACIÓN LLM

5.1 Signal Formatter

def format_signal_for_llm(metamodel_output, strategy_outputs, market_context):
    return f"""
## ML Prediction Summary for {market_context['symbol']}

### Ensemble Prediction
- Direction: {metamodel_output['direction']} (confidence: {metamodel_output['confidence']:.1%})
- Predicted Move: {metamodel_output['magnitude']:.2%}
- Strategy Agreement: {calculate_agreement(strategy_outputs)}/5

### Individual Strategies
| Strategy | Direction | Confidence | Weight |
|----------|-----------|------------|--------|
{format_strategy_table(strategy_outputs, metamodel_output['weights'])}

### Market Context
- Volatility Regime: {market_context['volatility_regime']}
- Current Trend: {market_context['trend']}
- Key Levels: {market_context['key_levels']}
"""

5.2 Decision Parsing

def parse_llm_decision(response: str) -> TradingDecision:
    """Parse LLM response into structured trading decision."""
    lines = response.strip().split('\n')
    decision = {}
    for line in lines:
        if line.startswith('DECISION:'):
            decision['action'] = line.split(':')[1].strip()
        elif line.startswith('ENTRY:'):
            decision['entry'] = float(line.split(':')[1].strip())
        elif line.startswith('STOP_LOSS:'):
            decision['stop_loss'] = float(line.split(':')[1].strip())
        elif line.startswith('TAKE_PROFIT:'):
            decision['take_profit'] = parse_tp_levels(line.split(':')[1])
        elif line.startswith('POSITION_SIZE:'):
            decision['position_size'] = float(line.split(':')[1].strip().rstrip('%'))
    return TradingDecision(**decision)

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6. MÉTRICAS Y VALIDACIÓN

6.1 Métricas por Estrategia

Métrica	Objetivo	Medición
Direction Accuracy	≥60%	Validation set
MAE (Magnitude)	≤1%	Validation set
Sharpe Ratio	≥1.0	Backtesting
Max Drawdown	≤20%	Backtesting

6.2 Métricas del Ensemble

Métrica	Objetivo	Medición
Efectividad Operaciones	≥80%	Backtesting con LLM
Sharpe Ratio	≥1.5	Backtesting
Max Drawdown	≤15%	Backtesting
Calibration Error	≤5%	Reliability diagram

6.3 Backtesting Protocol

1. Walk-Forward Validation: 80% train, 10% val, 10% test
2. Out-of-Sample Period: Últimos 6 meses
3. Slippage: 0.5 pips para forex, 0.1% para crypto
4. Commission: 0.1% por trade
5. Position Sizing: Kelly criterion con max 2%

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7. CONFIGURACIÓN

7.1 Config por Estrategia

# config/strategies/pva.yaml
strategy_name: "PVA"
architecture:
  encoder:
    type: "transformer"
    n_layers: 4
    n_heads: 8
    d_model: 256
    d_ff: 1024
    dropout: 0.1
  head:
    type: "xgboost"
    n_estimators: 500
    max_depth: 6
    learning_rate: 0.05

features:
  returns_periods: [1, 5, 10, 20]
  include_derivatives: true
  sequence_length: 100

training:
  batch_size: 256
  learning_rate: 0.0001
  epochs: 100
  early_stopping_patience: 10
  walk_forward_folds: 5

7.2 Config del Metamodel

# config/metamodel.yaml
metamodel_name: "NeuralGating"
architecture:
  n_strategies: 5
  d_hidden: 256
  dropout: 0.3

training:
  batch_size: 128
  learning_rate: 0.001
  epochs: 50
  entropy_regularization: 0.01

calibration:
  method: "isotonic"
  cv_folds: 5

---

8. ENDPOINTS API

8.1 Prediction Endpoint

POST /api/ml/predict/ensemble
Request:
{
  "symbol": "XAUUSD",
  "timeframe": "5m",
  "include_strategies": true
}

Response:
{
  "prediction": {
    "direction": 1,
    "magnitude": 0.0032,
    "confidence": 0.78
  },
  "weights": {
    "PVA": 0.25,
    "MRD": 0.20,
    "VBP": 0.15,
    "MSA": 0.25,
    "MTS": 0.15
  },
  "strategies": {
    "PVA": {"direction": 1, "confidence": 0.72},
    "MRD": {"direction": 1, "confidence": 0.65},
    "VBP": {"direction": 0, "confidence": 0.55},
    "MSA": {"direction": 1, "confidence": 0.80},
    "MTS": {"direction": 1, "confidence": 0.70}
  },
  "timestamp": "2026-01-25T12:00:00Z"
}

8.2 Strategy Detail Endpoint

GET /api/ml/strategy/{strategy_name}/{symbol}
Response:
{
  "strategy": "PVA",
  "symbol": "XAUUSD",
  "prediction": {...},
  "attention_scores": [...],
  "feature_importance": {...},
  "model_version": "1.0.0",
  "last_trained": "2026-01-25"
}

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9. DEPENDENCIAS

9.1 Python Packages

torch>=2.0.0
xgboost>=2.0.0
hmmlearn>=0.3.0
scikit-learn>=1.3.0
pandas>=2.0.0
numpy>=1.24.0
einops>=0.7.0

9.2 Infraestructura

• GPU: NVIDIA con 16GB+ VRAM
• PostgreSQL: Para almacenar modelos y predicciones
• Redis: Para caché de predicciones

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10. ROADMAP DE IMPLEMENTACIÓN

Fase	Descripción	Dependencia
1	Data Pipeline + Attention Architecture	-
2a	Strategy PVA	Fase 1
2b	Strategy MRD	Fase 1
2c	Strategy VBP	Fase 1
2d	Strategy MSA	Fase 1
2e	Strategy MTS	Fase 1
3a	Neural Gating Metamodel	Fase 2 (todas)
3b	LLM Integration	Fase 3a
4	Backtesting Validation	Fase 3b

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11. REFERENCIAS

• Tarea: TASK-2026-01-25-ML-TRAINING-ENHANCEMENT
• Documentación existente: ET-ML-001 a ET-ML-008
• Proyecto antiguo: C:\Empresas\WorkspaceOld\Projects\trading

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Estado: PLANIFICADO Próximo paso: Aprobación del plan e inicio de implementación