# 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

---

## 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.

---

## 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)                              │    │
│  └────────────────────────────────────────────────────────────────────────┘    │
│                                                                                  │
└─────────────────────────────────────────────────────────────────────────────────┘
```

---

## 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`

---

## 4. NEURAL GATING METAMODEL

### 4.1 Arquitectura

```python
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

```python
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

```python
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)
```

---

## 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%

---

## 7. CONFIGURACIÓN

### 7.1 Config por Estrategia

```yaml
# 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

```yaml
# 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"
}
```

---

## 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

---

## 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 |

---

## 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

---

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