diff --git a/src/api/main.py b/src/api/main.py
index 7dbf2be..a2023f8 100644
--- a/src/api/main.py
+++ b/src/api/main.py
@@ -24,7 +24,8 @@ from ..services.prediction_service import (
     initialize_prediction_service,
     Direction,
     AMDPhase as ServiceAMDPhase,
-    VolatilityRegime as ServiceVolatilityRegime
+    VolatilityRegime as ServiceVolatilityRegime,
+    HierarchicalResult
 )
 
 # API Models
@@ -123,6 +124,7 @@ class HealthResponse(BaseModel):
     status: str
     version: str
     models_loaded: bool
+    hierarchical_available: bool = False
     timestamp: datetime
 
 
@@ -199,10 +201,14 @@ async def shutdown_event():
 @app.get("/health", response_model=HealthResponse, tags=["System"])
 async def health_check():
     """Health check endpoint"""
+    hierarchical = False
+    if prediction_service:
+        hierarchical = prediction_service.hierarchical_available
     return HealthResponse(
         status="healthy",
         version="0.1.0",
         models_loaded=models_state["loaded"],
+        hierarchical_available=hierarchical,
         timestamp=datetime.utcnow()
     )
 
@@ -471,6 +477,163 @@ async def list_attention_models():
     }
 
 
+# =============================================================================
+# Hierarchical Pipeline Endpoints (L0→L1→L2)
+# =============================================================================
+
+class HierarchicalPredictionResponse(BaseModel):
+    """Full hierarchical L0→L1→L2 prediction response"""
+    symbol: str
+    timestamp: datetime
+    # Level 0: Attention
+    attention_score_5m: float = Field(..., description="Attention score for 5m timeframe")
+    attention_score_15m: float = Field(..., description="Attention score for 15m timeframe")
+    attention_class_5m: int = Field(..., description="Flow class 5m: 0=low, 1=med, 2=high")
+    attention_class_15m: int = Field(..., description="Flow class 15m: 0=low, 1=med, 2=high")
+    # Level 1: Base Models
+    pred_high_5m: float = Field(..., description="Predicted delta high from 5m model")
+    pred_low_5m: float = Field(..., description="Predicted delta low from 5m model")
+    pred_high_15m: float = Field(..., description="Predicted delta high from 15m model")
+    pred_low_15m: float = Field(..., description="Predicted delta low from 15m model")
+    # Level 2: Metamodel
+    delta_high_final: float = Field(..., description="Final synthesized delta high")
+    delta_low_final: float = Field(..., description="Final synthesized delta low")
+    confidence: bool = Field(..., description="Metamodel confidence flag")
+    confidence_proba: float = Field(..., description="Metamodel confidence probability")
+    # Trading signals
+    should_trade: bool = Field(..., description="Should trade based on attention+confidence")
+    trade_quality: str = Field(..., description="Trade quality: high, medium, low, skip")
+
+
+@app.get("/api/hierarchical/{symbol}", response_model=HierarchicalPredictionResponse, tags=["Hierarchical"])
+async def get_hierarchical_prediction(symbol: str):
+    """
+    Get full hierarchical L0→L1→L2 prediction for a symbol.
+
+    This is the main prediction endpoint that uses the 3-level architecture:
+    - Level 0 (Attention): Determines WHEN to pay attention to market
+    - Level 1 (Base Models): Symbol/timeframe specific predictions (5m + 15m)
+    - Level 2 (Metamodel): Synthesizes predictions into final values
+
+    The metamodel type (XGBoost or Neural Gating) is selected automatically
+    based on best performance per symbol.
+
+    Returns should_trade=True only when attention is sufficient AND
+    metamodel confidence exceeds threshold.
+    """
+    global prediction_service
+
+    if prediction_service is None:
+        prediction_service = get_prediction_service()
+
+    if not prediction_service.hierarchical_available:
+        raise HTTPException(
+            status_code=503,
+            detail="Hierarchical pipeline not available"
+        )
+
+    try:
+        result = await prediction_service.get_hierarchical_prediction(
+            symbol=symbol.upper()
+        )
+
+        if result is None:
+            raise HTTPException(
+                status_code=500,
+                detail=f"Hierarchical prediction failed for {symbol}"
+            )
+
+        return HierarchicalPredictionResponse(
+            symbol=result.symbol,
+            timestamp=result.timestamp,
+            attention_score_5m=result.attention_score_5m,
+            attention_score_15m=result.attention_score_15m,
+            attention_class_5m=result.attention_class_5m,
+            attention_class_15m=result.attention_class_15m,
+            pred_high_5m=result.pred_high_5m,
+            pred_low_5m=result.pred_low_5m,
+            pred_high_15m=result.pred_high_15m,
+            pred_low_15m=result.pred_low_15m,
+            delta_high_final=result.delta_high_final,
+            delta_low_final=result.delta_low_final,
+            confidence=result.confidence,
+            confidence_proba=result.confidence_proba,
+            should_trade=result.should_trade,
+            trade_quality=result.trade_quality
+        )
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Hierarchical prediction failed for {symbol}: {e}")
+        raise HTTPException(status_code=500, detail=f"Hierarchical prediction failed: {str(e)}")
+
+
+@app.get("/api/hierarchical/{symbol}/models", tags=["Hierarchical"])
+async def get_hierarchical_model_info(symbol: str):
+    """
+    Get information about loaded hierarchical models for a symbol.
+
+    Shows which models are loaded for each level of the hierarchy.
+    """
+    global prediction_service
+
+    if prediction_service is None:
+        prediction_service = get_prediction_service()
+
+    if not prediction_service.hierarchical_available:
+        return {
+            "available": False,
+            "symbol": symbol.upper(),
+            "models_loaded": False
+        }
+
+    try:
+        info = await prediction_service.get_hierarchical_model_info(symbol.upper())
+        return {
+            "available": True,
+            **info
+        } if info else {
+            "available": True,
+            "symbol": symbol.upper(),
+            "models_loaded": False,
+            "attention_models": [],
+            "base_models": [],
+            "metamodel_type": "none"
+        }
+    except Exception as e:
+        logger.error(f"Failed to get hierarchical model info: {e}")
+        return {
+            "available": False,
+            "error": str(e)
+        }
+
+
+@app.get("/api/hierarchical/status", tags=["Hierarchical"])
+async def get_hierarchical_status():
+    """
+    Get overall status of the hierarchical pipeline.
+
+    Returns availability and configuration info.
+    """
+    global prediction_service
+
+    if prediction_service is None:
+        prediction_service = get_prediction_service()
+
+    available = prediction_service.hierarchical_available
+
+    return {
+        "available": available,
+        "supported_symbols": ["XAUUSD", "EURUSD", "BTCUSD", "GBPUSD", "USDJPY"] if available else [],
+        "timeframes": ["5m", "15m"] if available else [],
+        "architecture": {
+            "level_0": "Attention Models (flow detection)",
+            "level_1": "Base Models (symbol/timeframe specific)",
+            "level_2": "Metamodels (XGBoost or Neural Gating)"
+        } if available else {}
+    }
+
+
 # Active signals endpoint - GET version for easy consumption
 class ActiveSignalsResponse(BaseModel):
     """Response with active signals for all symbols"""
diff --git a/src/services/prediction_service.py b/src/services/prediction_service.py
index 90f9586..292f103 100644
--- a/src/services/prediction_service.py
+++ b/src/services/prediction_service.py
@@ -34,6 +34,13 @@ from ..data.indicators import TechnicalIndicators
 # Attention provider for Level 0 features
 from .attention_provider import AttentionProvider, get_attention_provider
 
+# Hierarchical Pipeline for L0→L1→L2 predictions
+from ..pipelines.hierarchical_pipeline import (
+    HierarchicalPipeline,
+    PipelineConfig,
+    PredictionResult as HierarchicalPredictionResult
+)
+
 
 class Direction(Enum):
     LONG = "long"
@@ -86,6 +93,31 @@ class AttentionInfo:
     is_tradeable: bool  # True if attention_score >= 1.0
 
 
+@dataclass
+class HierarchicalResult:
+    """Result from hierarchical L0→L1→L2 pipeline"""
+    symbol: str
+    timestamp: datetime
+    # Level 0 outputs
+    attention_score_5m: float
+    attention_score_15m: float
+    attention_class_5m: int
+    attention_class_15m: int
+    # Level 1 outputs
+    pred_high_5m: float
+    pred_low_5m: float
+    pred_high_15m: float
+    pred_low_15m: float
+    # Level 2 outputs (final)
+    delta_high_final: float
+    delta_low_final: float
+    confidence: bool
+    confidence_proba: float
+    # Trading signals
+    should_trade: bool
+    trade_quality: str  # 'high', 'medium', 'low', 'skip'
+
+
 @dataclass
 class TradingSignal:
     """Complete trading signal"""
@@ -154,6 +186,7 @@ class PredictionService:
         self._tpsl_classifier = None
         self._amd_detector = None
         self._attention_provider = None  # Level 0 attention models
+        self._hierarchical_pipeline = None  # L0→L1→L2 pipeline
         self._models_loaded = False
 
         # Symbol-specific trainers (nuevos modelos por símbolo/timeframe)
@@ -211,6 +244,20 @@ class PredictionService:
                 logger.info("No attention models directory found, creating provider anyway")
                 self._attention_provider = AttentionProvider(attention_path)
 
+            # Load Hierarchical Pipeline (L0→L1→L2)
+            try:
+                pipeline_config = PipelineConfig(
+                    attention_model_path=os.path.join(self.models_dir, "attention"),
+                    base_model_path=os.path.join(self.models_dir, "symbol_timeframe_models"),
+                    metamodel_path=os.path.join(self.models_dir, "metamodels"),
+                    neural_gating_path=os.path.join(self.models_dir, "metamodels_neural")
+                )
+                self._hierarchical_pipeline = HierarchicalPipeline(pipeline_config)
+                logger.info("✅ HierarchicalPipeline initialized")
+            except Exception as e:
+                logger.warning(f"HierarchicalPipeline initialization failed: {e}")
+                self._hierarchical_pipeline = None
+
             self._models_loaded = True
 
             # Cargar modelos por símbolo si el feature flag está activo
@@ -589,6 +636,85 @@ class PredictionService:
         should_trade = attention.attention_score >= min_attention
         return should_trade, attention
 
+    @property
+    def hierarchical_available(self) -> bool:
+        """Check if hierarchical pipeline is available."""
+        return self._hierarchical_pipeline is not None
+
+    async def get_hierarchical_prediction(
+        self,
+        symbol: str,
+        load_models: bool = True
+    ) -> Optional[HierarchicalResult]:
+        """
+        Get full hierarchical L0→L1→L2 prediction.
+
+        This is the main method for getting predictions from the 3-level
+        architecture combining attention, base models, and metamodels.
+
+        Args:
+            symbol: Trading symbol (e.g., 'XAUUSD')
+            load_models: Whether to load models if not already loaded
+
+        Returns:
+            HierarchicalResult with all layer outputs, or None if unavailable
+        """
+        if not self._hierarchical_pipeline:
+            logger.warning("HierarchicalPipeline not available")
+            return None
+
+        # Load models for this symbol if needed
+        if load_models:
+            try:
+                self._hierarchical_pipeline.load_models(symbol)
+            except Exception as e:
+                logger.warning(f"Failed to load hierarchical models for {symbol}: {e}")
+                return None
+
+        # Get market data for both timeframes
+        try:
+            df_5m, df_15m = await asyncio.gather(
+                self.get_market_data(symbol, "5m", lookback_periods=300),
+                self.get_market_data(symbol, "15m", lookback_periods=150)
+            )
+
+            if df_5m.empty or df_15m.empty:
+                logger.warning(f"Insufficient data for hierarchical prediction: {symbol}")
+                return None
+
+            # Run hierarchical prediction
+            result = self._hierarchical_pipeline.predict(df_5m, df_15m, symbol)
+
+            # Convert to HierarchicalResult dataclass
+            return HierarchicalResult(
+                symbol=result.symbol,
+                timestamp=result.timestamp,
+                attention_score_5m=result.attention_score_5m,
+                attention_score_15m=result.attention_score_15m,
+                attention_class_5m=result.attention_class_5m,
+                attention_class_15m=result.attention_class_15m,
+                pred_high_5m=result.pred_high_5m,
+                pred_low_5m=result.pred_low_5m,
+                pred_high_15m=result.pred_high_15m,
+                pred_low_15m=result.pred_low_15m,
+                delta_high_final=result.delta_high_final,
+                delta_low_final=result.delta_low_final,
+                confidence=result.confidence,
+                confidence_proba=result.confidence_proba,
+                should_trade=result.should_trade,
+                trade_quality=result.trade_quality
+            )
+
+        except Exception as e:
+            logger.error(f"Hierarchical prediction failed for {symbol}: {e}")
+            return None
+
+    async def get_hierarchical_model_info(self, symbol: str) -> Optional[Dict]:
+        """Get information about loaded hierarchical models for a symbol."""
+        if not self._hierarchical_pipeline:
+            return None
+        return self._hierarchical_pipeline.get_model_info(symbol)
+
     async def generate_signal(
         self,
         symbol: str,