feat(ensemble): add ModelInferenceAdapter trait and types

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

ml/src/ensemble/inference_adapter.rs

@@ -0,0 +1,96 @@
+//! Model inference adapter trait for ensemble prediction
+//!
+//! Defines the contract that each model adapter must implement
+//! to participate in ensemble prediction.
+
+use crate::MLResult;
+
+/// Canonical feature vector for ensemble inference.
+/// 51-dim layout matching DQN state_dim:
+///   0-9: price features (OHLCV, VWAP, spread, tick)
+///   10-19: technical indicators (RSI, MACD, Bollinger, ATR)
+///   20-29: order book (depth levels, imbalance)
+///   30-39: microstructure (trade flow, toxicity)
+///   40-50: position/risk (PnL, exposure, drawdown)
+#[derive(Debug, Clone)]
+pub struct FeatureVector {
+    pub values: Vec<f64>,
+    pub timestamp: i64,
+}
+
+/// Prediction output from a single model adapter, normalized for ensemble aggregation.
+#[derive(Debug, Clone)]
+pub struct EnsemblePrediction {
+    pub model_name: String,
+    /// Directional signal: -1.0 (bearish) to 1.0 (bullish)
+    pub direction: f64,
+    /// Model confidence: 0.0 to 1.0
+    pub confidence: f64,
+    /// Model-specific metadata
+    pub metadata: PredictionMeta,
+}
+
+/// Optional model-specific metadata attached to predictions.
+#[derive(Debug, Clone, Default)]
+pub struct PredictionMeta {
+    /// Inference latency in microseconds
+    pub latency_us: u64,
+    /// Quantile forecasts (TFT only)
+    pub quantiles: Option<Vec<f64>>,
+    /// Attention weights (TFT only)
+    pub attention_weights: Option<Vec<f64>>,
+    /// Raw Q-values (DQN only)
+    pub q_values: Option<Vec<f64>>,
+}
+
+/// Adapter trait for running inference on a loaded model.
+///
+/// Each model type (DQN, PPO, TFT, Mamba2) implements this trait
+/// to handle its own feature-to-tensor mapping and output normalization.
+pub trait ModelInferenceAdapter: Send + Sync {
+    /// Human-readable model name for logging
+    fn model_name(&self) -> &str;
+
+    /// Run inference on a canonical feature vector.
+    /// Returns a normalized ensemble prediction.
+    fn predict(&self, features: &FeatureVector) -> MLResult<EnsemblePrediction>;
+
+    /// Whether this adapter has a loaded model ready for inference
+    fn is_ready(&self) -> bool;
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_feature_vector_creation() {
+        let fv = FeatureVector {
+            values: vec![0.1; 51],
+            timestamp: 1700000000_000_000,
+        };
+        assert_eq!(fv.values.len(), 51);
+        assert_eq!(fv.timestamp, 1700000000_000_000);
+    }
+
+    #[test]
+    fn test_ensemble_prediction_direction_bounds() {
+        let pred = EnsemblePrediction {
+            model_name: "test".to_string(),
+            direction: 0.75,
+            confidence: 0.9,
+            metadata: PredictionMeta::default(),
+        };
+        assert!(pred.direction >= -1.0 && pred.direction <= 1.0);
+        assert!(pred.confidence >= 0.0 && pred.confidence <= 1.0);
+    }
+
+    #[test]
+    fn test_prediction_meta_default() {
+        let meta = PredictionMeta::default();
+        assert_eq!(meta.latency_us, 0);
+        assert!(meta.quantiles.is_none());
+        assert!(meta.attention_weights.is_none());
+        assert!(meta.q_values.is_none());
+    }
+}

ml/src/ensemble/mod.rs

@@ -17,6 +17,7 @@ pub mod model;
 pub mod training_integration; // Training integration for ML service
 pub mod voting;
 pub mod weights;
+pub mod inference_adapter;
 
 // Re-export key types that are used across ensemble modules
 pub use ab_testing::{
@@ -43,6 +44,7 @@ pub use metrics::{
     CHECKPOINT_SWAP_LATENCY_MICROSECONDS, CHECKPOINT_VALIDATION_TOTAL,
 };
 pub use training_integration::EnsembleTrainingIntegration;
+pub use inference_adapter::{EnsemblePrediction, FeatureVector, ModelInferenceAdapter, PredictionMeta};
 
 /// Errors that can occur in ensemble operations
 #[derive(Error, Debug)]