diff --git a/crates/ml/src/trainers/tft_parquet.rs b/crates/ml/src/trainers/tft_parquet.rs
index 897358889..cce66daeb 100644
--- a/crates/ml/src/trainers/tft_parquet.rs
+++ b/crates/ml/src/trainers/tft_parquet.rs
@@ -23,7 +23,7 @@ impl TFTTrainer {
     ///
     /// # Arguments
     ///
-    /// * `parquet_path` - Path to Parquet file containing OHLCV bars and 54 features
+    /// * `parquet_path` - Path to Parquet file containing OHLCV bars
     ///
     /// # Returns
     ///
@@ -32,7 +32,7 @@ impl TFTTrainer {
     /// # Features
     ///
     /// - Lazy batch loading (10,000 rows at a time)
-    /// - 54 feature extraction from OHLCV bars (core features)
+    /// - 51-feature extraction from OHLCV bars (WAVE 10: 5 static + 10 known + 36 unknown)
     /// - Sliding window creation (lookback=60, horizon=10)
     /// - Memory-efficient processing for large datasets
     pub async fn train_from_parquet(&mut self, parquet_path: &str) -> MLResult<TrainingMetrics> {
@@ -373,15 +373,18 @@ impl TFTTrainer {
         for i in 0..(feature_vectors.len().saturating_sub(LOOKBACK + HORIZON)) {
             let static_feats = Array1::from_vec(feature_vectors[i + LOOKBACK][0..5].to_vec());
 
-            let mut hist_data = Vec::new();
+            // Historical features: fv[15..51] = 36 unknown features per timestep
+            // Feature vector is [f64; 51]: [0..5] static, [5..15] known, [15..51] unknown
+            const HIST_START: usize = 15;
+            const HIST_END: usize = 51;
+            const NUM_HIST_FEATURES: usize = HIST_END - HIST_START; // 36
+
+            let mut hist_data = Vec::with_capacity(LOOKBACK * NUM_HIST_FEATURES);
             for j in i..(i + LOOKBACK) {
-                let fv = &feature_vectors[j];
-                let start = 15.min(fv.len());
-                let end = 54.min(fv.len());
-                hist_data.extend_from_slice(&fv[start..end]);
+                hist_data.extend_from_slice(&feature_vectors[j][HIST_START..HIST_END]);
             }
             let historical_feats =
-                Array2::from_shape_vec((LOOKBACK, 39), hist_data).map_err(|e| {
+                Array2::from_shape_vec((LOOKBACK, NUM_HIST_FEATURES), hist_data).map_err(|e| {
                     MLError::ModelError(format!(
                         "Failed to create historical features array: {}",
                         e