fix(dqn): wire hyperopt weight_decay through trainer to agent

The DQNConfig construction in trainer.rs hardcoded weight_decay to 1e-4,
ignoring the hyperopt-tuned value in DQNHyperparameters. This meant PSO
optimization of weight_decay (search space index 17, range [1e-5, 1e-3])
had no effect on actual training runs.

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

crates/ml/src/trainers/dqn/trainer.rs

@@ -450,7 +450,7 @@ impl DQNTrainer {
             cvar_alpha: 0.05,
 
             minimum_profit_factor: 1.5,
-            weight_decay: 1e-4,
+            weight_decay: hyperparams.weight_decay,
             mixed_precision: hyperparams.mixed_precision.clone().or(mixed_precision_detected),
             entropy_coefficient: hyperparams.entropy_coefficient.unwrap_or(0.01),
             noisy_epsilon_floor: hyperparams.noisy_epsilon_floor.unwrap_or(0.05) as f32,
@@ -550,15 +550,9 @@ impl DQNTrainer {
         let multi_asset_portfolio: Option<Arc<crate::dqn::multi_asset::MultiAssetPortfolioTracker>> = None;
 
         // Stress testing for robustness validation
-        let stress_tester: Option<Arc<crate::dqn::stress_testing::DQNStressTester>> = if hyperparams.enable_stress_testing {
-            // Note: We need to create a dummy DQNTrainer first for stress testing
-            // For now, we'll initialize this as None and populate it after Self is created
-            // This is a circular dependency issue that will be resolved in the wiring phase
-            info!("Stress testing enabled (8 scenarios)");
-            None // Will be initialized after DQNTrainer construction
-        } else {
-            None
-        };
+        // Initialized as None here; call init_stress_tester() after construction
+        // to resolve the circular dependency (DQNStressTester needs a DQNTrainer).
+        let stress_tester: Option<Arc<crate::dqn::stress_testing::DQNStressTester>> = None;
 
         if enable_action_masking {
             info!(
@@ -858,6 +852,35 @@ impl DQNTrainer {
         })
     }
 
+    /// Two-phase stress tester initialization.
+    ///
+    /// Call this after constructing `DQNTrainer` to resolve the circular dependency:
+    /// `DQNStressTester::new()` requires a `DQNTrainer`, so the tester cannot be
+    /// created *during* trainer construction.  This method builds a lightweight
+    /// inner trainer (with stress testing itself disabled to avoid recursion) and
+    /// hands it to `DQNStressTester::new()`.
+    ///
+    /// No-op if `hyperparams.enable_stress_testing` is `false`.
+    pub fn init_stress_tester(&mut self) -> Result<()> {
+        if !self.hyperparams.enable_stress_testing {
+            return Ok(());
+        }
+
+        // Clone hyperparams with stress testing disabled so the inner trainer
+        // does not recursively try to initialise its own stress tester.
+        let mut inner_hp = self.hyperparams.clone();
+        inner_hp.enable_stress_testing = false;
+
+        let inner_trainer = Self::new_with_device(inner_hp, self.device.clone())
+            .context("Failed to create inner DQNTrainer for stress tester")?;
+
+        let tester = crate::dqn::stress_testing::DQNStressTester::new(inner_trainer);
+        self.stress_tester = Some(Arc::new(tester));
+        info!("Stress testing enabled (8 scenarios)");
+
+        Ok(())
+    }
+
     /// Get a reference to the double-buffered loader, if GPU is active.
     pub fn double_buffer(&self) -> Option<&crate::cuda_pipeline::double_buffer::DoubleBufferedLoader> {
         self.double_buffer.as_ref()
@@ -1333,6 +1356,25 @@ impl DQNTrainer {
             metrics.add_metric("hold_count", hold_count as f64);
         }
 
+        // Compute Q-value standard deviation across epochs for hyperopt stability penalty.
+        // self.q_value_history stores per-epoch average Q-values; their std measures
+        // how much Q-values fluctuate during training (volatility indicator).
+        if self.q_value_history.len() >= 2 {
+            let n = self.q_value_history.len() as f64;
+            let mean = self.q_value_history.iter().sum::<f64>() / n;
+            let variance = self
+                .q_value_history
+                .iter()
+                .map(|&q| (q - mean).powi(2))
+                .sum::<f64>()
+                / n;
+            let std_dev = variance.sqrt();
+            metrics.add_metric("q_value_std", std_dev);
+        } else {
+            // Not enough data points to compute std; default to 0.0 (no volatility signal)
+            metrics.add_metric("q_value_std", 0.0);
+        }
+
         if early_stopped {
             metrics.add_metric("early_stopped", 1.0);
         }