Closes #015 - Deploy Multi-Model Ensemble & Walk-Forward Radar

QUANT_ROADMAP.md

@@ -195,6 +195,22 @@ $$\mathbb{E}[\theta \mid \text{Data}] = \frac{\text{B}(\alpha_{\text{post}} + 1,
 #### 4. Expanded Workstation Formula
 $$P_{\text{Posterior}} = \frac{\alpha_{\text{prior}} + (P_{\text{ML}} \times w)}{\alpha_{\text{prior}} + \beta_{\text{prior}} + w}$$
 
+#### 5. Walk-Forward Validation & Multi-Model Ensemble
+To prevent look-ahead bias and structural overfitting, the system deploys a Walk-Forward Validation framework on a fixed 365-day rolling window across a fleet of 5 machine learning estimators: Random Forest (RF), XGBoost/Gradient Boosting (GB), ElasticNet Logistic Regression (LR), Support Vector Machines (SVM), and Multi-Layer Perceptrons (MLP).
+
+Predictions are generated across three distinct forecast horizons: \(T+1\), \(T+5\), and \(T+10\). To ensure absolute stationarity, all raw asset prices are stripped from the feature space, utilizing only Log-Returns, Rolling Volatility, RSI, Distance to Moving Averages, and Daily Spreads.
+
+##### Leakage Safeguards (Horizon Cutoff):
+For a training window ending at index \(T-1\) and forecasting horizon \(H \in \{1, 5, 10\}\):
+*   **T+1 Horizon**: Trains on features up to index \(T-2\), using target labels resolved up to index \(T-1\).
+*   **T+5 Horizon**: The training set is truncated by \(5\) steps, meaning the latest training features end at index \(T-6\) to ensure that the target labels (which require a 5-day future window) do not extend past index \(T-1\) (the window boundary).
+*   **T+10 Horizon**: The training set is truncated by \(10\) steps, ending features at index \(T-11\) to ensure zero leakage of post-boundary price data.
+
+##### Multi-Tracker Online Learning:
+The cockpit maintains 15 independent Beta-Posterior trackers (5 models \(\times\) 3 horizons) persisted inside the client browser. Each tracker is initialized with historical priors and updated dynamically in the background. The expected accuracy is calculated as:
+\[\mathbb{E}[\theta] = \frac{\alpha}{\alpha + \beta}\]
+where \(\alpha\) represents successes and \(\beta\) represents false alarms, calculated independently for each estimator-horizon pair.
+
 ---
 
 ### VI. Sandbox Portfolio Cockpit & Kelly Sizing