Closes #020 - Ticker Data Real-Time Alignment & ML Handbook Integration

QUANT_ROADMAP.md

@@ -32,6 +32,9 @@ This document serves as the permanent, centralized system architecture design an
 *   **Phase 6.0: Live Python Machine Learning Pipeline Integration**
     *   *Features*: Integrated local Miniconda3 Python environment to automatically install `scikit-learn`. Refactored `backend/core/pipeline.py` to ingest real-time market closing price candles for BTC-USD from Yahoo Finance and funding rates from Binance USDS-M Futures REST APIs. Trained the 5 ML estimators (RF, GB, LR, SVM, MLP) across T+1, T+5, and T+10 horizons using Walk-Forward validation, exporting forecasts to `public/data/ensemble_predictions.json` with `isShieldActive: false` to enable live probabilities in the frontend Walk-Forward Radar.
     *   *Status*: **Fully Operational (Production Lock)**.
+*   **Phase 6.5: Ticker Data Real-Time Alignment & ML Handbook Injection**
+    *   *Features*: Linked price asset cards dynamically to a 15-second `useEffect` polling loop querying live Yahoo Finance closing prices, Binance funding rates, and local CSV data. Dynamically scaled liquidation values. Injected mathematical specifications for all 5 ML models (RF, XGBoost, ElasticNet, SVM, MLP) as Section G of the quantitative handbook. Fixed modal viewport clipping. Expanded the Active Learning Feedback Loop table to preserve the 15-probability matrix layout and display separate consensuses for T+1, T+5, and T+10 with detailed model paths.
+    *   *Status*: **Fully Operational (Production Lock)**.
 
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