Closes #MASTER-RESET-024 - Implement layout, performance stats, and quant ML integration

This commit is contained in:
Antigravity Agent
2026-06-17 18:38:54 +02:00
parent 776a41f159
commit 71b7683370
11 changed files with 5469 additions and 458 deletions

View File

@@ -165,4 +165,13 @@ The workstation enforces zero silent caching or historical data ingestion:
* **PEAD Drift Radar**: Uses a dynamic state flag `isLivePeadApi` to toggle status rendering.
* *Green Badge (`🟢 LIVE EPS FEED`)*: FMP API endpoint responded with real-world quarterly reports (`isLiveApi: true`).
* *Amber Badge (`⚠️ ARCHIV-DATEN (API OFFLINE)`)*: Live API timeout, failure, or developer shield fallback active (`isLiveApi: false`).
* **Crypto Bayes Module**:
* *Full-Width Scannability*: Layout structured into 100%-width, centered grids containing the Walk-Forward Ensemble Radar and Active Learning Feedback Loop.
* *Countdown Formatter*: Remaining seconds under pending targets are formatted to human-readable durations (e.g. `Verbleibend: 1 Tag, 19 Std`) using `formatRemainingTime`.
* *Accordion Matrix*: Each log row is expandable via Chevron toggle, displaying the individual model prediction direction and success/failure correctness status checkmarks upon resolution.
* *Multi-Accuracy Tracking*: Shows distinct columns for `T+1 Acc`, `T+5 Acc`, and `T+10 Acc` rather than a single aggregated metric.
* *Global Performance Metrics Panel*: Mounted below the feedback loop, presenting Horizon Efficiency (Section A) and Estimator Hit Distribution (Section B) dynamically evaluated from `localStorage` logs.
* **Quant Python Pipeline (`pipeline.py`)**:
* *Intermarket Sentiment Ingestion*: Fetches daily close values for Nasdaq Composite (`^IXIC`), Gold Spot (`GC=F`), VIX (`^VIX`), and Crypto Fear & Greed (Alternative.me API). Incorporates automatic forward-fill (`ffill()`) and backward-fill (`bfill()`) to process data gaps.
* *Feature selection gateway*: Restricts features passed to the SVM and MLP estimators to those selected by a Random Forest feature importance selector (`SelectFromModel` with threshold `"mean"`), protecting non-linear algorithms from overfitting.