Closes #ISSUE-027-REGIME-UI - Implement regime indicator badge and dynamic meta-learner threshold

ARCHITECT_HANDOVER.md

@@ -174,7 +174,10 @@ The workstation enforces zero silent caching or historical data ingestion:
     *   *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.
+    *   *Regime Status Indicator*: Renders glassmorphic conditional badges in the header of the Walk-Forward Radar based on `"activeRegime"` in the JSON payload (1 for Calm, 2 for Turbulent, 3 for Churn).
 *   **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.
+    *   *Feature selection gateway*: Restricts features passed to the estimators to those selected by Boruta & PIMP filters, while explicitly prioritizing and bypassing pruning for all high-alpha metrics (`v_supply`, `asopr`, `sth_sopr`, `lth_sopr`, `theta`, `squeeze_risk`, `d_liq`, `f_comp`, `z_f`, `z_f_squeeze_trigger`, `cvd_inst`, `cvd_ret`, `div_cvd`, `lambda_kyle`).
+    *   *Dynamic Meta-Learner Calibrator*: Replaces the static $\theta_{\text{conf}} = 0.55$ with a dynamic calibration threshold computed as the mean training correctness probability (`np.mean(train_r_probs)`) inside each model loop, successfully resolving the 50% entropy block.
+    *   *Defensive Class Array Check*: Detects if the target classes array has a size smaller than 2, applying deterministic training probability fallbacks.
 

DEV_LOG.md

@@ -393,6 +393,25 @@ This document tracks all modifications, npm packages, active compilation states,
 *   **Active Bugs**: None.
 *   **Type Checker Status**: Verified 100% clean type verification (`npx tsc --noEmit` returns exit code 0).
 
+---
+
+## [2026-06-17] - Volatility Regime Badging & Selective Inference Calibrator (#ISSUE-027-REGIME-UI)
+
+### Added
+*   **Vol Regime Status Badges (CryptoDemo.tsx)**: Mounted high-visibility, glassmorphic conditional status badges adjacent to the "Explain Calibration" button in the Walk-Forward Ensemble Radar header. Dynamically renders:
+    *   `🟢 REGIME: CALM (MICROSTRUCTURE MODE)` for Regime 1 (Calm).
+    *   `🚨 REGIME: TURBULENT (LIQUIDATION CASCADE)` (flashing/pulsing) for Regime 2 (Turbulent).
+    *   `🟡 REGIME: MEAN-REVERTING CHURN` for Regime 3 (Mean-Reverting Churn).
+*   **Ensemble Active Regime Ingestion**: Bound `activeRegime` in the `fetchEnsemble` routine of [CryptoDemo.tsx](file:///c:/Users/jannr/.gemini/antigravity/scratch/investment-sandbox/components/modules/crypto/CryptoDemo.tsx) to read `"activeRegime"` from the JSON output of the python pipeline.
+*   **Active Volatility Regime Export**: Overhauled [pipeline.py](file:///c:/Users/jannr/.gemini/antigravity/scratch/investment-sandbox/backend/core/pipeline.py) to return `active_regime` from `train_and_forecast()` and write `"activeRegime": int(active_regime) + 1` (re-indexing Calm=0 to 1, Turbulent=1 to 2) inside the main JSON output payload (`ensemble_predictions.json`).
+*   **High-Alpha Feature Selector Prioritization**: Configured the Boruta & PIMP feature selection filters in [pipeline.py](file:///c:/Users/jannr/.gemini/antigravity/scratch/investment-sandbox/backend/core/pipeline.py) to explicitly prioritize and bypass pruning for all high-alpha metrics (`v_supply`, `asopr`, `sth_sopr`, `lth_sopr`, `theta`, `squeeze_risk`, `d_liq`, `f_comp`, `z_f`, `z_f_squeeze_trigger`, `cvd_inst`, `cvd_ret`, `div_cvd`, `lambda_kyle`), ensuring they are always routed to the estimators.
+*   **Dynamic Meta-Learner Calibrator**: Calibrated the Stage 2 Reliability Meta-Learner execution threshold ($\theta_{\text{conf}}$) dynamically against the empirical mean training correctness probability (`np.mean(train_r_probs)`) inside each model loop, successfully resolving the 50.0% zero-exposure entropy block.
+*   **Defensive Class Array Check**: Guarded against `IndexError` in the Meta-Learner confidence calculator by checking `len(meta_clf.classes_) >= 2`, providing stable execution fallbacks if the training correctness labels are uniform.
+
+### Active Bugs / Compile Status
+*   **Active Bugs**: None.
+*   **Type Checker Status**: Verified 100% clean type verification (`npx tsc --noEmit` returns exit code 0).
+
 
 
 

QUANT_ROADMAP.md

@@ -49,6 +49,9 @@ This document serves as the permanent, centralized system architecture design an
 *   **Phase 10.0: Alpha Unit Activation & Pure Quantum Fusion**
     *   *Features*: Deployed unified on-chain, perpetual derivatives, and order book microstructure ETL ingestion pipeline in `etl.py`. Refactored pipeline training loop with FFD-ADF memory search, rolling MAD scaling, MS-GJR-GARCH Student-t volatility regime routing matrices, PIMP feature validation shadow models, uLSIF density ratio weighting, and Stage 2 Reliability Meta-Learners.
     *   *Status*: **Fully Operational (Production Lock)**.
+*   **Phase 11.0: Volatility Regime Status Display & Dynamic Calibrator Threshold (#ISSUE-027-REGIME-UI)**
+    *   *Features*: Mounted glassmorphic conditional status badges for Calm, Turbulent, and Churn regimes in `CryptoDemo.tsx` Walk-Forward header. Overhauled `pipeline.py` to return the volatility regime, write it to the JSON payload, prioritize high-alpha features in Boruta & PIMP, and calibrate $\theta_{\text{conf}}$ dynamically using training accuracy to break the 50.0% zero-exposure block.
+    *   *Status*: **Fully Operational (Production Lock)**.
 
 ---
 

backend/core/pipeline.py

@@ -484,7 +484,7 @@ def train_and_forecast():
     """
     if not ML_LIBRARIES_AVAILABLE:
         print("Scikit-learn not available. Skipping model fitting.")
-        return get_mock_predictions()
+        return get_mock_predictions(), 0
 
     # Load data
     csv_path = os.path.join('backend', 'data', 'BTC-USD.csv')
@@ -572,11 +572,28 @@ def train_and_forecast():
         X_train_selected = X_train_scaled
         X_test_selected = X_test_scaled
         try:
+            high_alpha_cols = {
+                'v_supply', 'asopr', 'sth_sopr', 'lth_sopr', 'theta', 'squeeze_risk', 
+                'd_liq', 'f_comp', 'z_f', 'z_f_squeeze_trigger', 'cvd_inst', 'cvd_ret', 
+                'div_cvd', 'lambda_kyle'
+            }
+            # Identify indices of high-alpha features that are present in X_train
+            high_alpha_indices = [
+                i for i, col in enumerate(X_train.columns) 
+                if col in high_alpha_cols
+            ]
+            
             # Fit selector classifier (Random Forest)
             selector_clf = RandomForestClassifier(n_estimators=30, max_depth=4, random_state=42)
             
             # Boruta shadow model sweep
             boruta_idx = boruta_shadow_pruning(X_train_scaled, y_train)
+            
+            # Ensure high-alpha indices are retained in Boruta output
+            for idx in high_alpha_indices:
+                if idx not in boruta_idx:
+                    boruta_idx.append(idx)
+                    
             X_train_boruta = X_train_scaled[:, boruta_idx]
             
             # PIMP permutation feature filter
@@ -584,6 +601,12 @@ def train_and_forecast():
             
             # Map back to original indices
             selected_feature_indices = [boruta_idx[i] for i in pimp_idx]
+            
+            # Ensure high-alpha indices are retained in the final selection
+            for idx in high_alpha_indices:
+                if idx not in selected_feature_indices:
+                    selected_feature_indices.append(idx)
+                    
             X_train_selected = X_train_scaled[:, selected_feature_indices]
             X_test_selected = X_test_scaled[:, selected_feature_indices]
             print(f"Boruta & PIMP Selection ({h_label}): Reduced features from {X_train_scaled.shape[1]} to {X_train_selected.shape[1]}")
@@ -618,10 +641,16 @@ def train_and_forecast():
                 meta_clf.fit(X_train_micro, y_reliability)
                 
                 # Compute confidence score r_hat on test sample
-                r_pred = float(meta_clf.predict_proba(X_test_micro)[0][1])
+                if len(meta_clf.classes_) >= 2:
+                    r_pred = float(meta_clf.predict_proba(X_test_micro)[0][1])
+                    train_r_probs = meta_clf.predict_proba(X_train_micro)[:, 1]
+                else:
+                    r_pred = float(meta_clf.classes_[0])
+                    train_r_probs = np.full(len(X_train_micro), float(meta_clf.classes_[0]))
                 
-                # 3. Apply Ironclad Execution Rule: Execute ONLY if confidence exceeds threshold theta_conf = 0.55
+                theta_conf = float(np.mean(train_r_probs))
-                theta_conf = 0.55
+                
+                # 3. Apply Ironclad Execution Rule: Execute ONLY if confidence exceeds threshold theta_conf
                 if r_pred >= theta_conf:
                     # Retrieve expected direction probability
                     classes = list(clf.classes_)
@@ -644,7 +673,7 @@ def train_and_forecast():
                 print(f"Model {name} failed on horizon {h_label}: {e}")
                 predictions[name][h_label] = 0.5
 
-    return predictions
+    return predictions, active_regime
 
 
 def get_mock_predictions():
@@ -769,7 +798,7 @@ def main():
     # Ingest live data first
     fetch_real_data()
     
-    preds = train_and_forecast()
+    preds, active_regime = train_and_forecast()
     
     output_dir = os.path.join('public', 'data')
     os.makedirs(output_dir, exist_ok=True)
@@ -778,6 +807,7 @@ def main():
     
     payload = {
         "isShieldActive": not (ML_LIBRARIES_AVAILABLE and os.path.exists(os.path.join('backend', 'data', 'BTC-USD.csv'))),
+        "activeRegime": int(active_regime) + 1,
         "predictions": {
             "BTC": preds,
             "ETH": {

backend/data/BTC-USD.csv

@@ -729,4 +729,4 @@ Date,Open,High,Low,Close,Volume
 2026-06-14,64420.16796875,65749.78125,63634.0234375,65710.3984375,21572226975
 2026-06-15,65711.109375,67248.1328125,65315.8359375,66289.5,32927321950
 2026-06-16,66289.4609375,66928.609375,65315.0703125,65600.640625,25063963967
-2026-06-17,65710.09375,65849.53125,65333.8984375,65853.6796875,23256606720
+2026-06-17,65710.09375,65849.53125,65333.8984375,65996.8203125,23256606720

backend/data/GC-F.csv

@@ -502,4 +502,4 @@ Date,Open,High,Low,Close,Volume
 2026-06-12,4208.2998046875,4225.2998046875,4173.2001953125,4215.0,1167
 2026-06-15,4271.2001953125,4362.0,4269.10009765625,4328.0,1666
 2026-06-16,4309.5,4345.7998046875,4309.5,4330.89990234375,1666
-2026-06-17,4352.60009765625,4402.7998046875,4335.60009765625,4391.2001953125,74623
+2026-06-17,4352.60009765625,4402.7998046875,4335.60009765625,4394.7998046875,76633

backend/data/IXIC.csv

@@ -500,4 +500,4 @@ Date,Open,High,Low,Close,Volume
 2026-06-12,25783.359375,26010.310546875,25599.939453125,25888.83984375,10337400000
 2026-06-15,26447.23046875,26687.560546875,26438.76953125,26683.939453125,10590270000
 2026-06-16,26649.970703125,26788.619140625,26369.390625,26376.33984375,11132830000
-2026-06-17,26493.82421875,26511.5546875,26255.1640625,26378.064453125,6450463000
+2026-06-17,26493.82421875,26511.5546875,26255.1640625,26412.5859375,6597073000

backend/data/VIX.csv

@@ -501,4 +501,4 @@ Date,Open,High,Low,Close,Volume
 2026-06-12,19.510000228881836,19.850000381469727,17.59000015258789,17.68000030517578,0
 2026-06-15,16.780000686645508,16.850000381469727,15.979999542236328,16.200000762939453,0
 2026-06-16,16.200000762939453,16.440000534057617,15.770000457763672,16.40999984741211,0
-2026-06-17,16.079999923706055,17.079999923706055,16.020000457763672,16.899999618530273,0
+2026-06-17,16.079999923706055,17.079999923706055,16.020000457763672,16.84000015258789,0

components/modules/crypto/CryptoDemo.tsx

@@ -168,6 +168,7 @@ export default function CryptoDemo() {
   const [trackers, setTrackers] = useState<TrackersMap>({});
   const [ensemblePredictions, setEnsemblePredictions] = useState<any>(null);
   const [loadingEnsemble, setLoadingEnsemble] = useState(false);
+  const [activeRegime, setActiveRegime] = useState<number | null>(null);
   const [isShieldActive, setIsShieldActive] = useState(true);
   const [coins, setCoins] = useState<Record<string, CoinData>>(defaultCoins);
   const [feedbackFilterAsset, setFeedbackFilterAsset] = useState<'BTC' | 'ETH' | 'SOL'>('BTC');
@@ -235,6 +236,7 @@ export default function CryptoDemo() {
           const data = await res.json();
           setEnsemblePredictions(data.predictions || null);
           setIsShieldActive(data.isShieldActive !== undefined ? data.isShieldActive : true);
+          setActiveRegime(data.activeRegime !== undefined ? data.activeRegime : null);
         }
       } catch (err) {
         console.error("Failed to load ensemble predictions:", err);
@@ -947,6 +949,25 @@ export default function CryptoDemo() {
             </button>
           </div>
           <div className="flex items-center gap-3">
+            {rightColTab === 'radar' && activeRegime !== null && (
+              <>
+                {activeRegime === 1 && (
+                  <span className="flex items-center gap-1.5 px-3 py-1.5 rounded-lg bg-emerald-500/10 border border-emerald-500/20 backdrop-blur-md text-xs font-semibold text-emerald-400">
+                    <span>🟢</span> REGIME: CALM (MICROSTRUCTURE MODE)
+                  </span>
+                )}
+                {activeRegime === 2 && (
+                  <span className="flex items-center gap-1.5 px-3 py-1.5 rounded-lg bg-rose-500/10 border border-rose-500/30 backdrop-blur-md text-xs font-semibold text-rose-450 animate-pulse">
+                    <span className="animate-bounce">🚨</span> REGIME: TURBULENT (LIQUIDATION CASCADE)
+                  </span>
+                )}
+                {activeRegime === 3 && (
+                  <span className="flex items-center gap-1.5 px-3 py-1.5 rounded-lg bg-amber-500/10 border border-amber-500/20 backdrop-blur-md text-xs font-semibold text-amber-400">
+                    <span>🟡</span> REGIME: MEAN-REVERTING CHURN
+                  </span>
+                )}
+              </>
+            )}
             {rightColTab === 'radar' && (
               <button
                 onClick={() => setShowRadarExplanation(!showRadarExplanation)}

public/data/ensemble_predictions.json

@@ -1,14 +1,15 @@
 {
   "isShieldActive": false,
+  "activeRegime": 1,
   "predictions": {
     "BTC": {
       "rf": {
-        "T1": 0.405,
+        "T1": 0.513,
         "T5": 0.5,
         "T10": 0.5
       },
       "gb": {
-        "T1": 0.791,
+        "T1": 0.64,
         "T5": 0.5,
         "T10": 0.5
       },
@@ -23,19 +24,19 @@
         "T10": 0.5
       },
       "mlp": {
-        "T1": 0.669,
+        "T1": 0.037,
-        "T5": 0.5,
+        "T5": 0.706,
-        "T10": 0.324
+        "T10": 0.207
       }
     },
     "ETH": {
       "rf": {
-        "T1": 0.385,
+        "T1": 0.493,
         "T5": 0.51,
         "T10": 0.5
       },
       "gb": {
-        "T1": 0.801,
+        "T1": 0.65,
         "T5": 0.5,
         "T10": 0.47
       },
@@ -50,19 +51,19 @@
         "T10": 0.5
       },
       "mlp": {
-        "T1": 0.669,
+        "T1": 0.037,
-        "T5": 0.49,
+        "T5": 0.696,
-        "T10": 0.344
+        "T10": 0.227
       }
     },
     "SOL": {
       "rf": {
-        "T1": 0.435,
+        "T1": 0.543,
         "T5": 0.5,
         "T10": 0.48
       },
       "gb": {
-        "T1": 0.771,
+        "T1": 0.62,
         "T5": 0.52,
         "T10": 0.5
       },
@@ -77,9 +78,9 @@
         "T10": 0.5
       },
       "mlp": {
-        "T1": 0.689,
+        "T1": 0.057,
-        "T5": 0.5,
+        "T5": 0.706,
-        "T10": 0.304
+        "T10": 0.187
       }
     }
   }