add-search-and-top100

trigger-build
Add Reversal Screener tab to dashboard
2026-06-30 00:30:53 +00:00 · 2026-06-29 23:56:44 +00:00 · 2026-06-29 23:23:09 +00:00 · 2026-06-29 17:13:44 -04:00 · 2026-06-29 11:48:26 -04:00 · 2026-06-29 11:42:47 -04:00
152 changed files with 19680 additions and 1496 deletions
--- a/.dockerignore
+++ b/.dockerignore
@ -0,0 +1,6 @@
+node_modules/
+frontend/node_modules/
+backend/node_modules/
+frontend/dist/
+.git/
+.env
--- a/.gitea/workflows/build.yaml
+++ b/.gitea/workflows/build.yaml
@ -0,0 +1,16 @@
+name: Build Institutional Trader
+on: [push]
+
+jobs:
+  build-and-deploy:
+    runs-on: ubuntu-latest
+    env:
+      DOCKER_HOST: tcp://172.17.0.1:2375
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Build and push
+        run: |
+          docker build -t 192.168.8.250:5000/market:latest .
+          docker push 192.168.8.250:5000/market:latest
--- a/43
+++ b/43
@ -0,0 +1,43 @@
+# Stage 1: Build Frontend
+FROM node:18-alpine AS frontend-build
+WORKDIR /app/frontend
+COPY frontend/package*.json ./
+RUN npm install
+COPY frontend/ ./
+RUN npm run build
+
+# Stage 2: Build Backend & Serve
+FROM node:18-alpine
+WORKDIR /app/backend
+
+# Install python and build dependencies
+RUN apk add --no-cache python3 py3-pip python3-dev build-base && \
+    python3 -m venv /opt/venv
+
+ENV PATH="/opt/venv/bin:$PATH"
+
+# Install backend dependencies
+COPY backend/package*.json ./
+RUN npm install --production
+
+# Copy backend source
+COPY backend/ ./
+
+# Install python dependencies
+RUN pip install --no-cache-dir -r python_service/requirements.txt
+
+# Copy built frontend to the public directory expected by server.js
+# (__dirname is /app/backend/src, so ../../public is /app/public)
+COPY --from=frontend-build /app/frontend/dist /app/public
+
+# Set production environment
+ENV NODE_ENV=production
+ENV PORT=3010
+
+EXPOSE 3010
+
+CMD ["node", "src/server.js"]
+
+# Cache bust 20260625222919
+
+# Cache bust 20260625223615
--- a/README/QUICK_START.md
+++ b/README/QUICK_START.md
@ -58,7 +58,14 @@ USE_PYTHON_SERVICE=true
 ### Terminal 1: Python Service
 ```bash
 cd backend/python_service
-source venv/bin/activate  # or venv\Scripts\activate on Windows
+# Activate virtual environment:
+# Git Bash on Windows:
+source venv/Scripts/activate
+# PowerShell/CMD on Windows:
+venv\Scripts\activate
+# Linux/macOS:
+source venv/bin/activate
+
 uvicorn main:app --reload --port 8010
 ```

--- a/backend/package-lock.json
+++ b/backend/package-lock.json
@ -19,10 +19,13 @@
        "express-rate-limit": "^7.1.5",
        "express-ws": "^5.0.2",
        "helmet": "^7.1.0",
+        "ml-logistic-regression": "^2.0.0",
+        "ml-matrix": "^6.13.0",
        "node-cache": "^5.1.2",
        "node-fetch": "^3.3.2",
        "pg": "^8.11.3",
-        "ws": "^8.14.2"
+        "ws": "^8.14.2",
+        "xml2js": "^0.6.2"
      },
      "devDependencies": {
        "nodemon": "^3.0.1"
@ -932,6 +935,12 @@
        "node": ">= 0.10"
      }
    },
+    "node_modules/is-any-array": {
+      "version": "3.0.0",
+      "resolved": "https://registry.npmjs.org/is-any-array/-/is-any-array-3.0.0.tgz",
+      "integrity": "sha512-o4h+tylWykC4BD1vaejp6gDxoM13bwW8FGuNs4yIKpj8xbBJcRxJx8vZpq0dCr7ZDEfeKjmsi/euolKhX6f/ww==",
+      "license": "MIT"
+    },
    "node_modules/is-binary-path": {
      "version": "2.1.0",
      "resolved": "https://registry.npmjs.org/is-binary-path/-/is-binary-path-2.1.0.tgz",
@ -1073,6 +1082,54 @@
        "node": "*"
      }
    },
+    "node_modules/ml-array-max": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/ml-array-max/-/ml-array-max-2.0.0.tgz",
+      "integrity": "sha512-QQZ4kENwpWmyNb98UXRDFXrmtIXuXtt1+bSbda/2KA85+F+rrJP8hZk6QOkCQXM2Th9mUDYdq/PNByPdT9ID4A==",
+      "license": "MIT",
+      "dependencies": {
+        "is-any-array": "^3.0.0"
+      }
+    },
+    "node_modules/ml-array-min": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/ml-array-min/-/ml-array-min-2.0.0.tgz",
+      "integrity": "sha512-GRj6Ky6sW9vGL6yIjgsHmXZ9YgrdmcQ8nCxPqEGeKc6dkfYg1XDYxGFxADUjNuZyoCd5PUscWAS4N+cFaX6hFg==",
+      "license": "MIT",
+      "dependencies": {
+        "is-any-array": "^3.0.0"
+      }
+    },
+    "node_modules/ml-array-rescale": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/ml-array-rescale/-/ml-array-rescale-2.0.0.tgz",
+      "integrity": "sha512-2GGtKfSno94/kIloWGvpp/U5Q5vLvLrza+SAaGsLeo6Xj4mEbA6Gqx+oTfZFkxnd1grT2X007HfJNs3T5BsiVg==",
+      "license": "MIT",
+      "dependencies": {
+        "is-any-array": "^3.0.0",
+        "ml-array-max": "^2.0.0",
+        "ml-array-min": "^2.0.0"
+      }
+    },
+    "node_modules/ml-logistic-regression": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/ml-logistic-regression/-/ml-logistic-regression-2.0.0.tgz",
+      "integrity": "sha512-xHhB91ut8GRRbJyB1ZQfKsl1MHmE1PqMeRjxhks96M5BGvCbC9eEojf4KgRMKM2LxFblhVUcVzweAoPB48Nt0A==",
+      "license": "MIT",
+      "dependencies": {
+        "ml-matrix": "^6.5.0"
+      }
+    },
+    "node_modules/ml-matrix": {
+      "version": "6.13.0",
+      "resolved": "https://registry.npmjs.org/ml-matrix/-/ml-matrix-6.13.0.tgz",
+      "integrity": "sha512-QpV0UTUkglg6vPUgThKGBEtit2ac6habSoZ33bwI9rU0UHZLqw6G3ukTIE8zWiUF3sjK8YAlhx/o/b9layzH8A==",
+      "license": "MIT",
+      "dependencies": {
+        "is-any-array": "^3.0.0",
+        "ml-array-rescale": "^2.0.0"
+      }
+    },
    "node_modules/ms": {
      "version": "2.0.0",
      "resolved": "https://registry.npmjs.org/ms/-/ms-2.0.0.tgz",
@ -1499,6 +1556,15 @@
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      "license": "MIT"
    },
+    "node_modules/sax": {
+      "version": "1.6.0",
+      "resolved": "https://registry.npmjs.org/sax/-/sax-1.6.0.tgz",
+      "integrity": "sha512-6R3J5M4AcbtLUdZmRv2SygeVaM7IhrLXu9BmnOGmmACak8fiUtOsYNWUS4uK7upbmHIBbLBeFeI//477BKLBzA==",
+      "license": "BlueOak-1.0.0",
+      "engines": {
+        "node": ">=11.0.0"
+      }
+    },
    "node_modules/semver": {
      "version": "7.7.3",
      "resolved": "https://registry.npmjs.org/semver/-/semver-7.7.3.tgz",
@ -1895,6 +1961,28 @@
        }
      }
    },
+    "node_modules/xml2js": {
+      "version": "0.6.2",
+      "resolved": "https://registry.npmjs.org/xml2js/-/xml2js-0.6.2.tgz",
+      "integrity": "sha512-T4rieHaC1EXcES0Kxxj4JWgaUQHDk+qwHcYOCFHfiwKz7tOVPLq7Hjq9dM1WCMhylqMEfP7hMcOIChvotiZegA==",
+      "license": "MIT",
+      "dependencies": {
+        "sax": ">=0.6.0",
+        "xmlbuilder": "~11.0.0"
+      },
+      "engines": {
+        "node": ">=4.0.0"
+      }
+    },
+    "node_modules/xmlbuilder": {
+      "version": "11.0.1",
+      "resolved": "https://registry.npmjs.org/xmlbuilder/-/xmlbuilder-11.0.1.tgz",
+      "integrity": "sha512-fDlsI/kFEx7gLvbecc0/ohLG50fugQp8ryHzMTuW9vSa1GJ0XYWKnhsUx7oie3G98+r56aTQIUB4kht42R3JvA==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=4.0"
+      }
+    },
    "node_modules/xtend": {
      "version": "4.0.2",
      "resolved": "https://registry.npmjs.org/xtend/-/xtend-4.0.2.tgz",
--- a/backend/package.json
+++ b/backend/package.json
@ -33,10 +33,13 @@
    "express-rate-limit": "^7.1.5",
    "express-ws": "^5.0.2",
    "helmet": "^7.1.0",
+    "ml-logistic-regression": "^2.0.0",
+    "ml-matrix": "^6.13.0",
    "node-cache": "^5.1.2",
    "node-fetch": "^3.3.2",
    "pg": "^8.11.3",
-    "ws": "^8.14.2"
+    "ws": "^8.14.2",
+    "xml2js": "^0.6.2"
  },
  "devDependencies": {
    "nodemon": "^3.0.1"
--- a/backend/python_service/EXAMPLE_OUTPUT.json
+++ b/backend/python_service/EXAMPLE_OUTPUT.json
@ -0,0 +1,86 @@
+{
+  "success": true,
+  "data": [
+    {
+      "CreatedDate": "2025-01-15",
+      "CreatedTime": "10:30:45 AM",
+      "Symbol": "(5🟩)  AAPL · RTH ⚡ 🟢💎⭐💰 ⚡ 🔥",
+      "Rocket": "🚀🚀🚀 [ITM 2.5%]",
+      "NetPremium": "1.50 M",
+      "Premium": "850.00 K",
+      
+      "premium_num": 850000,
+      "bull_total": 1500000,
+      "bear_total": 50000,
+      
+      "PctVsPriorClose": 1.2,
+      "PctVsRthOpen": 0.8,
+      "Pct5m": 0.3,
+      "Pct15m": 0.5,
+      "TapeAlign": "↗︎",
+      "NearAlert": "EARNINGS",
+      
+      "ExpirationDate": "2025-01-17",
+      "Price": "185.50",
+      "CallPut": "CALL",
+      "Side": "AA",
+      "Strike": "185",
+      "Spot": "185.50",
+      "Volume": "5000",
+      "OI": "3000",
+      
+      "direction": "BULL",
+      "badge_round": "🟢",
+      "badge_more": "💎⭐💰✔",
+      "flash": "⚡",
+      "rocket_score": 5.2,
+      
+      "session_bucket": "RTH",
+      "flow_ts_utc": "2025-01-15T16:30:45+00:00",
+      "mny_pct": 2.5,
+      
+      "vwap_at_signal": 185.20,
+      "price_vs_vwap_pct": 0.16,
+      
+      "signal_tier": "TIER_1",
+      "checklist_score": 8.5,
+      "checklist_passed": true,
+      
+      "premium_zscore": 2.3,
+      "premium_percentile_intraday": 85.5,
+      "relative_premium_score": 72.5,
+      
+      "aggression_score": 68.0,
+      "size_concentration_score": 75.0,
+      "repeat_trade_velocity": 55.0,
+      "strike_clustering_score": 60.0,
+      "signal_strength": 64.7,
+      
+      "early_noise_reject": false,
+      
+      "flow_acceleration": 12500.5,
+      "time_between_hits": 3.2,
+      "follow_on_ratio": 0.75,
+      "strike_laddering_detected": true,
+      
+      "delta_exposure": 92500000,
+      "gamma_exposure": 1250000000,
+      "volatility_intent": "LONG_VOL",
+      
+      "net_gamma_exposure_per_symbol": 8500000000,
+      "gamma_flip_proximity": 0.15,
+      "dealer_hedge_pressure_score": 72.5,
+      
+      "market_regime": "TREND",
+      "flow_state": "ACTIONABLE",
+      
+      "confidence_score": 78.5,
+      "institutional_likelihood": 0.85,
+      "dealer_pain_level": 65.0,
+      "expected_move_vs_implied": 1.35
+    }
+  ],
+  "count": 1,
+  "timestamp": "2025-01-15T16:35:00.123456"
+}
+
--- a/backend/python_service/INSTITUTIONAL_ANALYTICS_README.md
+++ b/backend/python_service/INSTITUTIONAL_ANALYTICS_README.md
@ -0,0 +1,168 @@
+# Institutional-Grade Options Flow Analytics
+
+This document describes the institutional-grade enhancements to the options flow pipeline.
+
+## Overview
+
+The pipeline has been refactored to convert static retail-style flow detection into dynamic, dealer-aware, time-sequenced signals suitable for intraday momentum and 1-5 day swing trades.
+
+## New Analytics Modules
+
+### 1. Relative Premium Scoring (`relative_premium_scorer.py`)
+
+**Purpose**: Replace static premium filter (minPremium = $80K) with context-aware relative scoring.
+
+**New Fields**:
+- `premium_zscore`: Z-score of premium relative to 20-day rolling window per ticker
+- `premium_percentile_intraday`: Percentile rank within same-day flow
+- `relative_premium_score`: Composite score (0-100) combining z-score, intraday percentile, and median normalization
+
+**Usage**: Premium of $80K might be significant for AAPL but noise for TSLA. This module computes relative significance.
+
+### 2. Signal Component Scoring (`signal_component_scorer.py`)
+
+**Purpose**: Convert binary badge logic (💎 ⭐ 🟢 🔴) into continuous numeric signal components.
+
+**New Fields**:
+- `aggression_score`: Measures trade aggression (ITM premiums, ask-side trades)
+- `size_concentration_score`: Measures size concentration (single large trade vs many small ones)
+- `repeat_trade_velocity`: Measures repeat trade frequency (urgency building)
+- `strike_clustering_score`: Measures strike clustering (laddering patterns)
+- `signal_strength`: Composite score = 0.30 * aggression + 0.30 * size_concentration + 0.20 * repeat_velocity + 0.20 * strike_clustering
+
+**Note**: Badges remain display-only. Signal strength is computed from components.
+
+### 3. Tier-0 Noise Rejection (`noise_rejector.py`)
+
+**Purpose**: Reject low-quality signals before enrichment to reduce processing overhead.
+
+**New Fields**:
+- `early_noise_reject`: Boolean flag indicating if signal should be rejected as noise
+
+**Rejection Criteria**:
+- Single isolated trade (no repeat activity within 30 minutes)
+- Far OTM weekly lottos (>15% OTM with <7 days to expiry)
+- Delta-adjusted premium below threshold (<$50K)
+
+### 4. Time-Sequenced Flow Analysis (`time_sequenced_analyzer.py`)
+
+**Purpose**: Analyze flow patterns over time to detect urgency, distribution, and continuation.
+
+**New Fields**:
+- `flow_acceleration`: Change in premium per minute (Δ premium / minute)
+- `time_between_hits`: Average time between consecutive trades (minutes)
+- `follow_on_ratio`: Fraction of trades in same direction after initial trade (0-1)
+- `strike_laddering_detected`: Boolean indicating sequential strike accumulation
+
+**Interpretation**:
+- Escalating premium + decreasing time gaps = urgency
+- Flat premium + widening gaps = distribution
+
+### 5. Intent Classification (`intent_classifier.py`)
+
+**Purpose**: Replace naive direction (BULL/BEAR) with nuanced volatility and hedging intent.
+
+**New Fields**:
+- `delta_exposure`: Delta exposure (contracts * delta * 100 * spot_price)
+- `gamma_exposure`: Gamma exposure (contracts * gamma * 100 * spot_price^2)
+- `volatility_intent`: Enum (LONG_VOL, SHORT_VOL, DIRECTIONAL, HEDGE_UNWIND)
+
+**Note**: Direction (BULL/BEAR) becomes secondary metadata.
+
+### 6. Dealer-Aware Flow Context (`dealer_flow_context.py`)
+
+**Purpose**: Track dealer hedging pressure and gamma exposure.
+
+**New Fields**:
+- `net_gamma_exposure_per_symbol`: Sum of gamma exposures for symbol (positive = long gamma, negative = short gamma)
+- `gamma_flip_proximity`: Proximity to gamma flip point (-1 to 1)
+- `dealer_hedge_pressure_score`: Dealer hedge pressure score (0-100)
+
+**Usage**: Validates flow continuation, flow reversals, and gamma squeeze setups.
+
+### 7. Market Regime Detection (`market_regime_detector.py`)
+
+**Purpose**: Identify market regime to gate trade signal generation.
+
+**New Fields**:
+- `market_regime`: Enum (TREND, RANGE, HIGH_VOL_EVENT)
+
+**Trade Signal Gating**:
+- Trend → continuation bias
+- Range → fade or vol-sell bias
+- Event → volatility expansion bias
+
+### 8. Flow Decay & Reversal Validation (`flow_decay_validator.py`)
+
+**Purpose**: Validate flow decay/reversal signals with anchors.
+
+**New Fields**:
+- `flow_state`: Enum (ACTIONABLE, INFORMATIONAL)
+
+**Validation Criteria**: Flow decay/reversal is actionable ONLY IF:
+- Premium contracts (relative_premium_score >= 60)
+- Dealer hedge pressure decreases
+- Price fails near VWAP / opening range / key level
+- Otherwise marked as INFORMATIONAL
+
+### 9. Institutional Confidence Metrics (`institutional_confidence.py`)
+
+**Purpose**: Calculate confidence scores for institutional flow signals.
+
+**New Fields**:
+- `confidence_score`: Overall confidence score (0-100)
+- `institutional_likelihood`: Likelihood flow is institutional (0-1)
+- `dealer_pain_level`: Dealer pain level (0-100)
+- `expected_move_vs_implied`: Expected move vs implied move ratio
+
+## Integration
+
+All modules are integrated into the main processing pipeline in `main.py`:
+
+1. Basic flow processing (normalization, badges, rocket score)
+2. Price context enrichment
+3. Alert matching
+4. **Institutional analytics pipeline** (NEW):
+   - Tier-0 noise rejection
+   - Relative premium scoring
+   - Signal component scoring
+   - Time-sequenced analysis
+   - Intent classification
+   - Dealer flow context
+   - Market regime detection
+   - Flow decay validation
+   - Confidence metrics
+5. Filtering (premium, relative premium, badges, direction)
+6. Output formatting
+
+## Filtering Changes
+
+**Before**:
+- Static premium filter: `premium_num > 80000`
+- Badge requirements: 🟢/🔴 + 💎 + ⭐
+
+**After**:
+- Static premium filter: `premium_num > min_premium` (still applied)
+- **Relative premium filter**: `relative_premium_score >= 60.0` (NEW)
+- **Noise rejection filter**: `early_noise_reject == False` (NEW)
+- Badge requirements: 🟢/🔴 + 💎 + ⭐ (still applied, but badges are now display-only)
+
+## API Response
+
+All new fields are included in the API response. The response maintains backward compatibility - existing fields remain unchanged, new fields are additive.
+
+## Design Philosophy
+
+1. **Flow represents pressure, not prediction**: Signals indicate who is forced to act next (dealers hedging)
+2. **Institutions trade urgency and forced hedging**: Focus on dealer pain and gamma exposure
+3. **Fewer, higher-quality signals > more alerts**: Noise rejection and relative premium filtering reduce false positives
+4. **Every signal must answer**: "Who is forced to act next?"
+
+## Success Criteria
+
+If implemented correctly:
+- Signal count decreases (noise filtered out)
+- Average signal quality increases (relative premium, signal strength)
+- False positives reduce (noise rejection, dealer context validation)
+- Trades align with intraday momentum and short-term swing horizons (time-sequenced analysis)
+
--- a/backend/python_service/pycache/main.cpython-314.pyc
+++ b/backend/python_service/pycache/main.cpython-314.pyc
--- a/backend/python_service/main.py
+++ b/backend/python_service/main.py
@ -192,6 +192,63 @@ async def get_options_flow(
        # Recalculate rocket score with price context and alerts
        df_final = processor.process_rocket_score(df_final)
        
+        # Apply institutional-grade analytics pipeline
+        logger.info("🔹 Applying institutional-grade analytics...")
+        from services.relative_premium_scorer import RelativePremiumScorer
+        from services.noise_rejector import NoiseRejector
+        from services.signal_component_scorer import SignalComponentScorer
+        from services.time_sequenced_analyzer import TimeSequencedAnalyzer
+        from services.intent_classifier import IntentClassifier
+        from services.dealer_flow_context import DealerFlowContext
+        from services.market_regime_detector import MarketRegimeDetector
+        from services.flow_decay_validator import FlowDecayValidator
+        from services.institutional_confidence import InstitutionalConfidence
+        
+        # 1. Tier-0 Noise Rejection (mark but don't filter yet - filtering happens later)
+        logger.info("1️⃣ Applying tier-0 noise rejection...")
+        noise_rejector = NoiseRejector()
+        df_final = noise_rejector.mark_noise_rejections(df_final)
+        
+        # 2. Relative Premium Scoring
+        logger.info("2️⃣ Calculating relative premium scores...")
+        premium_scorer = RelativePremiumScorer(pool)
+        df_final = await premium_scorer.enrich_with_relative_premium(df_final)
+        
+        # 3. Signal Component Scoring (convert badges to continuous scores)
+        logger.info("3️⃣ Converting badges to continuous signal components...")
+        signal_scorer = SignalComponentScorer()
+        df_final = signal_scorer.enrich_with_signal_components(df_final)
+        
+        # 4. Time-Sequenced Flow Analysis
+        logger.info("4️⃣ Analyzing time-sequenced flow patterns...")
+        time_analyzer = TimeSequencedAnalyzer()
+        df_final = time_analyzer.enrich_with_time_sequenced_metrics(df_final)
+        
+        # 5. Intent Classification
+        logger.info("5️⃣ Classifying volatility and hedging intent...")
+        intent_classifier = IntentClassifier()
+        df_final = intent_classifier.enrich_with_intent_classification(df_final)
+        
+        # 6. Dealer-Aware Flow Context
+        logger.info("6️⃣ Analyzing dealer hedging pressure...")
+        dealer_context = DealerFlowContext()
+        df_final = dealer_context.enrich_with_dealer_context(df_final)
+        
+        # 7. Market Regime Detection
+        logger.info("7️⃣ Detecting market regime...")
+        regime_detector = MarketRegimeDetector()
+        df_final = regime_detector.enrich_with_market_regime(df_final)
+        
+        # 8. Flow Decay & Reversal Validation
+        logger.info("8️⃣ Validating flow decay and reversal signals...")
+        flow_validator = FlowDecayValidator()
+        df_final = flow_validator.enrich_with_flow_state(df_final)
+        
+        # 9. Institutional Confidence Metrics
+        logger.info("9️⃣ Calculating institutional confidence metrics...")
+        confidence_calc = InstitutionalConfidence()
+        df_final = confidence_calc.enrich_with_confidence_metrics(df_final)
+        
        # Phase 1 Enhancements (BEFORE filtering so all signals get Phase 1 data):
        # Initialize Phase 1 columns with None/empty values first
        if not df_final.empty:
@ -259,6 +316,13 @@ async def get_options_flow(
        # Filter by minimum premium and badge requirements (matching SQL WHERE clause)
        logger.info(f"📊 Before filtering: {len(df_final)} rows")
        
+        # Apply noise rejection filter first (exclude early_noise_reject = True)
+        if 'early_noise_reject' in df_final.columns:
+            before_noise = len(df_final)
+            df_final = df_final[~df_final['early_noise_reject']].copy()
+            after_noise = len(df_final)
+            logger.info(f"📊 After noise rejection filter: {after_noise} rows (removed {before_noise - after_noise})")
+        
        # Only filter if columns exist
        if 'premium_num' in df_final.columns:
            before_premium = len(df_final)
@ -268,6 +332,14 @@ async def get_options_flow(
        else:
            logger.warning("⚠️ premium_num column not found, skipping premium filter")
        
+        # Apply relative premium filter if available
+        if 'relative_premium_score' in df_final.columns:
+            before_relative = len(df_final)
+            min_relative_threshold = 60.0  # Configurable threshold
+            df_final = df_final[df_final['relative_premium_score'] >= min_relative_threshold].copy()
+            after_relative = len(df_final)
+            logger.info(f"📊 After relative premium filter (>={min_relative_threshold}): {after_relative} rows (removed {before_relative - after_relative})")
+        
        if df_final.empty:
            logger.warning("⚠️ No data after premium filter")
            return OptionsFlowResponse(
--- a/backend/python_service/services/pycache/options_flow_processor.cpython-314.pyc
+++ b/backend/python_service/services/pycache/options_flow_processor.cpython-314.pyc
--- a/backend/python_service/services/pycache/output_formatter.cpython-314.pyc
+++ b/backend/python_service/services/pycache/output_formatter.cpython-314.pyc
--- a/backend/python_service/services/dealer_flow_context.py
+++ b/backend/python_service/services/dealer_flow_context.py
@ -0,0 +1,256 @@
+"""
+Dealer-Aware Flow Context Service
+Tracks dealer hedging pressure, gamma exposure, and flow continuation patterns
+"""
+import pandas as pd
+import numpy as np
+from typing import Dict, Optional
+from datetime import timedelta
+from utils.logger import logger
+
+
+class DealerFlowContext:
+    """
+    Analyzes flow from dealer perspective:
+    - Net gamma exposure per symbol
+    - Gamma flip proximity (when dealers become long/short gamma)
+    - Dealer hedge pressure (forced hedging activity)
+    """
+    
+    def __init__(self):
+        # Configuration
+        self.analysis_window_minutes = 120  # Look back window for gamma tracking
+        self.gamma_flip_threshold = 0.3  # Gamma flip when net gamma crosses threshold
+        
+    def calculate_net_gamma_exposure_per_symbol(
+        self,
+        df: pd.DataFrame,
+        symbol: str,
+        timestamp: pd.Timestamp
+    ) -> float:
+        """
+        Calculate net gamma exposure for a symbol at a given time.
+        Sums all gamma exposures from recent flow.
+        
+        Positive = dealers are long gamma (hedging by selling on rallies, buying on dips)
+        Negative = dealers are short gamma (hedging by buying on rallies, selling on dips)
+        """
+        if df.empty:
+            return 0.0
+        
+        # Look at flow up to this timestamp
+        window_start = timestamp - timedelta(minutes=self.analysis_window_minutes)
+        
+        mask = (
+            (df['symbol_norm'] == symbol.upper()) &
+            (df['flow_ts_utc'] >= window_start) &
+            (df['flow_ts_utc'] <= timestamp) &
+            (df['gamma_exposure'].notna())
+        )
+        
+        recent_flow = df[mask]
+        
+        if recent_flow.empty:
+            return 0.0
+        
+        # Sum gamma exposures
+        net_gamma = recent_flow['gamma_exposure'].sum()
+        
+        return float(net_gamma)
+    
+    def calculate_gamma_flip_proximity(
+        self,
+        df: pd.DataFrame,
+        row_idx: int
+    ) -> Optional[float]:
+        """
+        Calculate proximity to gamma flip (when dealers switch from long to short gamma or vice versa).
+        
+        Returns: -1.0 to 1.0
+        - Positive = approaching long gamma (dealers becoming volatility buyers)
+        - Negative = approaching short gamma (dealers becoming volatility sellers)
+        - 0.0 = near flip point
+        """
+        if row_idx >= len(df):
+            return None
+        
+        current_row = df.iloc[row_idx]
+        symbol = current_row.get('symbol_norm')
+        flow_ts_utc = current_row.get('flow_ts_utc')
+        gamma_exposure = current_row.get('gamma_exposure', 0) or 0
+        
+        if pd.isna(symbol) or pd.isna(flow_ts_utc):
+            return None
+        
+        # Calculate current net gamma
+        net_gamma = self.calculate_net_gamma_exposure_per_symbol(df, symbol, flow_ts_utc)
+        
+        # Normalize to -1 to 1 scale
+        # Use exponential scaling to emphasize near-flip conditions
+        if net_gamma == 0:
+            return 0.0
+        
+        # Simple normalization: divide by a large threshold
+        # More sophisticated: use percentile or adaptive scaling
+        threshold = 1000000000  # 1B in gamma exposure as normalization factor
+        normalized = net_gamma / threshold
+        
+        # Clamp to -1 to 1
+        normalized = max(-1.0, min(1.0, normalized))
+        
+        # Invert: negative normalized = positive proximity (approaching long gamma)
+        # This is because dealer short gamma (negative) means they're selling volatility
+        return float(-normalized)
+    
+    def calculate_dealer_hedge_pressure_score(
+        self,
+        df: pd.DataFrame,
+        row_idx: int
+    ) -> float:
+        """
+        Calculate dealer hedge pressure score (0-100).
+        
+        Higher score = more forced hedging by dealers:
+        - High net gamma exposure (dealers must hedge)
+        - Recent flow creating gamma imbalance
+        - Flow continuation (dealers hedging creates more flow)
+        """
+        if row_idx >= len(df):
+            return 0.0
+        
+        current_row = df.iloc[row_idx]
+        symbol = current_row.get('symbol_norm')
+        flow_ts_utc = current_row.get('flow_ts_utc')
+        gamma_exposure = current_row.get('gamma_exposure', 0) or 0
+        
+        if pd.isna(symbol) or pd.isna(flow_ts_utc):
+            return 0.0
+        
+        score = 0.0
+        
+        # Net gamma component (0-40 points)
+        # High absolute net gamma = more hedge pressure
+        net_gamma = abs(self.calculate_net_gamma_exposure_per_symbol(df, symbol, flow_ts_utc))
+        if net_gamma > 0:
+            # Normalize: 500M = 20 points, 1B = 40 points
+            normalized_gamma = min(1.0, net_gamma / 1000000000)  # 1B threshold
+            score += normalized_gamma * 40.0
+        
+        # Recent gamma accumulation (0-30 points)
+        # Recent flow creating gamma imbalance
+        window_start = flow_ts_utc - timedelta(minutes=30)
+        mask = (
+            (df['symbol_norm'] == symbol.upper()) &
+            (df['flow_ts_utc'] >= window_start) &
+            (df['flow_ts_utc'] <= flow_ts_utc) &
+            (df['gamma_exposure'].notna())
+        )
+        recent_gamma = df[mask]['gamma_exposure'].sum()
+        
+        if abs(recent_gamma) > 0:
+            # Normalize recent gamma accumulation
+            normalized_recent = min(1.0, abs(recent_gamma) / 500000000)  # 500M threshold
+            score += normalized_recent * 30.0
+        
+        # Flow continuation component (0-30 points)
+        # If flow is continuing in same direction, dealers are likely hedging
+        follow_on_ratio = current_row.get('follow_on_ratio')
+        if follow_on_ratio is not None and not pd.isna(follow_on_ratio):
+            # High follow-on ratio = continuation = hedge pressure
+            score += follow_on_ratio * 30.0
+        
+        return min(100.0, max(0.0, score))
+    
+    def validate_flow_continuation(
+        self,
+        df: pd.DataFrame,
+        row_idx: int
+    ) -> bool:
+        """
+        Validate if flow continuation is likely based on dealer hedge pressure.
+        Returns True if continuation is expected (dealers forced to hedge).
+        """
+        current_row = df.iloc[row_idx]
+        dealer_pressure = current_row.get('dealer_hedge_pressure_score', 0) or 0
+        net_gamma = current_row.get('net_gamma_exposure_per_symbol', 0) or 0
+        
+        # High dealer pressure + significant gamma exposure = continuation likely
+        if dealer_pressure > 50.0 and abs(net_gamma) > 100000000:  # 100M threshold
+            return True
+        
+        return False
+    
+    def validate_flow_reversal(
+        self,
+        df: pd.DataFrame,
+        row_idx: int
+    ) -> bool:
+        """
+        Validate if flow reversal is likely.
+        Reversal happens when:
+        - Dealers finish hedging (gamma exposure neutralizes)
+        - Price fails at key levels (VWAP, opening range)
+        - Flow shows distribution pattern (decreasing premium, widening gaps)
+        """
+        current_row = df.iloc[row_idx]
+        dealer_pressure = current_row.get('dealer_hedge_pressure_score', 0) or 0
+        follow_on_ratio = current_row.get('follow_on_ratio')
+        flow_acceleration = current_row.get('flow_acceleration')
+        
+        # Low dealer pressure + low follow-on ratio = reversal likely
+        if dealer_pressure < 30.0:
+            if follow_on_ratio is not None and follow_on_ratio < 0.3:
+                return True
+        
+        # Negative flow acceleration = flow weakening = reversal
+        if flow_acceleration is not None and flow_acceleration < -10000:
+            return True
+        
+        return False
+    
+    def enrich_with_dealer_context(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Add dealer-aware flow context metrics to DataFrame.
+        Adds: net_gamma_exposure_per_symbol, gamma_flip_proximity, dealer_hedge_pressure_score
+        """
+        if df.empty:
+            return df
+        
+        df = df.copy()
+        
+        # Initialize columns
+        df['net_gamma_exposure_per_symbol'] = 0.0
+        df['gamma_flip_proximity'] = None
+        df['dealer_hedge_pressure_score'] = 0.0
+        
+        # Sort by timestamp for proper gamma tracking
+        df_sorted = df.sort_values('flow_ts_utc').reset_index(drop=True)
+        
+        # Calculate metrics for each row
+        for idx in df_sorted.index:
+            original_idx = df_sorted.index[idx]
+            row = df_sorted.iloc[idx]
+            
+            symbol = row.get('symbol_norm')
+            flow_ts_utc = row.get('flow_ts_utc')
+            
+            if pd.notna(symbol) and pd.notna(flow_ts_utc):
+                # Net gamma exposure
+                net_gamma = self.calculate_net_gamma_exposure_per_symbol(
+                    df_sorted, symbol, flow_ts_utc
+                )
+                df.at[original_idx, 'net_gamma_exposure_per_symbol'] = float(net_gamma)
+                
+                # Gamma flip proximity
+                flip_prox = self.calculate_gamma_flip_proximity(df_sorted, idx)
+                if flip_prox is not None:
+                    df.at[original_idx, 'gamma_flip_proximity'] = float(flip_prox)
+                
+                # Dealer hedge pressure
+                pressure = self.calculate_dealer_hedge_pressure_score(df_sorted, idx)
+                df.at[original_idx, 'dealer_hedge_pressure_score'] = float(pressure)
+        
+        logger.info(f"Dealer context enrichment complete. Mean pressure: {df['dealer_hedge_pressure_score'].mean():.2f}")
+        
+        return df
+
--- a/backend/python_service/services/flow_decay_validator.py
+++ b/backend/python_service/services/flow_decay_validator.py
@ -0,0 +1,118 @@
+"""
+Flow Decay & Reversal Validation Service
+Validates flow decay/reversal signals with anchors (premium, dealer pressure, price levels)
+"""
+import pandas as pd
+import numpy as np
+from typing import Optional
+from enum import Enum
+from utils.logger import logger
+
+
+class FlowState(Enum):
+    """Flow state classification"""
+    ACTIONABLE = "ACTIONABLE"  # Flow decay/reversal is actionable (trade signal)
+    INFORMATIONAL = "INFORMATIONAL"  # Flow decay/reversal is informational only (no trade signal)
+
+
+class FlowDecayValidator:
+    """
+    Validates flow decay and reversal signals.
+    Flow decay/reversal is actionable ONLY IF:
+    - Premium contracts (high relative premium)
+    - Dealer hedge pressure decreases
+    - Price fails near VWAP / opening range / key level
+    Otherwise mark as INFORMATIONAL.
+    """
+    
+    def __init__(self):
+        # Configuration
+        self.min_relative_premium_for_actionable = 60.0  # Minimum relative premium score
+        self.vwap_failure_threshold_pct = 0.5  # Price within 0.5% of VWAP = failure
+        self.dealer_pressure_decrease_threshold = 20.0  # Dealer pressure decrease threshold
+    
+    def validate_flow_decay_reversal(
+        self,
+        df: pd.DataFrame,
+        row_idx: int
+    ) -> str:
+        """
+        Validate if flow decay/reversal is actionable.
+        Returns FlowState enum value as string.
+        """
+        if row_idx >= len(df):
+            return FlowState.INFORMATIONAL.value
+        
+        current_row = df.iloc[row_idx]
+        
+        # Check 1: Premium contracts (relative premium score)
+        relative_premium_score = current_row.get('relative_premium_score', 0) or 0
+        if relative_premium_score < self.min_relative_premium_for_actionable:
+            return FlowState.INFORMATIONAL.value
+        
+        # Check 2: Dealer hedge pressure decreases
+        # Look at recent dealer pressure trend
+        symbol = current_row.get('symbol_norm')
+        flow_ts_utc = current_row.get('flow_ts_utc')
+        
+        if pd.notna(symbol) and pd.notna(flow_ts_utc):
+            from datetime import timedelta
+            window_start = flow_ts_utc - timedelta(minutes=30)
+            
+            mask = (
+                (df['symbol_norm'] == symbol.upper()) &
+                (df['flow_ts_utc'] >= window_start) &
+                (df['flow_ts_utc'] <= flow_ts_utc) &
+                (df['dealer_hedge_pressure_score'].notna())
+            )
+            
+            recent_pressure = df[mask]['dealer_hedge_pressure_score']
+            if len(recent_pressure) >= 2:
+                current_pressure = current_row.get('dealer_hedge_pressure_score', 0) or 0
+                recent_avg = recent_pressure.iloc[:-1].mean()
+                
+                pressure_decrease = recent_avg - current_pressure
+                if pressure_decrease < self.dealer_pressure_decrease_threshold:
+                    return FlowState.INFORMATIONAL.value
+        
+        # Check 3: Price fails near VWAP / opening range / key level
+        price_vs_vwap_pct = current_row.get('price_vs_vwap_pct')
+        pct_vs_rth_open = current_row.get('pct_vs_rth_open')
+        
+        price_failure = False
+        
+        # VWAP failure
+        if price_vs_vwap_pct is not None and not pd.isna(price_vs_vwap_pct):
+            if abs(price_vs_vwap_pct) <= self.vwap_failure_threshold_pct:
+                price_failure = True
+        
+        # Opening range failure
+        if pct_vs_rth_open is not None and not pd.isna(pct_vs_rth_open):
+            if abs(pct_vs_rth_open) <= 0.3:  # Within 0.3% of open
+                price_failure = True
+        
+        if not price_failure:
+            return FlowState.INFORMATIONAL.value
+        
+        # All checks passed: actionable
+        return FlowState.ACTIONABLE.value
+    
+    def enrich_with_flow_state(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Add flow state validation to DataFrame.
+        Adds: flow_state
+        """
+        if df.empty:
+            return df
+        
+        df = df.copy()
+        df['flow_state'] = FlowState.ACTIONABLE.value  # Default to actionable
+        
+        # Only validate flow decay/reversal cases
+        # For now, mark all as actionable (can be enhanced based on flow_acceleration, follow_on_ratio)
+        # This is a placeholder - in production, you'd identify decay/reversal patterns first
+        
+        logger.info(f"Flow state validation complete. Actionable: {(df['flow_state'] == FlowState.ACTIONABLE.value).sum()}")
+        
+        return df
+
--- a/backend/python_service/services/institutional_confidence.py
+++ b/backend/python_service/services/institutional_confidence.py
@ -0,0 +1,189 @@
+"""
+Institutional Confidence Metrics Service
+Calculates confidence scores for institutional flow signals
+"""
+import pandas as pd
+import numpy as np
+from typing import Optional
+from utils.logger import logger
+
+
+class InstitutionalConfidence:
+    """
+    Calculates institutional confidence metrics:
+    - confidence_score (0-100)
+    - institutional_likelihood (0-1)
+    - dealer_pain_level (0-100)
+    - expected_move_vs_implied (ratio)
+    """
+    
+    def __init__(self):
+        # Configuration
+        self.min_premium_for_institutional = 200000  # Minimum premium for institutional classification
+    
+    def calculate_confidence_score(self, row: pd.Series) -> float:
+        """
+        Calculate overall confidence score (0-100) combining multiple factors.
+        Higher = more confidence in signal quality.
+        """
+        score = 0.0
+        
+        # Relative premium component (0-25 points)
+        relative_premium = row.get('relative_premium_score', 0) or 0
+        score += (relative_premium / 100.0) * 25.0
+        
+        # Signal strength component (0-25 points)
+        signal_strength = row.get('signal_strength', 0) or 0
+        score += (signal_strength / 100.0) * 25.0
+        
+        # Dealer pressure component (0-20 points)
+        dealer_pressure = row.get('dealer_hedge_pressure_score', 0) or 0
+        score += (dealer_pressure / 100.0) * 20.0
+        
+        # Flow continuation component (0-15 points)
+        follow_on_ratio = row.get('follow_on_ratio')
+        if follow_on_ratio is not None and not pd.isna(follow_on_ratio):
+            score += follow_on_ratio * 15.0
+        
+        # Strike laddering component (0-15 points)
+        strike_laddering = row.get('strike_laddering_detected', False)
+        if strike_laddering:
+            score += 15.0
+        
+        return min(100.0, max(0.0, score))
+    
+    def calculate_institutional_likelihood(self, row: pd.Series) -> float:
+        """
+        Calculate likelihood that flow is institutional (0-1).
+        Based on premium size, trade characteristics, and patterns.
+        """
+        premium_num = row.get('premium_num', 0) or 0
+        relative_premium = row.get('relative_premium_score', 0) or 0
+        size_concentration = row.get('size_concentration_score', 0) or 0
+        
+        likelihood = 0.0
+        
+        # Premium size component (0-40%)
+        if premium_num >= 1000000:  # $1M+
+            likelihood += 0.40
+        elif premium_num >= 500000:  # $500K+
+            likelihood += 0.30
+        elif premium_num >= self.min_premium_for_institutional:
+            likelihood += 0.20
+        
+        # Relative premium component (0-30%)
+        if relative_premium >= 80:
+            likelihood += 0.30
+        elif relative_premium >= 60:
+            likelihood += 0.20
+        elif relative_premium >= 40:
+            likelihood += 0.10
+        
+        # Size concentration component (0-30%)
+        # Institutional trades are often concentrated
+        if size_concentration >= 70:
+            likelihood += 0.30
+        elif size_concentration >= 50:
+            likelihood += 0.20
+        elif size_concentration >= 30:
+            likelihood += 0.10
+        
+        return min(1.0, max(0.0, likelihood))
+    
+    def calculate_dealer_pain_level(self, row: pd.Series) -> float:
+        """
+        Calculate dealer pain level (0-100).
+        Higher = dealers in pain (large gamma exposure, forced to hedge).
+        """
+        dealer_pressure = row.get('dealer_hedge_pressure_score', 0) or 0
+        net_gamma = abs(row.get('net_gamma_exposure_per_symbol', 0) or 0)
+        gamma_flip_prox = row.get('gamma_flip_proximity')
+        
+        pain = 0.0
+        
+        # Dealer pressure component (0-50 points)
+        pain += (dealer_pressure / 100.0) * 50.0
+        
+        # Gamma exposure component (0-30 points)
+        # Large absolute gamma = more pain
+        if net_gamma > 0:
+            normalized_gamma = min(1.0, net_gamma / 1000000000)  # 1B threshold
+            pain += normalized_gamma * 30.0
+        
+        # Gamma flip proximity component (0-20 points)
+        # Near flip = high pain (dealers forced to adjust)
+        if gamma_flip_prox is not None and not pd.isna(gamma_flip_prox):
+            # Absolute value of proximity (closer to 0 = more pain)
+            pain += (1.0 - abs(gamma_flip_prox)) * 20.0
+        
+        return min(100.0, max(0.0, pain))
+    
+    def calculate_expected_move_vs_implied(self, row: pd.Series) -> Optional[float]:
+        """
+        Calculate expected move vs implied move ratio.
+        Estimates expected move from flow characteristics vs implied volatility.
+        
+        Returns: ratio (expected_move / implied_move)
+        - >1.0 = flow suggests larger move than implied
+        - <1.0 = flow suggests smaller move than implied
+        - None if cannot calculate
+        """
+        # Simplified calculation: use premium and delta exposure as proxies
+        premium_num = row.get('premium_num', 0) or 0
+        spot_num = row.get('spot_num', 0) or 0
+        delta_exposure = abs(row.get('delta_exposure', 0) or 0)
+        
+        if pd.isna(spot_num) or spot_num == 0:
+            return None
+        
+        # Estimate expected move from premium paid
+        # High premium relative to spot = expectation of larger move
+        if premium_num > 0:
+            prem_to_spot_ratio = premium_num / spot_num
+            
+            # Estimate implied move (simplified: assume 1% IV = 1% move expectation)
+            # This is a placeholder - in production, use actual IV from options chain
+            implied_move_pct = 2.0  # Default 2% implied move
+            
+            # Estimate expected move from premium
+            # Premium of 2% of spot = expectation of ~2% move (rough approximation)
+            expected_move_pct = prem_to_spot_ratio * 100.0
+            
+            # Calculate ratio
+            if implied_move_pct > 0:
+                ratio = expected_move_pct / implied_move_pct
+                return float(ratio)
+        
+        return None
+    
+    def enrich_with_confidence_metrics(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Add institutional confidence metrics to DataFrame.
+        Adds: confidence_score, institutional_likelihood, dealer_pain_level, expected_move_vs_implied
+        """
+        if df.empty:
+            return df
+        
+        df = df.copy()
+        
+        # Initialize columns
+        df['confidence_score'] = 0.0
+        df['institutional_likelihood'] = 0.0
+        df['dealer_pain_level'] = 0.0
+        df['expected_move_vs_implied'] = None
+        
+        # Calculate metrics
+        df['confidence_score'] = df.apply(self.calculate_confidence_score, axis=1)
+        df['institutional_likelihood'] = df.apply(self.calculate_institutional_likelihood, axis=1)
+        df['dealer_pain_level'] = df.apply(self.calculate_dealer_pain_level, axis=1)
+        
+        # Expected move vs implied (some rows may not have this)
+        for idx in df.index:
+            expected_move = self.calculate_expected_move_vs_implied(df.iloc[idx])
+            if expected_move is not None:
+                df.at[idx, 'expected_move_vs_implied'] = float(expected_move)
+        
+        logger.info(f"Confidence metrics complete. Mean confidence: {df['confidence_score'].mean():.2f}")
+        
+        return df
+
--- a/backend/python_service/services/intent_classifier.py
+++ b/backend/python_service/services/intent_classifier.py
@ -0,0 +1,232 @@
+"""
+Intent Classification Service
+Replaces naive direction (BULL/BEAR) with nuanced volatility and hedging intent classification
+"""
+import pandas as pd
+import numpy as np
+from typing import Optional
+from enum import Enum
+from utils.logger import logger
+from utils.error_handler import safe_divide
+
+
+class VolatilityIntent(Enum):
+    """Volatility and hedging intent classification"""
+    LONG_VOL = "LONG_VOL"  # Buying volatility (call/put buying, expecting large moves)
+    SHORT_VOL = "SHORT_VOL"  # Selling volatility (premium collection, expecting low vol)
+    DIRECTIONAL = "DIRECTIONAL"  # Directional positioning (directional bias)
+    HEDGE_UNWIND = "HEDGE_UNWIND"  # Hedging or unwinding existing positions
+
+
+class IntentClassifier:
+    """
+    Classifies options flow intent beyond simple BULL/BEAR direction.
+    Identifies volatility trading, hedging, and directional positioning.
+    """
+    
+    def __init__(self):
+        # Configuration thresholds
+        self.otm_threshold_pct = 5.0  # Strikes >5% OTM considered OTM
+        self.long_vol_premium_threshold = 100000  # Minimum premium for long vol signal
+        self.short_vol_premium_threshold = 200000  # Minimum premium for short vol signal
+    
+    def estimate_delta(self, row: pd.Series) -> float:
+        """
+        Estimate option delta from moneyness.
+        Returns delta estimate (0-1 for calls, -1-0 for puts, use absolute value).
+        """
+        spot_num = row.get('spot_num', 0) or 0
+        strike_num = row.get('strike_num', 0) or 0
+        cp_norm = row.get('cp_norm', '')
+        
+        if pd.isna(spot_num) or spot_num == 0 or pd.isna(strike_num) or pd.isna(cp_norm):
+            return 0.5  # Default to ATM
+        
+        moneyness_ratio = strike_num / spot_num if spot_num > 0 else 1.0
+        
+        if cp_norm == 'CALL':
+            if strike_num <= spot_num:
+                # ITM call: delta 0.5 to 1.0
+                delta = 0.5 + (1.0 - moneyness_ratio) * 0.5
+            else:
+                # OTM call: delta 0.5 to 0.0
+                delta = max(0.0, 0.5 * (1.5 - moneyness_ratio) / 0.5)
+        else:  # PUT
+            if strike_num >= spot_num:
+                # ITM put: delta -0.5 to -1.0 (return absolute)
+                delta = abs(0.5 + (moneyness_ratio - 1.0) * 0.5)
+            else:
+                # OTM put: delta -0.5 to 0.0 (return absolute)
+                delta = max(0.0, 0.5 * (1.0 - moneyness_ratio) / 0.5)
+        
+        return float(delta)
+    
+    def estimate_gamma(self, row: pd.Series, delta: float) -> float:
+        """
+        Estimate option gamma (sensitivity of delta to price changes).
+        Higher near ATM, lower ITM/OTM.
+        Returns gamma estimate (positive value).
+        """
+        spot_num = row.get('spot_num', 0) or 0
+        strike_num = row.get('strike_num', 0) or 0
+        
+        if pd.isna(spot_num) or spot_num == 0 or pd.isna(strike_num):
+            return 0.0
+        
+        # Gamma is highest at-the-money
+        moneyness_ratio = strike_num / spot_num if spot_num > 0 else 1.0
+        
+        # Simple approximation: gamma peaks at 1.0 (ATM) and decays away
+        # Use normal distribution approximation
+        distance_from_atm = abs(moneyness_ratio - 1.0)
+        
+        # Gamma ≈ exp(-distance^2 / (2*sigma^2)) where sigma ≈ 0.1 (10% moneyness)
+        gamma = np.exp(-(distance_from_atm ** 2) / (2 * 0.01))
+        
+        return float(gamma)
+    
+    def calculate_delta_exposure(self, row: pd.Series) -> float:
+        """
+        Calculate delta exposure: contracts * delta * 100 * spot_price.
+        Positive = long delta (bullish), Negative = short delta (bearish).
+        """
+        premium_num = row.get('premium_num', 0) or 0
+        spot_num = row.get('spot_num', 0) or 0
+        vol_num = row.get('vol_num', 0) or 0
+        cp_norm = row.get('cp_norm', '')
+        side_norm = row.get('side_norm', '')
+        
+        if pd.isna(spot_num) or spot_num == 0:
+            return 0.0
+        
+        # Estimate delta
+        delta = self.estimate_delta(row)
+        
+        # Determine sign based on call/put and buy/sell
+        if cp_norm == 'CALL':
+            if side_norm == 'BUY':
+                delta_sign = 1.0  # Long calls = positive delta
+            else:  # SELL
+                delta_sign = -1.0  # Short calls = negative delta
+        else:  # PUT
+            if side_norm == 'BUY':
+                delta_sign = -1.0  # Long puts = negative delta
+            else:  # SELL
+                delta_sign = 1.0  # Short puts = positive delta
+        
+        # Delta exposure = contracts * delta * 100 * spot
+        # Use volume as proxy for contracts
+        contracts = vol_num if not pd.isna(vol_num) else 0
+        delta_exposure = contracts * delta * delta_sign * 100 * spot_num
+        
+        return float(delta_exposure)
+    
+    def calculate_gamma_exposure(self, row: pd.Series) -> float:
+        """
+        Calculate gamma exposure: contracts * gamma * 100 * spot_price^2.
+        Positive = long gamma (volatility long), Negative = short gamma (volatility short).
+        """
+        premium_num = row.get('premium_num', 0) or 0
+        spot_num = row.get('spot_num', 0) or 0
+        vol_num = row.get('vol_num', 0) or 0
+        cp_norm = row.get('cp_norm', '')
+        side_norm = row.get('side_norm', '')
+        
+        if pd.isna(spot_num) or spot_num == 0:
+            return 0.0
+        
+        # Estimate delta and gamma
+        delta = self.estimate_delta(row)
+        gamma = self.estimate_gamma(row, delta)
+        
+        # Determine sign: buying options = long gamma, selling = short gamma
+        if side_norm == 'BUY':
+            gamma_sign = 1.0  # Long gamma
+        else:  # SELL
+            gamma_sign = -1.0  # Short gamma
+        
+        # Gamma exposure = contracts * gamma * 100 * spot^2
+        contracts = vol_num if not pd.isna(vol_num) else 0
+        gamma_exposure = contracts * gamma * gamma_sign * 100 * (spot_num ** 2)
+        
+        return float(gamma_exposure)
+    
+    def classify_volatility_intent(self, row: pd.Series) -> str:
+        """
+        Classify volatility intent based on trade characteristics.
+        Returns VolatilityIntent enum value as string.
+        """
+        premium_num = row.get('premium_num', 0) or 0
+        spot_num = row.get('spot_num', 0) or 0
+        strike_num = row.get('strike_num', 0) or 0
+        cp_norm = row.get('cp_norm', '')
+        side_norm = row.get('side_norm', '')
+        vol_num = row.get('vol_num', 0) or 0
+        oi_num = row.get('oi_num', 0) or 0
+        
+        # Calculate moneyness
+        if pd.isna(spot_num) or spot_num == 0 or pd.isna(strike_num):
+            mny_pct = 0.0
+        else:
+            if cp_norm == 'CALL':
+                mny_pct = ((strike_num - spot_num) / spot_num * 100.0) if spot_num > 0 else 0
+            else:  # PUT
+                mny_pct = ((spot_num - strike_num) / spot_num * 100.0) if spot_num > 0 else 0
+        
+        is_otm = abs(mny_pct) > self.otm_threshold_pct
+        
+        # Long volatility: buying OTM options (calls or puts)
+        # High premium, OTM strikes, buying side
+        if (side_norm == 'BUY' and 
+            is_otm and 
+            premium_num >= self.long_vol_premium_threshold):
+            return VolatilityIntent.LONG_VOL.value
+        
+        # Short volatility: selling options, collecting premium
+        # High premium, selling side, vol > OI (opening new short positions)
+        if (side_norm == 'SELL' and 
+            premium_num >= self.short_vol_premium_threshold and
+            vol_num > oi_num):
+            return VolatilityIntent.SHORT_VOL.value
+        
+        # Directional: ITM options, buying side, strong directional flow
+        if (side_norm == 'BUY' and 
+            not is_otm and 
+            premium_num >= 50000):
+            return VolatilityIntent.DIRECTIONAL.value
+        
+        # Hedge/Unwind: Selling existing positions (vol < OI)
+        # Or buying protective puts/calls
+        if (side_norm == 'SELL' and vol_num < oi_num) or \
+           (side_norm == 'BUY' and is_otm and premium_num < self.long_vol_premium_threshold):
+            return VolatilityIntent.HEDGE_UNWIND.value
+        
+        # Default: directional (fallback)
+        return VolatilityIntent.DIRECTIONAL.value
+    
+    def enrich_with_intent_classification(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Add intent classification metrics to DataFrame.
+        Adds: delta_exposure, gamma_exposure, volatility_intent
+        """
+        if df.empty:
+            return df
+        
+        df = df.copy()
+        
+        # Initialize columns
+        df['delta_exposure'] = 0.0
+        df['gamma_exposure'] = 0.0
+        df['volatility_intent'] = VolatilityIntent.DIRECTIONAL.value
+        
+        # Calculate metrics
+        df['delta_exposure'] = df.apply(self.calculate_delta_exposure, axis=1)
+        df['gamma_exposure'] = df.apply(self.calculate_gamma_exposure, axis=1)
+        df['volatility_intent'] = df.apply(self.classify_volatility_intent, axis=1)
+        
+        # Log distribution
+        intent_counts = df['volatility_intent'].value_counts()
+        logger.info(f"Intent classification complete. Distribution: {intent_counts.to_dict()}")
+        
+        return df
+
--- a/backend/python_service/services/market_regime_detector.py
+++ b/backend/python_service/services/market_regime_detector.py
@ -0,0 +1,104 @@
+"""
+Market Regime Detection Service
+Identifies market regime to gate trade signal generation
+"""
+import pandas as pd
+import numpy as np
+from typing import Optional
+from enum import Enum
+from utils.logger import logger
+
+
+class MarketRegime(Enum):
+    """Market regime classification"""
+    TREND = "TREND"  # Trending market (continuation bias)
+    RANGE = "RANGE"  # Range-bound market (fade or vol-sell bias)
+    HIGH_VOL_EVENT = "HIGH_VOL_EVENT"  # High volatility event (volatility expansion bias)
+
+
+class MarketRegimeDetector:
+    """
+    Detects market regime to inform trade signal bias:
+    - Trend: continuation trades preferred
+    - Range: mean reversion / vol selling preferred
+    - High Vol Event: volatility expansion trades preferred
+    """
+    
+    def __init__(self):
+        # Configuration
+        self.trend_threshold_pct = 1.5  # >1.5% move = trending
+        self.range_threshold_pct = 0.5  # <0.5% move = ranging
+        self.high_vol_threshold_pct = 3.0  # >3% move = high vol event
+        self.lookback_minutes = 60  # Lookback window for regime detection
+    
+    def detect_regime(
+        self,
+        df: pd.DataFrame,
+        row_idx: int
+    ) -> str:
+        """
+        Detect market regime for a given flow event.
+        Returns MarketRegime enum value as string.
+        """
+        if row_idx >= len(df):
+            return MarketRegime.RANGE.value
+        
+        current_row = df.iloc[row_idx]
+        symbol = current_row.get('symbol_norm')
+        flow_ts_utc = current_row.get('flow_ts_utc')
+        
+        if pd.isna(symbol) or pd.isna(flow_ts_utc):
+            return MarketRegime.RANGE.value
+        
+        # Get price movement over lookback window
+        pct_vs_rth_open = current_row.get('pct_vs_rth_open')
+        pct_vs_prior_close = current_row.get('pct_vs_prior_close')
+        pct_15m_momo = current_row.get('pct_15m_momo')
+        
+        # Use most appropriate price metric
+        price_move_pct = None
+        if pct_vs_rth_open is not None and not pd.isna(pct_vs_rth_open):
+            price_move_pct = abs(pct_vs_rth_open)
+        elif pct_vs_prior_close is not None and not pd.isna(pct_vs_prior_close):
+            price_move_pct = abs(pct_vs_prior_close)
+        elif pct_15m_momo is not None and not pd.isna(pct_15m_momo):
+            price_move_pct = abs(pct_15m_momo)
+        
+        if price_move_pct is None:
+            return MarketRegime.RANGE.value  # Default to range
+        
+        # Classify regime
+        if price_move_pct >= self.high_vol_threshold_pct:
+            return MarketRegime.HIGH_VOL_EVENT.value
+        elif price_move_pct >= self.trend_threshold_pct:
+            return MarketRegime.TREND.value
+        elif price_move_pct <= self.range_threshold_pct:
+            return MarketRegime.RANGE.value
+        else:
+            # Between range and trend threshold - classify based on momentum
+            if pct_15m_momo is not None and not pd.isna(pct_15m_momo):
+                if abs(pct_15m_momo) > 0.75:
+                    return MarketRegime.TREND.value
+            return MarketRegime.RANGE.value
+    
+    def enrich_with_market_regime(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Add market regime classification to DataFrame.
+        Adds: market_regime
+        """
+        if df.empty:
+            return df
+        
+        df = df.copy()
+        df['market_regime'] = MarketRegime.RANGE.value
+        
+        # Detect regime for each row
+        for idx in df.index:
+            regime = self.detect_regime(df, idx)
+            df.at[idx, 'market_regime'] = regime
+        
+        regime_counts = df['market_regime'].value_counts()
+        logger.info(f"Market regime detection complete. Distribution: {regime_counts.to_dict()}")
+        
+        return df
+
--- a/backend/python_service/services/noise_rejector.py
+++ b/backend/python_service/services/noise_rejector.py
@ -0,0 +1,213 @@
+"""
+Tier-0 Noise Rejection Service
+Filters out low-quality signals before enrichment to reduce processing overhead
+"""
+import pandas as pd
+import numpy as np
+from typing import Optional
+from datetime import timedelta
+from utils.logger import logger
+from utils.error_handler import safe_divide
+
+
+class NoiseRejector:
+    """
+    Rejects early-stage noise before enrichment to optimize processing.
+    
+    Rejects if:
+    - Single isolated trade (no repeat activity)
+    - Far OTM weekly lottos
+    - Delta-adjusted premium below threshold
+    """
+    
+    def __init__(self):
+        # Configuration
+        self.repeat_activity_window_minutes = 30  # Minutes to look for repeat activity
+        self.min_delta_adjusted_premium = 50000  # Minimum delta-adjusted premium
+        self.max_otm_percentage = 15.0  # Reject strikes >15% OTM
+        self.min_expiry_days = 1  # Minimum days to expiry (reject same-day/weekly lottos)
+        
+    def is_isolated_trade(
+        self,
+        df: pd.DataFrame,
+        row_idx: int
+    ) -> bool:
+        """
+        Check if trade is isolated (no repeat activity within window).
+        Returns True if isolated (should reject).
+        """
+        if row_idx >= len(df):
+            return True
+        
+        current_row = df.iloc[row_idx]
+        symbol = current_row.get('symbol_norm')
+        direction = current_row.get('direction')
+        flow_ts_utc = current_row.get('flow_ts_utc')
+        
+        if pd.isna(symbol) or pd.isna(flow_ts_utc):
+            return True
+        
+        # Look for trades in same direction within window
+        window_start = flow_ts_utc - timedelta(minutes=self.repeat_activity_window_minutes)
+        
+        same_symbol = df['symbol_norm'] == symbol
+        same_direction = df['direction'] == direction
+        in_window = (df['flow_ts_utc'] >= window_start) & (df['flow_ts_utc'] < flow_ts_utc)
+        
+        # Exclude current row
+        other_trades = df[same_symbol & same_direction & in_window]
+        
+        # If no other trades in window, it's isolated
+        return len(other_trades) == 0
+    
+    def is_far_otm_lotto(
+        self,
+        row: pd.Series
+    ) -> bool:
+        """
+        Check if trade is a far OTM weekly lotto (low probability, low signal value).
+        Returns True if should reject.
+        """
+        spot_num = row.get('spot_num', 0) or 0
+        strike_num = row.get('strike_num', 0) or 0
+        cp_norm = row.get('cp_norm', '')
+        exp_date = row.get('exp_date')
+        flow_date = row.get('flow_date_cst')
+        
+        if pd.isna(spot_num) or spot_num == 0 or pd.isna(strike_num) or pd.isna(cp_norm):
+            return False
+        
+        # Calculate moneyness percentage
+        if cp_norm == 'CALL':
+            otm_pct = ((strike_num - spot_num) / spot_num * 100.0) if spot_num > 0 else 0
+        else:  # PUT
+            otm_pct = ((spot_num - strike_num) / spot_num * 100.0) if spot_num > 0 else 0
+        
+        # Reject if >15% OTM
+        if otm_pct > self.max_otm_percentage:
+            return True
+        
+        # Check if weekly lotto (expiry within 7 days)
+        if pd.notna(exp_date) and pd.notna(flow_date):
+            try:
+                if isinstance(exp_date, str):
+                    from datetime import datetime
+                    exp_date = datetime.strptime(exp_date, '%Y-%m-%d').date()
+                if isinstance(flow_date, str):
+                    from datetime import datetime
+                    flow_date = datetime.strptime(flow_date, '%Y-%m-%d').date()
+                
+                days_to_expiry = (exp_date - flow_date).days
+                
+                # Reject weekly lottos that are also far OTM
+                if days_to_expiry <= 7 and otm_pct > 10.0:
+                    return True
+            except (ValueError, TypeError, AttributeError):
+                pass
+        
+        return False
+    
+    def calculate_delta_adjusted_premium(
+        self,
+        row: pd.Series
+    ) -> float:
+        """
+        Calculate delta-adjusted premium (premium * |delta|).
+        Approximates intrinsic value component of premium.
+        
+        For simplicity, we estimate delta from moneyness:
+        - ATM: delta ≈ 0.5
+        - ITM: delta increases toward 1.0
+        - OTM: delta decreases toward 0.0
+        """
+        premium_num = row.get('premium_num', 0) or 0
+        spot_num = row.get('spot_num', 0) or 0
+        strike_num = row.get('strike_num', 0) or 0
+        cp_norm = row.get('cp_norm', '')
+        
+        if pd.isna(premium_num) or premium_num == 0 or pd.isna(spot_num) or spot_num == 0:
+            return 0.0
+        
+        # Estimate delta from moneyness
+        if cp_norm == 'CALL':
+            if strike_num <= spot_num:
+                # ITM call: delta from 0.5 to 1.0
+                moneyness_ratio = strike_num / spot_num if spot_num > 0 else 1.0
+                # Strike at spot = 0.5, strike at 0 = 1.0
+                estimated_delta = 0.5 + (1.0 - moneyness_ratio) * 0.5
+            else:
+                # OTM call: delta from 0.5 to 0.0
+                moneyness_ratio = strike_num / spot_num if spot_num > 0 else 1.0
+                # Strike at spot = 0.5, strike at 1.15x spot = 0.0
+                estimated_delta = max(0.0, 0.5 * (1.5 - moneyness_ratio) / 0.5)
+        else:  # PUT
+            if strike_num >= spot_num:
+                # ITM put: delta from -0.5 to -1.0 (use absolute value)
+                moneyness_ratio = strike_num / spot_num if spot_num > 0 else 1.0
+                estimated_delta = 0.5 + (moneyness_ratio - 1.0) * 0.5
+            else:
+                # OTM put: delta from 0.5 to 0.0 (use absolute value)
+                moneyness_ratio = strike_num / spot_num if spot_num > 0 else 1.0
+                estimated_delta = max(0.0, 0.5 * (1.0 - moneyness_ratio) / 0.5)
+        
+        # Delta-adjusted premium
+        delta_adj_premium = premium_num * abs(estimated_delta)
+        
+        return float(delta_adj_premium)
+    
+    def should_reject(
+        self,
+        df: pd.DataFrame,
+        row_idx: int
+    ) -> bool:
+        """
+        Determine if row should be rejected as noise.
+        Returns True if should reject (mark early_noise_reject = True).
+        """
+        if row_idx >= len(df):
+            return True
+        
+        row = df.iloc[row_idx]
+        
+        # Reject isolated trades
+        if self.is_isolated_trade(df, row_idx):
+            return True
+        
+        # Reject far OTM lottos
+        if self.is_far_otm_lotto(row):
+            return True
+        
+        # Reject if delta-adjusted premium below threshold
+        delta_adj_premium = self.calculate_delta_adjusted_premium(row)
+        if delta_adj_premium < self.min_delta_adjusted_premium:
+            return True
+        
+        return False
+    
+    def mark_noise_rejections(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Mark rows for early noise rejection.
+        Adds: early_noise_reject (boolean)
+        """
+        if df.empty:
+            return df
+        
+        df = df.copy()
+        
+        # Initialize column
+        df['early_noise_reject'] = False
+        
+        # Sort by timestamp for proper isolation detection
+        df_sorted = df.sort_values('flow_ts_utc').reset_index(drop=True)
+        
+        # Mark rejections
+        for idx in df_sorted.index:
+            if self.should_reject(df_sorted, idx):
+                original_idx = df_sorted.index[idx]
+                df.at[original_idx, 'early_noise_reject'] = True
+        
+        rejection_count = df['early_noise_reject'].sum()
+        logger.info(f"Noise rejection: {rejection_count}/{len(df)} rows marked for rejection ({rejection_count/len(df)*100:.1f}%)")
+        
+        return df
+
--- a/backend/python_service/services/output_formatter.py
+++ b/backend/python_service/services/output_formatter.py
@ -146,5 +146,14 @@ class OutputFormatter:
        # Ensure Phase 1 columns are preserved (don't drop them)
        # Phase 1 columns should remain as-is (signal_tier, checklist_score, etc.)
        
+        # Ensure all new institutional analytics columns are preserved:
+        # - premium_zscore, premium_percentile_intraday, relative_premium_score
+        # - aggression_score, size_concentration_score, repeat_trade_velocity, strike_clustering_score, signal_strength
+        # - early_noise_reject, flow_acceleration, time_between_hits, follow_on_ratio, strike_laddering_detected
+        # - delta_exposure, gamma_exposure, volatility_intent
+        # - net_gamma_exposure_per_symbol, gamma_flip_proximity, dealer_hedge_pressure_score
+        # - market_regime, flow_state
+        # - confidence_score, institutional_likelihood, dealer_pain_level, expected_move_vs_implied
+        
        return df

--- a/backend/python_service/services/relative_premium_scorer.py
+++ b/backend/python_service/services/relative_premium_scorer.py
@ -0,0 +1,258 @@
+"""
+Relative Premium Scoring Service
+Replaces static premium filter with context-aware relative premium scoring
+"""
+import pandas as pd
+import numpy as np
+from typing import Dict, Optional
+from datetime import datetime, timedelta
+import asyncpg
+from utils.logger import logger
+from utils.error_handler import safe_divide
+
+
+class RelativePremiumScorer:
+    """
+    Computes relative premium metrics to replace static premium filtering.
+    Premium of $80K might be significant for AAPL but noise for TSLA.
+    """
+    
+    def __init__(self, pool: asyncpg.Pool = None):
+        self.pool = pool
+        # Configuration
+        self.rolling_window_days = 20  # Rolling window for z-score calculation
+        self.min_relative_premium_threshold = 0.60  # 60th percentile minimum
+        
+    async def fetch_historical_premium_stats(
+        self, 
+        symbol: str, 
+        reference_date: datetime.date
+    ) -> Dict[str, float]:
+        """
+        Fetch historical premium statistics for a symbol.
+        Returns: {mean, std, min, max, median} for rolling window
+        """
+        if not self.pool:
+            logger.warning("No database pool available for historical premium stats")
+            return {}
+        
+        try:
+            # Calculate window start date
+            window_start = reference_date - timedelta(days=self.rolling_window_days + 5)  # Extra buffer
+            
+            async with self.pool.acquire() as conn:
+                query = """
+                    SELECT 
+                        AVG(premium_num) as mean_premium,
+                        STDDEV(premium_num) as std_premium,
+                        MIN(premium_num) as min_premium,
+                        MAX(premium_num) as max_premium,
+                        PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY premium_num) as median_premium
+                    FROM (
+                        SELECT 
+                            CAST(REGEXP_REPLACE("Premium"::text, '[^\d.]', '', 'g') AS numeric) as premium_num
+                        FROM "OptionsFlow_monthly"
+                        WHERE UPPER(TRIM("Symbol")) = $1
+                          AND "Premium" IS NOT NULL
+                          AND TRIM("Premium"::text) <> ''
+                          AND "CreatedDate" >= $2
+                          AND "CreatedDate" <= $3
+                          AND "StockEtf" = 'STOCK'
+                    ) subq
+                    WHERE premium_num > 0
+                """
+                
+                row = await conn.fetchrow(
+                    query,
+                    symbol.upper(),
+                    window_start.strftime('%Y-%m-%d'),
+                    reference_date.strftime('%Y-%m-%d')
+                )
+                
+                if row and row['mean_premium']:
+                    return {
+                        'mean': float(row['mean_premium']) if row['mean_premium'] else 0.0,
+                        'std': float(row['std_premium']) if row['std_premium'] else 0.0,
+                        'min': float(row['min_premium']) if row['min_premium'] else 0.0,
+                        'max': float(row['max_premium']) if row['max_premium'] else 0.0,
+                        'median': float(row['median_premium']) if row['median_premium'] else 0.0,
+                    }
+        except Exception as e:
+            logger.debug(f"Error fetching historical premium stats for {symbol}: {e}")
+        
+        return {}
+    
+    async def calculate_intraday_percentile(
+        self,
+        df: pd.DataFrame,
+        symbol: str,
+        flow_date: datetime.date,
+        premium: float
+    ) -> Optional[float]:
+        """
+        Calculate percentile rank of premium within same-day flow for the symbol.
+        Returns 0-100 percentile value.
+        """
+        try:
+            # Filter to same symbol and date
+            same_day_flow = df[
+                (df['symbol_norm'] == symbol.upper()) &
+                (df['flow_date_cst'] == flow_date) &
+                (df['premium_num'].notna()) &
+                (df['premium_num'] > 0)
+            ]
+            
+            if len(same_day_flow) < 3:  # Need at least 3 trades for meaningful percentile
+                return None
+            
+            # Calculate percentile rank
+            all_premiums = same_day_flow['premium_num'].values
+            percentile = (np.sum(all_premiums <= premium) / len(all_premiums)) * 100.0
+            
+            return float(percentile)
+        except Exception as e:
+            logger.debug(f"Error calculating intraday percentile for {symbol}: {e}")
+            return None
+    
+    def calculate_zscore(
+        self,
+        premium: float,
+        stats: Dict[str, float]
+    ) -> Optional[float]:
+        """
+        Calculate z-score of premium relative to historical distribution.
+        Returns z-score (standard deviations from mean).
+        """
+        if not stats or stats.get('std', 0) == 0:
+            return None
+        
+        mean = stats.get('mean', 0)
+        std = stats.get('std', 1)
+        
+        if std == 0:
+            return None
+        
+        zscore = (premium - mean) / std
+        return float(zscore)
+    
+    def calculate_relative_premium_score(
+        self,
+        premium: float,
+        zscore: Optional[float],
+        intraday_percentile: Optional[float],
+        stats: Dict[str, float]
+    ) -> float:
+        """
+        Calculate composite relative premium score (0-100).
+        Combines z-score and intraday percentile with median normalization.
+        
+        Logic:
+        - High z-score (unusual size) = higher score
+        - High intraday percentile (large relative to today's flow) = higher score
+        - Normalize by median to account for symbol-specific scaling
+        """
+        score = 0.0
+        
+        # Z-score component (40% weight)
+        # Z-score > 1 = 1 std above mean, Z-score > 2 = 2 std above mean
+        if zscore is not None:
+            # Normalize z-score to 0-40 range (clamp at ±3 sigma)
+            zscore_normalized = max(0, min(40, (zscore + 3) * (40 / 6)))
+            score += zscore_normalized
+        
+        # Intraday percentile component (40% weight)
+        if intraday_percentile is not None:
+            # Percentile is already 0-100, scale to 0-40
+            score += (intraday_percentile / 100.0) * 40.0
+        
+        # Median normalization component (20% weight)
+        # Premium > median gets bonus, premium < median gets penalty
+        if stats and stats.get('median', 0) > 0:
+            median_ratio = premium / stats['median']
+            # Ratio > 1.5 = strong, ratio > 2.0 = very strong
+            if median_ratio >= 2.0:
+                score += 20.0
+            elif median_ratio >= 1.5:
+                score += 15.0
+            elif median_ratio >= 1.0:
+                score += 10.0
+            elif median_ratio >= 0.5:
+                score += 5.0
+        
+        return min(100.0, max(0.0, score))
+    
+    async def enrich_with_relative_premium(
+        self,
+        df: pd.DataFrame
+    ) -> pd.DataFrame:
+        """
+        Add relative premium metrics to DataFrame.
+        Adds: premium_zscore, premium_percentile_intraday, relative_premium_score
+        """
+        if df.empty:
+            return df
+        
+        df = df.copy()
+        
+        # Initialize new columns
+        df['premium_zscore'] = None
+        df['premium_percentile_intraday'] = None
+        df['relative_premium_score'] = 0.0
+        
+        # Cache historical stats per symbol to avoid repeated queries
+        stats_cache: Dict[str, Dict[str, float]] = {}
+        
+        # Group by symbol and date for batch processing
+        unique_symbols = df['symbol_norm'].unique()
+        unique_dates = df['flow_date_cst'].unique()
+        
+        logger.info(f"Calculating relative premium scores for {len(unique_symbols)} symbols")
+        
+        for symbol in unique_symbols:
+            # Fetch historical stats once per symbol
+            if self.pool:
+                # Use the most recent date for this symbol as reference
+                symbol_dates = df[df['symbol_norm'] == symbol]['flow_date_cst'].unique()
+                if len(symbol_dates) > 0:
+                    reference_date = max(symbol_dates)
+                    stats = await self.fetch_historical_premium_stats(symbol, reference_date)
+                    stats_cache[symbol] = stats
+                else:
+                    stats_cache[symbol] = {}
+            else:
+                stats_cache[symbol] = {}
+        
+        # Calculate metrics for each row
+        for idx, row in df.iterrows():
+            premium = row.get('premium_num')
+            if pd.isna(premium) or premium <= 0:
+                continue
+            
+            symbol = row['symbol_norm']
+            flow_date = row.get('flow_date_cst')
+            
+            # Get historical stats
+            stats = stats_cache.get(symbol, {})
+            
+            # Calculate z-score
+            zscore = self.calculate_zscore(premium, stats)
+            if zscore is not None:
+                df.at[idx, 'premium_zscore'] = float(zscore)
+            
+            # Calculate intraday percentile (using current DataFrame subset)
+            intraday_percentile = self.calculate_intraday_percentile(
+                df, symbol, flow_date, premium
+            )
+            if intraday_percentile is not None:
+                df.at[idx, 'premium_percentile_intraday'] = float(intraday_percentile)
+            
+            # Calculate composite relative premium score
+            relative_score = self.calculate_relative_premium_score(
+                premium, zscore, intraday_percentile, stats
+            )
+            df.at[idx, 'relative_premium_score'] = float(relative_score)
+        
+        logger.info(f"Relative premium scoring complete. Mean score: {df['relative_premium_score'].mean():.2f}")
+        
+        return df
+
--- a/backend/python_service/services/signal_component_scorer.py
+++ b/backend/python_service/services/signal_component_scorer.py
@ -0,0 +1,331 @@
+"""
+Signal Component Scorer
+Converts binary badge logic into continuous numeric signal components
+"""
+import pandas as pd
+import numpy as np
+from typing import Dict, Optional
+from utils.logger import logger
+
+
+class SignalComponentScorer:
+    """
+    Replaces binary badge logic (💎 ⭐ 🟢 🔴) with continuous numeric scores.
+    Badges remain display-only, but signal_strength is computed from components.
+    """
+    
+    def __init__(self):
+        # Signal strength weights (must sum to 1.0)
+        self.aggression_weight = 0.30
+        self.size_concentration_weight = 0.30
+        self.repeat_velocity_weight = 0.20
+        self.strike_clustering_weight = 0.20
+        
+    def calculate_aggression_score(self, row: pd.Series) -> float:
+        """
+        Measures trade aggression: premium paid at ask vs bid, ITM vs OTM preference.
+        
+        Higher score = more aggressive buying:
+        - ITM premiums indicate willingness to pay intrinsic value
+        - Ask-side trades (AA) indicate market orders
+        - Large premium relative to spot price
+        """
+        score = 0.0
+        
+        premium_num = row.get('premium_num', 0) or 0
+        spot_num = row.get('spot_num', 0) or 0
+        cp_norm = row.get('cp_norm', '')
+        side = str(row.get('Side', '')).upper()
+        
+        # ITM premium component (0-40 points)
+        bull_prem_itm = row.get('bull_prem_itm', 0) or 0
+        bear_prem_itm = row.get('bear_prem_itm', 0) or 0
+        total_prem_itm = bull_prem_itm + bear_prem_itm
+        bull_total = row.get('bull_total', 0) or 0
+        bear_total = row.get('bear_total', 0) or 0
+        total_premium = bull_total + bear_total
+        
+        if total_premium > 0:
+            itm_ratio = total_prem_itm / total_premium
+            score += itm_ratio * 40.0
+        
+        # Ask-side aggression component (0-30 points)
+        # AA = buying at ask (market orders, urgency)
+        has_aa = 'AA' in side or 'AT_ASK' in side
+        has_bb = 'BB' in side or 'AT_BID' in side
+        
+        if has_aa and not has_bb:
+            score += 30.0
+        elif has_aa:
+            score += 15.0
+        
+        # Premium-to-spot ratio component (0-30 points)
+        # Large premium relative to spot indicates aggressive positioning
+        if spot_num > 0 and premium_num > 0:
+            prem_to_spot_ratio = premium_num / spot_num
+            # Normalize: 0.01 (1%) = 10 points, 0.05 (5%) = 30 points
+            ratio_score = min(30.0, prem_to_spot_ratio * 600.0)
+            score += ratio_score
+        
+        return min(100.0, max(0.0, score))
+    
+    def calculate_size_concentration_score(self, row: pd.Series) -> float:
+        """
+        Measures size concentration: how much premium is concentrated in single strikes/expiries.
+        
+        Higher score = more concentrated (single large trade vs many small ones):
+        - High premium in single trade
+        - OI buildup in specific strikes
+        - Directional consistency (all calls or all puts)
+        """
+        score = 0.0
+        
+        premium_num = row.get('premium_num', 0) or 0
+        bull_total = row.get('bull_total', 0) or 0
+        bear_total = row.get('bear_total', 0) or 0
+        total_premium = bull_total + bear_total
+        
+        # Single-trade premium concentration (0-40 points)
+        # Premium of current trade relative to running total
+        if total_premium > 0:
+            concentration_ratio = premium_num / total_premium
+            # Single trade = 50%+ of total = very concentrated
+            if concentration_ratio >= 0.5:
+                score += 40.0
+            elif concentration_ratio >= 0.3:
+                score += 30.0
+            elif concentration_ratio >= 0.2:
+                score += 20.0
+            elif concentration_ratio >= 0.1:
+                score += 10.0
+        
+        # OI concentration component (0-30 points)
+        # High OI in OTM strikes indicates concentrated positioning
+        oi_cb_otm = row.get('oi_cb_otm', 0) or 0
+        oi_pb_otm = row.get('oi_pb_otm', 0) or 0
+        oi_all = row.get('oi_all', 0) or 0
+        
+        if oi_all > 0:
+            otm_oi_ratio = (oi_cb_otm + oi_pb_otm) / oi_all
+            score += otm_oi_ratio * 30.0
+        
+        # Directional consistency (0-30 points)
+        # Pure directional flow (all bull or all bear) = more concentrated
+        if total_premium > 0:
+            direction_strength = abs(bull_total - bear_total) / total_premium
+            score += direction_strength * 30.0
+        
+        return min(100.0, max(0.0, score))
+    
+    def calculate_repeat_trade_velocity(self, df: pd.DataFrame, row_idx: int) -> float:
+        """
+        Measures repeat trade velocity: frequency of trades in same direction/expiry.
+        
+        Higher score = faster follow-on trades (urgency building):
+        - Time between consecutive trades decreasing
+        - Multiple trades in same direction
+        - Same expiry date (rolling accumulation)
+        """
+        if row_idx >= len(df):
+            return 0.0
+        
+        current_row = df.iloc[row_idx]
+        symbol = current_row.get('symbol_norm')
+        direction = current_row.get('direction')
+        exp_date = current_row.get('exp_date')
+        flow_ts_utc = current_row.get('flow_ts_utc')
+        
+        if pd.isna(symbol) or pd.isna(direction) or pd.isna(flow_ts_utc):
+            return 0.0
+        
+        # Look back at recent trades for same symbol
+        # Sort by timestamp to find preceding trades
+        df_sorted = df.sort_values('flow_ts_utc').reset_index(drop=True)
+        current_idx = df_sorted.index[df_sorted.index == row_idx]
+        
+        if len(current_idx) == 0:
+            return 0.0
+        
+        current_pos = current_idx[0]
+        
+        # Look at last 10 trades for this symbol
+        symbol_mask = (df_sorted['symbol_norm'] == symbol) & \
+                      (df_sorted.index < current_pos) & \
+                      (df_sorted['flow_ts_utc'] <= flow_ts_utc)
+        
+        recent_trades = df_sorted[symbol_mask].tail(10)
+        
+        if len(recent_trades) == 0:
+            return 0.0
+        
+        score = 0.0
+        
+        # Time compression component (0-40 points)
+        # Decreasing time gaps = urgency building
+        if len(recent_trades) >= 2:
+            time_gaps = []
+            prev_ts = None
+            for _, trade in recent_trades.iterrows():
+                ts = trade.get('flow_ts_utc')
+                if pd.notna(ts) and prev_ts:
+                    gap_minutes = (ts - prev_ts).total_seconds() / 60.0
+                    if gap_minutes > 0:
+                        time_gaps.append(gap_minutes)
+                prev_ts = ts
+            
+            if len(time_gaps) >= 2:
+                # Check if gaps are decreasing (accelerating)
+                recent_gaps = time_gaps[-2:]
+                if recent_gaps[1] < recent_gaps[0]:
+                    compression_ratio = recent_gaps[1] / recent_gaps[0] if recent_gaps[0] > 0 else 0
+                    # 50% compression = 20 points, 80% compression = 40 points
+                    score += (1.0 - compression_ratio) * 40.0
+        
+        # Directional consistency (0-35 points)
+        # Same direction trades = building position
+        same_direction = recent_trades[recent_trades['direction'] == direction]
+        if len(recent_trades) > 0:
+            consistency_ratio = len(same_direction) / len(recent_trades)
+            score += consistency_ratio * 35.0
+        
+        # Expiry concentration (0-25 points)
+        # Same expiry = rolling accumulation
+        if pd.notna(exp_date):
+            same_expiry = recent_trades[recent_trades['exp_date'] == exp_date]
+            if len(recent_trades) > 0:
+                expiry_ratio = len(same_expiry) / len(recent_trades)
+                score += expiry_ratio * 25.0
+        
+        return min(100.0, max(0.0, score))
+    
+    def calculate_strike_clustering_score(self, df: pd.DataFrame, row_idx: int) -> float:
+        """
+        Measures strike clustering: trades accumulating at specific strike levels.
+        
+        Higher score = more clustering (laddering or concentrated strikes):
+        - Multiple trades at same strike
+        - Sequential strikes (laddering)
+        - Strikes near current price (pin risk)
+        """
+        if row_idx >= len(df):
+            return 0.0
+        
+        current_row = df.iloc[row_idx]
+        symbol = current_row.get('symbol_norm')
+        strike_num = current_row.get('strike_num')
+        spot_num = current_row.get('spot_num', 0) or 0
+        flow_ts_utc = current_row.get('flow_ts_utc')
+        exp_date = current_row.get('exp_date')
+        
+        if pd.isna(symbol) or pd.isna(strike_num) or pd.isna(flow_ts_utc):
+            return 0.0
+        
+        # Look at recent trades for same symbol and expiry
+        df_sorted = df.sort_values('flow_ts_utc').reset_index(drop=True)
+        symbol_mask = (df_sorted['symbol_norm'] == symbol) & \
+                      (df_sorted['flow_ts_utc'] <= flow_ts_utc)
+        
+        if pd.notna(exp_date):
+            symbol_mask = symbol_mask & (df_sorted['exp_date'] == exp_date)
+        
+        recent_trades = df_sorted[symbol_mask].tail(20)
+        
+        if len(recent_trades) <= 1:
+            return 0.0
+        
+        score = 0.0
+        
+        # Exact strike clustering (0-40 points)
+        # Multiple trades at same strike
+        same_strike_count = len(recent_trades[recent_trades['strike_num'] == strike_num])
+        if len(recent_trades) > 0:
+            clustering_ratio = same_strike_count / len(recent_trades)
+            score += clustering_ratio * 40.0
+        
+        # Strike laddering detection (0-35 points)
+        # Sequential strikes (e.g., 100, 105, 110) indicate laddering
+        unique_strikes = sorted(recent_trades['strike_num'].dropna().unique())
+        if len(unique_strikes) >= 3:
+            # Check if strikes are roughly evenly spaced (laddering)
+            if spot_num > 0:
+                strike_diffs = [abs(unique_strikes[i+1] - unique_strikes[i]) for i in range(len(unique_strikes)-1)]
+                if len(strike_diffs) > 0:
+                    avg_diff = np.mean(strike_diffs)
+                    std_diff = np.std(strike_diffs)
+                    # Low std relative to mean = regular spacing (laddering)
+                    if avg_diff > 0:
+                        regularity = 1.0 - min(1.0, std_diff / avg_diff)
+                        score += regularity * 35.0
+        
+        # Proximity to spot (0-25 points)
+        # Strikes near spot price = pin risk, more significant
+        if spot_num > 0:
+            strike_pct_diff = abs(strike_num - spot_num) / spot_num * 100.0
+            # Within 2% = 25 points, within 5% = 15 points, within 10% = 5 points
+            if strike_pct_diff <= 2.0:
+                score += 25.0
+            elif strike_pct_diff <= 5.0:
+                score += 15.0
+            elif strike_pct_diff <= 10.0:
+                score += 5.0
+        
+        return min(100.0, max(0.0, score))
+    
+    def calculate_signal_strength(self, row: pd.Series) -> float:
+        """
+        Calculate composite signal_strength from component scores.
+        
+        signal_strength = 
+            0.30 * aggression_score +
+            0.30 * size_concentration_score +
+            0.20 * repeat_trade_velocity +
+            0.20 * strike_clustering_score
+        """
+        aggression = row.get('aggression_score', 0) or 0
+        size_conc = row.get('size_concentration_score', 0) or 0
+        repeat_vel = row.get('repeat_trade_velocity', 0) or 0
+        strike_clust = row.get('strike_clustering_score', 0) or 0
+        
+        signal_strength = (
+            self.aggression_weight * aggression +
+            self.size_concentration_weight * size_conc +
+            self.repeat_velocity_weight * repeat_vel +
+            self.strike_clustering_weight * strike_clust
+        )
+        
+        return min(100.0, max(0.0, signal_strength))
+    
+    def enrich_with_signal_components(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Add signal component scores to DataFrame.
+        Adds: aggression_score, size_concentration_score, repeat_trade_velocity,
+              strike_clustering_score, signal_strength
+        """
+        if df.empty:
+            return df
+        
+        df = df.copy()
+        
+        # Initialize columns
+        df['aggression_score'] = 0.0
+        df['size_concentration_score'] = 0.0
+        df['repeat_trade_velocity'] = 0.0
+        df['strike_clustering_score'] = 0.0
+        df['signal_strength'] = 0.0
+        
+        # Calculate component scores (some require DataFrame context)
+        df['aggression_score'] = df.apply(self.calculate_aggression_score, axis=1)
+        df['size_concentration_score'] = df.apply(self.calculate_size_concentration_score, axis=1)
+        
+        # These require DataFrame context, so iterate
+        for idx in df.index:
+            df.at[idx, 'repeat_trade_velocity'] = self.calculate_repeat_trade_velocity(df, idx)
+            df.at[idx, 'strike_clustering_score'] = self.calculate_strike_clustering_score(df, idx)
+        
+        # Calculate composite signal_strength
+        df['signal_strength'] = df.apply(self.calculate_signal_strength, axis=1)
+        
+        logger.info(f"Signal component scoring complete. Mean signal_strength: {df['signal_strength'].mean():.2f}")
+        
+        return df
+
--- a/backend/python_service/services/time_sequenced_analyzer.py
+++ b/backend/python_service/services/time_sequenced_analyzer.py
@ -0,0 +1,319 @@
+"""
+Time-Sequenced Flow Analysis Service
+Analyzes flow patterns over time to detect urgency, distribution, and continuation signals
+"""
+import pandas as pd
+import numpy as np
+from typing import Dict, Optional
+from datetime import timedelta
+from utils.logger import logger
+
+
+class TimeSequencedAnalyzer:
+    """
+    Analyzes flow patterns over time to detect:
+    - Flow acceleration (urgency building)
+    - Distribution patterns (flow weakening)
+    - Strike laddering (sequential accumulation)
+    """
+    
+    def __init__(self):
+        # Configuration
+        self.analysis_window_minutes = 60  # Look back window for flow analysis
+        self.min_trades_for_analysis = 3  # Minimum trades needed for meaningful analysis
+        
+    def calculate_flow_acceleration(
+        self,
+        df: pd.DataFrame,
+        row_idx: int
+    ) -> Optional[float]:
+        """
+        Calculate flow acceleration: change in premium per minute.
+        Positive = accelerating (urgency building)
+        Negative = decelerating (flow weakening)
+        
+        Returns: Δ premium / minute (premium rate change)
+        """
+        if row_idx >= len(df):
+            return None
+        
+        current_row = df.iloc[row_idx]
+        symbol = current_row.get('symbol_norm')
+        direction = current_row.get('direction')
+        flow_ts_utc = current_row.get('flow_ts_utc')
+        
+        if pd.isna(symbol) or pd.isna(direction) or pd.isna(flow_ts_utc):
+            return None
+        
+        # Sort by timestamp
+        df_sorted = df.sort_values('flow_ts_utc').reset_index(drop=True)
+        
+        # Look at recent trades for same symbol and direction
+        window_start = flow_ts_utc - timedelta(minutes=self.analysis_window_minutes)
+        
+        mask = (
+            (df_sorted['symbol_norm'] == symbol) &
+            (df_sorted['direction'] == direction) &
+            (df_sorted['flow_ts_utc'] >= window_start) &
+            (df_sorted['flow_ts_utc'] <= flow_ts_utc)
+        )
+        
+        recent_trades = df_sorted[mask]
+        
+        if len(recent_trades) < self.min_trades_for_analysis:
+            return None
+        
+        # Calculate premium accumulation over time
+        recent_trades = recent_trades.sort_values('flow_ts_utc')
+        recent_trades = recent_trades.copy()
+        recent_trades['cumulative_premium'] = recent_trades['premium_num'].cumsum()
+        
+        # Fit linear trend to cumulative premium vs time
+        # Convert timestamps to minutes since window start
+        recent_trades['minutes_since_start'] = (
+            recent_trades['flow_ts_utc'] - window_start
+        ).dt.total_seconds() / 60.0
+        
+        # Calculate slope (premium per minute)
+        if len(recent_trades) >= 2:
+            x = recent_trades['minutes_since_start'].values
+            y = recent_trades['cumulative_premium'].values
+            
+            # Linear regression: y = mx + b, we want m (slope)
+            # Calculate acceleration as change in slope (second derivative approximation)
+            if len(recent_trades) >= 3:
+                # Split into two halves
+                mid_idx = len(recent_trades) // 2
+                first_half = recent_trades.iloc[:mid_idx]
+                second_half = recent_trades.iloc[mid_idx:]
+                
+                if len(first_half) >= 2 and len(second_half) >= 2:
+                    # Calculate slopes for each half
+                    x1 = first_half['minutes_since_start'].values
+                    y1 = first_half['cumulative_premium'].values
+                    slope1 = np.polyfit(x1, y1, 1)[0] if len(x1) > 1 else 0
+                    
+                    x2 = second_half['minutes_since_start'].values
+                    y2 = second_half['cumulative_premium'].values
+                    slope2 = np.polyfit(x2, y2, 1)[0] if len(x2) > 1 else 0
+                    
+                    # Acceleration = change in slope
+                    acceleration = slope2 - slope1
+                    return float(acceleration)
+        
+        return None
+    
+    def calculate_time_between_hits(
+        self,
+        df: pd.DataFrame,
+        row_idx: int
+    ) -> Optional[float]:
+        """
+        Calculate average time between consecutive trades (in minutes).
+        Lower = faster pace, higher urgency.
+        """
+        if row_idx >= len(df):
+            return None
+        
+        current_row = df.iloc[row_idx]
+        symbol = current_row.get('symbol_norm')
+        direction = current_row.get('direction')
+        flow_ts_utc = current_row.get('flow_ts_utc')
+        
+        if pd.isna(symbol) or pd.isna(direction) or pd.isna(flow_ts_utc):
+            return None
+        
+        # Sort by timestamp
+        df_sorted = df.sort_values('flow_ts_utc').reset_index(drop=True)
+        
+        # Look at recent trades
+        window_start = flow_ts_utc - timedelta(minutes=self.analysis_window_minutes)
+        
+        mask = (
+            (df_sorted['symbol_norm'] == symbol) &
+            (df_sorted['direction'] == direction) &
+            (df_sorted['flow_ts_utc'] >= window_start) &
+            (df_sorted['flow_ts_utc'] <= flow_ts_utc)
+        )
+        
+        recent_trades = df_sorted[mask].sort_values('flow_ts_utc')
+        
+        if len(recent_trades) < 2:
+            return None
+        
+        # Calculate time gaps between consecutive trades
+        time_gaps = []
+        prev_ts = None
+        for _, trade in recent_trades.iterrows():
+            ts = trade.get('flow_ts_utc')
+            if pd.notna(ts) and prev_ts:
+                gap_minutes = (ts - prev_ts).total_seconds() / 60.0
+                if gap_minutes > 0:
+                    time_gaps.append(gap_minutes)
+            prev_ts = ts
+        
+        if len(time_gaps) == 0:
+            return None
+        
+        avg_time_between = np.mean(time_gaps)
+        return float(avg_time_between)
+    
+    def calculate_follow_on_ratio(
+        self,
+        df: pd.DataFrame,
+        row_idx: int
+    ) -> Optional[float]:
+        """
+        Calculate follow-on ratio: fraction of trades in same direction after initial trade.
+        Higher = continuation, Lower = reversal/distribution.
+        
+        Returns: ratio of same-direction trades / total trades (0-1)
+        """
+        if row_idx >= len(df):
+            return None
+        
+        current_row = df.iloc[row_idx]
+        symbol = current_row.get('symbol_norm')
+        direction = current_row.get('direction')
+        flow_ts_utc = current_row.get('flow_ts_utc')
+        
+        if pd.isna(symbol) or pd.isna(direction) or pd.isna(flow_ts_utc):
+            return None
+        
+        # Sort by timestamp
+        df_sorted = df.sort_values('flow_ts_utc').reset_index(drop=True)
+        
+        # Look at trades after this one (within window)
+        window_end = flow_ts_utc + timedelta(minutes=self.analysis_window_minutes)
+        
+        mask = (
+            (df_sorted['symbol_norm'] == symbol) &
+            (df_sorted['flow_ts_utc'] > flow_ts_utc) &
+            (df_sorted['flow_ts_utc'] <= window_end)
+        )
+        
+        follow_on_trades = df_sorted[mask]
+        
+        if len(follow_on_trades) == 0:
+            return None
+        
+        # Count same-direction vs opposite-direction
+        same_direction = follow_on_trades[follow_on_trades['direction'] == direction]
+        follow_on_ratio = len(same_direction) / len(follow_on_trades) if len(follow_on_trades) > 0 else 0.0
+        
+        return float(follow_on_ratio)
+    
+    def detect_strike_laddering(
+        self,
+        df: pd.DataFrame,
+        row_idx: int
+    ) -> bool:
+        """
+        Detect strike laddering: sequential strikes in same direction.
+        Returns True if laddering pattern detected.
+        """
+        if row_idx >= len(df):
+            return False
+        
+        current_row = df.iloc[row_idx]
+        symbol = current_row.get('symbol_norm')
+        direction = current_row.get('direction')
+        cp_norm = current_row.get('cp_norm')
+        flow_ts_utc = current_row.get('flow_ts_utc')
+        exp_date = current_row.get('exp_date')
+        
+        if pd.isna(symbol) or pd.isna(direction) or pd.isna(cp_norm):
+            return False
+        
+        # Sort by timestamp
+        df_sorted = df.sort_values('flow_ts_utc').reset_index(drop=True)
+        
+        # Look at recent trades
+        window_start = flow_ts_utc - timedelta(minutes=self.analysis_window_minutes)
+        
+        mask = (
+            (df_sorted['symbol_norm'] == symbol) &
+            (df_sorted['direction'] == direction) &
+            (df_sorted['cp_norm'] == cp_norm) &
+            (df_sorted['flow_ts_utc'] >= window_start) &
+            (df_sorted['flow_ts_utc'] <= flow_ts_utc)
+        )
+        
+        if pd.notna(exp_date):
+            mask = mask & (df_sorted['exp_date'] == exp_date)
+        
+        recent_trades = df_sorted[mask].sort_values('flow_ts_utc')
+        
+        if len(recent_trades) < 3:
+            return False
+        
+        # Get unique strikes in order
+        strikes = recent_trades['strike_num'].dropna().unique()
+        strikes = sorted(strikes)
+        
+        if len(strikes) < 3:
+            return False
+        
+        # Check if strikes are sequential (laddering)
+        # Look for consistent spacing (e.g., 100, 105, 110 or 50, 55, 60)
+        strike_diffs = [strikes[i+1] - strikes[i] for i in range(len(strikes)-1)]
+        
+        if len(strike_diffs) >= 2:
+            # Check if differences are roughly equal (within 20% variance)
+            avg_diff = np.mean(strike_diffs)
+            if avg_diff > 0:
+                std_diff = np.std(strike_diffs)
+                cv = std_diff / avg_diff if avg_diff > 0 else float('inf')  # Coefficient of variation
+                
+                # Low coefficient of variation = regular spacing = laddering
+                if cv < 0.2:  # Less than 20% variation
+                    return True
+        
+        return False
+    
+    def enrich_with_time_sequenced_metrics(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Add time-sequenced flow metrics to DataFrame.
+        Adds: flow_acceleration, time_between_hits, follow_on_ratio, strike_laddering_detected
+        """
+        if df.empty:
+            return df
+        
+        df = df.copy()
+        
+        # Initialize columns
+        df['flow_acceleration'] = None
+        df['time_between_hits'] = None
+        df['follow_on_ratio'] = None
+        df['strike_laddering_detected'] = False
+        
+        # Sort by timestamp for proper analysis
+        df_sorted = df.sort_values('flow_ts_utc').reset_index(drop=True)
+        
+        # Calculate metrics for each row
+        for idx in df_sorted.index:
+            original_idx = df_sorted.index[idx]
+            
+            # Flow acceleration
+            acceleration = self.calculate_flow_acceleration(df_sorted, idx)
+            if acceleration is not None:
+                df.at[original_idx, 'flow_acceleration'] = float(acceleration)
+            
+            # Time between hits
+            time_between = self.calculate_time_between_hits(df_sorted, idx)
+            if time_between is not None:
+                df.at[original_idx, 'time_between_hits'] = float(time_between)
+            
+            # Follow-on ratio
+            follow_on = self.calculate_follow_on_ratio(df_sorted, idx)
+            if follow_on is not None:
+                df.at[original_idx, 'follow_on_ratio'] = float(follow_on)
+            
+            # Strike laddering
+            laddering = self.detect_strike_laddering(df_sorted, idx)
+            df.at[original_idx, 'strike_laddering_detected'] = bool(laddering)
+        
+        logger.info(f"Time-sequenced analysis complete. Strike laddering detected: {df['strike_laddering_detected'].sum()}")
+        
+        return df
+
--- a/backend/python_service/start.sh
+++ b/backend/python_service/start.sh
@ -3,7 +3,13 @@

 # Activate virtual environment if it exists
 if [ -d "venv" ]; then
+    # Try Windows Scripts path first (Git Bash on Windows)
+    if [ -f "venv/Scripts/activate" ]; then
+        source venv/Scripts/activate
+    # Fallback to Unix bin path (Linux/macOS)
+    elif [ -f "venv/bin/activate" ]; then
        source venv/bin/activate
+    fi
 fi

 # Start the service
--- a/backend/scripts/backtestNFLXPuts.js
+++ b/backend/scripts/backtestNFLXPuts.js
--- a/backend/scripts/init_signals.js
+++ b/backend/scripts/init_signals.js
@ -0,0 +1,35 @@
+import { rawQuery } from '../src/db.js';
+
+async function run() {
+  try {
+    await rawQuery(`
+      CREATE TABLE IF NOT EXISTS signals (
+        id SERIAL PRIMARY KEY,
+        ticker TEXT NOT NULL,
+        ts TIMESTAMPTZ NOT NULL,
+        lane TEXT NOT NULL,
+        score NUMERIC NOT NULL,
+        grade TEXT NOT NULL,
+        entry_price NUMERIC NOT NULL,
+        features JSONB NOT NULL,
+        regime JSONB NOT NULL,
+        data_age_sec INT,
+        resolved BOOLEAN DEFAULT FALSE,
+        intraday_move NUMERIC,
+        hit_2R_first BOOLEAN,
+        ret_5d NUMERIC,
+        ret_10d NUMERIC,
+        excess_3m NUMERIC,
+        excess_6m NUMERIC,
+        excess_12m NUMERIC
+      );
+      CREATE INDEX IF NOT EXISTS idx_signals_lane_grade ON signals(lane, grade, ts);
+    `);
+    console.log('Signals table initialized.');
+    process.exit(0);
+  } catch (err) {
+    console.error(err);
+    process.exit(1);
+  }
+}
+run();
--- a/backend/src/backtest/cache/AAPL_2y.json
+++ b/backend/src/backtest/cache/AAPL_2y.json
--- a/backend/src/backtest/cache/ABBV_2y.json
+++ b/backend/src/backtest/cache/ABBV_2y.json
--- a/backend/src/backtest/cache/ADBE_2y.json
+++ b/backend/src/backtest/cache/ADBE_2y.json
--- a/backend/src/backtest/cache/AMD_2y.json
+++ b/backend/src/backtest/cache/AMD_2y.json
--- a/backend/src/backtest/cache/AMZN_2y.json
+++ b/backend/src/backtest/cache/AMZN_2y.json
--- a/backend/src/backtest/cache/ARKK_2y.json
+++ b/backend/src/backtest/cache/ARKK_2y.json
--- a/backend/src/backtest/cache/AVGO_2y.json
+++ b/backend/src/backtest/cache/AVGO_2y.json
--- a/backend/src/backtest/cache/BAC_2y.json
+++ b/backend/src/backtest/cache/BAC_2y.json
--- a/backend/src/backtest/cache/CAT_2y.json
+++ b/backend/src/backtest/cache/CAT_2y.json
--- a/backend/src/backtest/cache/COIN_2y.json
+++ b/backend/src/backtest/cache/COIN_2y.json
--- a/backend/src/backtest/cache/COST_2y.json
+++ b/backend/src/backtest/cache/COST_2y.json
--- a/backend/src/backtest/cache/CRM_2y.json
+++ b/backend/src/backtest/cache/CRM_2y.json
--- a/backend/src/backtest/cache/CRWD_2y.json
+++ b/backend/src/backtest/cache/CRWD_2y.json
--- a/backend/src/backtest/cache/CVX_2y.json
+++ b/backend/src/backtest/cache/CVX_2y.json
--- a/backend/src/backtest/cache/DIA_2y.json
+++ b/backend/src/backtest/cache/DIA_2y.json
--- a/backend/src/backtest/cache/GLD_2y.json
+++ b/backend/src/backtest/cache/GLD_2y.json
--- a/backend/src/backtest/cache/GOOGL_2y.json
+++ b/backend/src/backtest/cache/GOOGL_2y.json
--- a/backend/src/backtest/cache/GS_2y.json
+++ b/backend/src/backtest/cache/GS_2y.json
--- a/backend/src/backtest/cache/HON_2y.json
+++ b/backend/src/backtest/cache/HON_2y.json
--- a/backend/src/backtest/cache/INTC_2y.json
+++ b/backend/src/backtest/cache/INTC_2y.json
--- a/backend/src/backtest/cache/IWM_2y.json
+++ b/backend/src/backtest/cache/IWM_2y.json
--- a/backend/src/backtest/cache/JNJ_2y.json
+++ b/backend/src/backtest/cache/JNJ_2y.json
--- a/backend/src/backtest/cache/JPM_2y.json
+++ b/backend/src/backtest/cache/JPM_2y.json
--- a/backend/src/backtest/cache/LLY_2y.json
+++ b/backend/src/backtest/cache/LLY_2y.json
--- a/backend/src/backtest/cache/MA_2y.json
+++ b/backend/src/backtest/cache/MA_2y.json
--- a/backend/src/backtest/cache/META_2y.json
+++ b/backend/src/backtest/cache/META_2y.json
--- a/backend/src/backtest/cache/MSFT_2y.json
+++ b/backend/src/backtest/cache/MSFT_2y.json
--- a/backend/src/backtest/cache/MSTR_2y.json
+++ b/backend/src/backtest/cache/MSTR_2y.json
--- a/backend/src/backtest/cache/MU_2y.json
+++ b/backend/src/backtest/cache/MU_2y.json
--- a/backend/src/backtest/cache/NFLX_2y.json
+++ b/backend/src/backtest/cache/NFLX_2y.json
--- a/backend/src/backtest/cache/NOW_2y.json
+++ b/backend/src/backtest/cache/NOW_2y.json
--- a/backend/src/backtest/cache/NVDA_2y.json
+++ b/backend/src/backtest/cache/NVDA_2y.json
--- a/backend/src/backtest/cache/ORCL_2y.json
+++ b/backend/src/backtest/cache/ORCL_2y.json
--- a/backend/src/backtest/cache/PFE_2y.json
+++ b/backend/src/backtest/cache/PFE_2y.json
--- a/backend/src/backtest/cache/PLTR_2y.json
+++ b/backend/src/backtest/cache/PLTR_2y.json
--- a/backend/src/backtest/cache/QCOM_2y.json
+++ b/backend/src/backtest/cache/QCOM_2y.json
--- a/backend/src/backtest/cache/QQQ_2y.json
+++ b/backend/src/backtest/cache/QQQ_2y.json
--- a/backend/src/backtest/cache/SNOW_2y.json
+++ b/backend/src/backtest/cache/SNOW_2y.json
--- a/backend/src/backtest/cache/SPY_2y.json
+++ b/backend/src/backtest/cache/SPY_2y.json
--- a/backend/src/backtest/cache/TLT_2y.json
+++ b/backend/src/backtest/cache/TLT_2y.json
--- a/backend/src/backtest/cache/TSLA_2y.json
+++ b/backend/src/backtest/cache/TSLA_2y.json
--- a/backend/src/backtest/cache/UNH_2y.json
+++ b/backend/src/backtest/cache/UNH_2y.json
--- a/backend/src/backtest/cache/V_2y.json
+++ b/backend/src/backtest/cache/V_2y.json
--- a/backend/src/backtest/cache/WFC_2y.json
+++ b/backend/src/backtest/cache/WFC_2y.json
--- a/backend/src/backtest/cache/WMT_2y.json
+++ b/backend/src/backtest/cache/WMT_2y.json
--- a/backend/src/backtest/cache/XLE_2y.json
+++ b/backend/src/backtest/cache/XLE_2y.json
--- a/backend/src/backtest/cache/XLF_2y.json
+++ b/backend/src/backtest/cache/XLF_2y.json
--- a/backend/src/backtest/cache/XLK_2y.json
+++ b/backend/src/backtest/cache/XLK_2y.json
--- a/backend/src/backtest/cache/XLV_2y.json
+++ b/backend/src/backtest/cache/XLV_2y.json
--- a/backend/src/backtest/cache/XOM_2y.json
+++ b/backend/src/backtest/cache/XOM_2y.json
--- a/backend/src/backtest/cache/^VIX_2y.json
+++ b/backend/src/backtest/cache/^VIX_2y.json
--- a/backend/src/backtest/config.js
+++ b/backend/src/backtest/config.js
@ -0,0 +1,16 @@
+export const LANES = {
+  swing: {
+    maxHoldDays: 10,
+    targetAtr: 2.0,
+    stopAtr: 1.0,
+    entry: 'next_open',
+    burnIn: 200,
+  },
+  overnight: {
+    maxHoldDays: 1,         // exit at close of day 1 (the entry day)
+    targetAtr: 1.0,         // +1 ATR (reachable intraday)
+    stopAtr: 1.0,           // -1 ATR
+    entry: 'next_open',     // signal at close[t], enter open[t+1]
+    burnIn: 200,
+  }
+};
--- a/backend/src/backtest/costs.js
+++ b/backend/src/backtest/costs.js
@ -0,0 +1,26 @@
+/**
+ * Apply slippage and spread costs
+ * @param {number} price - The raw price (e.g. open/close)
+ * @param {string} side - 'buy' or 'sell'
+ * @param {Array} candles - Full candles array
+ * @param {number} idx - Index of the candle to compute ADV from
+ * @param {number} orderDollar - Estimated order size (default $5000)
+ */
+export function applyCosts(price, side, candles, idx, orderDollar = 5000) {
+  // Compute ADV from the 10 days prior to idx, or default to 1M if not enough data
+  let advDollar = 1000000;
+  if (idx >= 10) {
+    let volSum = 0;
+    for (let i = idx - 10; i < idx; i++) {
+      volSum += (candles[i].volume || 0) * (candles[i].close || 0);
+    }
+    if (volSum > 0) advDollar = volSum / 10;
+  }
+
+  // tightest spread ~ 0.02%, scaling up for less liquid
+  const spreadPct = Math.max(0.0002, 0.01 / Math.sqrt(advDollar));
+  // size-aware slippage
+  const slippagePct = 0.0005 * Math.sqrt(orderDollar / advDollar); 
+  const drag = (spreadPct / 2) + slippagePct;
+  return side === 'buy' ? price * (1 + drag) : price * (1 - drag);
+}
--- a/backend/src/backtest/features.js
+++ b/backend/src/backtest/features.js
@ -0,0 +1,96 @@
+export function sma(candles, period) {
+  if (candles.length < period) return null;
+  const slice = candles.slice(-period);
+  return slice.reduce((a, c) => a + c.close, 0) / period;
+}
+
+export function atr14(candles, period = 14) {
+  if (!candles || candles.length <= period) return null;
+  const trs = [];
+  for (let i = 1; i < candles.length; i++) {
+    const { high, low } = candles[i];
+    const prevClose = candles[i - 1].close;
+    trs.push(Math.max(high - low, Math.abs(high - prevClose), Math.abs(low - prevClose)));
+  }
+  const recent = trs.slice(-period);
+  return recent.reduce((a, b) => a + b, 0) / recent.length;
+}
+
+export function rsi14(candles, period = 14) {
+  if (candles.length < period + 1) return null;
+  const recent = candles.slice(-period - 1);
+  let gains = 0, losses = 0;
+  for (let i = 1; i <= period; i++) {
+    const diff = recent[i].close - recent[i - 1].close;
+    if (diff >= 0) gains += diff;
+    else losses += Math.abs(diff);
+  }
+  if (losses === 0) return 100;
+  const rs = gains / losses;
+  return 100 - (100 / (1 + rs));
+}
+
+export function macd(candles, fast = 12, slow = 26, sig = 9) {
+  if (candles.length < slow + sig) return { macdLine: null, signalLine: null, hist: null };
+  const emaFast = sma(candles, fast); // simplified to SMA for backtest proxy
+  const emaSlow = sma(candles, slow);
+  const macdLine = emaFast - emaSlow;
+  // fake signal line for proxy (just using a small offset since we can't easily compute EMA of MACD over full history here without more state)
+  // Actually, let's just do a rough proxy
+  const signalLine = macdLine * 0.9; 
+  return { macdLine, signalLine, hist: macdLine - signalLine };
+}
+
+export function slope(candles, period = 50, slopeDays = 5) {
+  if (candles.length < period + slopeDays) return null;
+  const currentSma = sma(candles, period);
+  const oldSma = sma(candles.slice(0, candles.length - slopeDays), period);
+  return (currentSma - oldSma) / oldSma; // % change in SMA
+}
+
+export function volume(candles, period) {
+  if (candles.length < period) return null;
+  return candles.slice(-period).reduce((a, c) => a + c.volume, 0) / period;
+}
+
+export function avgVolume(candles, period) {
+  return volume(candles, period);
+}
+
+export function regimeScore(spyCandles, vixClose) {
+  if (!spyCandles || spyCandles.length < 50) return 0;
+  const spySma20 = sma(spyCandles, 20);
+  const spySma50 = sma(spyCandles, 50);
+  const spyClose = spyCandles[spyCandles.length - 1].close;
+  
+  if (spyClose > spySma20 && spyClose > spySma50 && vixClose < 20) return 1; // Risk-On
+  if (spyClose < spySma50 || vixClose > 25) return -1; // Risk-Off
+  return 0; // Neutral
+}
+
+/**
+ * Compute point-in-time features for the Swing lane.
+ * GUARANTEE: `candles` and `spyCandles` passed to this function must ALREADY
+ * be sliced to contain ONLY data up to and including the current day `t`.
+ */
+export function swingFeatures(candles, spyCandles, vix) {
+  const px = candles.at(-1).close;
+  const sma50 = sma(candles, 50);
+  const sma200 = sma(candles, 200);
+  const atr = atr14(candles);
+  const { macdLine, signalLine, hist } = macd(candles);
+  
+  if (!sma50 || !sma200 || !atr || macdLine === null) return null;
+
+  return {
+    dist50Atr: (px - sma50) / atr,
+    dist200Atr: (px - sma200) / atr,
+    sma50Slope: slope(candles, 50, 5),
+    macdHistNorm: hist / atr,
+    macdCrossUp: macdLine > signalLine ? 1 : 0,
+    rsi: rsi14(candles),
+    atrPct: (atr / px) * 100,
+    rvol: volume(candles, 1) / avgVolume(candles, 50),
+    regime: regimeScore(spyCandles, vix)
+  };
+}
--- a/backend/src/backtest/grades.js
+++ b/backend/src/backtest/grades.js
@ -0,0 +1,9 @@
+export function fitGradeThresholds(trainProbs) {
+  const sorted = [...trainProbs].sort((a, b) => a - b);
+  const q = p => sorted[Math.floor(p * sorted.length)];
+  return { aMin: q(0.80), bMin: q(0.60), cMin: q(0.40) }; // A=top20%, B=top40%, C=top60%
+}
+
+export function toGrade(prob, t) {
+  return prob >= t.aMin ? 'A' : prob >= t.bMin ? 'B' : prob >= t.cMin ? 'C' : 'D';
+}
--- a/backend/src/backtest/harness.js
+++ b/backend/src/backtest/harness.js
@ -0,0 +1,188 @@
+import fs from 'fs';
+import path from 'path';
+import { fileURLToPath } from 'url';
+import { UNIVERSE } from '../services/stockUniverseService.js';
+import { LANES } from './config.js';
+import { simulateSwing } from './outcomeSim.js';
+import { swingFeatures } from './features.js';
+import { StandardScaler } from './model/scaler.js';
+import { LogisticModel } from './model/logistic.js';
+import { fitGradeThresholds, toGrade } from './grades.js';
+import { generateResultsTable } from './report/resultsTable.js';
+import { calibration } from './report/calibration.js';
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = path.dirname(__filename);
+const CACHE_DIR = path.join(__dirname, 'cache');
+
+const BACKTEST_RANGE = '2y';
+const YF_BASE = 'https://query1.finance.yahoo.com';
+const BURN_IN_DAYS = 200; // Need 200 days for 200-SMA
+
+// Fetch historical data with caching
+async function fetchHistoricalData(symbol) {
+  const cachePath = path.join(CACHE_DIR, `${symbol}_${BACKTEST_RANGE}.json`);
+  if (fs.existsSync(cachePath)) {
+    return JSON.parse(fs.readFileSync(cachePath, 'utf8'));
+  }
+  console.log(`[Fetch] Downloading ${symbol} data...`);
+  const url = `${YF_BASE}/v8/finance/chart/${symbol}?interval=1d&range=${BACKTEST_RANGE}`;
+  const res = await fetch(url, { headers: { 'User-Agent': 'Mozilla/5.0' } });
+  if (!res.ok) return null;
+  const data = await res.json();
+  const result = data?.chart?.result?.[0];
+  if (result) fs.writeFileSync(cachePath, JSON.stringify(result));
+  return result;
+}
+
+function extractCandles(yfResult) {
+  const quotes = yfResult.indicators?.quote?.[0] || {};
+  const timestamps = yfResult.timestamp || [];
+  const candles = [];
+  for (let i = 0; i < timestamps.length; i++) {
+    if (quotes.high?.[i] != null && quotes.low?.[i] != null && quotes.close?.[i] != null && quotes.open?.[i] != null) {
+      candles.push({
+        ts: timestamps[i] * 1000,
+        date: new Date(timestamps[i] * 1000).toISOString().split('T')[0],
+        open: quotes.open[i],
+        high: quotes.high[i],
+        low: quotes.low[i],
+        close: quotes.close[i],
+        volume: quotes.volume?.[i] || 0
+      });
+    }
+  }
+  return candles;
+}
+
+async function runValidationGate() {
+  console.log(`\n======================================================`);
+  console.log(`   PATH C (SWING) VALIDATION GATE`);
+  console.log(`======================================================\n`);
+  
+  const cfg = LANES.swing;
+  const dataMap = new Map();
+  const symbols = ['SPY', '^VIX', ...UNIVERSE.map(s => s.symbol)];
+  
+  // 0. Load Data
+  process.stdout.write("Loading historical data... ");
+  for (const sym of symbols) {
+    const raw = await fetchHistoricalData(sym);
+    if (raw) dataMap.set(sym, extractCandles(raw));
+  }
+  console.log("Done.");
+
+  const spyCandles = dataMap.get('SPY');
+  const vixCandles = dataMap.get('^VIX');
+  
+  const allSignals = [];
+  
+  // 1. Generate ALL signals over full window
+  process.stdout.write("Generating signals (simulating multi-day paths)... ");
+  
+  for (let i = BURN_IN_DAYS; i < spyCandles.length - 1; i++) {
+    const currentSpyDate = spyCandles[i].date;
+    const currentVixDate = spyCandles[i].date; // approx
+    
+    const spySlice = spyCandles.slice(0, i + 1);
+    
+    // get VIX close
+    const vixSlice = vixCandles?.filter(c => c.date <= currentSpyDate);
+    const vixClose = vixSlice?.length ? vixSlice[vixSlice.length - 1].close : 15;
+
+    for (const u of UNIVERSE) {
+      const candles = dataMap.get(u.symbol);
+      if (!candles) continue;
+      
+      const stockIdx = candles.findIndex(c => c.date === currentSpyDate);
+      if (stockIdx === -1) continue;
+
+      const stockSlice = candles.slice(0, stockIdx + 1);
+      
+      // Compute point-in-time features (no lookahead!)
+      const features = swingFeatures(stockSlice, spySlice, vixClose);
+      if (!features) continue;
+
+      // Simulate outcome (multi-day) using the real entry index which is stockIdx + 1 (tomorrow's open)
+      const entryIdx = stockIdx + 1; 
+      const result = simulateSwing(candles, entryIdx, cfg);
+      
+      if (result) {
+        allSignals.push({
+          date: currentSpyDate, // The day the signal fired (market close)
+          symbol: u.symbol,
+          features,
+          outcome: result.outcome,
+          rMultiple: result.r
+        });
+      }
+    }
+  }
+  console.log(`Done. (${allSignals.length} signals generated)`);
+
+  if (allSignals.length === 0) {
+    console.error("No signals generated!");
+    return;
+  }
+
+  // 2. SPLIT: train = first 12 months, test = last 12 months
+  // We'll split roughly down the middle of the available dates
+  const uniqueDates = [...new Set(allSignals.map(s => s.date))].sort();
+  const splitDate = uniqueDates[Math.floor(uniqueDates.length / 2)];
+  
+  const trainSet = allSignals.filter(s => s.date < splitDate);
+  const testSet = allSignals.filter(s => s.date >= splitDate);
+  
+  console.log(`Split Date: ${splitDate}`);
+  console.log(`TRAIN Set: ${trainSet.length} signals`);
+  console.log(`TEST Set:  ${testSet.length} signals`);
+
+  // 3. scaler.fit(train.features)
+  const scaler = new StandardScaler();
+  scaler.fit(trainSet.map(s => s.features));
+  
+  // 4. model = logistic.fit(scaler.transform(train.features), train.win)
+  const model = new LogisticModel();
+  const trainFeaturesScaled = scaler.transformArray(trainSet.map(s => s.features));
+  const trainLabels = trainSet.map(s => s.outcome === 'WIN' ? 1 : 0);
+  
+  const coeffs = model.fit(trainFeaturesScaled, trainLabels);
+  
+  console.log(`\n=== MODEL FIT (Standardized Coefficients) ===`);
+  console.log(`Intercept:      ${coeffs.intercept.toFixed(4)}`);
+  for (const k of Object.keys(coeffs)) {
+    if (k !== 'intercept') console.log(`${k.padEnd(15)} ${coeffs[k].toFixed(4)}`);
+  }
+
+  // 5. thresholds = fitGradeThresholds(model.predict(train))
+  const trainProbs = model.predict(trainFeaturesScaled);
+  const thresholds = fitGradeThresholds(trainProbs);
+  
+  console.log(`\n=== GRADE THRESHOLDS (FROZEN ON TRAIN) ===`);
+  console.log(`Grade A min P(win): ${(thresholds.aMin*100).toFixed(1)}%`);
+  console.log(`Grade B min P(win): ${(thresholds.bMin*100).toFixed(1)}%`);
+  console.log(`Grade C min P(win): ${(thresholds.cMin*100).toFixed(1)}%`);
+
+  // ===================== NO TRAIN DATA PAST THIS POINT =====================
+
+  // 6. test.prob = model.predict(scaler.transform(test.features))
+  const testFeaturesScaled = scaler.transformArray(testSet.map(s => s.features));
+  const testProbs = model.predict(testFeaturesScaled);
+
+  for (let i = 0; i < testSet.length; i++) {
+    testSet[i].prob = testProbs[i];
+    testSet[i].grade = toGrade(testProbs[i], thresholds);
+  }
+
+  // 7. resultsTable(test)
+  generateResultsTable(testSet);
+
+  // 8. calibration(test)
+  calibration(testSet);
+  
+  console.log(`\n======================================================`);
+  console.log(`   VALIDATION GATE COMPLETE`);
+  console.log(`======================================================\n`);
+}
+
+runValidationGate().catch(console.error);
--- a/backend/src/backtest/harness_overnight.js
+++ b/backend/src/backtest/harness_overnight.js
@ -0,0 +1,188 @@
+import fs from 'fs';
+import path from 'path';
+import { fileURLToPath } from 'url';
+import { UNIVERSE } from '../services/stockUniverseService.js';
+import { LANES } from './config.js';
+import { simulateSwing } from './outcomeSim.js';
+import { swingFeatures } from './features.js';
+import { StandardScaler } from './model/scaler.js';
+import { LogisticModel } from './model/logistic.js';
+import { fitGradeThresholds, toGrade } from './grades.js';
+import { generateResultsTable } from './report/resultsTable.js';
+import { calibration } from './report/calibration.js';
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = path.dirname(__filename);
+const CACHE_DIR = path.join(__dirname, 'cache');
+
+const BACKTEST_RANGE = '2y';
+const YF_BASE = 'https://query1.finance.yahoo.com';
+const BURN_IN_DAYS = 200; // Need 200 days for 200-SMA
+
+// Fetch historical data with caching
+async function fetchHistoricalData(symbol) {
+  const cachePath = path.join(CACHE_DIR, `${symbol}_${BACKTEST_RANGE}.json`);
+  if (fs.existsSync(cachePath)) {
+    return JSON.parse(fs.readFileSync(cachePath, 'utf8'));
+  }
+  console.log(`[Fetch] Downloading ${symbol} data...`);
+  const url = `${YF_BASE}/v8/finance/chart/${symbol}?interval=1d&range=${BACKTEST_RANGE}`;
+  const res = await fetch(url, { headers: { 'User-Agent': 'Mozilla/5.0' } });
+  if (!res.ok) return null;
+  const data = await res.json();
+  const result = data?.chart?.result?.[0];
+  if (result) fs.writeFileSync(cachePath, JSON.stringify(result));
+  return result;
+}
+
+function extractCandles(yfResult) {
+  const quotes = yfResult.indicators?.quote?.[0] || {};
+  const timestamps = yfResult.timestamp || [];
+  const candles = [];
+  for (let i = 0; i < timestamps.length; i++) {
+    if (quotes.high?.[i] != null && quotes.low?.[i] != null && quotes.close?.[i] != null && quotes.open?.[i] != null) {
+      candles.push({
+        ts: timestamps[i] * 1000,
+        date: new Date(timestamps[i] * 1000).toISOString().split('T')[0],
+        open: quotes.open[i],
+        high: quotes.high[i],
+        low: quotes.low[i],
+        close: quotes.close[i],
+        volume: quotes.volume?.[i] || 0
+      });
+    }
+  }
+  return candles;
+}
+
+async function runValidationGate() {
+  console.log(`\n======================================================`);
+  console.log(`   PATH B (OVERNIGHT) VALIDATION GATE`);
+  console.log(`======================================================\n`);
+  
+  const cfg = LANES.overnight;
+  const dataMap = new Map();
+  const symbols = ['SPY', '^VIX', ...UNIVERSE.map(s => s.symbol)];
+  
+  // 0. Load Data
+  process.stdout.write("Loading historical data... ");
+  for (const sym of symbols) {
+    const raw = await fetchHistoricalData(sym);
+    if (raw) dataMap.set(sym, extractCandles(raw));
+  }
+  console.log("Done.");
+
+  const spyCandles = dataMap.get('SPY');
+  const vixCandles = dataMap.get('^VIX');
+  
+  const allSignals = [];
+  
+  // 1. Generate ALL signals over full window
+  process.stdout.write("Generating signals (simulating multi-day paths)... ");
+  
+  for (let i = BURN_IN_DAYS; i < spyCandles.length - 1; i++) {
+    const currentSpyDate = spyCandles[i].date;
+    const currentVixDate = spyCandles[i].date; // approx
+    
+    const spySlice = spyCandles.slice(0, i + 1);
+    
+    // get VIX close
+    const vixSlice = vixCandles?.filter(c => c.date <= currentSpyDate);
+    const vixClose = vixSlice?.length ? vixSlice[vixSlice.length - 1].close : 15;
+
+    for (const u of UNIVERSE) {
+      const candles = dataMap.get(u.symbol);
+      if (!candles) continue;
+      
+      const stockIdx = candles.findIndex(c => c.date === currentSpyDate);
+      if (stockIdx === -1) continue;
+
+      const stockSlice = candles.slice(0, stockIdx + 1);
+      
+      // Compute point-in-time features (no lookahead!)
+      const features = swingFeatures(stockSlice, spySlice, vixClose);
+      if (!features) continue;
+
+      // Simulate outcome (multi-day) using the real entry index which is stockIdx + 1 (tomorrow's open)
+      const entryIdx = stockIdx + 1; 
+      const result = simulateSwing(candles, entryIdx, cfg);
+      
+      if (result) {
+        allSignals.push({
+          date: currentSpyDate, // The day the signal fired (market close)
+          symbol: u.symbol,
+          features,
+          outcome: result.outcome,
+          rMultiple: result.r
+        });
+      }
+    }
+  }
+  console.log(`Done. (${allSignals.length} signals generated)`);
+
+  if (allSignals.length === 0) {
+    console.error("No signals generated!");
+    return;
+  }
+
+  // 2. SPLIT: train = first 12 months, test = last 12 months
+  // We'll split roughly down the middle of the available dates
+  const uniqueDates = [...new Set(allSignals.map(s => s.date))].sort();
+  const splitDate = uniqueDates[Math.floor(uniqueDates.length / 2)];
+  
+  const trainSet = allSignals.filter(s => s.date < splitDate);
+  const testSet = allSignals.filter(s => s.date >= splitDate);
+  
+  console.log(`Split Date: ${splitDate}`);
+  console.log(`TRAIN Set: ${trainSet.length} signals`);
+  console.log(`TEST Set:  ${testSet.length} signals`);
+
+  // 3. scaler.fit(train.features)
+  const scaler = new StandardScaler();
+  scaler.fit(trainSet.map(s => s.features));
+  
+  // 4. model = logistic.fit(scaler.transform(train.features), train.win)
+  const model = new LogisticModel();
+  const trainFeaturesScaled = scaler.transformArray(trainSet.map(s => s.features));
+  const trainLabels = trainSet.map(s => s.outcome === 'WIN' ? 1 : 0);
+  
+  const coeffs = model.fit(trainFeaturesScaled, trainLabels);
+  
+  console.log(`\n=== MODEL FIT (Standardized Coefficients) ===`);
+  console.log(`Intercept:      ${coeffs.intercept.toFixed(4)}`);
+  for (const k of Object.keys(coeffs)) {
+    if (k !== 'intercept') console.log(`${k.padEnd(15)} ${coeffs[k].toFixed(4)}`);
+  }
+
+  // 5. thresholds = fitGradeThresholds(model.predict(train))
+  const trainProbs = model.predict(trainFeaturesScaled);
+  const thresholds = fitGradeThresholds(trainProbs);
+  
+  console.log(`\n=== GRADE THRESHOLDS (FROZEN ON TRAIN) ===`);
+  console.log(`Grade A min P(win): ${(thresholds.aMin*100).toFixed(1)}%`);
+  console.log(`Grade B min P(win): ${(thresholds.bMin*100).toFixed(1)}%`);
+  console.log(`Grade C min P(win): ${(thresholds.cMin*100).toFixed(1)}%`);
+
+  // ===================== NO TRAIN DATA PAST THIS POINT =====================
+
+  // 6. test.prob = model.predict(scaler.transform(test.features))
+  const testFeaturesScaled = scaler.transformArray(testSet.map(s => s.features));
+  const testProbs = model.predict(testFeaturesScaled);
+
+  for (let i = 0; i < testSet.length; i++) {
+    testSet[i].prob = testProbs[i];
+    testSet[i].grade = toGrade(testProbs[i], thresholds);
+  }
+
+  // 7. resultsTable(test)
+  generateResultsTable(testSet);
+
+  // 8. calibration(test)
+  calibration(testSet);
+  
+  console.log(`\n======================================================`);
+  console.log(`   VALIDATION GATE COMPLETE`);
+  console.log(`======================================================\n`);
+}
+
+runValidationGate().catch(console.error);
--- a/backend/src/backtest/model/logistic.js
+++ b/backend/src/backtest/model/logistic.js
@ -0,0 +1,153 @@
+import fs from 'fs';
+
+function sigmoid(z) {
+  // Cap z to avoid overflow
+  if (z > 20) return 1.0;
+  if (z < -20) return 0.0;
+  return 1 / (1 + Math.exp(-z));
+}
+
+function computeLogLoss(features, labels, weights) {
+  let loss = 0;
+  for (let i = 0; i < features.length; i++) {
+    const f = features[i];
+    let z = weights[0];
+    for (let j = 0; j < f.length; j++) z += weights[j + 1] * f[j];
+    const p = sigmoid(z);
+    // clip p to avoid log(0)
+    const pSafe = Math.max(1e-15, Math.min(1 - 1e-15, p));
+    loss += -labels[i] * Math.log(pSafe) - (1 - labels[i]) * Math.log(1 - pSafe);
+  }
+  return loss / features.length;
+}
+
+export class LogisticModel {
+  constructor() {
+    this.weights = null;
+    this.featureKeys = [];
+  }
+
+  // Internal full-batch gradient descent
+  _train(X, Y, lambda, learningRate, maxSteps, tol) {
+    const numFeatures = X[0].length;
+    let w = new Array(numFeatures + 1).fill(0.0);
+    const N = X.length;
+
+    let prevLoss = Infinity;
+
+    for (let step = 0; step < maxSteps; step++) {
+      const grad = new Array(numFeatures + 1).fill(0.0);
+      
+      for (let i = 0; i < N; i++) {
+        let z = w[0];
+        for (let j = 0; j < numFeatures; j++) z += w[j + 1] * X[i][j];
+        
+        const p = sigmoid(z);
+        const err = p - Y[i];
+        
+        grad[0] += err;
+        for (let j = 0; j < numFeatures; j++) {
+          grad[j + 1] += err * X[i][j];
+        }
+      }
+
+      // Add L2 penalty (skip intercept grad[0])
+      for (let j = 0; j < numFeatures; j++) {
+        grad[j + 1] += lambda * w[j + 1];
+      }
+
+      // Update weights
+      let maxDelta = 0;
+      for (let j = 0; j < w.length; j++) {
+        const update = (learningRate * grad[j]) / N;
+        w[j] -= update;
+        if (Math.abs(update) > maxDelta) maxDelta = Math.abs(update);
+      }
+
+      if (maxDelta < tol) {
+        // Converged
+        break;
+      }
+    }
+    return w;
+  }
+
+  fit(featuresArray, labels) {
+    if (!featuresArray.length) return;
+    this.featureKeys = Object.keys(featuresArray[0]);
+    
+    // Convert to 2D array
+    const X = featuresArray.map(f => this.featureKeys.map(k => f[k]));
+    const Y = labels;
+    const N = X.length;
+
+    // 1. Carve a 80/20 validation slice from TRAIN to tune lambda
+    const splitIdx = Math.floor(N * 0.8);
+    const X_train = X.slice(0, splitIdx);
+    const Y_train = Y.slice(0, splitIdx);
+    const X_val = X.slice(splitIdx);
+    const Y_val = Y.slice(splitIdx);
+
+    // 2. Tune lambda
+    const lambdas = [0.01, 0.1, 1.0, 10.0, 100.0, 500.0, 1000.0];
+    let bestLambda = 0;
+    let bestLoss = Infinity;
+    
+    console.log(`\nTuning L2 Regularization (Lambda) on Validation Slice...`);
+    for (const l of lambdas) {
+      const w = this._train(X_train, Y_train, l, 0.5, 2000, 1e-5);
+      const loss = computeLogLoss(X_val, Y_val, w);
+      console.log(`  Lambda ${l.toString().padEnd(6)} -> LogLoss: ${loss.toFixed(4)}`);
+      if (loss < bestLoss) {
+        bestLoss = loss;
+        bestLambda = l;
+      }
+    }
+    
+    console.log(`Selected Best Lambda: ${bestLambda}`);
+
+    // 3. Refit on FULL TRAIN set using best lambda
+    this.weights = this._train(X, Y, bestLambda, 0.5, 5000, 1e-5);
+
+    return this.getCoefficients();
+  }
+
+  predict(featuresArray) {
+    if (!this.weights) throw new Error("Model not trained");
+    
+    const probs = [];
+    for (let i = 0; i < featuresArray.length; i++) {
+      const f = featuresArray[i];
+      let z = this.weights[0]; // intercept
+      for (let j = 0; j < this.featureKeys.length; j++) {
+        z += this.weights[j + 1] * f[this.featureKeys[j]];
+      }
+      probs.push(sigmoid(z));
+    }
+    
+    return probs;
+  }
+
+  getCoefficients() {
+    if (!this.weights) return {};
+    const coeffs = { intercept: this.weights[0] };
+    for (let i = 0; i < this.featureKeys.length; i++) {
+      coeffs[this.featureKeys[i]] = this.weights[i + 1];
+    }
+    return coeffs;
+  }
+
+  save(filepath) {
+    fs.writeFileSync(filepath, JSON.stringify({
+      featureKeys: this.featureKeys,
+      weights: this.weights
+    }, null, 2));
+  }
+
+  load(filepath) {
+    if (!fs.existsSync(filepath)) return;
+    const data = JSON.parse(fs.readFileSync(filepath, 'utf8'));
+    this.featureKeys = data.featureKeys;
+    this.weights = data.weights;
+  }
+}
--- a/backend/src/backtest/model/scaler.js
+++ b/backend/src/backtest/model/scaler.js
@ -0,0 +1,51 @@
+import fs from 'fs';
+import path from 'path';
+
+export class StandardScaler {
+  constructor() {
+    this.means = {};
+    this.stds = {};
+  }
+
+  fit(featuresArray) {
+    if (!featuresArray.length) return;
+    
+    // Get all feature keys
+    const keys = Object.keys(featuresArray[0]);
+
+    for (const key of keys) {
+      const values = featuresArray.map(f => f[key]);
+      const mean = values.reduce((sum, v) => sum + v, 0) / values.length;
+      
+      const variance = values.reduce((sum, v) => sum + Math.pow(v - mean, 2), 0) / values.length;
+      const std = Math.sqrt(variance);
+
+      this.means[key] = mean;
+      this.stds[key] = std || 1; // Avoid divide by 0
+    }
+  }
+
+  transform(features) {
+    const scaled = {};
+    for (const key of Object.keys(this.means)) {
+      if (features[key] === undefined) continue;
+      scaled[key] = (features[key] - this.means[key]) / this.stds[key];
+    }
+    return scaled;
+  }
+
+  transformArray(featuresArray) {
+    return featuresArray.map(f => this.transform(f));
+  }
+
+  save(filepath) {
+    fs.writeFileSync(filepath, JSON.stringify({ means: this.means, stds: this.stds }, null, 2));
+  }
+
+  load(filepath) {
+    if (!fs.existsSync(filepath)) return;
+    const data = JSON.parse(fs.readFileSync(filepath, 'utf8'));
+    this.means = data.means;
+    this.stds = data.stds;
+  }
+}
--- a/backend/src/backtest/model_weights.json
+++ b/backend/src/backtest/model_weights.json
@ -0,0 +1,36 @@
+{
+  "name": "LogisticRegression",
+  "numSteps": 1000,
+  "learningRate": 0.05,
+  "numberClasses": 2,
+  "classifiers": [
+    {
+      "numSteps": 1000,
+      "learningRate": 0.05,
+      "weights": [
+        [
+          -94.4420175451543,
+          -5.766427757134977,
+          -74.50180063587948,
+          -66.23257473781456,
+          -1817.0926888106162,
+          -166.35311896181236
+        ]
+      ]
+    },
+    {
+      "numSteps": 1000,
+      "learningRate": 0.05,
+      "weights": [
+        [
+          -84.54027063090014,
+          -50.36483530223822,
+          13.904576636453635,
+          37.27497755449738,
+          -395.0000542460034,
+          41.80818011695695
+        ]
+      ]
+    }
+  ]
+}
--- a/backend/src/backtest/noLookaheadTest.js
+++ b/backend/src/backtest/noLookaheadTest.js
@ -0,0 +1,46 @@
+import { swingFeatures } from './features.js';
+
+function runTest() {
+  console.log("Running no-lookahead guardrail test...");
+  
+  // Create mock candles where index = day
+  const candles = [];
+  for (let i = 0; i < 250; i++) {
+    candles.push({
+      date: `2024-01-${i}`,
+      open: 100 + i,
+      high: 105 + i,
+      low: 95 + i,
+      close: 102 + i,
+      volume: 1000000
+    });
+  }
+
+  const spyCandles = [...candles]; // clone
+  const vix = 15;
+
+  // We are at day index 210. 
+  const currentDayIdx = 210;
+  
+  // Guardrail: Pass ONLY the slice up to current day
+  const slicedCandles = candles.slice(0, currentDayIdx + 1);
+  const slicedSpy = spyCandles.slice(0, currentDayIdx + 1);
+
+  // Compute features
+  const feats = swingFeatures(slicedCandles, slicedSpy, vix);
+
+  if (!feats) {
+    throw new Error("Features returned null unexpectedly.");
+  }
+
+  // The slice literally does not contain any future data in memory.
+  // We can verify the length of the array inside the feature to be absolutely sure.
+  if (slicedCandles.length > currentDayIdx + 1) {
+    throw new Error("Lookahead leak: slicedCandles contains future data!");
+  }
+
+  console.log("PASS: Features successfully computed without lookahead bias.");
+  console.log("Computed Features:", feats);
+}
+
+runTest();
--- a/backend/src/backtest/outcomeSim.js
+++ b/backend/src/backtest/outcomeSim.js
@ -0,0 +1,44 @@
+import { applyCosts } from './costs.js';
+
+function atr14(candles, period = 14) {
+  if (!candles || candles.length <= period) return null;
+  const trs = [];
+  for (let i = 1; i < candles.length; i++) {
+    const { high, low } = candles[i];
+    const prevClose = candles[i - 1].close;
+    trs.push(Math.max(high - low, Math.abs(high - prevClose), Math.abs(low - prevClose)));
+  }
+  const recent = trs.slice(-period);
+  return recent.reduce((a, b) => a + b, 0) / recent.length;
+}
+
+export function simulateSwing(candles, entryIdx, cfg) {
+  // If entry is impossible (out of bounds)
+  if (entryIdx >= candles.length) return null;
+
+  const rawEntry = candles[entryIdx].open;
+  const entry = applyCosts(rawEntry, 'buy', candles, entryIdx);
+  
+  // ATR known at signal (close of previous day). candles slice goes UP TO the entryIdx (so it includes the signal bar, which is entryIdx - 1)
+  // Wait, if entryIdx is the open of tomorrow, we pass candles.slice(0, entryIdx) so the last candle is today (the signal bar).
+  const preEntryCandles = candles.slice(0, entryIdx);
+  const atr = atr14(preEntryCandles);
+  if (!atr) return null;
+
+  const stop = entry - cfg.stopAtr * atr;
+  const target = entry + cfg.targetAtr * atr;
+  const R = entry - stop; // 1 ATR worth of price
+
+  const end = Math.min(entryIdx + cfg.maxHoldDays - 1, candles.length - 1);
+  
+  for (let d = entryIdx; d <= end; d++) {
+    // pessimistic tie-break: if both hit same day, assume STOP first
+    if (candles[d].low <= stop) return { outcome: 'LOSS', r: -1.0, exitDay: d, stopPrice: stop, targetPrice: target, atr };
+    if (candles[d].high >= target) return { outcome: 'WIN', r: cfg.targetAtr, exitDay: d, stopPrice: stop, targetPrice: target, atr };
+  }
+  
+  // unresolved → exit at close of last day, net of exit costs
+  const rawExit = candles[end].close;
+  const exit = applyCosts(rawExit, 'sell', candles, end);
+  return { outcome: 'SCRATCH', r: (exit - entry) / R, exitDay: end, stopPrice: stop, targetPrice: target, atr };
+}
--- a/backend/src/backtest/report/calibration.js
+++ b/backend/src/backtest/report/calibration.js
@ -0,0 +1,31 @@
+export function calibration(testResults) {
+  // We bucket predicted probabilities into deciles (0-10%, 10-20%, etc)
+  const buckets = Array(10).fill(null).map(() => ({ total: 0, wins: 0, sumProb: 0 }));
+
+  for (const r of testResults) {
+    if (r.prob === undefined || r.prob === null) continue;
+    let bIdx = Math.floor(r.prob * 10);
+    if (bIdx > 9) bIdx = 9; // Handle p=1.0 edge case
+    if (bIdx < 0) bIdx = 0;
+    
+    buckets[bIdx].total++;
+    buckets[bIdx].sumProb += r.prob;
+    if (r.outcome === 'WIN') buckets[bIdx].wins++;
+  }
+
+  console.log(`\n=== CALIBRATION TABLE ===`);
+  console.log(`Bucket\t\tN\tPred P(Win)\tActual Win%\tDiff`);
+  console.log(`------------------------------------------------------------------`);
+  
+  for (let i = 0; i < 10; i++) {
+    const b = buckets[i];
+    if (b.total === 0) continue;
+    
+    const label = `${(i * 10).toString().padStart(2, '0')}-${((i + 1) * 10).toString().padStart(2, '0')}%`;
+    const predP = (b.sumProb / b.total) * 100;
+    const actP = (b.wins / b.total) * 100;
+    const diff = actP - predP;
+    
+    console.log(`[${label}]\t${b.total}\t${predP.toFixed(1)}%\t\t${actP.toFixed(1)}%\t\t${diff > 0 ? '+' : ''}${diff.toFixed(1)}pp`);
+  }
+}
--- a/backend/src/backtest/report/resultsTable.js
+++ b/backend/src/backtest/report/resultsTable.js
@ -0,0 +1,75 @@
+export function ciExpectancy(rValues) {
+  const n = rValues.length;
+  if (n === 0) return { mean: 0, lo: 0, hi: 0, n: 0 };
+  
+  const mean = rValues.reduce((a, b) => a + b, 0) / n;
+  
+  if (n === 1) return { mean, lo: mean, hi: mean, n };
+
+  const variance = rValues.reduce((a, b) => a + Math.pow(b - mean, 2), 0) / (n - 1);
+  const sd = Math.sqrt(variance);
+  const se = sd / Math.sqrt(n);
+  
+  return { mean, lo: mean - 1.96 * se, hi: mean + 1.96 * se, n };
+}
+
+export function generateResultsTable(testResults) {
+  const grades = ['A', 'B', 'C', 'D'];
+  const grouped = {
+    A: { wins: 0, losses: 0, scratches: 0, rVals: [] },
+    B: { wins: 0, losses: 0, scratches: 0, rVals: [] },
+    C: { wins: 0, losses: 0, scratches: 0, rVals: [] },
+    D: { wins: 0, losses: 0, scratches: 0, rVals: [] }
+  };
+  
+  for (const r of testResults) {
+    if (grouped[r.grade]) {
+      grouped[r.grade].rVals.push(r.rMultiple);
+      if (r.outcome === 'WIN') grouped[r.grade].wins++;
+      else if (r.outcome === 'LOSS') grouped[r.grade].losses++;
+      else grouped[r.grade].scratches++;
+    }
+  }
+
+  console.log(`\n=== OUT-OF-SAMPLE TEST RESULTS (EXPECTANCY) ===`);
+  console.log(`Grade\tExp(R)\t\t95% CI (Exp)\t\tWin%\tLoss%\tScratch%\tn`);
+  console.log(`-----------------------------------------------------------------------------------------`);
+
+  for (const g of grades) {
+    const st = grouped[g];
+    const n = st.rVals.length;
+    if (n === 0) continue;
+    
+    const pWin = (st.wins / n) * 100;
+    const pLoss = (st.losses / n) * 100;
+    const pScratch = (st.scratches / n) * 100;
+    
+    const ci = ciExpectancy(st.rVals);
+    
+    const meanFmt = `${ci.mean > 0 ? '+' : ''}${ci.mean.toFixed(3)}R`;
+    const ciFmt = `[${ci.lo.toFixed(3)}, ${ci.hi.toFixed(3)}]`;
+    
+    console.log(`${g}\t${meanFmt}\t\t${ciFmt}\t\t${pWin.toFixed(1)}%\t${pLoss.toFixed(1)}%\t${pScratch.toFixed(1)}%\t\t${n}`);
+  }
+
+  // Random Control = The entire test set
+  const allR = testResults.map(r => r.rMultiple);
+  const totalN = allR.length;
+  if (totalN > 0) {
+    const totalWins = testResults.filter(r => r.outcome === 'WIN').length;
+    const totalLosses = testResults.filter(r => r.outcome === 'LOSS').length;
+    const totalScratches = testResults.filter(r => r.outcome === 'SCRATCH').length;
+
+    const ciRand = ciExpectancy(allR);
+    const meanFmt = `${ciRand.mean > 0 ? '+' : ''}${ciRand.mean.toFixed(3)}R`;
+    const ciFmt = `[${ciRand.lo.toFixed(3)}, ${ciRand.hi.toFixed(3)}]`;
+    
+    const pWin = (totalWins / totalN) * 100;
+    const pLoss = (totalLosses / totalN) * 100;
+    const pScratch = (totalScratches / totalN) * 100;
+
+    console.log(`Rand\t${meanFmt}\t\t${ciFmt}\t\t${pWin.toFixed(1)}%\t${pLoss.toFixed(1)}%\t${pScratch.toFixed(1)}%\t\t${totalN}`);
+  }
+
+  console.log(`-----------------------------------------------------------------------------------------`);
+}
--- a/backend/src/backtest/results.csv
+++ b/backend/src/backtest/results.csv
--- a/backend/src/backtest/train_logistic.js
+++ b/backend/src/backtest/train_logistic.js
@ -0,0 +1,68 @@
+import fs from 'fs';
+import path from 'path';
+import { fileURLToPath } from 'url';
+import { parse } from 'csv-parse/sync';
+import LogisticRegression from 'ml-logistic-regression';
+import { Matrix } from 'ml-matrix';
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = path.dirname(__filename);
+
+async function train() {
+  console.log('Loading results.csv...');
+  const csvPath = path.join(__dirname, 'results.csv');
+  const fileContent = fs.readFileSync(csvPath, 'utf8');
+  
+  const records = parse(fileContent, {
+    columns: true,
+    skip_empty_lines: true
+  });
+
+  const X = [];
+  const Y = [];
+
+  for (const row of records) {
+    if (row.type !== 'signal') continue; // Skip controls if they exist
+    
+    // Parse features
+    const rsi = parseFloat(row.rsi);
+    const atrPct = parseFloat(row.atrPct);
+    const rvol = parseFloat(row.rvol);
+    const macd = parseFloat(row.macd);
+    const regime = parseFloat(row.regime_risk_on);
+    
+    // Skip rows with missing data
+    if (isNaN(rsi) || isNaN(atrPct) || isNaN(rvol) || isNaN(macd) || isNaN(regime)) continue;
+
+    X.push([
+      1.0, // Intercept
+      rsi / 100,
+      atrPct / 10,
+      rvol / 5,
+      macd / 2,
+      regime
+    ]);
+
+    // Target: 1 if win or win_partial, 0 otherwise
+    const isWin = (row.outcome === 'win' || row.outcome === 'win_partial') ? 1 : 0;
+    Y.push(isWin);
+  }
+
+  console.log(`Loaded ${X.length} valid training samples.`);
+
+  const xMat = new Matrix(X);
+  const yMat = Matrix.columnVector(Y);
+
+  console.log('Training Logistic Regression model...');
+  const logreg = new LogisticRegression({ numSteps: 1000, learningRate: 0.05 });
+  logreg.train(xMat, yMat);
+
+  const modelJson = logreg.toJSON();
+  
+  const outputPath = path.join(__dirname, 'model_weights.json');
+  fs.writeFileSync(outputPath, JSON.stringify(modelJson, null, 2));
+  
+  console.log(`Model trained and saved to ${outputPath}`);
+}
+
+train().catch(console.error);
--- a/backend/src/factorlab/tests/lookahead.test.js
+++ b/backend/src/factorlab/tests/lookahead.test.js
@ -0,0 +1,60 @@
+import { loadFundamentals, getFundamentalsAsOf } from '../fundamentals.js';
+
+function runTest() {
+  console.log("Running fundamentals lookahead test...");
+
+  const testData = [
+    { 
+      symbol: 'AAPL', 
+      datekey: '2020-02-15', // Actually filed on Feb 15
+      fiscalEnd: '2019-12-31', 
+      metrics: { roe: 0.10 } 
+    },
+    { 
+      symbol: 'AAPL', 
+      datekey: '2020-05-15', // Q1 filed on May 15
+      fiscalEnd: '2020-03-31', 
+      metrics: { roe: 0.12 } 
+    },
+    {
+      symbol: 'MSFT',
+      datekey: null,         // Missing datekey, should fallback to fiscalEnd + 120 days
+      fiscalEnd: '2019-12-31', // + 120 days = 2020-04-29
+      metrics: { roe: 0.15 }
+    }
+  ];
+
+  loadFundamentals(testData);
+
+  // Test 1: Jan 31, 2020. 
+  // The Dec 31 fiscal data isn't filed until Feb 15. We MUST return null.
+  const a1 = getFundamentalsAsOf('AAPL', '2020-01-31');
+  if (a1 !== null) throw new Error("Lookahead leak! Returned Q4 data before filing date.");
+
+  // Test 2: Feb 16, 2020.
+  // The Dec 31 fiscal data was filed Feb 15. We should get it.
+  const a2 = getFundamentalsAsOf('AAPL', '2020-02-16');
+  if (!a2 || a2.roe !== 0.10) throw new Error("Failed to return available data.");
+
+  // Test 3: May 14, 2020.
+  // Q1 isn't filed until May 15. We should still get Q4 (roe: 0.10).
+  const a3 = getFundamentalsAsOf('AAPL', '2020-05-14');
+  if (!a3 || a3.roe !== 0.10) throw new Error("Lookahead leak! Returned Q1 data early.");
+
+  // Test 4: May 16, 2020.
+  // Now we should get Q1 (roe: 0.12).
+  const a4 = getFundamentalsAsOf('AAPL', '2020-05-16');
+  if (!a4 || a4.roe !== 0.12) throw new Error("Failed to roll forward to new data.");
+
+  // Test 5: MSFT fallback logic. Target = April 28 (119 days after fiscalEnd). Should be null.
+  const m1 = getFundamentalsAsOf('MSFT', '2020-04-28');
+  if (m1 !== null) throw new Error("Lookahead leak! MSFT fallback data returned early.");
+
+  // Test 6: MSFT fallback logic. Target = April 30 (121 days after fiscalEnd). Should be available.
+  const m2 = getFundamentalsAsOf('MSFT', '2020-04-30');
+  if (!m2 || m2.roe !== 0.15) throw new Error("Failed to return fallback data after 120 days.");
+
+  console.log("PASS: Fundamentals guardrail strictly prevents lookahead bias.");
+}
+
+runTest();
--- a/backend/src/factorlab/tests/orientation.test.js
+++ b/backend/src/factorlab/tests/orientation.test.js
@ -0,0 +1,54 @@
+import { buildFactorProfiles } from '../descriptiveLens.js';
+
+test('strictly-better stock ranks higher on every factor', async () => {
+  const good = {
+    ticker: 'GOOD',
+    trailingPE: 8,
+    priceToBook: 1,
+    priceToSales: 0.5,
+    freeCashflow: 1e10,
+    marketCap: 1e11,
+    returnOnEquity: 0.30,
+    grossMargins: 0.60,
+    debtToEquity: 10,
+    mom_12_1: 0.25,
+    realizedVol252: 0.15
+  };
+
+  const bad = {
+    ticker: 'BAD',
+    trailingPE: 50,
+    priceToBook: 10,
+    priceToSales: 12,
+    freeCashflow: 1e8,
+    marketCap: 1e11,
+    returnOnEquity: 0.02,
+    grossMargins: 0.10,
+    debtToEquity: 300,
+    mom_12_1: -0.10,
+    realizedVol252: 0.60
+  };
+
+  // buildFactorProfiles supports injecting a universe payload directly for testing
+  const out = await buildFactorProfiles([good, bad]);
+
+  // 'out' returns a map { 'GOOD': { ... }, 'BAD': { ... } } during test injection
+  
+  for (const f of ['value', 'quality', 'lowVol', 'momentum', 'composite']) {
+    // A strictly better stock MUST have a higher percentile rank
+    if (out['GOOD'][f] <= out['BAD'][f]) {
+      throw new Error(`Sign catastrophe: GOOD ranked ${out['GOOD'][f]} on ${f}, but BAD ranked ${out['BAD'][f]}. GOOD must strictly outrank BAD.`);
+    }
+  }
+
+  console.log("PASS: Orientation logic correctly handles signs. 'Higher percentile' always means 'more favorable'.");
+});
+
+// Jest polyfill for simple node execution
+function test(name, fn) {
+  console.log(`Running test: ${name}`);
+  fn().catch(e => {
+    console.error(`FAIL: ${e.message}`);
+    process.exit(1);
+  });
+}
--- a/backend/src/factorlab/crossSection.js
+++ b/backend/src/factorlab/crossSection.js
@ -0,0 +1,142 @@
+import { getUniverseAsOf } from './universe.js';
+import { getFundamentalsAsOf } from './fundamentals.js';
+import { standardizeCrossSection, FACTORS } from './factors.js';
+import { spearman, calculateTurnover } from './metrics.js';
+
+// Global reference for forward returns in our testing environment
+// In production, this would query a price DB
+let globalReturnsMap = new Map();
+
+export function setForwardReturnsMap(map) {
+  globalReturnsMap = map;
+}
+
+function getForwardReturn(ticker, dateStr, horizonMonths, allDates) {
+  // Find the index of the current date
+  const idx = allDates.indexOf(dateStr);
+  if (idx === -1 || idx + horizonMonths >= allDates.length) return null; // Not enough forward data
+
+  let cumulativeRet = 0;
+  // Simple sum for log returns, or compound. We'll use simple compound: (1+r1)*(1+r2) - 1
+  let mult = 1.0;
+  for (let m = 0; m < horizonMonths; m++) {
+    const nextDate = allDates[idx + m];
+    const r = globalReturnsMap.get(`${ticker}_${nextDate}`);
+    if (r === undefined || r === null) return null; // missing data
+    mult *= (1 + r);
+  }
+  return mult - 1.0;
+}
+
+function bucketIntoDeciles(rankedScores) {
+  const deciles = Array(10).fill(null).map(() => []);
+  const n = rankedScores.length;
+  if (n === 0) return deciles;
+
+  for (let i = 0; i < n; i++) {
+    const d = Math.min(9, Math.floor((i / n) * 10));
+    deciles[d].push(rankedScores[i].ticker);
+  }
+  return deciles; // deciles[0] = highest scores (Top Decile), deciles[9] = lowest scores
+}
+
+export function runFactor(factorName, allDates, horizonMonths = 1, costPerSideBps = 5) {
+  const results = [];
+  let prevD10Weights = null;
+  let prevD1Weights = null;
+  const costPct = costPerSideBps / 10000.0;
+
+  for (const date of allDates) {
+    const members = getUniverseAsOf(date);
+    if (!members || members.length === 0) continue;
+
+    const rawScores = {};
+    for (const t of members) {
+      const fund = getFundamentalsAsOf(t, date);
+      if (!fund) continue;
+      
+      const score = FACTORS[factorName](fund);
+      if (score !== null && !isNaN(score)) {
+        rawScores[t] = score;
+      }
+    }
+
+    // Standardize cross-sectionally
+    const zScores = standardizeCrossSection(rawScores, false);
+    
+    // Sort descending (High Score = Best)
+    const ranked = Object.keys(zScores)
+      .map(t => ({ ticker: t, score: zScores[t] }))
+      .sort((a, b) => b.score - a.score);
+
+    if (ranked.length < 10) continue; // need enough names for deciles
+
+    const deciles = bucketIntoDeciles(ranked);
+    const d10Tickers = deciles[0]; // Top decile
+    const d1Tickers = deciles[9];  // Bottom decile
+
+    // Calculate equal-weight portfolio weights for this month
+    const curD10Weights = {};
+    d10Tickers.forEach(t => curD10Weights[t] = 1.0 / d10Tickers.length);
+    
+    const curD1Weights = {};
+    d1Tickers.forEach(t => curD1Weights[t] = 1.0 / d1Tickers.length);
+
+    // Calculate turnover
+    const d10Turnover = calculateTurnover(prevD10Weights, curD10Weights);
+    const d1Turnover = calculateTurnover(prevD1Weights, curD1Weights);
+
+    // Forward returns
+    const decileReturns = [];
+    let allFwdReturns = [];
+    let allZScores = [];
+
+    for (let d = 0; d < 10; d++) {
+      const decTickers = deciles[d];
+      let sumRet = 0;
+      let count = 0;
+      for (const t of decTickers) {
+        const ret = getForwardReturn(t, date, horizonMonths, allDates);
+        if (ret !== null) {
+          sumRet += ret;
+          count++;
+          
+          allFwdReturns.push(ret);
+          allZScores.push(zScores[t]);
+        }
+      }
+      decileReturns.push(count > 0 ? sumRet / count : 0);
+    }
+
+    if (allFwdReturns.length > 0) {
+      const ic = spearman(allZScores, allFwdReturns);
+      const universeMeanRet = allFwdReturns.reduce((a, b) => a + b, 0) / allFwdReturns.length;
+
+      // Gross Returns
+      const d10Gross = decileReturns[0];
+      const d1Gross = decileReturns[9];
+      
+      // Net Returns
+      const d10Net = d10Gross - (d10Turnover * costPct);
+      const d1Net = d1Gross - (d1Turnover * costPct);
+
+      results.push({
+        date,
+        ic,
+        decileReturns, // Gross decile returns for the staircase plot
+        d10Gross,
+        d1Gross,
+        d10Net,
+        d1Net,
+        universeMeanRet,
+        d10Turnover,
+        d1Turnover
+      });
+    }
+
+    prevD10Weights = curD10Weights;
+    prevD1Weights = curD1Weights;
+  }
+
+  return results;
+}
--- a/backend/src/factorlab/descriptiveLens.js
+++ b/backend/src/factorlab/descriptiveLens.js
@ -0,0 +1,228 @@
+import { FACTORS, standardizeCrossSection } from './factors.js';
+import { pullRawRecord } from './ingest.js';
+import { getUniverse } from '../services/stockUniverseService.js';
+
+/**
+ * Assigns percentiles (0-100) to a raw factor array using the standardizer.
+ * @param {Array} raw - Array of objects: { ticker, value: 0.5, quality: 1.2 }
+ * @param {String} factorKey - The factor to assign percentiles for (e.g. 'value')
+ */
+function assignPercentiles(raw, factorKey) {
+  const rawByTicker = {};
+  for (const r of raw) {
+    if (r[factorKey] !== null && r[factorKey] !== undefined) {
+      rawByTicker[r.ticker] = r[factorKey];
+    }
+  }
+
+  // Uses winsorization and z-scoring from factors.js
+  const zScores = standardizeCrossSection(rawByTicker, false);
+
+  // Convert z-scores to percentiles relative to the universe
+  const validTickers = Object.keys(zScores);
+  const sortedZScores = validTickers.map(t => zScores[t]).sort((a, b) => a - b);
+  
+  const n = sortedZScores.length;
+
+  for (const r of raw) {
+    const z = zScores[r.ticker];
+    if (z === undefined) {
+      r[`${factorKey}Percentile`] = null;
+      continue;
+    }
+    
+    // Find percentile rank
+    let index = sortedZScores.findIndex(val => val >= z);
+    if (index === -1) index = n - 1;
+    
+    r[`${factorKey}Percentile`] = Math.round((index / n) * 100);
+  }
+}
+
+/**
+ * Generates the descriptive Factor Profile for the entire universe based on CURRENT data.
+ * Does NOT generate forward predictions.
+ */
+let _cachedRawProfiles = null;
+let _cachedZScoreMaps = null;
+let _cachedRawTime = 0;
+
+export async function buildFactorProfiles(injectedUniverseRecords = null) {
+  const rawProfiles = [];
+  
+  // Allow test injection
+  if (injectedUniverseRecords) {
+    for (const data of injectedUniverseRecords) {
+      rawProfiles.push({
+        ticker: data.ticker || 'TEST',
+        value: FACTORS.value(data),
+        quality: FACTORS.quality(data),
+        lowVol: FACTORS.lowVol(data),
+        momentum: FACTORS.momentum(data)
+      });
+    }
+  } else {
+    // We now include ETFs in the factor ranking because we want to see momentum/value on country ETFs
+    const universe = getUniverse().map(s => s.symbol);
+    const BATCH_SIZE = 15;
+    
+    for (let i = 0; i < universe.length; i += BATCH_SIZE) {
+      const batch = universe.slice(i, i + BATCH_SIZE);
+      const promises = batch.map(async (ticker) => {
+        try {
+          const data = await pullRawRecord(ticker);
+          if (!data) return null;
+
+          return {
+            ticker,
+            value: FACTORS.value(data),
+            quality: FACTORS.quality(data),
+            lowVol: FACTORS.lowVol(data),
+            momentum: FACTORS.momentum(data)
+          };
+        } catch (e) {
+          console.warn(`[DescriptiveLens] Failed for ${ticker}:`, e.message);
+          return null;
+        }
+      });
+      
+      const results = await Promise.allSettled(promises);
+      for (const res of results) {
+        if (res.status === 'fulfilled' && res.value) {
+          rawProfiles.push(res.value);
+        }
+      }
+      
+      // Throttle delay between batches to respect free API limits
+      if (i + BATCH_SIZE < universe.length) {
+        await new Promise(r => setTimeout(r, 1000));
+      }
+    }
+  }
+
+  // Composite is the equal-weight average of the non-null standardized z-scores
+  // First, we must standardize the individual factors to compute composite z-score
+  const factors = ['value', 'quality', 'lowVol', 'momentum'];
+  const zScoreMaps = {};
+  
+  for (const f of factors) {
+    const rawByTicker = {};
+    for (const r of rawProfiles) {
+      if (r[f] !== null && r[f] !== undefined) rawByTicker[r.ticker] = r[f];
+    }
+    zScoreMaps[f] = standardizeCrossSection(rawByTicker, false);
+  }
+
+  // Compute composite raw score
+  for (const r of rawProfiles) {
+    let sum = 0;
+    let count = 0;
+    for (const f of factors) {
+      const z = zScoreMaps[f][r.ticker];
+      if (z !== undefined) {
+        sum += z;
+        count++;
+      }
+    }
+    r.composite = count > 0 ? (sum / count) : null;
+  }
+
+  // Cross-sectional standardization and percentile assignment
+  [...factors, 'composite'].forEach(f => assignPercentiles(rawProfiles, f));
+
+  // Cache the raw profiles for single-stock comparisons
+  if (!injectedUniverseRecords) {
+    _cachedRawProfiles = rawProfiles;
+    _cachedZScoreMaps = zScoreMaps;
+    _cachedRawTime = Date.now();
+  }
+
+  // If this is a test injection, return the raw profiles with percentiles for testing
+  if (injectedUniverseRecords) return rawProfiles.reduce((acc, p) => { acc[p.ticker] = p; return acc; }, {});
+
+  // Clean up the raw scores, we only return the percentiles for the UI
+  const cleanedProfiles = rawProfiles.map(p => ({
+    ticker: p.ticker,
+    value: p.valuePercentile,
+    quality: p.qualityPercentile,
+    lowVol: p.lowVolPercentile,
+    momentum: p.momentumPercentile,
+    composite: p.compositePercentile,
+    _disclaimer: "Descriptive only. Shows where this stock ranks vs. peers on each factor today. We have not validated that these ranks predict returns — and our own testing showed standard technical signals do not. We'll only call a factor predictive after it passes out-of-sample validation."
+  }));
+
+  return cleanedProfiles;
+}
+
+/**
+ * Computes a single stock's factor profile relative to the cached universe.
+ */
+export async function getSingleFactorProfile(symbol) {
+  if (!_cachedRawProfiles || (Date.now() - _cachedRawTime > 120000)) {
+    await buildFactorProfiles();
+  }
+
+  // If it's already in the universe, just return it
+  const existing = _cachedRawProfiles.find(p => p.ticker === symbol);
+  if (existing) {
+    return {
+      ticker: existing.ticker,
+      value: existing.valuePercentile,
+      quality: existing.qualityPercentile,
+      lowVol: existing.lowVolPercentile,
+      momentum: existing.momentumPercentile,
+      composite: existing.compositePercentile,
+      _disclaimer: "Descriptive only. Shows where this stock ranks vs. the core universe."
+    };
+  }
+
+  // Fetch out-of-universe data
+  const data = await pullRawRecord(symbol);
+  if (!data) return null;
+
+  const raw = {
+    ticker: symbol,
+    value: FACTORS.value(data),
+    quality: FACTORS.quality(data),
+    lowVol: FACTORS.lowVol(data),
+    momentum: FACTORS.momentum(data)
+  };
+
+  // Standardize single value using universe mean/std from zScoreMaps... wait, standardizer only gives cross-sectional z-scores.
+  // We can just re-run standardizer with the universe + this one stock.
+  const tempRaw = [..._cachedRawProfiles, raw];
+  
+  const factors = ['value', 'quality', 'lowVol', 'momentum'];
+  const zScoreMaps = {};
+  
+  for (const f of factors) {
+    const rawByTicker = {};
+    for (const r of tempRaw) {
+      if (r[f] !== null && r[f] !== undefined) rawByTicker[r.ticker] = r[f];
+    }
+    zScoreMaps[f] = standardizeCrossSection(rawByTicker, false);
+  }
+
+  let sum = 0;
+  let count = 0;
+  for (const f of factors) {
+    const z = zScoreMaps[f][raw.ticker];
+    if (z !== undefined) {
+      sum += z;
+      count++;
+    }
+  }
+  raw.composite = count > 0 ? (sum / count) : null;
+
+  [...factors, 'composite'].forEach(f => assignPercentiles(tempRaw, f));
+
+  return {
+    ticker: raw.ticker,
+    value: raw.valuePercentile,
+    quality: raw.qualityPercentile,
+    lowVol: raw.lowVolPercentile,
+    momentum: raw.momentumPercentile,
+    composite: raw.compositePercentile,
+    _disclaimer: "Descriptive only. Shows where this out-of-universe stock ranks vs. the core universe today."
+  };
+}
--- a/backend/src/factorlab/factors.js
+++ b/backend/src/factorlab/factors.js
@ -0,0 +1,106 @@
+function mean(arr) {
+  const valid = arr.filter(x => x !== null && x !== undefined && !isNaN(x));
+  if (valid.length === 0) return null;
+  return valid.reduce((a, b) => a + b, 0) / valid.length;
+}
+
+export function orientedComponents(r) {
+  const inv = x => (x != null && x > 0) ? 1 / x : null;  // guard neg/zero multiples
+  const neg = x => (x != null) ? -x : null;
+  const div = (a, b) => (a != null && b) ? a / b : null;
+  return {
+    value:    [ inv(r.trailingPE), div(r.freeCashflow, r.marketCap),
+                inv(r.priceToBook), inv(r.priceToSales) ],
+    quality:  [ r.returnOnEquity, r.grossMargins, neg(r.debtToEquity) ],
+    lowVol:   [ neg(r.realizedVol252) ],
+    momentum: [ r.mom_12_1 ],
+  };
+}
+
+export const FACTORS = {
+  value: r => mean(orientedComponents(r).value),
+  quality: r => mean(orientedComponents(r).quality),
+  lowVol: r => mean(orientedComponents(r).lowVol),
+  momentum: r => mean(orientedComponents(r).momentum),
+  // composite is an equal-weight average of the standardized scores, computed later
+};
+
+function winsorize(arr, trimPercent) {
+  if (arr.length < 5) return arr; // Don't winsorize tiny arrays
+  const sorted = [...arr].sort((a, b) => a - b);
+  const lowerIdx = Math.floor(arr.length * trimPercent);
+  let upperIdx = Math.floor(arr.length * (1 - trimPercent)) - 1;
+  if (upperIdx < lowerIdx) upperIdx = arr.length - 1;
+  
+  const lowerBound = sorted[lowerIdx];
+  const upperBound = sorted[upperIdx];
+
+  return arr.map(v => {
+    if (v < lowerBound) return lowerBound;
+    if (v > upperBound) return upperBound;
+    return v;
+  });
+}
+
+function avg(arr) {
+  if (!arr.length) return 0;
+  return arr.reduce((a, b) => a + b, 0) / arr.length;
+}
+
+function std(arr, meanVal) {
+  if (arr.length <= 1) return 1;
+  const variance = arr.reduce((sum, v) => sum + Math.pow(v - meanVal, 2), 0) / (arr.length - 1);
+  return Math.sqrt(variance) || 1;
+}
+
+export function standardizeCrossSection(rawByTicker, neutralizeSector = false, sectorsByTicker = {}) {
+  // Extract non-null values
+  const tickers = Object.keys(rawByTicker);
+  let validTickers = tickers.filter(t => rawByTicker[t] != null && !isNaN(rawByTicker[t]));
+  
+  if (validTickers.length === 0) return {};
+
+  const result = {};
+
+  if (neutralizeSector) {
+    // Group by sector
+    const sectorGroups = {};
+    for (const t of validTickers) {
+      const sec = sectorsByTicker[t] || 'UNKNOWN';
+      if (!sectorGroups[sec]) sectorGroups[sec] = [];
+      sectorGroups[sec].push(t);
+    }
+
+    for (const sec in sectorGroups) {
+      const secTickers = sectorGroups[sec];
+      const secVals = secTickers.map(t => rawByTicker[t]);
+      
+      const w = winsorize(secVals, 0.02);
+      const m = avg(w);
+      const s = std(w, m);
+
+      for (let i = 0; i < secTickers.length; i++) {
+        const t = secTickers[i];
+        let val = rawByTicker[t];
+        if (val < w[0]) val = w[0]; // approx winsorize clip (since w is same order)
+        // Actually, winsorize maps index-to-index.
+        const clipped = w[i];
+        result[t] = (clipped - m) / s;
+      }
+    }
+  } else {
+    // Global standard
+    const vals = validTickers.map(t => rawByTicker[t]);
+    const w = winsorize(vals, 0.02);
+    const m = avg(w);
+    const s = std(w, m);
+
+    for (let i = 0; i < validTickers.length; i++) {
+      const t = validTickers[i];
+      const clipped = w[i];
+      result[t] = (clipped - m) / s;
+    }
+  }
+
+  return result;
+}
--- a/backend/src/factorlab/fundamentals.js
+++ b/backend/src/factorlab/fundamentals.js
@ -0,0 +1,53 @@
+// In a real setup, this data would be loaded from Sharadar/Compustat.
+// For testing, we allow an injected dataset.
+
+let globalFundamentalsBySymbol = new Map(); 
+
+export function loadFundamentals(data) {
+  globalFundamentalsBySymbol.clear();
+  // Group by symbol
+  for (const record of data) {
+    if (!globalFundamentalsBySymbol.has(record.symbol)) {
+      globalFundamentalsBySymbol.set(record.symbol, []);
+    }
+    globalFundamentalsBySymbol.get(record.symbol).push(record);
+  }
+
+  // Sort each group by datekey ascending
+  for (const [sym, records] of globalFundamentalsBySymbol.entries()) {
+    records.sort((a, b) => new Date(a.datekey) - new Date(b.datekey));
+  }
+}
+
+export function getFundamentalsAsOf(symbol, dateStr) {
+  const targetDate = new Date(dateStr);
+  const records = globalFundamentalsBySymbol.get(symbol);
+  if (!records) return null;
+  
+  // Find the most recent fundamental report where filing date <= targetDate
+  let latest = null;
+  for (const record of records) {
+    
+    // GUARDRAIL: We absolutely require a filing date (datekey).
+    // If datekey <= targetDate, it's safe.
+    // If datekey is missing, fallback to fiscalEnd + 4 months (120 days).
+    let isAvailable = false;
+    if (record.datekey) {
+      if (new Date(record.datekey) <= targetDate) isAvailable = true;
+    } else if (record.fiscalEnd) {
+      const safeReleaseDate = new Date(record.fiscalEnd);
+      safeReleaseDate.setDate(safeReleaseDate.getDate() + 120); // Add 4 months roughly
+      if (safeReleaseDate <= targetDate) isAvailable = true;
+    } else {
+      throw new Error(`Record for ${symbol} lacks both datekey and fiscalEnd. Unsafe.`);
+    }
+
+    if (isAvailable) {
+      latest = record;
+    } else {
+      if (record.datekey && new Date(record.datekey) > targetDate) break;
+    }
+  }
+
+  return latest ? latest.metrics : null;
+}
--- a/backend/src/factorlab/gate.js
+++ b/backend/src/factorlab/gate.js
@ -0,0 +1,98 @@
+import { generateSyntheticData } from './synthetic.js';
+import { loadUniverse } from './universe.js';
+import { loadFundamentals } from './fundamentals.js';
+import { setForwardReturnsMap, runFactor } from './crossSection.js';
+import { mean, neweyWestStdErr } from './metrics.js';
+
+function printReport(title, results, horizonMonths) {
+  if (results.length === 0) {
+    console.log(`\n=== ${title} ===`);
+    console.log("No valid results.");
+    return;
+  }
+
+  const ics = results.map(r => r.ic);
+  const meanIc = mean(ics);
+  // We use Newey-West standard error for the t-stat if overlapping.
+  // For 1-month horizon, lags = 0 (simple std err). 
+  // For H-month horizon, lags = H - 1.
+  const lags = Math.max(0, horizonMonths - 1);
+  const seIc = neweyWestStdErr(ics, lags);
+  const tStatIc = meanIc / seIc;
+
+  // Average Decile Returns (Gross)
+  const deciles = Array(10).fill(0);
+  for (const r of results) {
+    for (let d = 0; d < 10; d++) {
+      deciles[d] += r.decileReturns[d];
+    }
+  }
+  for (let d = 0; d < 10; d++) deciles[d] /= results.length;
+
+  // Long/Short Spread (Net)
+  const lsSpreads = results.map(r => r.d10Net - r.d1Net);
+  const meanLsSpread = mean(lsSpreads);
+  const seLsSpread = neweyWestStdErr(lsSpreads, lags);
+  const tStatLs = meanLsSpread / seLsSpread;
+  const sharpeLs = (meanLsSpread / (seLsSpread * Math.sqrt(results.length))) * Math.sqrt(12 / horizonMonths); // Approx Annualized Gross Sharpe
+
+  // Long-Only Top-Decile Excess (Net)
+  const loExcess = results.map(r => r.d10Net - r.universeMeanRet);
+  const meanLoExcess = mean(loExcess);
+
+  // Consistency
+  const numPositiveLs = lsSpreads.filter(s => s > 0).length;
+  const consistencyPct = (numPositiveLs / lsSpreads.length) * 100;
+
+  // Turnover (Avg over periods)
+  const avgTurnoverD10 = mean(results.map(r => r.d10Turnover)) * 100;
+
+  console.log(`\n=== ${title} ===`);
+  console.log(`Periods (N):         ${results.length} months`);
+  console.log(`Horizon:             ${horizonMonths} month(s)`);
+  console.log(`Mean IC:             ${meanIc.toFixed(4)}`);
+  console.log(`IC t-stat:           ${tStatIc.toFixed(2)}  (Target: >= 3.0)`);
+  console.log(`L/S Spread (Net):    ${(meanLsSpread * 100).toFixed(2)}% per period (t-stat: ${tStatLs.toFixed(2)})`);
+  console.log(`Long-Only Excess:    ${(meanLoExcess * 100).toFixed(2)}% per period`);
+  console.log(`Consistency:         ${consistencyPct.toFixed(1)}% positive periods`);
+  console.log(`D10 Avg Turnover:    ${avgTurnoverD10.toFixed(1)}% per rebalance`);
+  
+  console.log(`\nDecile Monotonicity (Gross):`);
+  const maxRet = Math.max(...deciles);
+  const minRet = Math.min(...deciles);
+  for (let d = 0; d < 10; d++) {
+    // simple text bar chart
+    const pct = deciles[d] * 100;
+    const barLen = Math.max(1, Math.floor((deciles[d] - minRet) / (maxRet - minRet + 0.0001) * 20));
+    console.log(`  D${d+1}: ${pct.toFixed(2).padStart(5)}% | ${'#'.repeat(barLen)}`);
+  }
+}
+
+function runSyntheticTests() {
+  console.log("==================================================");
+  console.log("   SYNTHETIC KNOWN-ANSWER TESTS");
+  console.log("==================================================");
+
+  // Generate 25 years (300 months) of data for 500 stocks
+  // 1. NULL TEST (IC = 0)
+  const nullData = generateSyntheticData({ numMonths: 300, numStocks: 500, plantedIC: 0.0 });
+  loadUniverse(nullData.universe);
+  loadFundamentals(nullData.fundamentals);
+  setForwardReturnsMap(nullData.forwardReturnsMap);
+
+  const allDatesNull = nullData.universe.map(u => u.date);
+  const resultsNull = runFactor('value', allDatesNull, 1, 5); // 5 bps costs
+  printReport("TEST 1: NULL FACTOR (Target: IC ≈ 0, t-stat < 3)", resultsNull, 1);
+
+  // 2. PLANTED SIGNAL TEST (IC = 0.05)
+  const plantedData = generateSyntheticData({ numMonths: 300, numStocks: 500, plantedIC: 0.05 });
+  loadUniverse(plantedData.universe);
+  loadFundamentals(plantedData.fundamentals);
+  setForwardReturnsMap(plantedData.forwardReturnsMap);
+
+  const allDatesPlanted = plantedData.universe.map(u => u.date);
+  const resultsPlanted = runFactor('value', allDatesPlanted, 1, 5); // 5 bps costs
+  printReport("TEST 2: PLANTED SIGNAL (Target: IC ≈ 0.05, t-stat >= 3, Monotonic)", resultsPlanted, 1);
+}
+
+runSyntheticTests();
--- a/backend/src/factorlab/ingest.js
+++ b/backend/src/factorlab/ingest.js
@ -0,0 +1,75 @@
+import { fetchQuoteSummary } from '../services/fundamentalsService.js';
+
+const DEFAULT_HEADERS = {
+  'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
+  'Accept': 'application/json',
+};
+
+async function fetchHistory(symbol) {
+  const url = `https://query1.finance.yahoo.com/v8/finance/chart/${symbol}?range=2y&interval=1d`;
+  try {
+    const res = await fetch(url, { headers: DEFAULT_HEADERS });
+    if (!res.ok) return null;
+    const data = await res.json();
+    const result = data?.chart?.result?.[0];
+    if (!result) return null;
+    return result.indicators?.quote?.[0]?.close || [];
+  } catch (e) {
+    return null;
+  }
+}
+
+function momentum12_1(closes) {
+  if (!closes || closes.length < 252) return null;
+  const n = closes.length;
+  // 12-1 month: close[-21] / close[-252] - 1
+  const current = closes[n - 21];
+  const past = closes[n - 252];
+  if (!current || !past) return null;
+  return (current / past) - 1;
+}
+
+function realizedVol252(closes) {
+  if (!closes || closes.length < 252) return null;
+  const n = closes.length;
+  const returns = [];
+  for (let i = n - 251; i < n; i++) {
+    const prev = closes[i - 1];
+    const cur = closes[i];
+    if (prev && cur) returns.push((cur - prev) / prev);
+  }
+  if (returns.length < 200) return null;
+  
+  const mean = returns.reduce((a, b) => a + b, 0) / returns.length;
+  const variance = returns.reduce((a, b) => a + Math.pow(b - mean, 2), 0) / (returns.length - 1);
+  // Annualize daily vol
+  return Math.sqrt(variance) * Math.sqrt(252);
+}
+
+export async function pullRawRecord(ticker, asOf = new Date().toISOString().split('T')[0]) {
+  const info = await fetchQuoteSummary(ticker);
+  if (!info) return null;
+  
+  const closes = await fetchHistory(ticker);
+
+  return {
+    snapshot_date: asOf,
+    ticker,
+    price: info.price,
+    marketCap: info.market_cap,
+    
+    // raw inputs
+    trailingPE: info.pe_ttm,
+    priceToBook: info.price_to_book,
+    priceToSales: info.price_to_sales,
+    freeCashflow: info.free_cash_flow,
+    returnOnEquity: info.return_on_equity,
+    grossMargins: info.gross_margins,
+    debtToEquity: info.debt_to_equity,
+    
+    mom_12_1: momentum12_1(closes),
+    realizedVol252: realizedVol252(closes),
+    
+    fundamentals_period_end: info.fiscal_end
+  };
+}
--- a/backend/src/factorlab/metrics.js
+++ b/backend/src/factorlab/metrics.js
@ -0,0 +1,84 @@
+export function rank(arr) {
+  const sorted = arr.map((val, ind) => ({ val, ind })).sort((a, b) => a.val - b.val);
+  const ranks = new Array(arr.length);
+  for (let i = 0; i < sorted.length; i++) {
+    ranks[sorted[i].ind] = i + 1;
+  }
+  // Handle ties (average rank) if needed, but simple rank is fine for large N
+  return ranks;
+}
+
+export function spearman(x, y) {
+  if (x.length !== y.length || x.length === 0) return null;
+  const rankX = rank(x);
+  const rankY = rank(y);
+  const n = x.length;
+  let dSqSum = 0;
+  for (let i = 0; i < n; i++) {
+    dSqSum += Math.pow(rankX[i] - rankY[i], 2);
+  }
+  return 1 - ((6 * dSqSum) / (n * (Math.pow(n, 2) - 1)));
+}
+
+// Simple mean
+export function mean(arr) {
+  if (!arr || arr.length === 0) return 0;
+  return arr.reduce((a, b) => a + b, 0) / arr.length;
+}
+
+// Simple standard deviation
+export function std(arr, m) {
+  if (!arr || arr.length <= 1) return 1;
+  const variance = arr.reduce((sum, v) => sum + Math.pow(v - m, 2), 0) / (arr.length - 1);
+  return Math.sqrt(variance) || 1;
+}
+
+/**
+ * Calculate Newey-West standard error for a time-series of means
+ * Lags = 0 is equivalent to standard error.
+ * For N-month overlapping returns, lag = N - 1.
+ */
+export function neweyWestStdErr(ts, lags) {
+  const n = ts.length;
+  if (n <= 1) return 1;
+  const m = mean(ts);
+  
+  // Variance
+  let s0 = 0;
+  for (let i = 0; i < n; i++) {
+    s0 += Math.pow(ts[i] - m, 2);
+  }
+  s0 = s0 / n;
+
+  // Covariances
+  let sLags = 0;
+  for (let l = 1; l <= lags; l++) {
+    let cov = 0;
+    for (let i = l; i < n; i++) {
+      cov += (ts[i] - m) * (ts[i - l] - m);
+    }
+    cov = cov / n;
+    const weight = 1 - (l / (lags + 1));
+    sLags += 2 * weight * cov;
+  }
+
+  const S = s0 + sLags;
+  return Math.sqrt(S / n) || (std(ts, m) / Math.sqrt(n)); // fallback
+}
+
+/**
+ * Compute turnover given two sets of weights.
+ * w1, w2 are objects: { 'AAPL': 0.05, 'MSFT': 0.02, ... }
+ * turnover = 0.5 * sum(|w2_i - w1_i|)
+ */
+export function calculateTurnover(wOld, wNew) {
+  if (!wOld) return 1.0; // 100% turnover on first month
+  let turnover = 0;
+  const allTickers = new Set([...Object.keys(wOld), ...Object.keys(wNew)]);
+  for (const t of allTickers) {
+    const oldWt = wOld[t] || 0;
+    const newWt = wNew[t] || 0;
+    turnover += Math.abs(newWt - oldWt);
+  }
+  return 0.5 * turnover;
+}
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Antigravity	0ac5176df6	add-search-and-top100 Build Institutional Trader / build-and-deploy (push) Successful in 3m9s Details	2026-06-30 00:30:53 +00:00
Antigravity	f177252d6d	trigger-build Build Institutional Trader / build-and-deploy (push) Successful in 28s Details	2026-06-29 23:56:44 +00:00
Deep Koluguri	e784bc46eb	Add Reversal Screener tab to dashboard Build Institutional Trader / build-and-deploy (push) Successful in 7m35s Details	2026-06-29 23:23:09 +00:00
Deep Koluguri	2b3355cddb	merge: resolve conflicts between local production fixes and remote updates Build Institutional Trader / build-and-deploy (push) Successful in 5m41s Details	2026-06-29 17:13:44 -04:00
Deep Koluguri	fe48536e9b	Update app.yaml with python sidecar Build Institutional Trader / build-and-deploy (push) Successful in 2m39s Details	2026-06-29 11:48:26 -04:00
Deep Koluguri	03a7e73b37	Update Dockerfile for python Build Institutional Trader / build-and-deploy (push) Has been cancelled Details	2026-06-29 11:42:47 -04:00
Deep Koluguri	5241f18052	style: Add explanatory tooltips to factor lens rows Build Institutional Trader / build-and-deploy (push) Successful in 54s Details	2026-06-27 16:00:46 -04:00
Deep Koluguri	85e11dca88	feat: Expand universe to ~125 symbols covering EM and sectors, optimize fetching with concurrency and rate limit delays Build Institutional Trader / build-and-deploy (push) Successful in 23s Details	2026-06-27 15:12:10 -04:00
Deep Koluguri	1278b5d4eb	style: Move StockDetailPanel directly under the search bar for better UX Build Institutional Trader / build-and-deploy (push) Successful in 50s Details	2026-06-27 15:02:34 -04:00
Deep Koluguri	85e9e523d9	fix: Replace Kaniko with standard docker build to avoid context mounting issues Build Institutional Trader / build-and-deploy (push) Successful in 1m37s Details	2026-06-27 14:24:36 -04:00
Deep Koluguri	189d33e65e	fix: Revert JOB_CONTAINER extraction now that zombies are cleared Build Institutional Trader / build-and-deploy (push) Failing after 21s Details	2026-06-27 14:21:16 -04:00
Deep Koluguri	9b2420ed8d	fix: Kaniko mounting wrong workspace due to race condition Build Institutional Trader / build-and-deploy (push) Failing after 16s Details	2026-06-25 23:14:58 -04:00
Deep Koluguri	ab1e3f28f6	feat: Add dynamic Fundamental Factor Lens to individual stock search view Build Institutional Trader / build-and-deploy (push) Failing after 17s Details	2026-06-25 22:59:16 -04:00
Deep Koluguri	ae21b0df90	chore: add --no-cache to kaniko pipeline Build Institutional Trader / build-and-deploy (push) Failing after 16s Details	2026-06-25 22:44:13 -04:00
Deep Koluguri	7907fa30f2	feat: Add 1W, 1M, 1Y returns to macro dashboard Build Institutional Trader / build-and-deploy (push) Successful in 2m9s Details	2026-06-25 22:36:15 -04:00
Deep Koluguri	e43361fadd	chore: cache bust Build Institutional Trader / build-and-deploy (push) Successful in 2m3s Details	2026-06-25 22:29:20 -04:00
Deep Koluguri	6e323967a1	feat: swap generic mkts for specific regions (EWJ, FXI, INDA, VGK) Build Institutional Trader / build-and-deploy (push) Successful in 1m59s Details	2026-06-25 21:31:51 -04:00
Deep Koluguri	9625979954	feat: Add Global Macro Indicators strip to dashboard Build Institutional Trader / build-and-deploy (push) Successful in 2m9s Details	2026-06-25 21:04:43 -04:00
Deep Koluguri	cd78058072	UI Honesty Pivot: Remove predictive claims, add methodology, descriptive flow Build Institutional Trader / build-and-deploy (push) Successful in 2m8s Details	2026-06-25 20:32:15 -04:00
Deep Koluguri	2e2efaf530	fix(backend): restore calculateRocketScore to fix crash Build Institutional Trader / build-and-deploy (push) Successful in 2m3s Details	2026-06-25 19:40:16 -04:00
Deep Koluguri	8677489043	fix(ui): tear out predictive swimlanes and replace with descriptive factor lens grid Build Institutional Trader / build-and-deploy (push) Successful in 2m10s Details	2026-06-25 19:28:12 -04:00
Deep Koluguri	a5e06b44b1	feat(factorlab): pivot to honest transparent research accelerator, implement factor pipeline, descriptive lens, and fundamental snapshotter Build Institutional Trader / build-and-deploy (push) Successful in 2m9s Details	2026-06-25 00:29:34 -04:00
Deep Koluguri	4dfe340345	style: remove nested scrollbars from swimlanes, let main window scroll Build Institutional Trader / build-and-deploy (push) Successful in 2m3s Details	2026-06-24 19:38:55 -04:00
Deep Koluguri	dfae02c851	fix: use relative URLs for API requests to fix CSP block in production Build Institutional Trader / build-and-deploy (push) Successful in 1m57s Details	2026-06-24 19:30:05 -04:00
Deep Koluguri	7c67786e80	fix: relax CORS middleware to prevent 500 errors when CORS_ORIGIN is missing Build Institutional Trader / build-and-deploy (push) Successful in 1m58s Details	2026-06-24 19:25:10 -04:00
Deep Koluguri	43ba386da9	fix: CORP header for Cloudflare proxy Build Institutional Trader / build-and-deploy (push) Has been cancelled Details	2026-06-24 19:23:18 -04:00
Deep Koluguri	7344d5a2b1	fix: static files before routes, fix CSP for assets and Cloudflare, SPA fallback safety Build Institutional Trader / build-and-deploy (push) Successful in 1m57s Details	2026-06-24 19:17:43 -04:00
Deep Koluguri	78b408057c	fix: health returns 200 for degraded, probe tolerant of optional services Build Institutional Trader / build-and-deploy (push) Successful in 2m2s Details	2026-06-24 19:13:56 -04:00
Deep Koluguri	911693da88	feat: swimlane screener suitability scoring global ticker search remove side panels Build Institutional Trader / build-and-deploy (push) Successful in 2m7s Details	2026-06-24 18:53:11 -04:00
Deep Koluguri	f91534e976	feat: implement stock evaluation filters, fundamentals styling, and AI trading profiles Build Institutional Trader / build-and-deploy (push) Successful in 2m1s Details	2026-06-22 23:19:38 -04:00
Deep Koluguri	c981faf3a6	fix: resolve WebSocket path conflicts by manually routing HTTP upgrade events Build Institutional Trader / build-and-deploy (push) Successful in 2m1s Details	2026-06-22 22:10:53 -04:00
Deep Koluguri	2ba6425eed	fix: trust proxy for express-rate-limit to prevent crash behind APISIX Build Institutional Trader / build-and-deploy (push) Successful in 2m4s Details	2026-06-22 22:03:32 -04:00
Deep Koluguri	a0c54feee5	fix: import getApiUrl in AlertsFeed to fix reference error Build Institutional Trader / build-and-deploy (push) Successful in 2m6s Details	2026-06-22 21:54:17 -04:00
Deep Koluguri	b3373d6d85	fix: explicitly allow wss in CSP connectSrc Build Institutional Trader / build-and-deploy (push) Successful in 2m13s Details	2026-06-22 19:47:00 -04:00
Deep Koluguri	2dba57191b	fix: change hardcoded localhost URLs to relative URLs so frontend works on production domain Build Institutional Trader / build-and-deploy (push) Successful in 2m10s Details	2026-06-22 19:43:18 -04:00
Deep Koluguri	cd83c18555	fix: update CSP for Cloudflare and set CORS_ORIGIN to fix 500 errors Build Institutional Trader / build-and-deploy (push) Successful in 2m8s Details	2026-06-22 19:40:24 -04:00
Deep Koluguri	76c24aaec1	fix: disable python service health check to prevent pod crashes Build Institutional Trader / build-and-deploy (push) Has been cancelled Details	2026-06-22 19:38:15 -04:00
Deep Koluguri	4d00f978e2	feat: provision dedicated postgres database via CloudNativePG Build Institutional Trader / build-and-deploy (push) Successful in 2m19s Details	2026-06-22 19:29:22 -04:00
Deep Koluguri	f68b3e5b75	chore: trigger Kaniko build for institutional-trader Build Institutional Trader / build-and-deploy (push) Successful in 2m11s Details	2026-06-22 17:24:39 -04:00
Deep Koluguri	f6f35dba00	Update package-lock	2026-06-22 16:40:15 -04:00
Deep Koluguri	274dc38794	Add .dockerignore to prevent Windows binaries from breaking Kaniko	2026-06-22 16:34:52 -04:00
Deep Koluguri	7c2e19552f	Fix Vite config Rollup missing dependency error	2026-06-22 16:30:34 -04:00
Deep Koluguri	947c50302e	Fix trailing comma in frontend package.json	2026-06-22 16:21:46 -04:00
Deep Koluguri	95cdafa34a	Add SSL to ingress	2026-06-22 16:00:43 -04:00
Deep Koluguri	4774890415	Deploy to Talos K8s: add Market Analysis and Docker/K8s manifests	2026-06-22 15:49:06 -04:00