docs: add Phase 6 (range compression) and Phase 7 (volume profile) to roadmap
Added two new optimization phases for future implementation: PHASE 6: TradingView Range Compression Metrics (PLANNED) - Target: November 2025 (after frequency penalties validated) - Adds range%, priceChange5bars, ADX-momentum mismatch to alerts - Detects fake trends (ADX passes but price not moving) - Penalties: -20 pts for compressed range, -20 pts for momentum mismatch - Implementation: 1-2 hours (TradingView alert modifications) PHASE 7: Volume Profile Integration (ADVANCED) - Target: December 2025 or Q1 2026 - Uses Volume S/R Zones V2 indicator for volume node detection - Identifies high-probability chop zones (price stuck in volume node) - Penalties: -25 to -35 pts for volume node entries - Bonuses: +10 to +15 pts for breakout setups - Implementation: 2-3 hours + Pine Script expertise - Most powerful but also most complex Also documented Phase 1.5 completion (signal frequency penalties). Milestones updated with realistic timelines for each phase.
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mindesbunister
@@ -78,6 +78,56 @@ ORDER BY MIN(signalQualityScore) DESC;
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---
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## Phase 1.5: Signal Frequency Penalties ✅ DEPLOYED Nov 14, 2025
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**Status:** Production deployment complete
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**Commit:** 111e3ed
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**Deployment Time:** 07:28 CET, November 14, 2025
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### What Was Implemented
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Real-time database analysis that detects overtrading and flip-flop patterns before trade execution.
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### Penalties Applied
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1. **Overtrading (3+ signals in 30min):** -20 points
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- Counts both executed trades AND blocked signals
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- Prevents excessive trading in consolidation zones
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2. **Flip-flop (opposite direction <15min):** -25 points
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- Blocks rapid long→short→long whipsaws
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- Example: SHORT 10:00 → LONG 10:12 = blocked
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3. **Alternating pattern (last 3 trades):** -30 points
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- Detects choppy market conditions
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- Pattern: long→short→long = chop detection
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### Technical Details
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- **Function:** `getRecentSignals()` in `lib/database/trades.ts`
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- **Architecture:** `scoreSignalQuality()` now async
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- **Endpoints updated:** check-risk, execute, reentry-check
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- **Performance:** Indexed queries, <10ms overhead
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### Expected Impact
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- Eliminate tight-range flip-flops (Nov 14 chart: $141-145 SOL)
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- Reduce overtrading during sideways markets
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- Target: +5-10% win rate improvement
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- Better capital preservation in chop
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### Monitoring
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Watch for penalty messages in logs:
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```
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⚠️ Overtrading zone: 3 signals in 30min (-20 pts)
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⚠️ Flip-flop detected: opposite direction 12min ago (-25 pts)
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⚠️ Chop pattern: last 3 trades alternating (long → short → long) (-30 pts)
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```
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### Validation Plan
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1. Monitor next 5-10 signals
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2. Verify penalties trigger correctly
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3. Analyze if blocked signals would have lost
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4. If effective, proceed to Phase 6 (range compression)
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---
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## Phase 2: Pattern Analysis 🔜 NEXT
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**Prerequisites:** 10-20 blocked signals collected
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@@ -258,21 +308,163 @@ Monitoring Job (runs every 30 min)
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---
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## Phase 5: ML-Based Optimization 🧠 DISTANT FUTURE
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## Phase 5: ML-Based Scoring (DISTANT FUTURE) 🤖
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**Goal:** Use machine learning to optimize scoring weights
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**Prerequisites:** 200+ trades with quality scores, 100+ blocked signals
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**Complexity:** High
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**Status:** Future consideration - requires extensive data
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**Estimated Start:** Q2 2026 or later
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**Prerequisites:** 500+ trades with quality scores, proven manual optimization
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### Approach
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1. Extract features: ATR, ADX, RSI, volume, price position, timeframe
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2. Train model on: executed trades (outcome = P&L)
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3. Validate on: blocked signals (if price analysis complete)
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4. Generate: Optimal scoring weights for each feature
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5. Implement: Dynamic threshold adjustment based on market conditions
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1. Build ML model to predict trade success from metrics
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2. Use predicted success rate as quality score
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3. Continuously learn from new trades
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4. Auto-adjust weights based on market regime
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### Not Implemented Yet
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This is a future consideration only. Current data-driven approach is sufficient.
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### Requirements
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- Sufficient training data (500+ trades minimum)
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- Market regime classification system
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- ML infrastructure (model training, deployment)
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- Monitoring for model drift
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### Risks
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- Overfitting to past data
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- Model degrades as markets change
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- Black box decision-making
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- Increased system complexity
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**Note:** Only pursue if manual optimization plateaus or if pursuing ML as learning exercise.
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---
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## Phase 6: TradingView Range Compression Metrics (PLANNED) 📏
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**Status:** Planned - Next after frequency penalties prove effective
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**Estimated Start:** November 2025 (after Phase 1 validation)
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**Prerequisites:** Phase 1 deployed, 5-10 signals monitored
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### What It Does
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Adds NEW metrics to TradingView alerts to detect range compression and momentum mismatches that indicate choppy markets.
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### New TradingView Calculations
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```javascript
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// 1. Range compression (20-bar high/low range as % of price)
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rangePercent = ((highest(high, 20) - lowest(low, 20)) / close) * 100
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// 2. Price change over 5 bars (momentum check)
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priceChange5bars = ((close - close[5]) / close[5]) * 100
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// 3. ADX-momentum mismatch (ADX says trend but price not moving)
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adxMismatch = (adx > 15 AND abs(priceChange5bars) < 0.3)
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```
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### Quality Score Penalties
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- **Range < 1.5× ATR**: -20 points (compressed range = chop likely)
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- **ADX 15+ but price change < 0.3%**: -20 points (fake trend signal)
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- **Price oscillating around MA**: -15 points (whipsaw zone)
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### Why This Helps
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Current system can pass ADX 12-22 even when price just bouncing in tight zone. This detects the mismatch between "ADX says trending" vs "price says chopping."
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**Example from Nov 14 chart:** Multiple signals in $141-145 range passed quality check despite obvious consolidation. Range compression would have caught this.
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### Implementation Steps
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1. Add calculations to TradingView strategy (30 min)
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2. Update webhook JSON to include new fields (15 min)
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3. Modify `scoreSignalQuality()` to use range metrics (30 min)
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4. Test alerts on historical data (1 hour)
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5. Deploy and monitor (ongoing)
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### Expected Impact
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- Catch "fake trends" where ADX misleads
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- Reduce entries in tight consolidation zones
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- Improve win rate by 3-5% in choppy markets
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- Complement frequency penalties (Phase 1)
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### Success Metrics
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- Reduction in flip-flop losses
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- Fewer blocked signals in validated trending moves
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- Better P&L in sideways market conditions
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---
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## Phase 7: Volume Profile Integration (ADVANCED) 📊
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**Status:** Future consideration - most complex but most powerful
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**Estimated Start:** December 2025 or Q1 2026
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**Prerequisites:** Phase 6 completed, Volume S/R Zones V2 indicator expertise
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### What It Does
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Uses Volume S/R Zones V2 indicator to detect when price is stuck in high-volume consolidation nodes where flip-flops are most likely.
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### How Volume Profile Works
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- Shows horizontal bars representing volume at each price level
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- **Volume nodes** (thick bars) = high volume = price gets stuck (S/R zones)
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- **Thin zones** (low volume) = price moves through quickly (breakout zones)
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- Price bouncing inside volume node = high probability chop
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### Required Indicator Modifications
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Expose these values from Volume S/R Zones V2:
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```javascript
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// New indicator outputs (requires Pine Script modifications)
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inVolumeNode: true/false // Is price inside thick volume bar?
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nearVolumeEdge: true/false // Near top/bottom of node? (breakout setup)
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nodeStrength: 0-100 // Volume concentration at this level
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distanceFromNode: % // How far from nearest node?
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volumeNodeWidth: % // How wide is current node? (tight = strong)
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```
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### Quality Score Adjustments
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**Penalties:**
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- **In volume node (stuck)**: -25 points (high chop probability)
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- **Node strength > 80**: -30 points (VERY strong S/R = rejection likely)
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- **Tight node width (<1%)**: -35 points (extreme consolidation)
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**Bonuses:**
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- **Near volume edge + high volume**: +10 points (breakout setup)
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- **Distance from node > 2%**: +5 points (free movement zone)
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- **Breaking through node with volume**: +15 points (momentum trade)
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### Why This Is Powerful
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**Example from Nov 14 chart:** Price bouncing $141-145. Volume profile would show THICK volume node at that exact level - instant warning. System would:
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1. Detect price in node → apply -25 to -35 penalty
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2. Block most entries in that zone
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3. Wait for breakout above/below node
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4. Bonus points when price clears node with volume
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### Implementation Complexity
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**High - Requires:**
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1. Modify Volume S/R Zones indicator source code (Pine Script)
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2. Expose new variables in indicator settings
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3. Add webhook outputs from indicator (JSON formatting)
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4. Parse in n8n workflow (new data structure)
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5. Update quality scorer with volume logic (complex conditionals)
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6. Test on historical data with indicator overlays
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7. Validate against manual chart analysis
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**Estimated Time:** 2-3 hours + TradingView Pine Script knowledge
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### Risks & Considerations
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- Indicator must stay updated (TradingView updates can break)
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- Volume profile changes dynamically (recalculates with new data)
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- May over-filter in ranging markets (miss valid mean-reversion trades)
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- Complexity increases debugging difficulty
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### Success Metrics
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- Elimination of entries in obvious consolidation zones
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- Higher win rate specifically in ranging markets
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- Reduction in whipsaw losses (target: 30-50% fewer)
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- Improved P&L per trade (better entries near node edges)
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### Alternatives to Consider
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- Use simpler "volume concentration" metric (easier to calculate)
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- Implement fixed support/resistance zones instead of dynamic profile
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- Combine with Phase 6 range compression (may be sufficient)
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**Recommendation:** Only implement if Phase 1 + Phase 6 don't adequately solve flip-flop problem. Volume profile is most powerful but also most fragile.
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---
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## Progress Tracking
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---
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@@ -303,10 +495,14 @@ This is a future consideration only. Current data-driven approach is sufficient.
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## Progress Tracking
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### Milestones
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- [x] Nov 11, 2025: Phase 1 infrastructure complete
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- [ ] Target: ~Nov 20-25, 2025: Phase 1 complete (10-20 blocked signals)
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- [ ] Target: ~Nov 25-30, 2025: Phase 2 analysis complete
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- [ ] TBD: Phase 3 implementation (conditional)
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- [x] Nov 11, 2025: Phase 1 infrastructure complete (blocked signals tracking)
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- [x] Nov 14, 2025: Phase 1.5 complete (signal frequency penalties deployed)
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- [ ] Target: ~Nov 20-25, 2025: Phase 2 analysis complete (10-20 blocked signals)
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- [ ] Target: ~Nov 25-30, 2025: Phase 3 implementation (threshold adjustment)
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- [ ] Target: ~Dec 1-7, 2025: Phase 6 implementation (TradingView range metrics)
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- [ ] Target: ~Dec 15-31, 2025: Phase 7 evaluation (Volume Profile integration)
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- [ ] TBD: Phase 4 automation (blocked signal price tracking)
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- [ ] TBD: Phase 5 ML-based scoring (Q2 2026 or later)
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### Metrics to Watch
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- **Blocked signals collected:** 0/10 minimum
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