Enhancement #6 - SL Distance Validation (Data Collection Phase): - Added slDistanceAtEntry field to StopHunt schema - Calculates distance from revenge entry to stop zone (LONG vs SHORT logic) - Logs distance in dollars + × ATR multiplier - Purpose: Collect 20+ revenge trade samples for optimal multiplier analysis - Created comprehensive analysis guide with SQL queries - Decision deferred until empirical data collected Enhancement #1 - ADX Confirmation (Implementation Plan): - Documented complete 1-minute TradingView alert strategy - Storage analysis: 19.44 MB/month for 3 symbols (negligible) - Two-phase approach: Cache-only MVP → Optional DB persistence - Provided TradingView Pine Script (ready to use) - Cost breakdown: Pro subscription $49.95/month required - Benefits: Real-time ADX, pattern recognition, ML features - Implementation checklist with validation phases Files Changed: - prisma/schema.prisma: +1 field (slDistanceAtEntry) - lib/trading/stop-hunt-tracker.ts: +10 lines (distance calculation + logging) - docs/1MIN_MARKET_DATA_IMPLEMENTATION.md: NEW (comprehensive plan) - docs/ENHANCEMENT_6_ANALYSIS_GUIDE.md: NEW (SQL queries + decision matrix) Status: Enhancement #4 and #10 deployed (previous commit) Enhancement #6 data collection enabled (this commit) Awaiting 20+ revenge trades for Enhancement #6 decision
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mindesbunister
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docs/ENHANCEMENT_6_ANALYSIS_GUIDE.md
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# Enhancement #6 - SL Distance Analysis Guide
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**Status:** DATA COLLECTION ENABLED (Nov 27, 2025)
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**Purpose:** Gather revenge trade data to determine optimal SL distance multiplier
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---
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## What We're Tracking Now
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### Database Fields Added
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```sql
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-- In StopHunt table:
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slDistanceAtEntry Float? -- Distance from entry to stop zone (absolute value)
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revengeOutcome String? -- "TP1", "TP2", "SL", "TRAILING_SL"
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revengePnL Float? -- Actual P&L from revenge trade
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revengeFailedReason String? -- "stopped_again" if re-stopped
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originalATR Float? -- ATR at original stop-out
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```
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### What Gets Calculated
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```typescript
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// When revenge trade executes:
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const slDistance = direction === 'long'
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? currentPrice - stopHuntPrice // LONG: Room below entry
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: stopHuntPrice - currentPrice // SHORT: Room above entry
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// Stored: Math.abs(slDistance)
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// Logged: Distance in $ and in ATR multiples
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// Example: "$1.48 distance (2.47× ATR)"
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```
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---
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## Analysis Queries (After 20+ Revenge Trades)
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### Query 1: SL Distance vs Outcome
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```sql
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-- Do tighter entries get re-stopped more often?
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SELECT
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CASE
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WHEN "slDistanceAtEntry" / "originalATR" < 1.0 THEN '<1× ATR (Very Tight)'
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WHEN "slDistanceAtEntry" / "originalATR" < 1.5 THEN '1-1.5× ATR (Tight)'
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WHEN "slDistanceAtEntry" / "originalATR" < 2.0 THEN '1.5-2× ATR (Moderate)'
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WHEN "slDistanceAtEntry" / "originalATR" < 2.5 THEN '2-2.5× ATR (Safe)'
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ELSE '2.5×+ ATR (Very Safe)'
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END as distance_tier,
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COUNT(*) as total_trades,
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-- Win rate
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ROUND(100.0 * SUM(CASE
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WHEN "revengeOutcome" IN ('TP1', 'TP2', 'TRAILING_SL') THEN 1
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ELSE 0
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END) / COUNT(*), 1) as win_rate,
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-- Re-stopped rate
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ROUND(100.0 * SUM(CASE
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WHEN "revengeFailedReason" = 'stopped_again' THEN 1
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ELSE 0
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END) / COUNT(*), 1) as restopped_rate,
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-- Average P&L
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ROUND(AVG("revengePnL"), 2) as avg_pnl,
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-- Total P&L
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ROUND(SUM("revengePnL"), 2) as total_pnl
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FROM "StopHunt"
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WHERE "revengeExecuted" = true
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AND "slDistanceAtEntry" IS NOT NULL
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AND "originalATR" IS NOT NULL
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GROUP BY distance_tier
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ORDER BY MIN("slDistanceAtEntry" / "originalATR");
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```
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**Expected Output:**
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```
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distance_tier | total | win_rate | restopped_rate | avg_pnl | total_pnl
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-----------------------+-------+----------+----------------+---------+-----------
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<1× ATR (Very Tight) | 5 | 40.0 | 60.0 | -25.50 | -127.50
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1-1.5× ATR (Tight) | 8 | 62.5 | 25.0 | 15.25 | 122.00
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1.5-2× ATR (Moderate) | 12 | 75.0 | 16.7 | 42.30 | 507.60
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2-2.5× ATR (Safe) | 7 | 85.7 | 14.3 | 68.45 | 479.15
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2.5×+ ATR (Very Safe) | 3 | 100.0 | 0.0 | 92.30 | 276.90
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```
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**Decision Logic:**
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- If <1× ATR has high re-stop rate (>50%): Filter needed
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- If 1.5-2× ATR has best risk/reward: Use 1.5× multiplier
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- If 2-2.5× ATR has highest win rate: Use 2.0× multiplier
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- If Very Safe (2.5×+) rarely happens: Lower multiplier to catch more
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---
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### Query 2: Missed Opportunities
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```sql
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-- How many revenge opportunities didn't execute because reversal wasn't deep enough?
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SELECT
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COUNT(*) as total_stop_hunts,
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-- How many reversed but didn't enter
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SUM(CASE
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WHEN "revengeExecuted" = false
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AND "revengeWindowExpired" = true
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AND ("lowestPriceAfterStop" < "originalEntryPrice" -- LONG reversed
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OR "highestPriceAfterStop" > "originalEntryPrice") -- SHORT reversed
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THEN 1 ELSE 0
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END) as missed_reversals,
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-- Average distance of missed reversals
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ROUND(AVG(CASE
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WHEN "revengeExecuted" = false
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AND "revengeWindowExpired" = true
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THEN ABS("lowestPriceAfterStop" - "stopHuntPrice")
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END), 2) as avg_missed_distance
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FROM "StopHunt"
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WHERE "originalATR" IS NOT NULL;
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```
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**Tells us:** If we filter too strictly (e.g., 3× ATR), how many opportunities do we lose?
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---
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### Query 3: Time to Re-Stop
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```sql
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-- How quickly do re-stopped revenge trades fail?
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SELECT
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CASE
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WHEN EXTRACT(EPOCH FROM ("Trade"."exitTime" - "StopHunt"."revengeTime")) < 300 THEN '<5min (Instant)'
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WHEN EXTRACT(EPOCH FROM ("Trade"."exitTime" - "StopHunt"."revengeTime")) < 900 THEN '5-15min (Fast)'
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WHEN EXTRACT(EPOCH FROM ("Trade"."exitTime" - "StopHunt"."revengeTime")) < 1800 THEN '15-30min (Moderate)'
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ELSE '30min+ (Slow)'
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END as time_to_restop,
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COUNT(*) as count,
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ROUND(AVG("StopHunt"."slDistanceAtEntry" / "StopHunt"."originalATR"), 2) as avg_atr_multiple
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FROM "StopHunt"
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INNER JOIN "Trade" ON "StopHunt"."revengeTradeId" = "Trade"."id"
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WHERE "StopHunt"."revengeFailedReason" = 'stopped_again'
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GROUP BY time_to_restop
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ORDER BY MIN(EXTRACT(EPOCH FROM ("Trade"."exitTime" - "StopHunt"."revengeTime")));
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```
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**Insight:** If most re-stops happen <5min, they're wicks. Wider SL distance helps.
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---
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### Query 4: Direction-Specific Analysis
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```sql
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-- Do LONGs vs SHORTs need different SL distances?
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SELECT
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"direction",
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COUNT(*) as total,
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ROUND(AVG("slDistanceAtEntry" / "originalATR"), 2) as avg_atr_multiple,
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ROUND(100.0 * SUM(CASE
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WHEN "revengeFailedReason" = 'stopped_again' THEN 1 ELSE 0
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END) / COUNT(*), 1) as restopped_rate,
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ROUND(AVG("revengePnL"), 2) as avg_pnl
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FROM "StopHunt"
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WHERE "revengeExecuted" = true
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AND "slDistanceAtEntry" IS NOT NULL
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GROUP BY "direction";
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```
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**Possible outcome:** SHORTs need wider distance (more volatile reversals)
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---
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## Decision Matrix (After 20+ Trades)
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### Scenario A: Tight Entries Work
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**Data shows:** 1-1.5× ATR has 70%+ win rate, low re-stop rate
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**Decision:** Use 1.5× ATR multiplier (or no filter at all)
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**Trade-off:** More revenge opportunities, slightly higher re-stop risk
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### Scenario B: Moderate Distance Optimal
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**Data shows:** 1.5-2× ATR has best risk/reward
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**Decision:** Use 1.75× or 2.0× ATR multiplier
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**Trade-off:** Balanced approach (recommended starting point)
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### Scenario C: Wide Distance Required
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**Data shows:** <2× ATR has >40% re-stop rate
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**Decision:** Use 2.5× or 3.0× ATR multiplier
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**Trade-off:** Fewer opportunities, but much higher win rate
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### Scenario D: Direction-Specific
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**Data shows:** LONGs work at 1.5×, SHORTs need 2.5×
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**Decision:** Implement separate multipliers per direction
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**Trade-off:** More complex but optimized
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---
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## Implementation Plan (After Data Collection)
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### Step 1: Review Data (After 20 Revenge Trades)
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```bash
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# Run Query 1 in database
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docker exec trading-bot-postgres psql -U postgres -d trading_bot_v4 -c "
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[Query 1 from above]
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"
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# Save results to CSV
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# Analyze in spreadsheet or share with me
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```
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### Step 2: Calculate Optimal Multiplier
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```python
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# Simple calculation:
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optimal_multiplier = (
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sum(distance * pnl for each tier) /
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sum(pnl for each tier)
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)
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# Weight by win rate:
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optimal_multiplier_weighted = (
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sum(distance * win_rate * count for each tier) /
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sum(win_rate * count for each tier)
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)
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```
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### Step 3: Implement Filter
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```typescript
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// In stop-hunt-tracker.ts shouldExecuteRevenge()
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const slDistance = stopHunt.direction === 'long'
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? currentPrice - stopHunt.stopHuntPrice
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: stopHunt.stopHuntPrice - currentPrice
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const minSafeDistance = stopHunt.originalATR * 2.0 // Use data-driven value
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if (Math.abs(slDistance) < minSafeDistance) {
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console.log(`⚠️ SL distance too tight: ${Math.abs(slDistance).toFixed(2)} < ${minSafeDistance.toFixed(2)}`)
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return false
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}
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```
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### Step 4: A/B Test (Optional)
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```typescript
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// Randomly assign filter on/off
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const useFilter = Math.random() > 0.5
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if (useFilter && Math.abs(slDistance) < minSafeDistance) {
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await prisma.stopHunt.update({
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where: { id: stopHunt.id },
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data: {
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notes: 'Would have been filtered by SL distance check',
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filterTestGroup: 'A'
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}
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})
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return false
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}
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// Compare groups after 40 trades (20 each)
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```
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---
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## Monitoring Dashboard
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### Key Metrics to Track
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1. **Re-Stop Rate:** % of revenge trades that hit SL immediately
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2. **Average SL Distance:** Median ATR multiple at entry
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3. **Win Rate by Distance:** Scatterplot (x=distance, y=outcome)
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4. **Missed Opportunities:** Reversals that didn't execute
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### Alert Thresholds
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- **High re-stop rate:** >30% revenge trades fail = tighten filter
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- **Low execution rate:** <50% stop hunts execute = loosen filter
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- **Negative total P&L:** Sum of revenge P&L < 0 = pause system
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---
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## Timeline
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**Week 1-2:** Data collection (first 5-10 revenge trades)
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**Week 3:** Preliminary analysis (spot obvious patterns)
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**Week 4:** Full analysis (20+ trades, statistical significance)
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**Week 5:** Implement optimal filter based on data
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**Week 6+:** Monitor improvement, adjust if needed
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**Current Status:** ✅ Data collection enabled, awaiting first revenge trades
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---
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## Example Real-World Analysis
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### Hypothetical Results After 25 Revenge Trades
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```
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Distance Tier | Trades | Win Rate | Avg P&L | Total P&L
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---------------------|--------|----------|---------|----------
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<1× ATR | 3 | 33% | -$45 | -$135
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1-1.5× ATR | 7 | 57% | $12 | $84
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1.5-2× ATR | 9 | 78% | $58 | $522
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2-2.5× ATR | 4 | 75% | $62 | $248
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2.5×+ ATR | 2 | 100% | $89 | $178
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```
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**Conclusion from data:**
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- <1× ATR: High failure rate, negative P&L → FILTER OUT
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- 1-1.5× ATR: Break-even, mediocre win rate → QUESTIONABLE
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- 1.5-2× ATR: **SWEET SPOT** - 9 trades, 78% WR, +$522
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- 2-2.5× ATR: Good but fewer opportunities
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- 2.5×+ ATR: Perfect WR but rarely happens
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**Optimal setting:** 1.5× ATR minimum distance
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**Expected impact:** Block 10 bad trades, keep 15 good trades = +$606 net improvement
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---
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## Questions for Future AI Agents
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1. **What's the optimal multiplier?** → Check Query 1 results
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2. **Are we missing too many opportunities?** → Check Query 2 results
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3. **Do re-stops happen instantly or slowly?** → Check Query 3 results
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4. **Do LONGs vs SHORTs need different filters?** → Check Query 4 results
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5. **Should we implement the filter?** → If re-stop rate >30%, YES
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**Remember:** Data > theory. Don't implement filter until data proves it helps.
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