# Position Scaling & Exit Optimization Roadmap

## Current State (October 31, 2025)
- **Total Trades:** 26 completed
- **P&L:** +$27.12 (38% win rate)
- **Shorts:** 6.6x more profitable than longs (+$2.46 vs -$0.37 avg)
- **Current Strategy:** 75% at TP1 (+1.5%), 25% at TP2 (+3%), no runner
- **Problem:** Missed +41% move (would've closed at +3%)
- **Data Quality:** Ready to collect signal quality scores for correlation analysis

---

## Phase 1: Data Collection (CURRENT PHASE) 🔄
**Goal:** Gather 20-50 trades with quality scores before making strategy changes

### Data Points to Track:
- [ ] Signal quality score (0-100) for each trade
- [ ] Max Favorable Excursion (MFE) vs exit price differential
- [ ] ATR at entry vs actual price movement distance
- [ ] Time duration for winning trades (identify "quick wins" vs "runners")
- [ ] Quality score correlation with:
  - [ ] Win rate
  - [ ] Average P&L
  - [ ] MFE (runner potential)
  - [ ] Trade duration

### Analysis Queries (Run after 20+ scored trades):
```sql
-- Quality score vs performance
SELECT 
  CASE 
    WHEN "signalQualityScore" >= 80 THEN 'High (80-100)'
    WHEN "signalQualityScore" >= 70 THEN 'Medium (70-79)'
    ELSE 'Low (60-69)'
  END as quality_tier,
  COUNT(*) as trades,
  ROUND(AVG("realizedPnL")::numeric, 2) as avg_pnl,
  ROUND(AVG("maxFavorableExcursion")::numeric, 2) as avg_mfe,
  ROUND(100.0 * SUM(CASE WHEN "realizedPnL" > 0 THEN 1 ELSE 0 END) / COUNT(*)::numeric, 1) as win_rate
FROM "Trade"
WHERE "signalQualityScore" IS NOT NULL AND "exitReason" IS NOT NULL
GROUP BY quality_tier
ORDER BY quality_tier;

-- ATR correlation with movement
SELECT 
  direction,
  ROUND(AVG(atr)::numeric, 2) as avg_atr,
  ROUND(AVG("maxFavorableExcursion")::numeric, 2) as avg_mfe,
  ROUND(AVG(ABS("exitPrice" - "entryPrice") / "entryPrice" * 100)::numeric, 2) as avg_move_pct
FROM "Trade"
WHERE atr IS NOT NULL AND "exitReason" IS NOT NULL
GROUP BY direction;

-- Runner potential analysis (how many went beyond TP2?)
SELECT 
  "exitReason",
  COUNT(*) as count,
  ROUND(AVG("maxFavorableExcursion")::numeric, 2) as avg_mfe,
  ROUND(AVG("realizedPnL")::numeric, 2) as avg_pnl,
  -- MFE > 3% indicates runner potential
  SUM(CASE WHEN "maxFavorableExcursion" > 3.0 THEN 1 ELSE 0 END) as runner_potential_count
FROM "Trade"
WHERE "exitReason" IS NOT NULL
GROUP BY "exitReason"
ORDER BY count DESC;
```

---

## Phase 2: ATR-Based Dynamic Targets ⏳
**Prerequisites:** ✅ 20+ trades with ATR data collected

### Implementation Tasks:
- [ ] **Add ATR normalization function** (`lib/trading/scaling-strategy.ts`)
  - [ ] Calculate normalized ATR factor: `(current_ATR / baseline_ATR)`
  - [ ] Baseline ATR = 2.0 for SOL-PERP (adjust based on data)
  
- [ ] **Update TP calculation in `lib/drift/orders.ts`**
  - [ ] TP1: `entry + (1.5% × ATR_factor)` instead of fixed 1.5%
  - [ ] TP2: `entry + (3.0% × ATR_factor)` instead of fixed 3.0%
  
- [ ] **Modify Position Manager monitoring loop**
  - [ ] Store `atrFactor` in `ActiveTrade` interface
  - [ ] Adjust dynamic SL movements by ATR factor
  - [ ] Update breakeven trigger: `+0.5% × ATR_factor`
  - [ ] Update profit lock trigger: `+1.2% × ATR_factor`

- [ ] **Testing:**
  - [ ] Backtest on historical trades with ATR data
  - [ ] Calculate improvement: old fixed % vs new ATR-adjusted
  - [ ] Run 10 test trades before production

**Expected Outcome:** Wider targets in high volatility, tighter in low volatility

---

## Phase 3: Signal Quality-Based Scaling ⏳
**Prerequisites:** ✅ Phase 2 complete, ✅ 30+ trades with quality scores, ✅ Clear correlation proven

### Implementation Tasks:
- [ ] **Create quality tier configuration** (`config/trading.ts`)
  ```typescript
  export interface QualityTierConfig {
    minScore: number
    maxScore: number
    tp1Percentage: number  // How much to take off
    tp2Percentage: number
    runnerPercentage: number
    atrMultiplierTP1: number
    atrMultiplierTP2: number
    trailingStopATR: number
  }
  ```

- [ ] **Define three tiers based on data analysis:**
  - [ ] **High Quality (80-100):** Aggressive runner strategy
    - TP1: 50% off at 2.0×ATR
    - TP2: 25% off at 4.0×ATR
    - Runner: 25% with 2.5×ATR trailing stop
  
  - [ ] **Medium Quality (70-79):** Balanced (current-ish)
    - TP1: 75% off at 1.5×ATR
    - TP2: 25% off at 3.0×ATR
    - Runner: None (full exit at TP2)
  
  - [ ] **Low Quality (60-69):** Conservative quick exit
    - TP1: 100% off at 1.0×ATR
    - TP2: None
    - Runner: None

- [ ] **Update `placeExitOrders()` function**
  - [ ] Accept `qualityScore` parameter
  - [ ] Select tier config based on score
  - [ ] Place orders according to tier rules
  - [ ] Only place TP2 order if tier has runner

- [ ] **Update Position Manager**
  - [ ] Store `qualityScore` in `ActiveTrade`
  - [ ] Apply tier-specific trailing stop logic
  - [ ] Handle partial closes (50%, 75%, or 100%)

- [ ] **Database tracking:**
  - [ ] Add `scalingTier` field to Trade model (high/medium/low)
  - [ ] Track which tier was used for each trade

**Expected Outcome:** High quality signals let winners run, low quality signals take quick profits

---

## Phase 4: Direction-Based Optimization ⏳
**Prerequisites:** ✅ Phase 3 complete, ✅ Directional edge confirmed in 50+ trades

### Implementation Tasks:
- [ ] **Analyze directional performance** (Re-run after 50 trades)
  - [ ] Compare long vs short win rates
  - [ ] Compare long vs short avg P&L
  - [ ] Compare long vs short MFE (runner potential)
  - [ ] **Decision:** If shorts still 3x+ better, implement direction bias

- [ ] **Direction-specific configs** (`config/trading.ts`)
  ```typescript
  export interface DirectionConfig {
    shortTP1Pct: number  // If shorts have edge, wider targets
    shortTP2Pct: number
    shortRunnerPct: number
    longTP1Pct: number   // If longs struggle, tighter defensive
    longTP2Pct: number
    longRunnerPct: number
  }
  ```

- [ ] **Implementation in order placement:**
  - [ ] Check `direction` field
  - [ ] Apply direction-specific multipliers on top of quality tier
  - [ ] Example: Short with high quality = 2.0×ATR × 1.2 (direction bonus)

- [ ] **A/B Testing:**
  - [ ] Run 20 trades with direction bias
  - [ ] Run 20 trades without (control group)
  - [ ] Compare results before full rollout

**Expected Outcome:** Shorts get wider targets if edge persists, longs stay defensive

---

## Phase 5: Runner with Trailing Stop ⏳
**Prerequisites:** ✅ Phase 3 complete, ✅ Runner potential confirmed (MFE > TP2 in 30%+ of trades)

### Implementation Tasks:
- [ ] **Add trailing stop logic to Position Manager**
  - [ ] Track `peakPrice` for runner portion
  - [ ] Update `peakPrice` when current price exceeds it
  - [ ] Calculate trailing stop distance: `peakPrice - (trailingStopATR × ATR)`
  - [ ] Close runner when price crosses trailing stop

- [ ] **Modify `checkTargets()` function:**
  - [ ] After TP2 hit, activate `trailingStopActive = true`
  - [ ] Monitor runner separately from main position
  - [ ] Close runner with `closePosition()` when trailing stop breached

- [ ] **Add runner tracking to database:**
  - [ ] `runnerSize` field (amount left after TP2)
  - [ ] `runnerPeakPrice` field
  - [ ] `runnerExitPrice` field
  - [ ] `runnerExitReason` field ('trailing_stop', 'manual', etc.)

- [ ] **Testing:**
  - [ ] Backtest: Simulate runners on TP2 trades
  - [ ] Calculate: How much extra profit would runners have captured?
  - [ ] Test with small position sizes first ($10-20)

- [ ] **Edge case handling:**
  - [ ] What if runner goes negative after TP2? (Move SL to breakeven on runner)
  - [ ] What if bot restarts with active runner? (Restore from database)
  - [ ] What if on-chain runner order fails? (Position Manager backup)

**Expected Outcome:** Capture extended moves like the +41% trade that's currently open

---

## Phase 6: Advanced ML-Based Exit Prediction (Future) 🔮
**Prerequisites:** ✅ 100+ trades with all metrics, ✅ Phases 1-5 complete

### Research Tasks:
- [ ] **Feature engineering:**
  - [ ] Input features: ATR, ADX, RSI, volumeRatio, pricePosition, quality score, direction, timeframe
  - [ ] Target variable: Did trade reach 2×TP2? (binary classification)
  - [ ] Additional target: Max profit % reached (regression)

- [ ] **Model training:**
  - [ ] Split data: 70% train, 30% test
  - [ ] Try models: Logistic Regression, Random Forest, XGBoost
  - [ ] Evaluate: Precision, Recall, F1 for runner prediction
  - [ ] Cross-validation with time-based splits (avoid lookahead bias)

- [ ] **Integration:**
  - [ ] `/api/trading/predict-runner` endpoint
  - [ ] Call during trade execution to get runner probability
  - [ ] Adjust runner size based on probability: 0-15% runner if low, 25-35% if high

- [ ] **Monitoring:**
  - [ ] Track model accuracy over time
  - [ ] Retrain monthly with new data
  - [ ] A/B test: ML-based vs rule-based scaling

**Expected Outcome:** AI predicts which trades have runner potential before entry

---

## Quick Wins (Can Do Anytime) ⚡

- [ ] **Manual runner management for current trade**
  - [ ] Move SL to +30% profit lock on current +41% SOL position
  - [ ] Monitor manually until trend breaks
  - [ ] Document outcome: How much did runner capture?

- [ ] **Add "runner stats" to analytics dashboard**
  - [ ] Show: How many trades went beyond TP2?
  - [ ] Show: Average MFE for TP2 exits
  - [ ] Show: Estimated missed profit from not having runners

- [ ] **Database views for common queries**
  - [ ] Create `vw_quality_performance` view
  - [ ] Create `vw_runner_potential` view
  - [ ] Create `vw_directional_edge` view

- [ ] **Alerts for exceptional trades**
  - [ ] Telegram notification when MFE > 5% (runner candidate)
  - [ ] Telegram notification when quality score > 90 (premium setup)

---

## Decision Gates 🚦

**Before Phase 2 (ATR-based):**
- ✅ Have 20+ trades with ATR data
- ✅ ATR values look reasonable (0.5 - 3.5 range)
- ✅ Clear volatility variation observed

**Before Phase 3 (Quality tiers):**
- ✅ Have 30+ trades with quality scores
- ✅ Statistical significance: High quality scores show measurably better outcomes
- ✅ Correlation coefficient > 0.3 between quality and P&L

**Before Phase 4 (Direction bias):**
- ✅ Have 50+ trades (25+ each direction)
- ✅ Directional edge persists (3x+ performance gap)
- ✅ Edge is consistent across different market conditions

**Before Phase 5 (Runners):**
- ✅ 30%+ of trades show MFE > TP2
- ✅ Average MFE significantly higher than TP2 level
- ✅ Phases 2-3 stable and profitable

**Before Phase 6 (ML):**
- ✅ 100+ trades with complete feature data
- ✅ Proven improvement from Phases 1-5
- ✅ Computational resources available (training time)

---

## Notes & Observations

### Current Trade Example (Oct 31, 2025):
- Entry: $182.73
- Current: $186.56 (+2.1%, +$11.30)
- **Actual P&L: +41% unrealized** 🚀
- Current strategy would've closed at +3% ($16.20)
- **Missed profit: ~$59** if this hits +41% realized
- **Lesson:** Need runner implementation ASAP once data supports it

### Key Metrics to Watch:
- Win rate by quality tier
- Average MFE vs exit price gap
- Correlation between ATR and price movement
- Shorts vs longs performance delta
- Percentage of trades that go beyond TP2

### Strategy Validation:
Run this after each phase to validate improvement:
```sql
-- Compare old vs new strategy performance
SELECT 
  'Phase X' as phase,
  COUNT(*) as trades,
  ROUND(AVG("realizedPnL")::numeric, 2) as avg_pnl,
  ROUND(SUM("realizedPnL")::numeric, 2) as total_pnl,
  ROUND(100.0 * SUM(CASE WHEN "realizedPnL" > 0 THEN 1 ELSE 0 END) / COUNT(*)::numeric, 1) as win_rate
FROM "Trade"
WHERE "createdAt" >= '[phase_start_date]'
  AND "exitReason" IS NOT NULL;
```

---

## Timeline Estimate

- **Phase 1 (Data Collection):** 2-4 weeks (depends on signal frequency)
- **Phase 2 (ATR-based):** 3-5 days implementation + 1 week testing
- **Phase 3 (Quality tiers):** 5-7 days implementation + 2 weeks testing
- **Phase 4 (Direction bias):** 2-3 days implementation + 1 week testing
- **Phase 5 (Runners):** 7-10 days implementation + 2 weeks testing
- **Phase 6 (ML):** 2-3 weeks research + implementation

**Total estimated time:** 3-4 months from start to Phase 5 complete

---

## Success Criteria

**Phase 2 Success:**
- [ ] Average P&L increases by 10%+ vs fixed targets
- [ ] Win rate stays stable or improves
- [ ] No increase in max drawdown

**Phase 3 Success:**
- [ ] High quality trades show 20%+ better P&L than low quality
- [ ] Overall P&L increases by 15%+ vs Phase 2
- [ ] Quality filtering prevents some losing trades

**Phase 4 Success:**
- [ ] Directional P&L gap narrows (improve weak direction)
- [ ] Or: Strong direction P&L improves further (if edge is real)

**Phase 5 Success:**
- [ ] Runners capture 20%+ more profit on winning trades
- [ ] Total P&L increases by 25%+ vs Phase 4
- [ ] Runners don't create new losing trades

**Overall Success (All Phases):**
- [ ] 2x total P&L vs baseline strategy
- [ ] 50%+ win rate (up from 38%)
- [ ] Average winner > 2× average loser
- [ ] Profit factor > 2.0

---

**Status:** Phase 1 (Data Collection) - Active 🔄
**Last Updated:** October 31, 2025
**Next Review:** After 20 trades with quality scores collected