9989f75955
- 6-phase development plan: data collection → ATR-based → quality tiers → direction bias → runners → ML - Each phase has clear prerequisites, implementation tasks, and success criteria - Decision gates based on data validation (20+ trades for Phase 2, 30+ for Phase 3, etc.) - Includes SQL queries for analysis and performance validation - Documents current +41% trade as motivation for runner implementation - Estimated 3-4 months timeline to complete Phases 1-5
382 lines
13 KiB
Markdown
382 lines
13 KiB
Markdown
# Position Scaling & Exit Optimization Roadmap
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## Current State (October 31, 2025)
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- **Total Trades:** 26 completed
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- **P&L:** +$27.12 (38% win rate)
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- **Shorts:** 6.6x more profitable than longs (+$2.46 vs -$0.37 avg)
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- **Current Strategy:** 75% at TP1 (+1.5%), 25% at TP2 (+3%), no runner
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- **Problem:** Missed +41% move (would've closed at +3%)
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- **Data Quality:** Ready to collect signal quality scores for correlation analysis
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---
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## Phase 1: Data Collection (CURRENT PHASE) 🔄
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**Goal:** Gather 20-50 trades with quality scores before making strategy changes
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### Data Points to Track:
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- [ ] Signal quality score (0-100) for each trade
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- [ ] Max Favorable Excursion (MFE) vs exit price differential
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- [ ] ATR at entry vs actual price movement distance
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- [ ] Time duration for winning trades (identify "quick wins" vs "runners")
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- [ ] Quality score correlation with:
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- [ ] Win rate
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- [ ] Average P&L
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- [ ] MFE (runner potential)
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- [ ] Trade duration
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### Analysis Queries (Run after 20+ scored trades):
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```sql
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-- Quality score vs performance
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SELECT
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CASE
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WHEN "signalQualityScore" >= 80 THEN 'High (80-100)'
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WHEN "signalQualityScore" >= 70 THEN 'Medium (70-79)'
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ELSE 'Low (60-69)'
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END as quality_tier,
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COUNT(*) as trades,
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ROUND(AVG("realizedPnL")::numeric, 2) as avg_pnl,
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ROUND(AVG("maxFavorableExcursion")::numeric, 2) as avg_mfe,
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ROUND(100.0 * SUM(CASE WHEN "realizedPnL" > 0 THEN 1 ELSE 0 END) / COUNT(*)::numeric, 1) as win_rate
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FROM "Trade"
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WHERE "signalQualityScore" IS NOT NULL AND "exitReason" IS NOT NULL
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GROUP BY quality_tier
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ORDER BY quality_tier;
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-- ATR correlation with movement
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SELECT
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direction,
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ROUND(AVG(atr)::numeric, 2) as avg_atr,
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ROUND(AVG("maxFavorableExcursion")::numeric, 2) as avg_mfe,
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ROUND(AVG(ABS("exitPrice" - "entryPrice") / "entryPrice" * 100)::numeric, 2) as avg_move_pct
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FROM "Trade"
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WHERE atr IS NOT NULL AND "exitReason" IS NOT NULL
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GROUP BY direction;
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-- Runner potential analysis (how many went beyond TP2?)
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SELECT
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"exitReason",
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COUNT(*) as count,
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ROUND(AVG("maxFavorableExcursion")::numeric, 2) as avg_mfe,
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ROUND(AVG("realizedPnL")::numeric, 2) as avg_pnl,
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-- MFE > 3% indicates runner potential
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SUM(CASE WHEN "maxFavorableExcursion" > 3.0 THEN 1 ELSE 0 END) as runner_potential_count
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FROM "Trade"
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WHERE "exitReason" IS NOT NULL
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GROUP BY "exitReason"
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ORDER BY count DESC;
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```
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---
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## Phase 2: ATR-Based Dynamic Targets ⏳
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**Prerequisites:** ✅ 20+ trades with ATR data collected
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### Implementation Tasks:
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- [ ] **Add ATR normalization function** (`lib/trading/scaling-strategy.ts`)
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- [ ] Calculate normalized ATR factor: `(current_ATR / baseline_ATR)`
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- [ ] Baseline ATR = 2.0 for SOL-PERP (adjust based on data)
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- [ ] **Update TP calculation in `lib/drift/orders.ts`**
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- [ ] TP1: `entry + (1.5% × ATR_factor)` instead of fixed 1.5%
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- [ ] TP2: `entry + (3.0% × ATR_factor)` instead of fixed 3.0%
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- [ ] **Modify Position Manager monitoring loop**
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- [ ] Store `atrFactor` in `ActiveTrade` interface
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- [ ] Adjust dynamic SL movements by ATR factor
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- [ ] Update breakeven trigger: `+0.5% × ATR_factor`
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- [ ] Update profit lock trigger: `+1.2% × ATR_factor`
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- [ ] **Testing:**
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- [ ] Backtest on historical trades with ATR data
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- [ ] Calculate improvement: old fixed % vs new ATR-adjusted
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- [ ] Run 10 test trades before production
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**Expected Outcome:** Wider targets in high volatility, tighter in low volatility
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---
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## Phase 3: Signal Quality-Based Scaling ⏳
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**Prerequisites:** ✅ Phase 2 complete, ✅ 30+ trades with quality scores, ✅ Clear correlation proven
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### Implementation Tasks:
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- [ ] **Create quality tier configuration** (`config/trading.ts`)
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```typescript
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export interface QualityTierConfig {
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minScore: number
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maxScore: number
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tp1Percentage: number // How much to take off
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tp2Percentage: number
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runnerPercentage: number
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atrMultiplierTP1: number
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atrMultiplierTP2: number
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trailingStopATR: number
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}
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```
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- [ ] **Define three tiers based on data analysis:**
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- [ ] **High Quality (80-100):** Aggressive runner strategy
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- TP1: 50% off at 2.0×ATR
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- TP2: 25% off at 4.0×ATR
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- Runner: 25% with 2.5×ATR trailing stop
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- [ ] **Medium Quality (70-79):** Balanced (current-ish)
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- TP1: 75% off at 1.5×ATR
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- TP2: 25% off at 3.0×ATR
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- Runner: None (full exit at TP2)
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- [ ] **Low Quality (60-69):** Conservative quick exit
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- TP1: 100% off at 1.0×ATR
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- TP2: None
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- Runner: None
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- [ ] **Update `placeExitOrders()` function**
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- [ ] Accept `qualityScore` parameter
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- [ ] Select tier config based on score
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- [ ] Place orders according to tier rules
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- [ ] Only place TP2 order if tier has runner
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- [ ] **Update Position Manager**
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- [ ] Store `qualityScore` in `ActiveTrade`
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- [ ] Apply tier-specific trailing stop logic
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- [ ] Handle partial closes (50%, 75%, or 100%)
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- [ ] **Database tracking:**
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- [ ] Add `scalingTier` field to Trade model (high/medium/low)
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- [ ] Track which tier was used for each trade
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**Expected Outcome:** High quality signals let winners run, low quality signals take quick profits
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---
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## Phase 4: Direction-Based Optimization ⏳
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**Prerequisites:** ✅ Phase 3 complete, ✅ Directional edge confirmed in 50+ trades
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### Implementation Tasks:
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- [ ] **Analyze directional performance** (Re-run after 50 trades)
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- [ ] Compare long vs short win rates
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- [ ] Compare long vs short avg P&L
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- [ ] Compare long vs short MFE (runner potential)
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- [ ] **Decision:** If shorts still 3x+ better, implement direction bias
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- [ ] **Direction-specific configs** (`config/trading.ts`)
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```typescript
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export interface DirectionConfig {
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shortTP1Pct: number // If shorts have edge, wider targets
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shortTP2Pct: number
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shortRunnerPct: number
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longTP1Pct: number // If longs struggle, tighter defensive
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longTP2Pct: number
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longRunnerPct: number
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}
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```
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- [ ] **Implementation in order placement:**
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- [ ] Check `direction` field
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- [ ] Apply direction-specific multipliers on top of quality tier
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- [ ] Example: Short with high quality = 2.0×ATR × 1.2 (direction bonus)
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- [ ] **A/B Testing:**
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- [ ] Run 20 trades with direction bias
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- [ ] Run 20 trades without (control group)
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- [ ] Compare results before full rollout
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**Expected Outcome:** Shorts get wider targets if edge persists, longs stay defensive
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---
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## Phase 5: Runner with Trailing Stop ⏳
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**Prerequisites:** ✅ Phase 3 complete, ✅ Runner potential confirmed (MFE > TP2 in 30%+ of trades)
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### Implementation Tasks:
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- [ ] **Add trailing stop logic to Position Manager**
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- [ ] Track `peakPrice` for runner portion
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- [ ] Update `peakPrice` when current price exceeds it
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- [ ] Calculate trailing stop distance: `peakPrice - (trailingStopATR × ATR)`
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- [ ] Close runner when price crosses trailing stop
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- [ ] **Modify `checkTargets()` function:**
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- [ ] After TP2 hit, activate `trailingStopActive = true`
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- [ ] Monitor runner separately from main position
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- [ ] Close runner with `closePosition()` when trailing stop breached
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- [ ] **Add runner tracking to database:**
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- [ ] `runnerSize` field (amount left after TP2)
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- [ ] `runnerPeakPrice` field
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- [ ] `runnerExitPrice` field
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- [ ] `runnerExitReason` field ('trailing_stop', 'manual', etc.)
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- [ ] **Testing:**
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- [ ] Backtest: Simulate runners on TP2 trades
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- [ ] Calculate: How much extra profit would runners have captured?
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- [ ] Test with small position sizes first ($10-20)
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- [ ] **Edge case handling:**
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- [ ] What if runner goes negative after TP2? (Move SL to breakeven on runner)
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- [ ] What if bot restarts with active runner? (Restore from database)
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- [ ] What if on-chain runner order fails? (Position Manager backup)
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**Expected Outcome:** Capture extended moves like the +41% trade that's currently open
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---
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## Phase 6: Advanced ML-Based Exit Prediction (Future) 🔮
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**Prerequisites:** ✅ 100+ trades with all metrics, ✅ Phases 1-5 complete
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### Research Tasks:
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- [ ] **Feature engineering:**
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- [ ] Input features: ATR, ADX, RSI, volumeRatio, pricePosition, quality score, direction, timeframe
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- [ ] Target variable: Did trade reach 2×TP2? (binary classification)
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- [ ] Additional target: Max profit % reached (regression)
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- [ ] **Model training:**
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- [ ] Split data: 70% train, 30% test
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- [ ] Try models: Logistic Regression, Random Forest, XGBoost
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- [ ] Evaluate: Precision, Recall, F1 for runner prediction
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- [ ] Cross-validation with time-based splits (avoid lookahead bias)
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- [ ] **Integration:**
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- [ ] `/api/trading/predict-runner` endpoint
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- [ ] Call during trade execution to get runner probability
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- [ ] Adjust runner size based on probability: 0-15% runner if low, 25-35% if high
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- [ ] **Monitoring:**
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- [ ] Track model accuracy over time
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- [ ] Retrain monthly with new data
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- [ ] A/B test: ML-based vs rule-based scaling
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**Expected Outcome:** AI predicts which trades have runner potential before entry
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---
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## Quick Wins (Can Do Anytime) ⚡
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- [ ] **Manual runner management for current trade**
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- [ ] Move SL to +30% profit lock on current +41% SOL position
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- [ ] Monitor manually until trend breaks
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- [ ] Document outcome: How much did runner capture?
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- [ ] **Add "runner stats" to analytics dashboard**
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- [ ] Show: How many trades went beyond TP2?
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- [ ] Show: Average MFE for TP2 exits
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- [ ] Show: Estimated missed profit from not having runners
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- [ ] **Database views for common queries**
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- [ ] Create `vw_quality_performance` view
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- [ ] Create `vw_runner_potential` view
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- [ ] Create `vw_directional_edge` view
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- [ ] **Alerts for exceptional trades**
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- [ ] Telegram notification when MFE > 5% (runner candidate)
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- [ ] Telegram notification when quality score > 90 (premium setup)
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---
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## Decision Gates 🚦
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**Before Phase 2 (ATR-based):**
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- ✅ Have 20+ trades with ATR data
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- ✅ ATR values look reasonable (0.5 - 3.5 range)
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- ✅ Clear volatility variation observed
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**Before Phase 3 (Quality tiers):**
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- ✅ Have 30+ trades with quality scores
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- ✅ Statistical significance: High quality scores show measurably better outcomes
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- ✅ Correlation coefficient > 0.3 between quality and P&L
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**Before Phase 4 (Direction bias):**
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- ✅ Have 50+ trades (25+ each direction)
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- ✅ Directional edge persists (3x+ performance gap)
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- ✅ Edge is consistent across different market conditions
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**Before Phase 5 (Runners):**
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- ✅ 30%+ of trades show MFE > TP2
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- ✅ Average MFE significantly higher than TP2 level
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- ✅ Phases 2-3 stable and profitable
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**Before Phase 6 (ML):**
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- ✅ 100+ trades with complete feature data
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- ✅ Proven improvement from Phases 1-5
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- ✅ Computational resources available (training time)
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---
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## Notes & Observations
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### Current Trade Example (Oct 31, 2025):
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- Entry: $182.73
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- Current: $186.56 (+2.1%, +$11.30)
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- **Actual P&L: +41% unrealized** 🚀
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- Current strategy would've closed at +3% ($16.20)
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- **Missed profit: ~$59** if this hits +41% realized
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- **Lesson:** Need runner implementation ASAP once data supports it
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### Key Metrics to Watch:
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- Win rate by quality tier
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- Average MFE vs exit price gap
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- Correlation between ATR and price movement
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- Shorts vs longs performance delta
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- Percentage of trades that go beyond TP2
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### Strategy Validation:
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Run this after each phase to validate improvement:
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```sql
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-- Compare old vs new strategy performance
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SELECT
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'Phase X' as phase,
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COUNT(*) as trades,
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ROUND(AVG("realizedPnL")::numeric, 2) as avg_pnl,
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ROUND(SUM("realizedPnL")::numeric, 2) as total_pnl,
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ROUND(100.0 * SUM(CASE WHEN "realizedPnL" > 0 THEN 1 ELSE 0 END) / COUNT(*)::numeric, 1) as win_rate
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FROM "Trade"
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WHERE "createdAt" >= '[phase_start_date]'
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AND "exitReason" IS NOT NULL;
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```
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---
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## Timeline Estimate
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- **Phase 1 (Data Collection):** 2-4 weeks (depends on signal frequency)
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- **Phase 2 (ATR-based):** 3-5 days implementation + 1 week testing
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- **Phase 3 (Quality tiers):** 5-7 days implementation + 2 weeks testing
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- **Phase 4 (Direction bias):** 2-3 days implementation + 1 week testing
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- **Phase 5 (Runners):** 7-10 days implementation + 2 weeks testing
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- **Phase 6 (ML):** 2-3 weeks research + implementation
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**Total estimated time:** 3-4 months from start to Phase 5 complete
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---
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## Success Criteria
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**Phase 2 Success:**
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- [ ] Average P&L increases by 10%+ vs fixed targets
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- [ ] Win rate stays stable or improves
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- [ ] No increase in max drawdown
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**Phase 3 Success:**
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- [ ] High quality trades show 20%+ better P&L than low quality
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- [ ] Overall P&L increases by 15%+ vs Phase 2
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- [ ] Quality filtering prevents some losing trades
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**Phase 4 Success:**
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- [ ] Directional P&L gap narrows (improve weak direction)
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- [ ] Or: Strong direction P&L improves further (if edge is real)
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**Phase 5 Success:**
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- [ ] Runners capture 20%+ more profit on winning trades
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- [ ] Total P&L increases by 25%+ vs Phase 4
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- [ ] Runners don't create new losing trades
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**Overall Success (All Phases):**
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- [ ] 2x total P&L vs baseline strategy
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- [ ] 50%+ win rate (up from 38%)
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- [ ] Average winner > 2× average loser
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- [ ] Profit factor > 2.0
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---
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**Status:** Phase 1 (Data Collection) - Active 🔄
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**Last Updated:** October 31, 2025
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**Next Review:** After 20 trades with quality scores collected
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