trading_bot_v4/POSITION_SCALING_ROADMAP.md

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:
  • TP1 at +1.5%: Close 75%
  • TP2 at +3.0%: Close 80% of remaining (20% total)
  • Runner: 5% of position with 0.3% trailing stop ✅ ALREADY IMPLEMENTED
• Problem: Small runner size (5%) + tight trailing stop (0.3%) may be suboptimal
• Data Quality: Ready to collect signal quality scores for correlation analysis

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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):

-- 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;

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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)

  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)

  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: Optimize Runner Size & Trailing Stop ⏳

Prerequisites: ✅ Phase 3 complete, ✅ Runner data collected (10+ trades with runners)

Current Implementation: ✅ Runner with trailing stop already exists!

• Runner size: 5% (configurable via TAKE_PROFIT_2_SIZE_PERCENT=80)
• Trailing stop: 0.3% fixed (configurable via TRAILING_STOP_PERCENT=0.3)

Implementation Tasks:

• Analyze runner performance from existing trades

  • Query trades where runner was active (TP2 hit)
  • Calculate: How many runners hit trailing stop vs kept going?
  • Calculate: Average runner profit vs optimal exit
  • Calculate: Was 0.3% trailing stop too tight? (got stopped out too early?)

• Optimize runner size by quality tier:

  • High quality (80-100): 25% runner (TP2 closes 0%, all becomes runner)
  • Medium quality (70-79): 10% runner (TP2 closes 60% of remaining)
  • Low quality (60-69): 5% runner (current behavior)

• Make trailing stop ATR-based:

  • Change from fixed 0.3% to (1.5 × ATR) or (2.0 × ATR)
  • Add trailingStopATRMultiplier config option
  • Update Position Manager to use ATR-based trailing distance
  • Store ATR value in ActiveTrade for dynamic calculations

• Add runner-specific analytics:

  • Dashboard widget: Runner performance stats
  • Show: Total profit from runners vs TP1/TP2
  • Show: Average runner hold time
  • Show: Runner win rate

• Testing:

  • Backtest: Simulate larger runners (10-25%) on historical TP2 trades
  • Backtest: Simulate ATR-based trailing stop vs fixed 0.3%
  • A/B test: Run 10 trades with optimized settings before full rollout

Expected Outcome: Capture more profit from extended moves, reduce premature trailing stop exits

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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

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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)

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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)

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Notes & Observations

Current Trade Example (Oct 31, 2025):

• Entry: $182.73
• Current: $186.56 (+2.1%, +$11.30)
• Actual P&L: +41% unrealized 🚀
• Status: TP1 hit (closed 75%), TP2 hit (closed 20%), 5% runner still active with trailing stop
• Lesson: The 5% runner captured this move! But could a larger runner (10-25%) capture even more?
• Trailing stop: 0.3% below peak might be too tight, ATR-based (1.5-2.0×ATR) might work better

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:

-- 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;

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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

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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

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