trading_bot_v4

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Author	SHA1	Message	Date
copilot-swe-agent[bot]	0dedfecde5	docs: Final summary - All bugs fixed with comprehensive tests and documentation Co-authored-by: mindesbunister <32161838+mindesbunister@users.noreply.github.com>	2025-12-09 22:32:18 +00:00
mindesbunister	a8c1b2ca06	feat: Phase 2 Smart Entry Timing - COMPLETE Implementation of 1-minute data enhancements Phase 2: - Queue signals when price not at favorable pullback level - Monitor every 15s for 0.15-0.5% pullback (LONG=dip, SHORT=bounce) - Validate ADX hasn't dropped >2 points (trend still strong) - Timeout at 2 minutes → execute at current price - Expected improvement: 0.2-0.5% per trade = ,600-4,000 over 100 trades Files: - lib/trading/smart-entry-timer.ts (616 lines, zero TS errors) - app/api/trading/execute/route.ts (integrated smart entry check) - .env (SMART_ENTRY_* configuration, disabled by default) Next steps: - Test with SMART_ENTRY_ENABLED=true in development - Monitor first 5-10 trades for improvement verification - Enable in production after successful testing	2025-11-27 11:40:23 +01:00
mindesbunister	bfdb0ba779	feat: Implement adaptive leverage based on signal quality score - Quality-based risk adjustment: 95+ = 15x, 90-94 = 10x, <90 = blocked - Data-driven decision: v8 quality 95+ = 100% WR (4/4 wins) - Config fields: useAdaptiveLeverage, highQualityLeverage, lowQualityLeverage, qualityLeverageThreshold - Helper function: getLeverageForQualityScore() returns appropriate leverage tier - Position sizing: Modified getActualPositionSizeForSymbol() to accept optional qualityScore param - Execute endpoint: Calculate quality score early (before sizing) for leverage determination - Test endpoint: Uses quality 100 for maximum leverage on manual test trades - ENV variables: USE_ADAPTIVE_LEVERAGE, HIGH_QUALITY_LEVERAGE, LOW_QUALITY_LEVERAGE, QUALITY_LEVERAGE_THRESHOLD - Impact: 33% less exposure on borderline quality signals (90-94) - Example: $540 × 10x = $5,400 vs $8,100 (saves $2,700 exposure on volatile signals) - Files changed: * config/trading.ts (interface, config, ENV, helper function, position sizing) * app/api/trading/execute/route.ts (early quality calculation, pass to sizing) * app/api/trading/test/route.ts (quality 100 for test trades)	2025-11-24 00:47:09 +01:00

Author

SHA1

Message

Date

copilot-swe-agent[bot]

0dedfecde5

docs: Final summary - All bugs fixed with comprehensive tests and documentation

Co-authored-by: mindesbunister <32161838+mindesbunister@users.noreply.github.com>

2025-12-09 22:32:18 +00:00

mindesbunister

a8c1b2ca06

feat: Phase 2 Smart Entry Timing - COMPLETE

Implementation of 1-minute data enhancements Phase 2:
- Queue signals when price not at favorable pullback level
- Monitor every 15s for 0.15-0.5% pullback (LONG=dip, SHORT=bounce)
- Validate ADX hasn't dropped >2 points (trend still strong)
- Timeout at 2 minutes → execute at current price
- Expected improvement: 0.2-0.5% per trade = ,600-4,000 over 100 trades

Files:
- lib/trading/smart-entry-timer.ts (616 lines, zero TS errors)
- app/api/trading/execute/route.ts (integrated smart entry check)
- .env (SMART_ENTRY_* configuration, disabled by default)

Next steps:
- Test with SMART_ENTRY_ENABLED=true in development
- Monitor first 5-10 trades for improvement verification
- Enable in production after successful testing

2025-11-27 11:40:23 +01:00

mindesbunister

bfdb0ba779

feat: Implement adaptive leverage based on signal quality score

- Quality-based risk adjustment: 95+ = 15x, 90-94 = 10x, <90 = blocked
- Data-driven decision: v8 quality 95+ = 100% WR (4/4 wins)
- Config fields: useAdaptiveLeverage, highQualityLeverage, lowQualityLeverage, qualityLeverageThreshold
- Helper function: getLeverageForQualityScore() returns appropriate leverage tier
- Position sizing: Modified getActualPositionSizeForSymbol() to accept optional qualityScore param
- Execute endpoint: Calculate quality score early (before sizing) for leverage determination
- Test endpoint: Uses quality 100 for maximum leverage on manual test trades
- ENV variables: USE_ADAPTIVE_LEVERAGE, HIGH_QUALITY_LEVERAGE, LOW_QUALITY_LEVERAGE, QUALITY_LEVERAGE_THRESHOLD
- Impact: 33% less exposure on borderline quality signals (90-94)
- Example: $540 × 10x = $5,400 vs $8,100 (saves $2,700 exposure on volatile signals)
- Files changed:
  * config/trading.ts (interface, config, ENV, helper function, position sizing)
  * app/api/trading/execute/route.ts (early quality calculation, pass to sizing)
  * app/api/trading/test/route.ts (quality 100 for test trades)

2025-11-24 00:47:09 +01:00

3 Commits