- Add market data cache service (5min expiry) for storing TradingView metrics
- Create /api/trading/market-data webhook endpoint for continuous data updates
- Add /api/analytics/reentry-check endpoint for validating manual trades
- Update execute endpoint to auto-cache metrics from incoming signals
- Enhance Telegram bot with pre-execution analytics validation
- Support --force flag to override analytics blocks
- Use fresh ADX/ATR/RSI data when available, fallback to historical
- Apply performance modifiers: -20 for losing streaks, +10 for winning
- Minimum re-entry score 55 (vs 60 for new signals)
- Fail-open design: proceeds if analytics unavailable
- Show data freshness and source in Telegram responses
- Add comprehensive setup guide in docs/guides/REENTRY_ANALYTICS_QUICKSTART.md
Phase 1 implementation for smart manual trade validation.
- Added signalQualityVersion field to Trade model
- Tracks which scoring logic version was used for each trade
- v1: Original logic (price position < 5% threshold)
- v2: Added volume compensation for low ADX
- v3: CURRENT - Stricter logic requiring ADX > 18 for extreme positions (< 15%)
This enables future analysis to:
- Compare performance between logic versions
- Filter trades by scoring algorithm
- Data-driven improvements based on clean datasets
All new trades will be marked as v3. Old trades remain null/v1 for comparison.
- Detect position size mismatches (>50% variance) after opening
- Save phantom trades to database with expectedSizeUSD, actualSizeUSD, phantomReason
- Return error from execute endpoint to prevent Position Manager tracking
- Add comprehensive documentation of phantom trade issue and solution
- Enable data collection for pattern analysis and future optimization
Fixes oracle price lag issue during volatile markets where transactions
confirm but positions don't actually open at expected size.
CRITICAL: Cooldown was global across ALL symbols, causing missed opportunities
Example: ETH trade at 10:00 blocked SOL trade at 10:04 (5min cooldown)
Changes:
- Added getLastTradeTimeForSymbol() function to query last trade per symbol
- Updated check-risk endpoint to use symbol-specific cooldown
- Each coin (SOL/ETH/BTC) now has independent cooldown timer
- Cooldown message shows symbol: 'Must wait X min before next SOL-PERP trade'
Result: Can trade ETH and SOL simultaneously without interference
Example: ETH LONG at 10:00, SOL SHORT at 10:01 = both allowed
- Add signalQualityScore field to Trade model (0-100)
- Calculate quality score in execute endpoint using same logic as check-risk
- Save score with every trade for correlation analysis
- Create database migration for new field
- Enables future analysis: score vs win rate, P&L, etc.
This allows data-driven decisions on dynamic position sizing
- Add getLastTrade() function to database service
- Create /api/analytics/last-trade endpoint
- Display last trade with full details on analytics page
- Show entry/exit prices, P&L, position size, targets
- Visual indicators for trade direction and exit reason
- Helps quickly diagnose where trades went (TP1, TP2, or SL)
Bug: MAE/MFE was tracked in memory during trades but not saved to database on exit
Cause: updateTradeExit() wasn't receiving or saving MAE/MFE parameters
Changes:
- Added MAE/MFE fields to UpdateTradeExitParams interface
- Modified updateTradeExit() to save maxFavorableExcursion, maxAdverseExcursion, maxFavorablePrice, maxAdversePrice
- Updated both updateTradeExit() calls in Position Manager to pass MAE/MFE values
- Enhanced exit logging to show final MAE/MFE percentages
Impact: Future trades will now properly save MAE/MFE data for analytics
Note: Past 2 trades (from before this fix) don't have MAE/MFE saved
- Added getFundingRate() method to DriftService
- Capture expectedEntryPrice from oracle before order execution
- Capture fundingRateAtEntry from Drift Protocol
- Save market context fields to database (expectedEntryPrice, fundingRateAtEntry)
- Calculate entry slippage percentage in createTrade()
- Fixed template literal syntax errors in execute endpoint
Database fields populated:
- expectedEntryPrice: Oracle price before order
- entrySlippagePct: Calculated from entrySlippage
- fundingRateAtEntry: Current funding rate from Drift
Next: Phase 3 (analytics API) or test market context on next trade
- Add Position Manager state persistence to survive restarts
- Auto-restore open trades from database on startup
- Save state after TP1, SL adjustments, profit locks
- Persist to configSnapshot JSON field
- Add automatic order cancellation
- Cancel all TP/SL orders when position fully closed
- New cancelAllOrders() function in drift/orders.ts
- Prevents orphaned orders after manual closes
- Improve stop loss management
- Move SL to +0.35% after TP1 (was +0.15%)
- Gives more breathing room for retracements
- Still locks in half of TP1 profit
- Add database sync when Position Manager closes trades
- Auto-update Trade record with exit data
- Save P&L, exit reason, hold time
- Fix analytics showing stale data
- Add trade state management functions
- updateTradeState() for Position Manager persistence
- getOpenTrades() for startup restoration
- getInitializedPositionManager() for async init
- Create n8n database analytics workflows
- Daily report workflow (automated at midnight)
- Pattern analysis (hourly/daily performance)
- Stop loss effectiveness analysis
- Database analytics query workflow
- Complete setup guide (N8N_DATABASE_SETUP.md)
- Fixed Prisma client not being available in Docker container
- Added isTestTrade flag to exclude test trades from analytics
- Created analytics views for net positions (matches Drift UI netting)
- Added API endpoints: /api/analytics/positions and /api/analytics/stats
- Added test trade endpoint: /api/trading/test-db
- Updated Dockerfile to properly copy Prisma client from builder stage
- Database now successfully stores all trades with full details
- Supports position netting calculations to match Drift perpetuals behavior
- Add PostgreSQL database with Prisma ORM
- Trade model: tracks entry/exit, P&L, order signatures, config snapshots
- PriceUpdate model: tracks price movements for drawdown analysis
- SystemEvent model: logs errors and system events
- DailyStats model: aggregated performance metrics
- Implement dual stop loss system (enabled by default)
- Soft stop (TRIGGER_LIMIT) at -1.5% to avoid wicks
- Hard stop (TRIGGER_MARKET) at -2.5% to guarantee exit
- Configurable via USE_DUAL_STOPS, SOFT_STOP_PERCENT, HARD_STOP_PERCENT
- Backward compatible with single stop modes
- Add database service layer (lib/database/trades.ts)
- createTrade(): save new trades with all details
- updateTradeExit(): close trades with P&L calculations
- addPriceUpdate(): track price movements during trade
- getTradeStats(): calculate win rate, profit factor, avg win/loss
- logSystemEvent(): log errors and system events
- Update execute endpoint to use dual stops and save to database
- Calculate dual stop prices when enabled
- Pass dual stop parameters to placeExitOrders
- Save complete trade record to database after execution
- Add test trade button to settings page
- New /api/trading/test endpoint for executing test trades
- Displays detailed results including dual stop prices
- Confirmation dialog before execution
- Shows entry price, position size, stops, and TX signature
- Generate Prisma client in Docker build
- Update DATABASE_URL for container networking
