Start Date: Feb 3, 2026
Goal: Live trading ready in 3-4 weeks with profitable strategy
Current Status:
- EMA(32/45) → -0.11% (LOSING) ❌
- Need to find parameters with:
- Win rate > 40%
- Profit factor > 1.2
- Sharpe ratio > 0.5
- Run
run_optimization_fast.pywith wider EMA ranges- Fast: 5-50 (step 2)
- Slow: 20-200 (step 10)
- ~437 combinations to test
- Test multiple EMA strategies:
- Very fast: EMA(5-15) / EMA(20-40)
- Medium: EMA(15-30) / EMA(40-80)
- Slow: EMA(30-50) / EMA(100-200)
- Save top 10 results
- Take top 3 EMA pairs from optimization
- Walk-forward validation on each:
- Split data: Train (60 days) → Test (30 days) → OOS (30 days)
- Must perform similar in OOS as training
- Check for overfitting
- Select BEST performer (highest Sharpe in OOS)
- For best EMA:
- Adjust stop-loss: 3% → 1.5% (reduce drawdown)
- Adjust take-profit: 5% → 10% (bigger winners)
- Run sensitivity analysis
- Check drawdown periods
- Generate detailed backtest report
Week 1 Goal: Have 1 profitable strategy ready
- Implement live data feed (CCXT streaming)
- Real-time 1h candles
- Proper timestamp handling
- Latency tolerance
- Create
paper_trader.pythat:- Pulls live CCXT data
- Runs strategy in real-time (not backtest)
- Simulates order execution
- Tracks fills, commissions, slippage
- Sends Telegram alerts on trades
- Test for 2-3 days
- Monitor logs
- Verify candle timing
- Check order mechanics
- Compare:
- Historical backtest metrics
- vs. Paper trading metrics (first week)
- Should be similar (±2-3% variance)
- If matches → proceed to live
- If different → debug & adjust
Week 2 Goal: Paper trading running, metrics validated
- Configure Binance margin account connection
- Set trading capital: $100-500 (test amount)
- Strict position size: 0.001-0.01 BTC max
- Emergency stop-loss at account level
- Deploy to production
- Monitor 24/7 (set Telegram alerts)
- Track all trades in database
- Daily P&L review
- Document any issues
Week 3 Goal: 10-20 real trades, validated system
- Increase position size gradually
- Add more strategies (multi-asset)
- Implement advanced risk management
- Set up performance dashboard
| Factor | Target | Current | Status |
|---|---|---|---|
| Win Rate | >40% | 7.7% | ❌ NEEDS FIX |
| Profit Factor | >1.2 | 0.39 | ❌ NEEDS FIX |
| Sharpe Ratio | >0.5 | N/A | |
| Max Drawdown | <15% | N/A | |
| Backtest vs Paper Match | <5% diff | N/A |
run_optimization_fast.py # 437 combinations in 2-3 minsrc/data/stream.py # Real-time CCXT candlesscripts/paper_trader.py # Simulate live tradingscripts/live_trader.py # Real order executionsrc/monitoring/dashboard.py # Grafana/web dashboardWeekly Goals:
| Week | Metric | Target |
|---|---|---|
| 1 | Find profitable params | Sharpe > 0.5 in OOS |
| 2 | Paper trading match | Backtest ≈ Paper ±2-3% |
| 3 | Live P&L | Positive in first 20 trades |
| 4+ | Consistent returns | Monthly Sharpe > 1.0 |
- Start with small capital ($100)
- Hard stop-loss at -2% account loss
- Position limits to 0.5% account per trade
- Daily review of all trades
- Immediate rollback if something breaks
✅ Live Trading Success = First month with:
- At least 15 trades
- Win rate > 35%
- Positive P&L
- Zero system errors
- All Telegram alerts working
Then: Scale to $1,000+ capital
Last Updated: Feb 3, 2026
Next Review: After Week 1 optimization complete