The Proving Ground
The first 30 days of live trading reveal whether your system works in reality, not just in theory. This period requires heightened attention and careful analysis.
Week 1: Validation Mode
Primary goal: verify everything works mechanically.
Daily Tasks:
- •Review every single trade
- •Compare fills to expected prices
- •Verify position tracking accuracy
- •Check for any errors or warnings
- •Confirm notifications fired correctly
What Youre Looking For:
- •Execution matches signals
- •No unexpected behavior
- •System stability
- •Acceptable latency
Red Flags:
- •Trades not matching signals
- •Position mismatches
- •Excessive slippage
- •System crashes or errors
Keep position sizes minimal. Youre testing, not trading for profit.
Week 2: Early Performance
Primary goal: initial performance assessment.
Metrics to Track:
- •Win rate (compare to backtest)
- •Average win/loss (compare to backtest)
- •Signal frequency (compare to expectations)
- •Slippage (expected vs actual)
What Youre Looking For:
- •Results roughly matching expectations
- •No systematic issues
- •Stable operation
Red Flags:
- •Win rate significantly below backtest
- •Much higher slippage than expected
- •Far fewer or more signals than expected
If metrics diverge significantly, investigate before increasing size.
Week 3: Stress Testing
Primary goal: see how system handles variety.
Watch For:
- •Performance across different market conditions
- •Behavior during high volatility
- •Handling of rapid signal bursts
- •Weekend/off-hours stability
Expand Gradually:
- •Add additional edges if primary is stable
- •Slightly increase position sizes
- •Enable additional coins if multi-asset
Red Flags:
- •Degraded performance in certain conditions
- •Instability during volatility
- •Edge cases causing problems
Week 4: Calibration
Primary goal: tune based on live data.
Adjustments to Consider:
- •Position sizing refinement
- •Slippage assumptions update
- •Risk limit adjustments
- •Notification threshold tuning
What Youre Looking For:
- •Convergence with expectations
- •Stable, predictable behavior
- •Confidence in scaling
Red Flags:
- •Continued divergence from expectations
- •Unresolved issues
- •Gut feeling something is wrong
Key Metrics Dashboard
Track daily:
| Metric | Expected | Actual | Status |
|---|---|---|---|
| Win Rate | 60% | ? | - |
| Avg Win | 2.5% | ? | - |
| Avg Loss | 1.5% | ? | - |
| Signals/Day | 5 | ? | - |
| Slippage | 0.1% | ? | - |
Visualize trends. Degrading metrics warn of problems.
Common First-30-Day Issues
Issue: Win rate below expected Possible causes: Edge decay, execution problems, parameter issues. Action: Analyze losing trades. Pattern?
Issue: Excessive slippage Possible causes: Poor liquidity, order type wrong, timing issues. Action: Review execution strategy. Consider limit orders.
Issue: Fewer signals than expected Possible causes: Data issues, filter too strict, calculation bug. Action: Verify data pipeline. Check signal logic.
Issue: System instability Possible causes: Memory leaks, connection issues, resource constraints. Action: Review logs. Profile system. Upgrade resources.
Decision Points
End of Week 1: Go/No-Go on continuing. If mechanical issues, fix before proceeding.
End of Week 2: Assess early performance. Major divergence requires investigation.
End of Week 4: Full assessment. Ready to scale or need more validation?
Scaling Decision Framework
Ready to scale when:
- •4 weeks stable operation
- •Metrics within acceptable range of expectations
- •No unresolved issues
- •Confidence in the system
Not ready if:
- •Any unresolved bugs
- •Metrics significantly below expectations
- •Unanswered questions about behavior
- •Gut feeling of uncertainty
When in doubt, extend validation period.
Takeaway
The first 30 days are not about making money. Theyre about building justified confidence that your system works.
Rushing this period is the most common mistake. Patient validation prevents expensive lessons.
Next: continuous improvement without overfitting.