Great algorithms are simple, testable, and resilient. Focus on clear entry/exit rules, robust risk limits, and realistic assumptions about slippage and fees.
- Define risk per trade and max daily drawdown.
- Keep inputs minimal—avoid overfitting.
- Use broker-stable APIs and proper logging.
Start small, deploy in stages, and iterate based on live feedback.
In real markets, reliability beats raw speed. Monitoring, graceful restarts, and state recovery keep you profitable when networks hiccup or APIs throttle.
Aim for consistent sub‑second reactions with strong observability rather than micro‑optimizations that compromise stability.
Validate your ideas using out‑of‑sample tests, walk‑forward analysis, and realistic cost models.
- Split data chronologically to avoid leakage.
- Add fees/slippage with conservative estimates.
- Track drawdowns and time‑to‑recovery, not just CAGR.
The goal is robustness, not a perfect historical equity curve.