Algotrading Best Practices – Ek Tech Expert Ki Nazar Se
Aaj kal har doosra trader algo trading ki taraf dekh raha hai. Lekin algo trading best practices sirf strategy likhne tak limited nahi hoti — ye ek engineering discipline hai.
Main aapko bataunga ki ek tech architect ke nazariye se algotrading best practise kya hoti hai — taaki aap ek clean, scalable, aur profitable algo infra build kar sako.
1. Backtest Engine Banaye Real Market Jaise
Galti jo sabse zyada traders karte hain:
Excel ya random Python script pe strategy test karte hain bina slippage, latency, bid-ask, ya brokerage ke hisaab se.
Best Practice:
→ Aise backtesting engine ka use karo jo real execution ko simulate kare
→ Delay, brokerage, partial fills, sab consider karo
→ Out-of-sample aur forward test mandatory hai
2. Overfitting Se Bacho — Simplicity Sabse Zyada Kaam Karti Hai
Bahut log strategy mein 10 variables ghusa dete hain aur lagta hai edge mil gaya.
Reality: Sirf past data pe kaam karta hai.
Best Practice:
→ 2-3 signals se strategy banao
→ Cross-validation karo
→ Simpler models zyada generalize hote hain
3. Infrastructure = Production Code
Algo trading ka matlab hai real-time money pe kaam ho raha hai.
Toh weekend project ki tarah code mat likho.
Top Practice:
→ Docker/Kubernetes jaise tools se infra containerize karo
→ Logging, alerts, dashboards mandatory hai
→ Version control aur auto-restarts setup karo
Trader nahi, DevOps engineer ki tarah socho.
4. Latency = Paisa
Milliseconds matter karte hain — specially jab market volatile ho.
Best Practice:
→ Algo co-located servers pe deploy karo (agar allowed ho)
→ Fast APIs use karo
→ Execution path ko streamline karo
1 second ka delay = Missed order = Missed profit
5. Capital Allocation Dynamic Hona Chahiye
Ek strategy kabhi bhi hamesha nahi chalegi.
Market regime badalte hain.
Top Practice:
→ Capital rotate karo based on recent performance
→ Rolling sharpe, DD monitor karo
→ Underperformance pe auto-disable logic daalo
Paise ko wahan lagao jahan performance ho — emotions se nahi.
6. Logging is Insurance
Sab kuch log karo. Har cheez. Har trade.
Log karo:
Signal kab trigger hua
Order sent, filled
Slippage kitna tha
API errors
Daily MTM, exposure, risk
Log = Lifesaver jab kuch galat ho.
7. Stress Test for Chaos Days
Market kabhi bhi crash ho sakta hai.
Algo sirf "normal" days ke liye banana bekaar hai.
Best Practice:
→ High IV, earnings day, RBI days pe logic test karo
→ Circuit breakers daalo
→ Tail events simulate karo
8. Compliance Chhodna = Suicide
India mein algo trading ke rules clear hain:
SEBI registration (agar public ko offer kar rahe ho)
Broker-approved APIs
Position limits aur margin rules ka dhyan
Tech smart ho toh bhi legal fool nahi ban sakte.
9. Har Algo Ka Ek End Hota Hai
Ek aur common galti — dead strategy ko zinda rakhna.
Kab algo kill karo:
→ Win-rate gir gaya
→ DD > expected DD
→ Logic market regime ke saath match nahi karta
→ Liquidity khatam
Algo bandh karna weakness nahi, maturity hai.
10. Architecture Modular Rakho
Agar 5 trades/day se 500 trades/day jana hai, toh architecture bhi scale hona chahiye.
Top Practice:
→ Algo ko 3 parts mein baanto: signal engine, execution engine, risk engine
→ Kafka/Redis jaise real-time systems use karo
→ Failover planning rakho (local + cloud backup)
Bonus: Documentation = Scalability
Har strategy ka ek clean doc hona chahiye:
Kya hai strategy
Logic ka summary
Assumptions
Backtest stats
Risk logic
Kab use na karein
Agar aap nahi ho, tab bhi team run kar paye — tabhi scalable hai.
Summary: Algotrading Best Practise Checklist ✅
| Area | Best Practise |
|---|---|
| Backtesting | Realistic, clean data, no overfit |
| Infra | Containerized, logs, alerts, restart |
| Execution | Low-latency, co-located, fast APIs |
| Monitoring | Log signals, orders, slippage |
| Risk | Dynamic capital, circuit breakers |
| Compliance | SEBI, broker API, limits |
| Algo Lifecycle | Auto-disable, exit rules |
| Documentation | Clear, modular, shareable |