Most trading software fails on first contact with real markets.
Developers build it like any other application. Trading systems need fundamentally different architecture.
Why brilliant strategies collapse from basic engineering mistakes
I've seen too many institutional investors watch perfect backtests turn into catastrophic losses. The math was flawless. The strategy was sound. The implementation was amateur.
Trading systems operate in a completely different universe than web apps. Markets don't wait for your code to catch up. Miss the architectural fundamentals and your money vanishes when markets get real.
Use WebSocket or FIX for real-time data and orders
REST APIs are too slow for serious trading. WebSocket is easier to implement and works for most brokers. FIX protocol offers more control but requires deeper integration work.
Choose based on your speed requirements, but never choose REST for live trading.
Store order states in a database, not memory
Use PostgreSQL with ACID transactions. Log every order state change with timestamps. When your process crashes during market hours, you need to know exactly which orders are live.
I've seen accounts blown up because systems lost track of position size during crashes.
Implement exponential backoff for API rate limits
Build retry logic that backs off exponentially when you hit limits. Use separate API keys for market data vs order management.
This keeps you trading when other systems get locked out during volatile sessions.
Set up proper position reconciliation
Pull positions from your broker every 30 seconds. Compare against your internal tracking. If they don't match within your tolerance, halt all trading immediately and alert.
This single check prevents the position size errors that compound into disasters.
Use market, limit, and stop orders strategically
Market orders guarantee execution but terrible fills. Limit orders get better prices but may not fill. Stop orders trigger market orders at specific price levels.
Use market orders for emergency exits, limits for most entries, stops for risk management. Specific use cases may vary.
Build kill switches you can actually use
Create an emergency stop that cancels all open orders and closes all positions. Test it monthly. Make sure it works even if your main trading process is frozen.
When systematic trading goes wrong, seconds determine survival.
Log everything with microsecond timestamps
Use structured logging. Every order, fill, rejection, and market data tick gets logged. When regulators ask questions, you need audit trails that prove compliance.
What actually matters
Successful automated trading isn't about smarter algorithms. It's about building systems robust enough to execute consistently when markets don't behave like your backtests.
At autotradelab, we build trading systems with institutional-grade infrastructure that survives market chaos. Because that's scientific trading.