Diagnosing Trading API Errors and Understanding Backtest Limits
Summary
This beginner guide groups common automated-trading failures into syntax errors, runtime bugs, configuration mistakes, and exchange API errors. It gives examples such as malformed symbol formats, invalid API credentials, network timeouts, request-rate violations, and exchange rejections of unsupported parameters. It recommends using error messages and platform debugging tools to identify bad inputs, while noting that an API call can return invalid data that causes a separate runtime failure if the strategy lacks error handling.
The article also compares backtesting modes. Simulated backtests infer price movements from candle ranges, so a strategy can appear strong by fitting a path that did not occur; the guide says this mode is more informative for some trend strategies than for high-frequency ones. Live-tick-style backtests use finer historical data but cover shorter periods because of their size. The guide describes platform and custom data sources, but provides no evidence that backtest results predict live performance.
Key ideas
- Trading failures can come from code, runtime logic, configuration, network access, or exchange business rules.
- Error messages and debugging tools can help distinguish invalid inputs from connectivity and rate-limit problems.
- Strategies should handle failed API responses because unusable return values can trigger runtime errors.
- Simulated backtests generate prices within candle ranges and can mislead strategies that depend on the generated path.
- Finer-grained backtests can better represent frequent trading but often cover less historical time.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.