Python & tooling
A practical Python research project structure
Keep data, experiments, reports, and production candidates separate so your research can be reproduced months later.
Read articleTopic / Software engineering
Architecture, testing, state management, debugging, and maintainable trading code.
9 guides
Python & tooling
Keep data, experiments, reports, and production candidates separate so your research can be reproduced months later.
Read articleEngineering discipline
Use commits, experiment notes, and configuration hashes to know exactly which code produced a trading result.
Read articleEngineering discipline
Small automated tests can catch sizing, stop, time, and state errors before they reach an account.
Read articleEngineering discipline
A bot that cannot explain its state is difficult to trust. Design logs that help you distinguish no signal from no execution.
Read articleArchitecture
Explicit states make order handling, restart recovery, and protection logic easier to reason about than scattered flags.
Read articleOperations
A compact dashboard should answer whether the terminal is alive, what risk is open, and why the system is or is not trading.
Read articleDeployment
A staged checklist for moving from a promising backtest to a monitored system without skipping the uncomfortable evidence.
Read articleTroubleshooting
A step-by-step operational diagnosis for silent Expert Advisors, from connection and symbol checks to signal and protection state.
Read articleProcess
Treat every input change as a new experiment so a live system does not drift away from the evidence that justified it.
Read articleAsk a setup question, share a backtest, or compare notes with other algorithmic traders. Use a display name; your email remains private.
Create an account or sign in above to join the conversation.
Loading discussion…