Trading bots and risk controls

Risk limits before optimisation

Before refining strategies, set ceilings on positions, order sizes, and per-venue exposure. Hard stops and circuit breakers keep experiments from becoming incidents. These controls should live close to execution, not only in dashboards.

Limits change over time, so make them data-driven and easy to update. Static config files age quickly in live trading environments.

Make decisions and inputs visible

Every automated decision should be explainable: which inputs were used, which model or rule fired, and what downstream actions followed. Logging and dashboards that show this in plain language help traders and support teams trust the automation.

Pair logs with alerts that highlight anomalies or stuck orders. The goal is fewer surprises when markets move fast.

Plan for failure modes

Bots fail in boring ways: missing data, API errors, or rate limits. Plan explicit fallbacks and ensure manual stop paths are tested. A runbook with restart steps and known safe states avoids scrambling when latency spikes.