A fleet of autonomous trading bots operating 24/7 across multiple platforms, executing forex strategies with automated risk management.
Manual forex trading is emotionally exhausting and time-intensive. Profitable strategies exist, but executing them consistently — at scale, across time zones, without fatigue — requires automation. Prop firm challenges demand strict drawdown limits that no human can monitor 24/7.
A fleet of 5+ trading bots, each running unique strategies (supply/demand zone detection, London breakout, momentum) across multiple broker platforms. A VPS runs the signal generator and Expert Advisors around the clock. A Python backtesting pipeline validates strategies across 26+ forex pairs before going live.
The hardest lesson was security. Early on, broker credentials were committed to git in plaintext — a dangerous mistake. I performed a full git history scrub using git-filter-repo, then rebuilt the credential architecture with environment variables and a custom config resolver. Now every bot loads secrets at runtime from .env, and account configs only contain $ENV_VAR references. Another key lesson: backtesting in Python gives fast discovery, but you must validate with the broker's own strategy tester to account for real spreads and slippage.