AI Trading Bot

The Question

Can a disciplined system outperform emotional trading?

After years of watching myself — and every trader I know — make the same mistakes at the same points in market cycles, I wanted to build something that trades on rules, not feelings. Not because I believe markets are efficient, but because I know I'm not.

What I Built

A Python-based algorithmic trading system that runs on Bybit. 50,000 lines of code, 266 passing tests, fully Dockerised with CI/CD.

The architecture is built around three principles:

• Signal generation — multiple data sources feeding into a thesis-aware scoring system
• Risk management — position sizing, stop losses, and portfolio-level exposure limits that cannot be overridden
• Execution — automated order management with slippage protection

The hardest lesson wasn't technical. It was accepting that the bot is only as good as the thesis it's built on. No amount of engineering sophistication compensates for a wrong read on market structure.

What I Learned

Building a trading bot is building a mirror. Every bias, every shortcut, every assumption you've internalised about how markets work — you have to make it explicit and write it down as a rule. And when the rule doesn't work, you can't blame emotion. You have to confront the gap between what you think you know and what the market actually does.

Status

Phase 1 complete and live. Running on Bybit with real capital. Phase 2 — multi-strategy and improved signal generation — is in planning.