I’ve been building software for more than 20 years, and over the last few years I became increasingly interested in systematic trading. Like many developers entering quantitative trading, I started with a familiar workflow: find an idea, write some code, run a backtest, adjust parameters, run another backtest, and repeat. Then AI made strategy development dramatically faster. But it also created another problem. Generating strategies became easy. Evaluating them properly did not. I could ask an LLM to create a strategy in seconds, but I still needed to answer much harder questions: Is the idea actually valid? Is the backtest misleading? Is the strategy overfit? Should I optimize it yet? Should I test another symbol? Is poor performance caused by implementation or by the idea itself? When should I stop? That led me to build Algo Trader Intelligence. ATI started as tooling for my own strategy research and gradually evolved into a platform designed around one principle: strategy development should behave more like research than prompt engineering.

Algo Trader Intelligence

AI-native research for systematic trading strategies

AI-native research for systematic trading strategies