I Built a Genetic Algorithm That Discovers Trading Strategies - Here's What 89 Generations Found

I wanted a system that could discover trading strategies without me hand-tuning every parameter. So I built one. finclaw is an open-source quantitative finance engine in Python. One of its core features is an evolution engine that uses genetic algorithm principles to mutate, evaluate, and improve trading strategies automatically. After running it for 89 generations on NVDA data, here's what I learned. The Problem With Manual Strategy Tuning Every quant hits the same wall: you write a strategy, backtest it, tweak a parameter, backtest again. Repeat 200 times. You end up overfitting to historical data without realizing it. I wanted something that could explore the strategy space systematically — try combinations I wouldn't think of, and discard what doesn't work through a principled selection process rather than gut feeling. How The Evolution Engine Works The core loop is deceptively simple: Seed — Start with a YAML strategy definition (entry rules, exit rules, filters) Evaluate —