What 180 Generations of Genetic Algorithm Trading Taught Me About Overfitting

What 180 Generations of Genetic Algorithm Trading Taught Me About Overfitting I've been building an open-source genetic algorithm engine that evolves trading strategies. The idea is simple: instead of manually picking indicators and thresholds, let evolution find the optimal combination from 484 technical factors. After 180 generations of evolution, here's what I learned. The Setup 484 factors : RSI variants, MACD, volume patterns, order flow proxies, Bollinger derivatives, candlestick patterns, and more Walk-forward validation : train/test split per generation — no peeking at future data Multi-objective optimization : NSGA-III balancing return, drawdown, and turnover Running on : 500 A-share stocks and 17 crypto pairs simultaneously Each generation takes about 2 hours. Three engines run in parallel on a single machine, pure Python, zero cloud cost. The Bug That Showed 34,000% Returns Around generation 69, the engine produced a strategy claiming 34,889% annual returns with only 7.38% m