Beyond Manual Tuning: Genetic Algorithms Discover Optimal Trading Strategies

This article demonstrates how genetic algorithms can systematically discover and optimize trading strategies without manual parameter tuning—a perennial problem in quantitative finance that leads to overfitting. By showcasing results from 89 generations on NVDA data, it proves that evolutionary optimization can achieve measurable improvements in strategy performance. The finclaw open-source framework makes this advanced technique accessible to traders beyond specialized quant teams.