Why Genetic Algorithms Beat Deep RL for Trading (At Least for Me)

I spent the better part of six months trying to make Deep Reinforcement Learning work for stock trading. PPO, SAC, A2C — I tried them all. Tweaked reward functions until 3 AM. Watched my agent learn to do absolutely nothing because that minimized drawdown. Then I switched to genetic algorithms, and within two weeks I had something that actually traded profitably. This isn't a "GA is always better" post. It's a "here's what I learned the hard way" post. The DRL Dream vs. Reality The pitch for Deep RL in trading sounds amazing. Your agent learns optimal actions from raw market data. No hand-crafted features needed. It adapts to changing markets. AlphaGo beat the world champion, so surely it can beat the S&P 500, right? Here's what actually happens. Reward shaping is black magic. Do you reward realized PnL? Unrealized? Risk-adjusted returns? Sharpe ratio? Every choice creates perverse incentives. I had an agent that learned to open and close positions every single bar because the transact