Overfitting in Retail ML Trading

Overfitting = memorizing noise as signal. Symptoms: 95% in-sample accuracy, -10% live. Fixes: walk-forward (never train on future), regularization (max_depth 4-6, subsample 0.8), feature count < samples/10, out-of-sample Sharpe > 0.5x in-sample. The content moat is honest methodology, not a PnL screenshot. Publish the walk-forward equity curve with holes (out-of-sample shaded). Retail gullibility funds overfit models; discipline builds cited assets.