Overfitting means memorizing noise as signal. Symptoms: 95 percent in-sample accuracy, minus 10 percent live. Fixes: walk-forward (never train on the future), regularization (max_depth 4-6, subsample 0.8), feature count below samples divided by ten, out-of-sample Sharpe above half the 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. When a model looks too good, audit feature importance for leakage first.
Q: What is overfitting?
A: Memorizing training noise so the model fails on unseen data.
Q: How do I detect it?
A: Compare in-sample versus out-of-sample performance; a large drop signals overfit.
Q: What regularization helps?
A: Shallow trees, subsampling, and limiting feature count relative to samples.
Q: Why publish methodology?
A: Reproducible honest method builds trust; a PnL screenshot alone proves nothing.
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By Shakti Tiwari · Options AI research pillar. NISM XII certified. Educational only, not investment advice; verify before acting.