Options Buyer ML Model V2: How V1 Failed and What the Rebuild Fixed

A developer has documented the rebuild of a machine learning system designed to predict options-buying decisions in Indian equity markets, detailing why the first version (V1) consistently underperformed. V1 used a single XGBoost model to directly predict CE or PE direction from raw premium data, causing the model to learn noise as much as signal. Critical bugs — including a partition shift error that silently zeroed out a key feature and a rollup config that compressed training data from 295,000+ sequences to just 387 — contributed to a live paper-trading result of 31.6% win rate and a loss of ₹90,300. The rebuilt V2 separates the problem into narrow, task-specific model heads for underlying price movement and contract-level return targets, while applying strict hyperparameter tuning and an overfitting gate based on train-test metric gaps. The author emphasizes that the key lessons are to ask precise questions, keep decision trees shallow, and only promote models based on out-of-sample performance rather than training scores.