# Options Buyer ML: Why One Model Fails (and the V2 Fix) > Source: > Published: 2026-08-19 06:55:09+00:00 Lessons from a real rebuild of an options-buyer prediction system. No profit claims — just the architecture that fixes the chronic bugs of V1. V1 asked one XGBoost model one big fuzzy question: **"CE ya PE?"** — directly from raw CE/PE premium data. Premium is a *transformed* signal (underlying move × delta × gamma × IV × theta × spread × strike distance × liquidity). The model learned noise as much as signal. Concrete evidence from the research logs: `lr=0.02, depth=3` defaults used throughout; Optuna existed but was never run).`iv_change_1d` shift inside single-row groups) silently zeroed a whole feature for the entire history. ``` php underlying mechanics --> side, range, ETA, invalidation option chain scanner --> is the buyer contract worth paying for? XGBoost (many heads) --> thin calibrated learner on clean mechanics ``` Rule: **underlying decides side; option contract decides execution eligibility.** CE/PE premium is validated against, never learned as, direction. Instead of one CE/PE answer, V2 trains separate narrow heads: `underlying_up/down_touch_{15,30,60}m` `ce_1p3x / ce_1p5x / ce_2p0x` and `pe_1p3x / pe_1p5x / pe_2p0x` (SEPARATE CE and PE)`no_trade_quality` This single change removes most of the CE/PE confusion V1 fought for months. ``` learning_rate = 0.015–0.035 n_estimators = 800–2000 (early stop) max_depth = 2–3 min_child_weight = 12–40 gamma = 0.1–2.0 subsample = 0.65–0.90 colsample_bytree = 0.55–0.85 reg_alpha = 0.5–3.0 reg_lambda = 6.0–20.0 scale_pos_weight = min(neg/pos, 8.0) ``` V1's intraday head had only **8 of 1280 features** with non-zero gain — most of the bloat was pure noise the regularizer had to prune. Shallow + hard-regularized is the answer. `overfit_gap = train_metric − test_metric` . **Flag if > 0.15.** A model is NOT promoted just because train metrics look good. Log the gap automatically on every head, every retrain. V2 is a cleaner architecture, but it is still **research**. The lesson that transfers: stop asking fuzzy questions, declare your nulls, keep trees shallow, and gate promotion on out-of-sample gap — not training score. *Research only. Not investment advice.*