Machine Learning for Trading: From Research to Production

You take a fragile backtest from the case studies and identify whether the source is the label, features, validation, costs, or capacity.

You score it on the same rubric used in your capstone — leakage, multiple testing, regime sensitivity, cost under-modeling, capacity ceilings.

Diagnosis points to the next improvement, not just a verdict.

You pick universe, label horizon, validation protocol, cost model, and execution problem for one of nine real case studies.

You prioritize ideas by binding constraint, not by novelty — knowing which moves will actually move the needle in that market.

Same workflow, bent across ETFs, equities, futures, FX, crypto, and options.

You tune a regularized baseline and a GBM on the same panel, then read both tearsheets honestly (`ml4t-models`).

You reason out loud about why different families win in different data regimes — on real feature panels, not benchmarks.

Linear, gradient boosting, latent factor, and causal models — when each helps and when each doesn't.

You ship a backtest with realistic per-share-and-spread costs, a risk overlay, and a paper-trading hook on the ETF hub.

You walk the shadow → paper → live progression and write the deployment-readiness checklist for your own strategy.

Feature pipelines with batch/online parity, model registries, signal and risk monitoring.

You install the `ml4t-skills` companion repo (61 ML4T failure-mode skills) so your agent picks up the guardrails on day one.

You name what agents accelerate, what still needs human review, and which failure modes bite without ML4T-specific guardrails.

Calibrated read on what is reliable now, what still needs human review, and where this is heading.

You take one strategy from a clean baseline through two-to-three honest improvement cycles to a measurably better v2 OOS.

You defend the source of the gain in a 30-minute one-on-one with Stefan, against a rubric published day one.

The iteration loop the book points at but cannot run for you — finally run, scored, reviewed.