Credential program on the practitioner path
Certificate in Machine Learning for Trading
Five courses and a capstone on applying machine learning to markets without fooling yourself: labeling, features, purged validation, deep and language models, regimes and reinforcement learning.
Machine learning finds patterns, and this certificate in machine learning is about which ones to believe. In market data most patterns are noise, and a flexible model will find them anyway. The methods that work in finance are the ones built around that fact: labels that reflect how a trade is actually managed, cross-validation that respects time, and tests that estimate how much of a backtest is luck.
What does the certificate cover?
Time series foundations; supervised learning for return and direction prediction; the methods of financial machine learning, including triple-barrier labeling, meta-labeling, purged and embargoed cross-validation and feature importance; deep learning with sequence models and transformers; language models and alternative data; Bayesian methods and hidden Markov models for regimes; and reinforcement learning for execution and allocation.
Who is it for?
Data scientists moving into finance, quantitative researchers adding machine learning to their toolkit, and engineers who need to evaluate machine learning trading claims.
The courses in this program
- Machine Learning for Trading Applying machine learning to trading problems without fooling yourself: building features and labels from market data, linear, tree and neural network models, time-series cross-validation, feature importance, and turning predictions into a tested strategy.
- Financial Machine Learning: Deep and Reinforcement Learning The research methods developed for machine learning on financial data: sampling with information-driven bars, triple-barrier labels and meta-labeling, sample weights for overlapping outcomes, purged cross-validation, feature importance, and statistics of backtest overfitting. It also teaches deep learning for finance and reinforcement learning for trading.
- Alternative Data and NLP for Finance Alternative data and natural language processing as funds use them: turning news, filings and earnings calls into signals with language models, judging satellite, card and web data, and testing whether any of it adds return once costs and timing are honest.
- GARCH Models, Market Regimes and Bayesian Methods The GARCH model and its family, from Engle's original ARCH to asymmetric and multivariate GARCH: derivation, maximum likelihood estimation, diagnostics and forecasting. It also teaches hidden Markov models for market regimes and Bayesian statistics for estimation under uncertainty.
- Time Series Analysis and Financial Econometrics The time series methods behind trading signals and risk forecasts: stationarity, autocorrelation, ARMA models, unit roots, volatility clustering and honest forecast evaluation. It also teaches financial econometrics: regression, its failures and their fixes.
What every course contains
Every paid course comes with the first seven and the certificate, whichever level it sits at. The proctored exam and the credential come when the course is taken as part of a Certificate, Diploma or Fellowship. Everything is self-paced: the material is yours to study in any order, at any hour, for the whole of the license.
- Workbooks. Written to train the thinking patterns and the important concepts of the course, with worked problems and the reasoning behind each answer, so that you learn to think the way the subject is thought.
- Audio edition. Focused, well-presented recordings with explainers and examples of the material, made to be interesting, relevant and entertaining, so that the general subject can be learned away from the desk.
- Notes. Pre-produced notes with every fact the course relies on, written out in full, so that nothing has to be transcribed from the films or the audio.
- Cheat sheet. One sheet with every important learning concept of the course on it, to keep beside you during the work and to revise from before the exam.
- Named sources. Every fact in the notes names its source, the paper, the book or the dataset, for anyone who wants to dig in further.
- Explainer films. Focused films, in the style of the sample films on the catalog page, that walk through the most difficult concepts of the course with visual explanations of a high standard.
- Transcript. A full transcript of every film and every audio recording, searchable and quotable.
- Proctored exam. Taken as part of a Certificate, Diploma or Fellowship, the course ends in a proctored multiple-choice questionnaire, with a pass mark of 60% and three retakes included. A course taken on its own is assessed by its coursework and has no exam.
- Verifiable certificate. A certificate of completion that a third party can verify: an employer sends the code on it to the credentials office and receives written confirmation of the course, the date and the result.
- Credential of the program. The Certificate, Diploma or Fellowship of the program, track or path the course belongs to, awarded when every course in it is passed. Which one depends on the program you choose.
What should you know before you start?
The courses in this program build on the courses below. They are not part of the program and not included in its price. If you have not studied this material, take them first. Admissions can tell you whether your background already covers them: the academy office.
- Python and SQL for Finance A Python for finance course with the Python every later course assumes: loading and cleaning market data with pandas, computing returns and risk with NumPy, plotting, and writing code another person can rerun. It also teaches SQL for finance, so your data lives in a database rather than in files.
- Probability for Quantitative Finance, with Statistics The probability every quant course assumes: distributions, expectation, conditioning and the limit theorems, with each idea tested on real return data. It also teaches the statistics for finance that every model rests on.
- Order Books, Execution Algorithms and Trading Costs How the limit order book works and what it reveals: matching rules, queue priority, depth and liquidity, order flow imbalance, and the short-term price dynamics that execution and market-making strategies depend on. It also teaches VWAP and TWAP execution algorithms, transaction cost analysis and smart order routing across venues.
What is the machine learning capstone?
You build a machine learning trading model end to end: labels, features, purged cross-validation, a backtest with costs and an estimate of how likely the result is to be overfit. The report must state what the model learned in terms a portfolio manager could check, and the faculty weigh the validation as heavily as the performance.
What does the credential record?
It records that you passed the proctored exam of every course listed on this page and the capstone, assessed by the Institute's faculty. The credential is issued by the Institute. Each one carries a verification code that an employer can check with the credentials office.
Questions applicants ask
Can machine learning predict the stock market?
Markets are noisy and adapt to whatever works, so most models that look good in a backtest are overfit. The program spends as much time on detecting overfitting as on building models.
Which libraries are used?
Python with scikit-learn, LightGBM and PyTorch. Courses explain the methods first and the libraries second.
How long does the certificate take?
56 hours of material across the five courses, each hour with its audio edition and workbook, and a capstone of 20 hours of your own work. The license runs 12 months, so you set the pace around a job.