Practitioner course in machine learning
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.
Machine learning for trading gives a trader flexible models that can find patterns a linear regression would miss. It also gives the trader many more ways to find patterns that are not there. Financial data is noisy and changes over time, and the methods that work in image or text tasks often fail on it. This course teaches the methods and the discipline together.
How do you avoid overfitting a machine learning trading model?
By validating it in a way that respects time. Ordinary cross-validation shuffles data and leaks information from the future into training. Walk-forward testing, and cross-validation with purging and embargo periods, prevent that. Counting how many models and settings you tried, and adjusting your expectations for it, prevents the subtler error of selecting a lucky one.
What makes a good feature for a trading model?
It must be known at the time of the decision, it should have an economic reason to relate to future returns, and it should be stable enough that its relationship holds out of sample. You will build features from price, volume and fundamental data, and test which ones carry information.
Who is this course for?
Quant analysts, data scientists moving into finance, and traders who want to use machine learning properly.
What you will be able to do
- Build features and labels from price, volume and fundamental data without look-ahead.
- Train and compare regularized linear models, gradient-boosted trees and neural networks.
- Validate models with walk-forward and purged cross-validation.
- Measure feature importance and recognize unstable results.
- Turn model predictions into positions and test the strategy with costs.
Syllabus, module by module
The problem setup
What to predict, at what horizon, and why the choice matters more than the model.
Features
Returns, volatility, volume, cross-sectional ranks and fundamentals, all point-in-time.
Labels
Fixed-horizon returns, direction, and labels that reflect how a trade would exit.
Linear models
Ridge, lasso and elastic net as strong baselines.
Tree models
Random forests and gradient boosting, and tuning them on noisy data.
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.
How the certificate is earned
An assessed project: you build a machine learning strategy on a universe of your choice, validate it with a time-aware method, test it out of sample with costs, and submit the code with a report. On a pass you receive the Course certificate in Machine Learning for Trading, issued by the Institute with a verification code. A course taken as part of a Certificate, Diploma or Fellowship ends in a proctored online exam with a pass mark of 60%. The fee includes three retakes; each attempt after that carries a reduced proctored exam fee. A course taken on its own is assessed by its coursework and has no exam. Read how certification works.
Before you start
Working Python with pandas, statistics to regression level, and the material of Time Series Analysis and Financial Econometrics.
This course counts toward
- Certificate in Algorithmic TradingSeven courses and a capstone that take a strategy from idea to a costed, sized and validated system, traded on paper and reported the way a trading desk would read it.
- Certificate in Statistical ArbitrageSeven courses and a capstone on market-neutral trading: time series foundations, pairs and baskets, cointegration, regimes, costs and the validation that keeps spread strategies honest.
- Certificate in Machine Learning for TradingFive 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.
- Professional Diploma in Algorithmic TradingThe top credential on the practitioner path. Twenty-one courses and an 80-hour capstone covering everything between the mathematics and a book you could defend to a risk committee.
- Fellowship in Computational FinanceThe top credential on the researcher path. Twenty-one courses and a dissertation supervised in writing and examined by two faculty members.
Questions applicants ask
Can machine learning predict stock prices?
It can find weak but real patterns in some settings, particularly across many assets and at short to medium horizons. It cannot reliably predict the price of a single stock far ahead. The course is built around measuring how weak the edge is and whether it survives costs.
Which machine learning model is best for trading?
On tabular financial data, regularized linear models and gradient-boosted trees are strong and hard to beat. Neural networks help in some problems with large data. The course compares them on the same task.