Researcher course in machine learning
Financial Machine 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.
Machine learning methods were built for data with many independent samples and a stable relationship to learn. Financial data has neither. Over the past decade, researchers have developed financial machine learning: methods specific to finance, for how to sample, label, weight and validate, and how to judge whether a backtest is the product of skill or of many trials. This course teaches those methods at research level.
What is the triple-barrier method?
A way of labelling trading outcomes that matches how a position is actually managed. From each entry point, three barriers are set: a profit target, a stop-loss and a time limit. The label records which barrier is touched first. It produces labels that reflect real trade outcomes rather than an arbitrary fixed-horizon return.
How do you tell if a backtest is overfit?
By accounting for how many strategies or settings were tried. The deflated Sharpe ratio adjusts a Sharpe ratio for the number of trials and the shape of the return distribution, and the probability of backtest overfitting estimates how often the best in-sample choice underperforms out of sample. You will apply both to your own research.
Who is this course for?
Quant researchers and data scientists working on systematic strategies who want research-grade methods. It counts toward the Certificate in Machine Learning for Trading and the Fellowship in Computational Finance.
What you will be able to do
- Build volume, dollar and imbalance bars and compare their statistical properties.
- Label events with the triple-barrier method and apply meta-labeling.
- Weight samples whose outcomes overlap in time.
- Apply purged and combinatorial cross-validation.
- Estimate the probability of backtest overfitting and the deflated Sharpe ratio.
Syllabus, module by module
Why finance is different
Low signal-to-noise, non-stationarity, overlapping outcomes and few independent samples.
Sampling
Time, tick, volume and dollar bars, imbalance bars and event-based sampling.
Labelling
The triple-barrier method, meta-labeling and bet sizing from probabilities.
Sample weights
Concurrency, uniqueness and sequential bootstrap.
Stationarity and memory
Fractional differentiation and the trade-off between stationarity and information.
Cross-validation
Purging, embargo and combinatorial purged cross-validation.
Feature importance
Mean decrease impurity, permutation importance, clustered importance and substitution effects.
Backtest overfitting
The probability of backtest overfitting and the deflated Sharpe ratio.
Assessment preparation
Supervised work on the written assessment.
How the certificate is earned
A written paper-style assessment with a computational component: you apply the methods of the course to a research question of your choice and write up the design, results and robustness checks as a paper. On a pass you receive the Course certificate in Financial Machine Learning, issued by the Institute with a verification code. Read how certification works.
Before you start
The material of Machine Learning for Trading, probability and statistics to graduate entry level, and fluent Python.
This course counts toward
- Certificate in Machine Learning for TradingFive courses and a capstone on applying machine learning to markets without fooling yourself: labelling, features, purged validation, overfitting tests and reinforcement learning.
- Fellowship in Computational FinanceThe top credential on the researcher path. Nine courses, eight of them at researcher level, and a supervised dissertation, examined by two faculty members.
Questions applicants ask
Is this course based on Advances in Financial Machine Learning?
It covers many of the methods that Marcos López de Prado set out in that book and in related papers, alongside work by other authors. The book is recommended reading; the course is independent and not affiliated with the author or publisher.
What is meta-labeling?
A two-stage approach: a primary model or rule decides the side of a trade, and a secondary machine learning model decides whether to take it and how large to make it. It separates the direction decision from the sizing decision.