ML for quant
3 readings
Learn statistical learning, establish simple baselines, and evaluate financial predictions without leaking the future.
- Who this is for
- Readers comfortable with Python, linear algebra and basic statistics.
- What you will work towards
- Design a chronological experiment that compares a financial ML model with an interpretable baseline.
Before you start
Use these SMQ lessons to check the ideas the readings assume. Lesson pages show the outline and how to access the full lesson.
- Vectors, Matrices and Linear Systems
Work with vectors and matrix operations before fitting a model.
- Linear Regression
Establish and interpret a simple predictive baseline.
Read in this order
An Introduction to Statistical Learning with Applications in Python
Start with accessible statistical learning and Python implementations.
Mathematics for Machine Learning
Fill the mathematical gaps behind the models you choose.
Empirical Asset Pricing via Machine Learning
See how financial prediction changes model evaluation.
Continue your study on SMQ
- Finance-aware validation
Preserve time order and test for leakage before evaluating a model.
- Feature importance
Check what information the fitted model actually uses.
Follow the connected lessons, worked examples, and exercises in the app. Full lesson access depends on the topic and your membership.
Continue with Financial Machine Learning