Qlib-based Practical Program for Teaching Quantitative Investment
Qlib is particularly well suited as a platform for teaching quantitative investing and the following teaching methods are recommended:
- Modular Knowledge Breakdown::
- Foundation: Adoption
D.features()API Demo Financial Data Feature Extraction - Advanced: Using Presets
Alpha158Factor pooling explains multifactor modeling - Practical: based on
TopkDropoutStrategyBuild a complete trading strategy
- Foundation: Adoption
- Jupyter Notebook Interactive Teaching: Create interactive courseware with code samples + theoretical explanations + visualization results using Qlib's perfect support for Jupyter.
plot_graphFunctions can be directly plotted on instructional graphs such as yield curves. - Course Experimental Design: It is proposed to set up three progressive experiments:
- Experiment 1: Replicating the traditional CAPM model
- Experiment 2: Constructing a machine learning-based multi-factor stock selection model
- Experiment 3: Complete Strategy Development and Backtesting
Teaching Suggestion: Use China A-share market (cn_data) data which is closer to domestic students' knowledge. For the first time, you can start by modifying the official sample code, and then gradually move on to self-development. qlib's perfect error alerts can significantly reduce the learning curve.
This answer comes from the articleQlib: an AI quantitative investment research tool developed by MicrosoftThe
May not be reproduced without permission:AI productivity tools " How can Qlib be applied to teach quantitative investment fundamentals in an educational scenario?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil