AIQUANT 2026 spring – Artificial Intelligence and Quantitative Finance

人工智能在量化金融中的应用

2025 Spring

Lecturer: Jian Li ( lapordge at gmail dot com)

TA: Yu Shi (shi-y23@mails.tsinghua.edu.cn), Yuebo Sun (sun-yb25@mails.tsinghua.edu.cn)

time: every Monday 19:20-21:45pm

Room: 6教 6A-213

We intend to cover a subset of the following topics (tentative):

(1) I assume you already took an undergrad level machine learning or AI course (basic machine learning concept, supervised learning, unsupervised learning, stochastic gradient descent, gradient boosting, deep learning basics, CNN, RNN, please see my undergrad course). If you don't know much machine learning (e.g., you do not know how to train a standard MLP model yet), please do NOT take this course. I will recall some concepts briefly when necessary.

It is a graduate course.

I will cover classic and modern topics related to AI and its application in Finance..

(1) classical idea in quantitative trading (portfolio theory, factor models, time series, risk models etc.)

(2) trading strategies for stocks, futures and other derivatives

(2) Applications of ML and DL to finance

(3) Applications of LLM and Agent to finance

It is a very diverse set of topics. I don't expect you to be interested in everything. But if you have enough undergrad AI background, and are interested in 1-2 topics above, you can take this course.

It may be a good opportunity for you to get a flavor of other topics. Anyway there is no closed-book exam.

Basic machine learning knowledge is a must. Andrew Ng's undergrad lecture notes

Only standard CS undergrad math and machine learning knowledge are required, otherwise the course will be self-contained. But certain math maturity and coding skill is required.

Some knowledge about convex optimization may be useful. See this course (by S. Boyd) and a previous course by myself. But it will be fine if you didn't take those courses.

Grading:

1. Homeworks (40pts, written homeworks+2-3 coding homeworks)
2. Course projects (60 pts. 10pts for mid-report, 10pts for final presentation, 25pts for final report, 15pts for code)
3. No close-book exam. 

Schedule:

References:

[Book] Advances in Financial Machine Learning

[Book] Options, Futures and Other Derivatives  

[Book] Active Portfolio Management

[Book] Learning, Prediction and Games

Python is the default programming language we will use in the course.

A standard combination for this class is Python+numpy (a numeric lib for python)+scipy (a scientific computing lib for python)+matplotlab (for generating nice plots)

Another somewhat easier way is to install Anaconda (it is a free Python distribution with most popular packages).