Shaoxiong (Steven) Yuan

Course Textbook: Mathematical Statistics and Data Analysis by John A. Rice

Course Syllabus

Midterm Exam 1

Midterm Exam 2

Final

There are 38 lecture notes, but the final only covers 1 through 19. There are some practice problems after lecture 19.

Course Textbook: A First Course in Probability (9th edition)

All Lecture Notes

Quiz 1

Quiz 2

Midterm Exam

Final Exam with Solution

Professor Paul Tokorcheck taught this class in a very good fashion. Although “Number Theory” sounds intimidating, it is not. The course content, though abstract, was greatly demonstrated and proved by the instructor. He was also kind enough to write a recommendation letter for the Continuing Students Scholarship.

Course Textbook: Elementary Number Theory by David M. Burton

Additional Useful Reference: Book of Proof

Note: This class has no official lecture notes, but one good way to refer to the content is through the textbook.

Course Syllabus

Exercise List

Midterm Exam 1

Midterm Exam 2

Final Exam

Course Textbook: Differential Equations and Linear Algebra by Stephen W. Goode

All Lecture Notes

Weekly Quizzes

Practice Midterm

Practice Final

Midterm Exam

Final Exam

Course Textbook: Essential Calculus by James Stewart

Lecture Notes

Exercise List

Midterm Exam 1

Midterm Exam 2

Final Review

Final Review Solutions

Final Exam

Course Textbook: Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig (4th edition)

Course Syllabus

Projects

Homework

Course Textbook: Introduction to Java Programming and Data Structures by Y. Daniel Liang (12th edition)

Course Syllabus

Programming Assignments

My solution to the first two programming assignments can be found here.

Please refer to the course website for more information including course syllabus and assignments!

Course Textbook: Algorithm Design by Jon Kleinberg and Eva Tardos

A Useful Course on Coursera: Data Structures and Algorithms Specialization

Please refer to the course website for more information!

My solution to the six programming assignments can be found at this GitHub repository.

Course Textbook: Essential Discrete Mathematics for Computer Science, by Harry Lewis and Rachel Zax

Course Syllabus

Midterm

Final Exam

Course Textbook: Not Available

Midterm

Final

Final Project: Golden Cross & Death Cross EMA Trading Strategy

A group project implementing a momentum-based trading strategy using 8-period and 25-period Exponential Moving Averages (EMAs) on the S&P 500 Energy Sector. The strategy uses Golden Cross signals (8EMA crossing above 25EMA) to trigger buys and Death Cross signals to trigger sells, achieving a 17% annual return vs. 10% buy-and-hold, with a Sharpe Ratio of 0.68 vs. 0.37 for the benchmark.

Final Project: Stock Return Predictability with XGBoost and Random Forest

A group project examining whether combining momentum and fundamental factors can produce a strategy that captures momentum gains while remaining effective long-term. Using CRSP/Compustat data (2012–2024), the study trains XGBoost and Random Forest models to forecast next-month stock returns, evaluated against a Fama-French 3-Factor benchmark via decile portfolios and a Winner-Minus-Loser (WML) long-short portfolio. XGBoost achieved a 26.11% WML return and Sharpe of 0.96 in the test period (2022–2024).

Final Project: Regime-Based Factor Investing Strategy

A group project developing a novel regime-aware long-short factor investing strategy using CRSP data (1990–2023). Market regimes are identified along two dimensions — macroeconomic regime (Bull/Bear/Sideways via term spread between the 10-year Treasury and 3-month T-Bill) and market concentration regime (Narrow/Normal/Broad via Gini coefficient and HHI) — forming a 3×3 matrix of nine distinct regimes. For each regime, an optimal combination of six factor signals (momentum, growth, quality, volatility, value, profitability) is selected, and stocks are ranked by composite Z-score into top/bottom quintile long-short portfolios rebalanced monthly. The strategy generates statistically significant CAPM and FF3 alpha, outperforms the market across all major bear market episodes (151.51% during the GFC), and achieves a maximum drawdown of −45.32% vs. the market’s −50.31%.

Official Website: https://pioneeracademics.com/

In this program, I learned several fascinating identities about the Fibonacci sequence as well as other sequences such as Lucas numbers.

At the end of the Pioneer Academics, I undertook an independent research project with Prof. Dresden of Washington and Lee University, writing a paper on continued fraction, an iterative process of number patterns.

Professor Gregory Dresden’s Homepage: https://dresden.academic.wlu.edu/

Link to my final paper on continued fractions: Final Paper

Program Textbook: Proofs That Really Count

Link to my entry in Online Encyclopedia of Integer Sequences: https://oeis.org/A049669

Note: I followed Corey Schafer (His YouTube Channel) to write this project.

The source code can be found here.

The source code can be found here.

The source code can be found here.

This project also uses Nginx to handle static requests such as HTML templates, CSS files, and JS files. To download nginx, visit this link. The configuration file can be found here. The static files can be found here.

This project trained the GPT model from scratch to allow the computer to predict what my next line of Python code is. The complete tutorial is taught by sentdex in Generating Python code with GPT. The trained model using 80G of python files can be found at this link.

The source code can be found here.

The source code can be found here.