Open Formal LaTeX Documentation
Project by Yehyun Lee, Aung Zwe Maw and Wonjae Lee
Copyright and Usage Information
===============================
This file is provided solely for the personal and private use of our group
memebers at the University of Toronto St. George campus. All forms of
distribution of this code, whether as given or with any changes, are
expressly prohibited. For more information on copyright for this project,
please consult Yehyun Lee at [email protected].
This file is Copyright (c) 2023 Yehyun Lee, Aung Zwe Maw and Wonjae Lee.
⠀ As an investor, the question is, which stocks are most profitable in the long-term, and what factors contribute to their success? Our team will use a tree data structure to identify the promising factors and the most likely companies to invest in and utilize the backtesting approach to determine the profitability of the investments.
⠀ We aim to determine the most promising stocks for investment by analyzing the correlation between their performance metrics. Based on the analysis, we will rank the stocks using a binary tree to group the stock by ranking for investment. We will then simulate backtesting results based on the conclusions drawn from the correlation analysis and historical data.
⠀ One of the reasons for this project is the increase in interest in stocks during and after the pandemic. Many people have turned to invest in stocks to make passive income or grow their savings during a time of economic uncertainty. However, with so many companies to choose from, it can be challenging to know which ones are most likely to provide a good return on investment. By using past datasets, we can analyze historical data, current market trends, and other relevant factors to identify the companies that are confused to perform well in the future. This can help investors make more informed ed decisions about where to put their money and maximize their returns. Another motivation for developing a stock market investment program is the passion of one of our partners, Yehyun Lee for stock market trends. His passion positively affected us and we were also willing to explore the trends along with him.
For more information about our project, please visit LaTeX File.
Credits
Yehyun Lee, Aung Zwe Maw, Wonjae Lee
General Credits:
General code architecture and design were done by Yehyun Lee, with great support from Aung Zwe Maw with LaTeX, mathematical formulas, file management, and help with coding. Thanks to Wonjae Lee for the detailed docstrings and help with coding.
Overall
We worked together as a team! Instruction led by Yehyun Lee.
All docstring done mostly by Wonjae Lee, Aung Zwe Maw
LaTeX and documentation done mostly by Aung Zwe Maw
Rest of all codes possibly not mentioned done by Yehyun Lee
Part 1:
- All code: "user_input", "run_program", "visualization" solely by Yehyun
- Docstring: Wonjae Lee
Part 2:
- "read_csv", "top_half", by Wonjae Lee
- "get_percentage_growth", "get_percentage_growth_of_stocks" worked together by Yehyun Lee, Wonjae Lee
- Rest of all code: "filter_stocks", "obtain_factor_data", "get_factors_data", "clean_and_merge_data", "correlation", "all_factors_correlation", "filter_nan", "determining_best_factor" solely by Yehyun Lee
- Docstring: Wonjae Lee, Aung Zwe Maw
Part 3:
- "RecommendationTree", "create_recommendation_tree" by Yehyun Lee, Aung Zwe Maw, Wonjae Lee
- "ranked_choices_of_stocks" by Aung Zwe Maw
- "determining_buy_stocks" by Yehyun Lee, Aung Zwe Maw
- Docstring: Wonjae Lee, Aung Zwe Maw
Part 4:
- All code: "get_price", "benchmark_simulation", "recommendation_tree_simulation" solely by Yehyun Lee
- Docstring: Wonjae Lee, Aung Zwe Maw
Special words from Yehyun Lee:
I would like to thank my group members, Aung Zwe Maw and Wonjae Lee, for working very hard and following my instructions. Thanks a lot! You guys were very supportive and motivated me to work throughout the project. The atmosphere we had during work was very enjoyable!