This project is part of a competition at WUTIS - Academic Trading And Investment Society, focusing on leveraging graph representations to understand and capitalize on group trends within stock market data. Our approach, recognized through a first-place victory in an Algorithmic Trading pitch competition, involves creating a dynamic graph representation based on the cross-correlation of stock price time-series data, identifying coherent and deviating group trends among stocks. More details are in the presentation file, with sample slides below:
Our project is split into three sections:
1. data_collection_graph_analysis.ipynb - Collecting the data, constructing the representation graph and analyzing potential group parameters.
2. group_trends_trading_strategy.ipynb - Formulating a trading strategy around the stocks that deviate and potentially returning to group trends.
3. parameter_optimization_strategy.ipynb - Backtesting and ranking based on the historical data to find optimal parameters.
Data is downloaded from the yahoo finance Python library - https://pypi.org/project/yfinance/.
Python file where all used functions are stored - trading_functions.py