Optimal Algorithms for Stock Trading Decisions

This project is centered on creating optimal algorithms to solve the classic problem of determining the best times to buy and sell stocks in order to maximize profits. Given stock price data over several days, our algorithms analyze and predict the most lucrative trading opportunities.

Project Overview:

• Algorithm Development: Designed three distinct algorithms for each of two problems, varying in complexity from brute force to more sophisticated dynamic programming approaches. These were tailored to optimize trading strategies over different scenarios.
• Performance Evaluation: Conducted rigorous performance evaluations for each algorithm, measuring efficiency and effectiveness through computational benchmarks and comparative analysis.
• Detailed Comparisons: Provided detailed comparisons of the algorithms, showcasing their potential in real-world trading scenarios and supporting strategic financial decision-making.
• Technological Implementation: Implemented the algorithms in Java, ensuring they run efficiently on specified platforms, and integrated comprehensive performance metrics to guide users in choosing the most suitable algorithm for their needs.

Through this project, we demonstrated that it is possible to significantly reduce the number of features (i.e., days and stocks considered) while still maintaining high accuracy in predictions, thereby reducing computational costs and simplifying the decision-making process for stock trading.