Developed an autonomous stock trading algorithm. Each group member created their own trading system, and parts of each system were combined into a single system. The final system consists of a separate algorithm for selecting stocks, buying stocks, allocation money, and selling stocks. The buy process uses a Random Forest machine learning algorithm trained on historical stock prices and modified bars based on volume to predict when to buy. The system and backtesting software were developed in Python using modules Numpy, Pandas, Scikit-learn, and Matplotlib. The system was tested across short, intermediate and long term periods. The average annualized return on investment for the three time periods was 26.67%.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Battles, John William
• Wurts, Alexander Jay
• Albert, Nicholas Scott

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-042919-211151

Keyword

• Economic Growth, Stability, and Development

Advisor

• Somasse, Gbetonmasse B.
• Ludwig, Reinhold

Year

• 2019

Date created

• 2019-04-29

Resource type

• Interactive Qualifying Project

Rights statement

• In Copyright

Last modified

• 2021-02-03