Student Work

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%.

Last modified

• 02/03/2021

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

License

• All rights reserved

Relationships

In Collection:

• Interactive Qualifying Projects

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	IQP_Final_Report.pdf	2020-08-16	Public	Press to Select an action Download