Video summary: https://youtu.be/nnNDyLz46Yc
Abstract: The finance industry is often quick to adopt new technology and the application of natural computing techniques is a contemporary research area. This project aimed to derive and test genetically evolved trading strategies with a grammatical evolution algorithm. Four S&P 500 stocks were modelled over a 17-year period and trading strategies were generated. The results were marginally more profitable than a benchmark buy and hold strategy.
To run the program, copy all of the GE Program folder into a new directory. Execute "run_ponyge.py" in the folder GE Program\src
The following files were modified from PonyGE2 for this project: \src\fitness\pymax.py "trading.py" - The fitness function and stock selection \grammars\pymax.pybnf "trading.pybnf" - The grammar \parameters\pymax.txt "trading.txt" - Parameters file
Contact me with any questions and I would be happy to help!
Thanks to Michael Fenton for his support in implementing the powerful PonyGE2 library.
Eoin Carroll