Faculty Advisor

Marcel Y. Blais

Identifier

etd-050117-071501

Abstract

Building automated trading systems has long been one of the most cutting-edge and exciting fields in the financial industry. In this research project, we built a trading system based on machine learning methods. We used the Recurrent Reinforcement Learning (RRL) algorithm as our fundamental algorithm, and by introducing Genetic Algorithms (GA) in the optimization procedure, we tackled the problems of picking good initial values of parameters and dynamically updating the learning speed in the original RRL algorithm. We call this optimization algorithm the Evolutionary Recurrent Reinforcement Learning algorithm (ERRL), or the GA-RRL algorithm. ERRL allows us to find many local optimal solutions easier and faster than the original RRL algorithm. Finally, we implemented the GA-RRL system on EUR/USD at a 5-minute level, and the backtest performance showed that our GA-RRL system has potentially promising profitability. In future research we plan to introduce some risk control mechanism, implement the system on different markets and assets, and perform backtest at higher frequency level.

Publisher

Worcester Polytechnic Institute

Degree Name

MS

Department

Mathematical Sciences

Project Type

Thesis

Date Accepted

2017-05-01

Accessibility

Restricted-WPI community only

Subjects

Genetic Algorithms, Reinforcement Learning, Foreign Exchange, Algorithmic Trading, Machine Learning

Available for download on Friday, May 01, 2020

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