Evolutionary Algorithm Based Approach for Modeling Autonomously Trading Agents

Authors

Anil Yaman, CUNY City CollegeFollow
Stephen Lucci, CUNY City CollegeFollow
Izidor Gertner, CUNY City CollegeFollow

Document Type

Article

Publication Date

2014

Abstract

The autonomously trading agents described in this paper produce a decision to act such as: buy, sell or hold, based on the input data. In this work, we have simulated autonomously trading agents using the Echo State Network (ESNs) model. We generate a collection of trading agents that use different trading strategies using Evolutionary Programming (EP). The agents are tested on EUR/ USD real market data. The main goal of this study is to test the overall performance of this collection of agents when they are active simultaneously. Simulation results show that using different agents concurrently outperform a single agent acting alone.

Comments

This article was originally published in Intelligent Information Management, available at DOI: 10.4236/iim.2014.62007.

This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).