Evolutionary Algorithm Based Approach for Modeling Autonomously Trading Agents

Anil Yaman, Stephen Lucci, Izidor Gertner

Department of Computer Science, The City College of New York, New York, USA.

DOI: 10.4236/iim.2014.62007                

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.

Keywords

Artificial Intelligence; Autonomous Agents; Artificial Life; Evolutionary Computation; Neural Networks; FOREX

Conflicts of Interest

The authors declare no conflicts of interest.

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