TITLE:
Evolutionary Algorithm Based Approach for Modeling Autonomously Trading Agents
AUTHORS:
Anil Yaman, Stephen Lucci, Izidor Gertner
KEYWORDS:
Artificial Intelligence; Autonomous Agents; Artificial Life; Evolutionary Computation; Neural Networks; FOREX
JOURNAL NAME:
Intelligent Information Management,
Vol.6 No.2,
March
28,
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.