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Investigations into Novel Strategies for Intelligent Agent-based Decision Making (2008)
BibTeX
@MISC{Schoreels08investigationsinto,
author = {Cyril Schoreels},
title = {Investigations into Novel Strategies for Intelligent Agent-based Decision Making},
year = {2008}
}
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Abstract
The role of automation in modern trading environments has increased dramatically over the last decade, even exhibiting a fundamental shift in market behaviour as a direct consequence of algorithmic trading and a significant increase in trading frequency. As a self-propagating consequence, the need for automation in both analysis and reactivity to market movements is one of the fastest developing areas in industry and the eld of computer science. A large variety of approaches exist in analysing market data and deriving indications as to the market's future movement. The focus of this work addressed the choice of trading methodology in simulation, using popular technical analysis tools, the capital asset pricing model and a hybrid implementation of the two. The algorithmic design of the genetic algorithm used in evolving agent populations and the choice and impact of the associated tness function was studied in depth. Furthermore, the choice of a static approach, in which agents are first trained and then exposed to testing data, versus that of an adaptive approach, in which agents are continuously
Keyphrases
novel strategy intelligent agent-based decision making self-propagating consequence direct consequence last decade algorithmic trading agent population capital asset pricing model hybrid implementation algorith-mic design associated tness function future movement market behaviour computer science large variety trading methodology genetic algorithm signicant increase modern trading environment adaptive approach market data fundamental shift popular technical analysis tool market movement static approach
