A Framework for Testing Algorithmic Trading Strategies

Srinivas Raghavendra () and Daniel Paraschiv
Additional contact information
Daniel Paraschiv: Department of Economics, National University of Ireland, Galway

No 139, Working Papers from National University of Ireland Galway, Department of Economics

Abstract: Algorithmic trading and artificial stock markets have generated huge interest not only among brokers and traders in the financial markets but also across various disciplines in the academia. The emergence of algorithmic trading has created a new environment where the classic way of trading requires new approaches. In order to understand the impact of such a trading process on the functioning of the market, new tools, theories and approaches need to be created. Thus artificial stock markets have emerged as simulation environments to test, understand and model the impact of algorithmic trading, where humans and software agents may compete on the same market. The purpose of this paper is to create a framework to test and analyse various trading strategies in a dedicated artificial environment.1

Keywords: Artificial Stock Market; Double Auction; Back Testing; Algorithmic Trading; MACD (search for similar items in EconPapers)
Date: 2008, Revised 2008
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.economics.nuig.ie/resrch/paper.php?pid=146 First version, 2008 (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found
http://www.economics.nuig.ie/resrch/paper.php?pid=146 Revised version, 2008 (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nig:wpaper:0139

Access Statistics for this paper

More papers in Working Papers from National University of Ireland Galway, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Srinivas Raghavendra ().