Abstract
Evolving information technologies have brought computational power and real-time facilities into the stock market. Automated stock trading draws much interest from both the fields of computer science and of business, since it promises to provide superior ability in a trading market to any individual trader. Trading strategies have been proposed and practiced from the perspectives of Artificial Intelligence, market making, external information feedback, and technical analysis among others. This paper examines two automated stock-trading agents in the context of the Penn-Lehman Automated Trading (PLAT) simulator [1], which is a real-time, real-data market simulator. The first agent devises a market-making strategy exploiting market volatility without predicting the exact direction of the stock price movement. The second agent uses technical analysis. It might seem natural to buy when the market is on the rise and sell when it’s on the decline, but the second agent does exactly the opposite. As a result, we call it the reverse strategy. The strategies used by both agents are adapted for automated trading. Both agents performed well in a PLAT live competition. In this paper, we analyze the performance of these two automated trading strategies. Comparisons between them are also provided.
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References
Kearns, M., Ortiz, L.: The Penn-Lehman Automated Trading Project. IEEE Intelligent Systems 18(6), 22–31 (2003)
Island, http://www.island.com
Archipelago, http://www.tradearca.com
Bloomberg, http://www.bloomberg.com
The 2002 annual report of Securities Industry Foundation for Economic Education (2001), http://www.sia.com/about_sia/pdf/annual2002.pdf
Virtual Stock Exchange, http://www.virtualstockexchange.com
Freedman, R., et al. (eds.): Artificial Intelligence in Capital Markets. Probus Pub., Chicago (1995)
Skabar, A., Cloete, I.: Discovery of Financial Trading Rules. In: Proc. Artificial Intelligence and Applications, pp. 121–125 (2001)
Kingdon, J.: Intelligent Systems and Financial Forecasting. Springer, New York (1997)
Donaldson, R., Kamstra, M.: Forcast Combining With Neural Networks. Journal of Forecasting 15(1), 49–61 (1996)
Sheimo, M.: Stock Market Rules: 50 of the most widely held investment axioms explained, examined and exposed. Probus Pub., Chicago (1991)
Stone, P., Greenwald, A.: The First International Trading Agent Competition: Autonomous Bidding Agents. Electronic Commerce Research 5(1) (2005) (to appear)
The Penn-Lehman Automated Trading Project, http://www.cis.upenn.edu/~mkearns/projects/pat.html
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© 2004 Springer-Verlag Berlin Heidelberg
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Feng, Y., Yu, R., Stone, P. (2004). Two Stock-Trading Agents: Market Making and Technical Analysis. In: Faratin, P., Parkes, D.C., Rodríguez-Aguilar, J.A., Walsh, W.E. (eds) Agent-Mediated Electronic Commerce V. Designing Mechanisms and Systems. AMEC 2003. Lecture Notes in Computer Science(), vol 3048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25947-3_2
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DOI: https://doi.org/10.1007/978-3-540-25947-3_2
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