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Two Stock-Trading Agents: Market Making and Technical Analysis.
Yi Feng, Ronggang Yu, and Peter
Stone.
In Peyman Faratin, David C. Parkes, Juan A. Rodriguez-Aguilar,
and William E. Walsh, editors, Agent Mediated Electronic Commerce V: Designing Mechanisms and Systems, Lecture
Notes in Artificial Intelligence, pp. 18–36, Springer Verlag, 2004.
AMEC-2003
[PDF]512.0kB [postscript]912.4kB
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 than any individual trader. Trading strategies have been proposed and practiced from the perspectives of Artificial Intelligence, market making, external information feedback, technical analysis etc. This paper examines two automated stock-trading agents in the context of the Penn-Lehman Automated Trading (PLAT) simulator, 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 the PLAT live competition. In this paper, we analyze the performance of these two automated trading strategies. Comparisons between them are also provided.
@InCollection(AMEC03,
author = "Yi Feng and Ronggang Yu and Peter Stone",
title="Two Stock-Trading Agents: Market Making and Technical Analysis",
booktitle="Agent Mediated Electronic Commerce {V}: Designing Mechanisms and Systems",
editor="Peyman Faratin and David C.\ Parkes and Juan A.\ Rodriguez-Aguilar and William E.\ Walsh",
series="Lecture Notes in Artificial Intelligence",
publisher="Springer Verlag",
volume="3048",
year="2004",
pages="18--36",
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 than any
individual trader. Trading strategies have been
proposed and practiced from the perspectives of
Artificial Intelligence, market making, external
information feedback, technical analysis etc. This
paper examines two automated stock-trading agents in
the context of the Penn-Lehman Automated Trading
(PLAT) simulator, 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 the PLAT live competition. In this paper, we
analyze the performance of these two automated
trading strategies. Comparisons between them are
also provided.
},
wwwnote={<a href="http://www.iiia.csic.es/amecv/">AMEC-2003</a>},
)
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