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Three Automated Stock-Trading Agents: A Comparative Study.
Alexander
Sherstov and Peter Stone.
In P. Faratin and J.A. Rodriguez-Aguilar,
editors, Agent Mediated Electronic Commerce VI: Theories for and Engineering of Distributed Mechanisms and Systems (AMEC
2004), Lecture Notes in Artificial Intelligence, pp. 173–187, Springer Verlag, Berlin, 2005.
Official version
from Publisher's Webpage© Springer-Verlag
[PDF]167.0kB [postscript]248.6kB
This paper documents the development of three autonomous stock-trading agents within the framework of the Penn Exchange Simulator (PXS), a novel stock-trading simulator that takes advantage of electronic crossing networks to realistically mix agent bids with bids from the real stock market. The three approaches presented take inspiration from reinforcement learning, myopic trading using regression-based price prediction, and market making. These approaches are fully implemented and tested with results reported here, including individual evaluations using a fixed opponent strategy and a comparative analysis of the strategies in a joint simulation. The market-making strategy described in this paper was the winner in the fall 2003 PLAT live competition and the runner-up in the spring 2004 live competition, exhibiting consistent profitability. The strategy's performance in the live competitions is presented and analyzed.
@InCollection(AMEC04-plat,
author="Alexander Sherstov and Peter Stone",
title="Three Automated Stock-Trading Agents: A Comparative Study",
booktitle="Agent Mediated Electronic Commerce {VI}: Theories for and Engineering of Distributed Mechanisms and Systems (AMEC 2004)",
editor="P.~Faratin and J.A.~Rodriguez-Aguilar",
series="Lecture Notes in Artificial Intelligence",
volume="3435",
Publisher="Springer Verlag",address="Berlin",year="2005",
pages="173--187",
abstract={
This paper documents the development of three
autonomous stock-trading agents within the framework
of the Penn Exchange Simulator (PXS), a novel
stock-trading simulator that takes advantage of
electronic crossing networks to realistically mix
agent bids with bids from the real stock market. The
three approaches presented take inspiration from
reinforcement learning, myopic trading using
regression-based price prediction, and market
making. These approaches are fully implemented and
tested with results reported here, including
individual evaluations using a fixed opponent
strategy and a comparative analysis of the
strategies in a joint simulation. The market-making
strategy described in this paper was the winner in
the fall 2003 PLAT live competition and the
runner-up in the spring 2004 live competition,
exhibiting consistent profitability. The strategy's
performance in the live competitions is presented
and analyzed.
},
wwwnote={Official version from <a href="http://dx.doi.org/10.1007/11575726_13">Publisher's Webpage</a>© Springer-Verlag},
)
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