An Improved Platform for Multi-Agent Based Stock Market Simulation in Distributed Environment
Authored by Ce Yu, Xiang Chen, Chunyu Wang, Hutong Wu, Jizhou Sun, Yuelei Li, Xiaotao Zhang
Date Published: 2015
DOI: 10.1587/transinf.2015edp7050
Sponsors:
Chinese National Natural Science Foundation
Platforms:
No platforms listed
Model Documentation:
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Abstract
Multi-agent based simulation has been widely used in behavior finance, and several single-processed simulation platforms with Agent-Based
Modeling (ABM) have been proposed. However, traditional simulations of
stock markets on single processed computers are limited by the computing
capability since financial researchers need larger and larger number of
agents and more and more rounds to evolve agents' intelligence and get
more efficient data. This paper introduces a distributed multi-agent
simulation platform, named PSSPAM, for stock market simulation focusing
on large scale of parallel agents, communication system and simulation
scheduling. A logical architecture for distributed artificial stock
market simulation is proposed, containing four loosely coupled modules:
agent module, market module, communication system and user interface.
With the customizable trading strategies inside, agents are deployed to
multiple computing nodes. Agents exchange messages with each other and
with the market based on a customizable network topology through a
uniform communication system. With a large number of agent threads, the
round scheduling strategy is used during the simulation, and a worker
pool is applied in the market module. Financial researchers can design
their own financial models and run the simulation through the user
interface, without caring about the complexity of parallelization and
related problems. Two groups of experiments are conducted, one with
internal communication between agents and the other without
communication between agents, to verify PSSPAM to be compatible with the
data from Euronext-NYSE. And the platform shows fair scalability and
performance under different parallelism configurations.
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