The highly intelligent virtual agents
for modeling financial markets
G. Yang1, Y. Chen2 and J. P. Huang1
2Department
1Department of Physics, Fudan University.
of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo.
Introduction
Agent-based modeling is proven to be a
very promising method to deal with the
complexity of economic or social systems.
The bottom-up approach is a most
significant feature, which resembles the
methods in statistical physics. The
difference is that the micro-units in agentbased models (ABMs) are artificial agents,
while those in physics systems are entities
with no observing, learning or adapting
abilities. Among complex human systems,
modeling of financial markets is the most
attracting project for the researchers.
Three roles of ABMs for modeling financial
markets:
 A microscopy for underlying dynamics:
reproduce the stylized facts, and explain
the micro-dynamics behind the phenomena
that appear in the macro-states. This can
give us a better understanding of human
behaviors in real markets.
 An evaluator for system properties: find
how the statistics of financial markets
evolves under the change of macroscopic
or microscopic environments. This can
offer suggestions to policy-makers.
 A forecaster for future states: predict the
future market movements. This can be
used to make trading strategies .
Three Principles
 A high information processing ability:
agents can easily observe, collect and
organize multiple information from the
environment.
 A high learning ability: agents can learn
from trial and error to optimize the
internal states of their own decisionmaking models.
 A high adaptation ability: agents can
replace their strategies to improve their
adaptability, by using self evolutionary
algorithms.
Building Agents
iAgents:
 iAgents observe three sign series of information flows: the price change flow, the volume change flow and the volatility change flow.
 1
 1
Impact factor of a certain sign series on an iAgent is expressed as: I (t )   M  exp( T )  exp( T ) . The position-changing level of an
iAgent is defined as: L(t )   eM I M (t )   eD I D (t )   eY IY (t ) , and his/her position is set as: P(t )  [ L(t )* Pmax ] .
 iAgents’ strategy form: { ,  ,  , eM , eD , e Y , TM , TD , TY }.
 Dynamic Genetic Algorithm (DGA) is adopted to update iAgents’ strategies.
Random traders: change positions randomly.
WG agents: a type of agents from wealth game (a modified minority game to study financial markets).
WG-DGA agents: WG agents equipped with DGA process.
TM
M
WG agents
Evaluation of the agents
Table 2. Comparison of agents' mean wealth from index trading of S&P at
the end of the optimization and the test periods respectively.
 1
TM
t 
M
 1
M
WG-DGA agents
iAgents
WG-DGA agents have slightly more intelligence than WG agents. But both the two types
of agents are more like passive fund managers whose main goal is just to follow the
market. However, the mean wealth of iAgents on the test period is 8.56 times the market
performance. In addition, the mean position of iAgents shows a degree of clustering. This
implies that trading strategies may be constructed on the iAgents’ decision-making model.
Conclusions
We have designed a kind of highly intelligent virtual agents called iAgents based on three
principles. Intelligence of iAgents have been tested through virtual index trading on S&P
(also on NKY in the paper), along with random traders, WG agents and WG-DGA agents.
It has been shown that trading behaviors of WG agents and WG-DGA agents are more
like those of passive fund managers. However, iAgents can in average outperform the
market greatly, which means that the three principles give iAgents a higher intelligence.
G. Yang, Y. Chen, and J. P. Huang, The highly intelligent virtual agents for modeling financial markets,
Physica A: Statistical Mechanics and its Applications 443, 98 (2016).