Information Technology Reference
In-Depth Information
4
Artificial Traders: From Basic Reactive Agents to Highly
Sophisticated Entities
Artificial agents, market participants, comply with basic agent-based modelling con-
cepts [17].
- Autonomy means that an agent is not passive subject to a global, external flow of
control in its actions. An agent has own objectives, abilities to accept information,
then to analyze it and based on these results to make decision about further actions.
- Proactivity means that the agents act in order to achieve its objectives or goals. In
terms of artificial financial market, agents trade (set up the buy and sell orders) to
maximize their wealth.
Many ASM can run large populations of homogeneous, respectively heterogeneous ar-
tificial traders. This is also the case for ATOM, moreover it allows facilities which are
not available in other platforms. Generally speaking, artificial traders are characterized
by their a) available set of actions (buy, sell) and possibility to switch between these
activities (from buyer to seller) b) decision making rules , for instance, buyers cannot
buy at a price higher than their buyer value and sellers cannot sell for a price below
their seller cost c) scheduling of action: how often agent is able to send the orders
in respond to market request, some agent participate one time per hour, while others
tradeeveryminuted) information consideration, in the mean which information agent
requires from market or external word in order to make decision and what kind of in-
formation she shares for others e) possibility to describe status in mean of number of
assets and available cash or current budget. Agents heterogeneity is driven by different
combinations of these properties. For example, the following types of agents can be
implemented:
Zero Intelligence Traders (ZIT). This behaviour is merely based on stochastic choices:
there are equal possibilities to send ask or bid order, ZIT do not observe and do not ask
any information to set up prices and quantities, that are random variable. Concerning
scheduling, such traders respond to every market request. This kind of behaviour has
been popularized in economics by [18]. Despite their extreme simplicity, these agents
are widely used because more sophisticated forms of rationality appear to be useless to
explain the emergence of the main financial stylized facts at the intraday level.
Technical Traders. ”Chartists” are a specific population of technical traders. These
agents try to identify patterns in past prices (using charts or statistical signals) that could
be used to predict future prices and henceforth send appropriate orders. One can find
an example of such behaviour in [19]. From a software engineering perspective, these
agents need to have some feedback from the market and some kind of learning process
as well (reinforcement learning for large sets of rules is generally used). At the same
time, technical traders ignore the actual nature of the company, currency or commodity.
This lead to some complex algorithmic issues. For example, if one considers a popula-
tion of a few thousand Technical Traders, it is highly desirable to avoid that each agent
compute the same indicators, or simply store themselves the whole price series.
 
Search WWH ::




Custom Search