Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/54307
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Type: Book chapter
Title: Evolving trading rules
Author: Ghandar, A.
Michalewicz, Z.
Schmidt, M.
To, T.
Zurbrugg, R.
Citation: Success in Evolutionary Computation, 2008 / Yang, A., Shan, Y., Bui, L. (ed./s), vol.92, pp.95-119
Publisher: Springer
Publisher Place: Germany
Issue Date: 2008
Series/Report no.: Studies in computational intelligence ; 92
ISBN: 9783540762850
Editor: Yang, A.
Shan, Y.
Bui, L.
Statement of
Responsibility: 
Adam Ghandar, Zbigniew Michalewicz, Martin Schmidt, Thuy-Duong Tô and Ralf Zurbrugg
Abstract: This chapter describes a computational intelligence system for portfolio management and provides a comparison of the relative performance of portfolios of managed by the system using stocks selected from the ASX (Australian Stock Exchange). The core of the system is the development of trading rules to guide portfolio management. The rules the system develops are adapted to dynamic market conditions. An integrated process for stock selection and portfolio management enables a search specification that produces highly adaptive and dynamic rule bases. Rule base solutions are represented using fuzzy logic and an evolutionary process facilitates a search for high performing fuzzy rule bases. Performance is defined using a novel evaluation function involving simulated trading on a recent historical data window. The system is readily extensible in terms of the information set used to construct rules, however to produce the results given in this chapter information derived only from price and volume history of stock prices was used. The approach is essentially referred to as technical analysis - as opposed to using information from outside the market such as fundamental accounting and macroeconomic data. A set of possible technical indicators commonly used by traders forms the basis for rule construction. The fuzzy rule base representation enables intuitive natural language interpretation of trading signals and implies a search space of possible rules that corresponds to trading rules a human trader could construct. An example of a typical technical trading rule such as "buy when the price of a stock X´s price becomes higher than the single moving average of the stock X´s price for the last, say, 20 days" (indicating a possible upward trend) could be encoded using a fuzzy logic rule such as "if single moving average buy signal is High then rating is 1"; conversely we could have a trading rule such as "sell stocks with high volatility when the portfolio value is relatively low" encoded by a fuzzy rule: "if Price Change is High and Portfolio Value is Extremely Low then rating is 0.1". The fuzzy rule bases undergo an evolutionary process. An initial population of rule bases (genotypes) is selected at random and may be seeded with some rule bases that correspond to accepted technical trading strategies. Further generations are then evolved by evaluating the rule bases comprising the initial population and then taking offspring of the best performing rule bases from the previous generation. Fuzzy rule base performance is evaluated through analysis of the results of applying a particular rule base to simulated trading. In the simulated scenario an initial capital is allocated to construct an initial portfolio at the beginning of the simulation period. This initial portfolio is managed over the rest of a data window. The empirical results from testing the system on historical data show that the system can produce quite impressive results over the test period with respect to a range of portfolio evaluation tools. Particularly given that we impose both costs to trading and restrictions on how trades can occur and limit the information set used to information derived only from price and volume data.
DOI: 10.1007/978-3-540-76286-7_5
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