Genetic Algorithms and Trading Strategies: New Evidences from Financially Interesting Time Series
In this paper, the performance of canonical GA-based trading strategies are evaluated under different time series. The time series considered include a variety of financial time series, ranging from linear and nonlinear stationary time series to chaotic time series. Unlike many existing applications of computational intelligence in financial engineering, for each performance criterion, we provide rigourous asymptotic statistical tests based on a Monte Carlo simulation. In addition, the criteria chosen are much more extensive than in the existing literature. These include the profit ratio, risk, the Sharpe ratio, maximum drawdown, and the luck coefficient. As a result, this study provides a thorough understanding of the effectiveness of canonical GAs for generating trading strategies under different financial time series.
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|Date of creation:||01 Mar 1999|
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Web page: http://fmwww.bc.edu/CEF99/
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