Genetic Algorithms and Trading Strategies: New Evidences from Financially Interesting Time Series
AbstractIn 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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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 552.
Date of creation: 01 Mar 1999
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