This book reconciles the existence of technical trading with the Efficient Market Hypothesis. By analyzing a well-known agent-based model, the Santa Fe Institute Artificial Stock Market (SFI-ASM), it finds that when selective forces are weak, financial evolution cannot guarantee that only the fittest trading rules will survive.Its main contribution lies in the application of standard results from population genetics which have widely been neglected in the agent-based community. This has led to various misinterpretations of previous simulation results. The book is able to finally establish the emergence of technical trading for faster learning speeds in the SFI-ASM beyond a doubt. In emphasizing the importance of genetic drift as an important evolutionary factor and analyzing its effects on various mutation operators, this book provides agent-based modelers with several tools to design better evolutionary algorithms.

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