Who’s Smart and Who’s Lucky? Inferring Trading Strategy, Learning and Adaptation in Financial Markets through Data Mining

  • Christopher R. Stephens
  • José Luis Gordillo
  • Enrique Martinéz Miranda
Part of the Studies in Computational Intelligence book series (SCI, volume 185)


Trading “profits” can be obtained by luck or by the implementation of a superior trading strategy. In this chapter we discuss the difficulties of distinguishing between the two. First, a suitable characterization of profit that distinguishes between trading gains and market gains is required. Secondly, one needs to be able to characterize trading “strategies”. To achieve this, we introduce the notion of a genotype-phenotype map to finance, where the genotype is associated with the information set and associated decision rules that lead to a given set of trading decisions for a given trader, while the phenotype is described by the set of observable trading decisions themselves. In AI based systems, such as agent-based markets, a strategy is implemented algorithmically and so the genotype is explicitly known. In real markets however, the genotypic trading strategy of one trader is hidden from the rest. The phenotype however, is, in principle, observable. A microscopic description at the level of the set of individual trades, however, is not sufficient to understand or characterize the strategies at a more macroscopic and intuitive one. By introducing a set of coarse grained variables that can be used to classify strategy types, we show how these variables can then be data mined to understand what differs between an intelligent and a lucky strategy. We show that these variables can be used to distinguish between different strategy types and can be further used to infer the presence of learning and adaptation in the market. We illustrate all of the above using data from an experimental political market.


Trading Strategy Sharpe Ratio Limit Order Winning Strategy Real Market 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Christopher R. Stephens
    • 1
  • José Luis Gordillo
    • 2
  • Enrique Martinéz Miranda
    • 3
  1. 1.C3 - Centro de Ciencias de la Complejidad and Instituto de Ciencias NuclearesUniversidad Nacional Autónoma de MéxicoMéxicoMexico
  2. 2.Dept. de Supercomputo, DGSCAUniversidad Nacional Autónoma de MéxicoMéxicoMexico
  3. 3.Facultad de CienciasUniversidad Nacional Autónoma de MéxicoMéxicoMexico

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