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Algorithmic Daily Trading Based on Experts’ Recommendations

  • Andrzej RutaEmail author
  • Dymitr Ruta
  • Ling Cen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10352)

Abstract

Trading financial products evolved from manual transactions, carried out on investors’ behalf by well informed market experts to automated software machines trading with millisecond latencies on continuous data feeds at computerised market exchanges. While high-frequency trading is dominated by the algorithmic robots, mid-frequency spectrum, around daily trading, seems left open for deep human intuition and complex knowledge acquired for years to make optimal trading decisions. Banks, brokerage houses and independent experts use these insights to make daily trading recommendations for individual and business customers. How good and reliable are they? This work explores the value of such expert recommendations for algorithmic trading utilising various state of the art machine learning models in the context of ISMIS 2017 Data Mining Competition. We point at highly unstable nature of market sentiments and generally poor individual expert performances that limit the utility of their recommendations for successful trading. However, upon a thorough investigation of different competitive classification models applied to sparse features derived from experts’ recommendations, we identified several successful trading strategies that showed top performance in ISMIS 2017 Competition and retrospectively analysed how to prevent such models from over-fitting.

Keywords

Algorithmic trading Feature selection Classification Gradient boosting decision trees Sparse features K-nn 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.ING Bank SlaskiKatowicePoland
  2. 2.Emirates ICT Innovation Center, EBTICKhalifa UniversityAbu DhabiUAE

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