Algorithmic daily trading based on experts’ recommendations

Andrzej Ruta, Dymitr Ruta, Ling Cen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 util-ising 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.

Original languageBritish English
Title of host publicationFoundations of Intelligent Systems - 23rd International Symposium, ISMIS 2017, Proceedings
EditorsMarzena Kryszkiewicz, Henryk Rybinski, Zbigniew W. Ras, Dominik Slezak, Andrzej Skowron, Annalisa Appice, Andrzej Skowron
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319604374
StatePublished - 2017
Event23rd International Symposium on Methodologies for Intelligent Systems, ISMIS 2017 - Warsaw, Poland
Duration: 26 Jun 201729 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10352 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Symposium on Methodologies for Intelligent Systems, ISMIS 2017


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


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