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Big Data Framework for Finding Patterns in Multi-market Trading Data

  • Daya Ram Budhathoki
  • Dipankar DasguptaEmail author
  • Pankaj Jain
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10968)

Abstract

In the United States, multimarket trading is becoming very popular for investors, professionals and high-frequency traders. This research focuses on 13 exchanges and applies data mining algorithm, an unsupervised machine learning technique for discovering the relationships between stock exchanges. In this work, we used an association rule (FP-growth) algorithm for finding trading pattern in exchanges. Thirty days NYSE Trade and Quote (TAQ) data were used for these experiments. We implemented a big data framework of Spark clusters on the top of Hadoop to conduct the experiment. The rules and co-relations found in this work seems promising and can be used by the investors and traders to make a decision.

Keywords

Multimarket Exchanges Association rules FP-Growth Hadoop Spark TAQ Clusters 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Daya Ram Budhathoki
    • 1
  • Dipankar Dasgupta
    • 1
    Email author
  • Pankaj Jain
    • 1
  1. 1.University of MemphisMemphisUSA

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