Automated trading with machine learning on big data

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

18 Scopus citations

Abstract

Financial markets are now extremely efficient,nevertheless there are still many investment funds that generatealpha systematically beating markets' return benchmarks. Theemergence of big data gave professional traders the newterritory, leverage and evidence and renewed opportunitiesof their profitable exploitation by Machine Learning (ML)models, increasingly taking over the trading floor by 24/7automated trading in response to the continuously fed datastreams. Rapidly increasing data sizes and strictly real-timerequirements of the trading models render large subset ofML methods intractable, overcomplex and impossible to applyin practise. In this work we demonstrate how to efficientlyapproach the problem of automated trading with large portfoliostrategy that continuously consumes streams of data acrossmultiple diverse markets. We demonstrate a simple scalabletrading model that learns to generate profit from multiple intermarketprice predictions and markets' correlation structure.We also introduce the stochastic trade diffusion technique tomaximise trading turnover while reducing strategy's exposureto market impact and construct the efficient risk-mitigatingportfolio that backtests with the strong positive return.

Original languageBritish English
Title of host publicationProceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014
EditorsPeter Chen, Peter Chen, Hemant Jain
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages824-830
Number of pages7
ISBN (Electronic)9781479950577
DOIs
StatePublished - 22 Sep 2014
Event3rd IEEE International Congress on Big Data, BigData Congress 2014 - Anchorage, United States
Duration: 27 Jun 20142 Jul 2014

Publication series

NameProceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014

Conference

Conference3rd IEEE International Congress on Big Data, BigData Congress 2014
Country/TerritoryUnited States
CityAnchorage
Period27/06/142/07/14

Keywords

  • classification
  • Keywords-machine learning
  • logistic regression

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