Data stream mining for market-neutral algorithmic trading

Authors:
Giovanni Montana;Kostas Triantafyllopoulos;Theodoros Tsagaris
Affiliations:
Imperial College London, London, UK;University of Sheffield, Sheffield, UK;Imperial College London, London, UK
Venue:
Proceedings of the 2008 ACM symposium on Applied computing
Year:
2008

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Abstract

In algorithmic trading applications, a large number of co-evolving financial data streams are observed and analyzed. A recurrent and important task is to determine how a given stream depends on others, over time, accounting for dynamic dependence patterns and without imposing any probabilistic law governing this dependence. We demonstrate how Flexible Least Squares (FLS), a penalized version of ordinary least squares that accommodates for dynamic regression coefficients, can be deployed successfully in this context. We describe a market-neutral algorithmic trading system based on a combined use of on-line feature extraction and recursive regression. The system has been proved to perform successfully when trading the S&P 500 Futures Index.