Data stream mining for market-neutral algorithmic trading

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.