Computational Intelligence within Finance

Advances in computing have profoundly changed our society. It has provided us with the ability to capture and process massive volumes of meaningful data. We never before have had the amount of financial data available for analysis nor have had the processing power to analysis a complete market in real-time. This in conjunction with the advances in computational methods, we have been recently equipped to examine financial data in real-time and more efficiently than anytime previously.

It is proposed that thier is a large scientific and commercial relevance exposed as a result of the convergence of four advanced and diverse fields of; (i) model-driven trading, (ii) computational intelligence, (iii) the availability of high frequency market data, and (iv) the evolution of enabling technologies; such as available 64-bit processors, high performance data managers and grid computing.

We sould be able demonstrate the unique power of this technology convergence by analyzing quote depth, which is still not commercially available in historic form, in real-time and identifying important non-seasonal patterns. One can examine the BID-ASK depth of the NASDAQ cash equity market by loading and committing inhomogeneous time-series market data into cache memory. By applying the dataset to a continuously adaptive and biologically-inspired computational method high speed pattern recognition can be conducted. The resultant identified patterns should indicate market anomalies and will form stylized facts that in turn can be used to supply a paradigm for model-driven trading. Because of the technology barriers to entry and a high level of domain specific knowledge required the method described here has not been attempted by any known large non-bank entities and is truly ground breaking.