The Insider's Guide to Next-Generation BPM
By Colin Clark, VP Customers, Streambase Systems Inc. , 04/02/2008
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*Editor's note: If this topic interests you, be sure to sign up for our April 16th panel discussion on IT in Financial Markets. Sign up here.
As the volume and velocity of financial market data continues to soar, staying ahead of the competition requires the right trading tools and infrastructure. The purpose of this article is to present an overview of algorithmic trading and to detail the key components that comprise a real-time algorithmic trading system.
Algorithmic trading is the implementation of some form of statistical or quantitative analysis for making transaction decisions in the financial markets. The key principles behind this analysis involve identifying patterns within historical data to predict future market behavior or looking for arbitrage opportunities within various markets. Algorithmic trading is typically broken down into either profit seeking (signal generation) or execution management based implementations.
The use of algorithmic trading started on the sell side to assist brokers in placing large equities orders into the market in a manner that would not impact the market and would achieve the best price avoiding short term variations. These execution strategies were initially very straightforward. Based upon either volume or time-weighted averages of executions occurring in the market place, corresponding orders were placed for small slices of the larger order. In this manner, the entire order is executed throughout some period of time, and the theory is that the average execution price will be either the time or volume weighted price for that period.
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