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Algorithmic Trading Part 2 Editor's Note: This article is a continuation of last week's first installment on key components for real-time algorithmic trading. Be sure to read part one if you missed it last week. Remember to sign up for our April 16th discussion on IT in financial markets as well.

> Trading Strategies/Signal Generation

Figure 3Statistical trading strategies are predictions of future market behavior based upon the detection of a pattern within current and historical data. The deduction of the strategy is normally performed by careful analysis of historical data. The trading strategy is implemented as a pattern detection process that triggers buy or sell orders.

What is classically called Statistical Arbitrage is usually a simple form of this pattern detection that applies to very short periods of time. When strategies are required to respond very rapidly it becomes necessary to perform the pattern detection and trigger the order automatically in real time.

As algorithmic trading becomes more complex, trading strategies have been and continue to be developed. Each strategy primarily consists of the detection of one or more patterns and placing an order when these patterns are detected. The detection of the patterns requires:

  • The calculation of aggregated values
  • A snapshot of the market
  • Calculations to be performed over windows of data
  • Variability in the coefficients
  • Multiple inter-related phases

Many patterns are based upon the comparison of current point data with some form of aggregated data. In the very simple patterns this may mean comparison of current price with some set of mean prices or the comparison between a set of mean prices. As the strategies have grown in complexity the number and complexity of aggregation has grown. This will continue to be true and thus the ability to extend the calculation capability of any system is important.


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