There are two common approaches to the development and implementation of an automated trading system: (1) rules-based (IF/THEN rules proposed by a human) and (2) predictive modeling.

 

A rules-based trading system requires that the user specify the exact rules that make trade decisions, although one or more parameters associated with these rules may be optimized by the development software. Here is a simple example of an algorithm-based trading system. IF the short-term moving average of prices exceeds the long-term moving average of prices, THEN hold a long position during the next bar. The algorithm explicitly states the rule that decides positions bar-by-bar, although the exact definition of ‘short-term’ and ‘long-term’ is left open. The developer might use software to find moving-average look-back distances that maximize some measure of performance. 

Predictive modeling employs mathematically sophisticated software to examine indicators derived from historical data such as price, volume, and open interest, with the goal of discovering repeatable patterns that have predictive power. A predictive model is essentially a mathematical or logical formula that relates these patterns to a forward-looking variable called a target or dependent variable, such as the market’s return over the next week. This is the approach used by Empirical and it has several advantages over algorithm-based system development.  

 

Intelligent modeling software utilizing machine learning can discover patterns that are so complex or buried under random noise that no human could ever see them. Once a predictive model trading system is developed, it is usually easy to tweak its operation to adjust the risk/reward ratio to suit applications ranging across a wide spectrum. It can obtain a desired trade off between numerous signals with a lower probability of success and fewer signals with a higher probability of success. This is accomplished by adjusting a threshold that converts model predictions into discrete buy and sell signals. In general, predictive modeling is more amenable to advanced statistical analysis than rules-based system development. Sophisticated analysis algorithms to test the statistical soundness of its discoveries can be incorporated into the model-generating process more easily than they can be incorporated into systems based on human-specified rules. 

The predictive modeling approach to trading system development relies on a basic property of market price movement: all markets contain patterns that tend to repeat throughout history, and hence can often be used to predict future activity. For example, under some conditions a trend can be expected to continue until the move is exhausted. Under other conditions, a different pattern manifests: a trend is more likely to be followed by a retracement toward the recent mean price. A predictive model studies historical market data and attempts to discover the features that discriminate these two patterns.


The goal of predictive modeling then is finding patterns that repeat often enough to be profitable. Once discovered, the model will be on the lookout for the pattern to reoccur. Based on historical observations, the model will then be able to predict whether the market will soon rise, fall, or remain about the same. These predictions can be translated into buy/sell decisions by applying thresholds to the model’s predictions.