Trading financial instruments in an objective systematic fashion has numerous advantages over subjective approaches:
Automated trading systems are usually used for one or both of two applications. TSSB (Trading System Synthesis and Boosting) is a state-of-the-art program that is able to generate both applications: (1) a complete, stand-alone trading system which makes all trading decisions and (2) a model which may be used to filter the trades of an existing trading system in order to improve performance. We refer to this as “boosting”. It is often the case that by intelligently selecting a subset of the signals generated by an existing trading system, and rejecting the others, we can improve the risk/reward ratio.
Whether the user’s goal is development of a stand-alone trading system or a filtering system to boost the performance of an existing trading system, there are two common approaches to its development and implementation: (1) rules-based (IF/THEN rules proposed by a human) and 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 above 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 lookback distances that maximize some measure of performance. Programs such as TradeStation® include a proprietary language (EasyLanguage® in this case) by which the developer can specify trading rules.
With the widespread availability of high-speed desktop computers, an alternative approach to trading system development has become feasible. 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 formulate 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 TSSB, and it has several advantages over algorithm-based system development:
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.
Predictive models do not normally work with raw market data. Rather, the market prices and other series, such as volume, are usually transformed into two classes of variables called indicators and targets. This is the data used by the model during its training, testing, and ultimate real time use. It is in the definition of these variables that the developer exerts his or her own influence on the trading system.
Indicators are variables that look strictly backwards in time. When trading in real time, as of any given bar an indicator will be computable, assuming that we are in possession of sufficient historical price data to satisfy the definition of the indicator. For example, someone may define an indicator called trend as the percent change of market price from the close of a bar five bars ago to the close of this bar. As long as we know these two prices, we can compute this trend indicator. TSSB can compute over a hundred types of indicators that quantify numerous features of market behavior.
Targets are variables that look strictly forward in time. (In classical regression modeling, the target is often referred to as the dependent variable.) Targets reveal the future behavior of the market. We can compute targets for historical data as long as we have a sufficient number of future bars to satisfy the definition of the target. Obviously, though, when we are actually trading the system we cannot know the targets unless we have a phenomenal crystal ball. For example, we may define an indicator called day_return as the percent market change from the open of the next day to the open of the day after the next. If we have a historical record of prices, we can compute this target for every bar except the last two in the dataset. TSSB can compute a variety of target variable types.
In summary, the fundamental idea behind predictive modeling is that indicators may contain information that can be used to predict targets. The task of predictive model is to find and exploit any such information.
— By David Aronson
Part 2 of this series can be found here, Predictive-Model Based Trading Systems Part II
David Aronson is a pioneer in machine learning and nonlinear trading system development and signal boosting/filtering. Aronson is Co-designer of TSSB (Trading System Synthesis and Boosting) a software platform for the automated development of statistically sound predictive model based trading systems. He has worked in this field since 1979 and has been a Chartered Market Technician certified by The Market Technicians Association since 1992. He was an adjunct professor of finance, and regularly taught to MBA and financial engineering students a graduate-level course in technical analysis, data mining and predictive analytics. His recently released book, Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments, is a in-depth look at developing predictive-model-based trading systems using TSSB.
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