FOREX TRADE INTRODUCTION

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FOREX TRADE INTRODUCTION

There is one thing that most traders have in common: They have taken on the challenge of forecasting and trading the financial markets, of searching for those small islands of lucrative inefficiency in a vast sea of efficient market behavior.

For one of the authors, Jeffrey Katz, this challenge was initially a means to indulge an obsession with mathematics. Over a decade ago, he developed a model that provided entry signals for the Standard & Poor’s 500 (S&P 500) and OEX. While these signals were, at that time, about 80% accurate, Katz found himself second-guessing them. Moreover, he had to rely on his own subjective determinations of such critical factors as what kind of order to use for entry, when to exit, and where to place stops. These determinations, the essence of discretionary trading, were often driven more by the emotions of fear and avarice than by reason and knowledge.

As a result, he churned and vacillated, made bad decisions, and lost more often than won. For Katz, like for most traders, discretionary trading did not work.

If discretionary trading did not work, then what did? Perhaps system trading was the answer. Katz decided to develop a completely automated trading system in the form of a computer program that could generate buy, sell, stop, and other necessary orders without human judgment or intervention. A good mechanical system, logic suggested, would avoid the problems associated with discretionary trading, if the discipline to follow it could be mustered. Such a system would provide explicit and well-defined entries, “normal” or profitable exits, and “abnormal” or money management exits designed to control losses on bad trades, A fully automated system would also make it possible to conduct historical tests, unbiased by hindsight, and to do such tests on large quantities of data.

Thorough testing was the only way to determine whether a system really worked and would be profitable to trade, Katz reasoned. Due to familiarity with the data series, valid tests could not be performed by eye. If Katz looked at a chart and “believed” a given formation signaled a good place to enter the market, he could not trust that belief because he had already seen what happened after the formation occurred. Moreover, if charts of previous years were examined to find other examples of the formation, attempts to identify the pattern by “eyeballing” would be biased. On the other hand, if the pattern to be tested could be formally defined and explicitly coded, the computer could then objectively do all the work: It would run the code on many years of historical data, look for the specified formation, and evaluate (without hindsight) the behavior of the market after each instance. In this way, the computer could indicate whether he was indeed correct in his hypothesis that a given formation was a profitable one. Exit rules could also be evaluated objectively.

Finally, a well-defined mechanical trading system would allow such things as commissions, slippage, impossible tills, and markets that moved before he could to be factored in. This would help avoid unpleasant shocks when moving from computer simulations to real-world trading. One of the problems Katz had in his earlier trading attempt was failing to consider the high transaction costs involved in trading OEX options. Through complete mechanization, he could ensure that the system tests would include all such factors. In this way, potential surprises could be eliminated, and a very realistic assessment could be obtained of how any system or system element would perform. System trading might, he thought, be the key to greater success in the markets.

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