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Trading as a Business:
Optimization, The Double-Edged Sword
By Charlie Wright
The issue of optimization, or to be more exact over-optimization, has taken on greater importance in the last ten years with the advent of personal computers and software such as TradeStation.
Optimization is the process of using historical data to test the effects of slight changes in indicator or strategy criteria. The goal of optimization is to uncover the most profitable or optimal setting for a particular indicator or price pattern traded on a particular security. If, for instance, we want to trade a moving average strategy on Coffee futures, we might arbitrarily pick the 18-period moving average and design a strategy around it. Or, we could test all the moving averages between 1 and 50 and pick the most profitable. This latter process is what is called optimization.
Ever since I have been trading, there has been a continuing debate as to whether optimization is a valid process. There are strong opinions on both sides. The basic controversy centers on the argument that the results of a historical test are not valid because the market never does the exact same thing twice. The market prices will never exactly move in the future as they have in the past. One side of the argument says that because prices will never move exactly the same, optimization is really fitting the strategy to historical data and is therefore a useless process that simply serves to give historical performance data that is irrelevant in the future.
The anti-optimization argument goes on to say that if the trading method has been exactly “fitted” to the historical data, it stands to reason that the technique will not work in the future because future data has no relation to past data. The traders that take this position usually opt for “soft” trading methods such as the Elliott Wave, Gann techniques, the Market Profile or other generally intuitive approaches to trading.
The irony is that the individuals who decry the perils of optimization also use a type of historical testing to see if their techniques have worked in the past. These soft techniques are “back-tested” by looking at historical charts and estimating where and under what circumstances they would have made a trade. It is very easy to curve-fit the Elliott Wave theory and Gann techniques to historical data, but very difficult to trade them in real time.
I have never seen any performance statistics for those who trade the Elliott Wave or other soft techniques that are superior to the average, statistically sound (and optimized!) trading strategy. Ponder this very important point.
Check out the Commodity Traders Consumer Report, the Hulbert Digest or other trading and investing rating services. What you will find is that all of these trading advisors have trading statistics that are no better than an average trading strategy. Most of those who argue in favor of optimization do realize that there is a risk of over-optimizing. But our solution is to minimize the chances of over-optimization and curve fitting rather than not use it altogether. Just because over-optimization is a risk does not mean that you should throw the baby out with the bath water and not optimize at all. Just because there is a risk of an accident does not meant you should not drive a car. You just have to know the risks and be careful.
We have to start with the assumption that back testing using quantifiable historical data is a valid method for analyzing price activity and projecting trading profits for stocks and futures, despite the risk of curve-fitting. The reason we make this assumption is that historical data is all we have to go by.
If you think about it, all investments are bought and sold based on some type of historical record. Before we make any investment, we want to see an historical track record. We want to know what return the particular investment advisor has achieved over the last few years in relation to the Dow Jones Average. We want to see how the venture capital fund’s investments have performed over the last few years, or the history of the fund. We want to know how real estate has fared in the area we are buying, and whether the developer has achieved profits on the last few projects.
Sales pitches for common stocks point out the average 15% or so annual return over the last x number of years. The perennial futures strategy seller promotes the strategy based on the historical track record, either an actual or simulated performance history. And the arguments are similar for numismatic coins and precious metals, bonds and asset allocation strategies, etc.
The sales pitch for all investments is either the trend argument, that the trend is up and will continue up and you should purchase the investment, or the long-term support argument, that the price is at an historic low and the item is so cheap you should buy it now. Both of these arguments reference historical data. You simply can’t get away from it.