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Developing and testing a trading method

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Technical analysis is perhaps the single most valuable tool used in the development of positive expectancy trading models. According to technicians, the reason that technical analysis helps in the development of such models is due to the notion that "price has memory." What does this mean? It means that when crude oil traded at $40 a barrel in 1990, this linear, horizontal resistance area would again act as resistance when retested in 2003 (see Figure 1). This reality drives economists crazy because, according to economic theory, it makes absolutely no sense for crude oil to sell off at $40 a barrel in 2003, since the purchasing power of the U.S. dollar in 2003 is different from its purchasing power in 1990. Nevertheless, according to technical analysis, the selloff at $40 a barrel in 2003 made perfect sense because price has memory. Price has memory means that traders experienced pain, pleasure, and regret associated with the linear price level of $40 a barrel. Let's look at this in greater detail.

Figure 1: Rolling Front-Month Quarterly CME Group Crude Oil Futures Showing $40 a Barrel Horizontal Resistance


Source: CQG, Inc. © 2010. All rights reserved worldwide.

Price has memory because back in 1990 a group of traders bought oil at $40 a barrel. They had all sorts of reasons for their purchase: Saddam Hussein had invaded Kuwait, global demand for oil products was strong, and so on. However, if these buyers were honest with themselves, as oil prices tumbled, all these reasons evaporated and were replaced with one thought and one thought only—usually expressed in prayer form—"Please, God, let it go back to $40 a barrel and I swear I'll never trade crude oil again." When it does rally back to $40 a barrel, that linear price represents the termination of the painful experience of loss for such traders. And so they create selling pressure at this linear, $40-a-barrel price level.

The Inefficient Market

Incredibly, academics and economists with strong science backgrounds have put forth a theory of an efficient market without any statistical evidence of market efficiency, despite much evidence to the contrary. The markets have always been inefficient, have always cycled from panic to bubble to panic again, and will always continue to do so. In fact, as stated earlier, this cyclical nature of market behavior is one of the few things we as traders can actually count on.

Ludicrous as it sounds, according to efficient market hypothesis there can be no such thing as a bubble because markets are always trading at their correct, or efficient, price levels. In other words, according to these theorists, a tulip in Holland that was correctly priced at 2,500 guilders on February 2, 1637, was also correctly priced at 2 guilders on February 3, 1637.

Irrationally priced markets tend to become even more irrationally priced—this is the nature of an inefficient, fat-tailed market—before crashing, and no one can know where the top is until after that top has been proved through the printing of lower prices. Wait for the evidence of a top to start selling and wait for evidence of a bottom to start buying.

But why does the inefficiency of markets matter to us as traders? It is this inefficiency that allows us to develop positive expectancy trading models. This inefficient behavior of markets leads to what statisticians call a leptokurtic—as opposed to a normal—distribution of asset prices (see Figure 2). This means that prices display a greater propensity toward mean reversion than would occur if markets were efficient, and, when they are not in this mean reverting mode, they have a greater propensity to trending action (statisticians call this propensity for trending action the fat tail of the distribution).

Figure 2: Leptokurtic versus Normal Distribution of Asset Prices


Source: www.risk.glossary.com.

It is because markets display this leptokurtic price distribution that positive expectancy trading models tend to fall into two categories:

  1. Countertrend models that capitalize on the market's propensity toward reversion to the mean.
  2. Trend-following models that take advantage of those times when markets undergo a fat tail event.

It is no coincidence that two of the three major types of technical indicators are oscillators that signal when markets are—at least temporarily—overbought or oversold and trend-following indicators like moving averages, moving average convergence divergence, Ichimoku clouds, and so on, which signal when markets are displaying bullish- or bearish-trending behavior.

If It Feels Good, Don’t Do It

Well, speculative trading sounds simple enough. Markets can do only two things, either trade in a range or trend, and volatility indicators can be used to clue you in to which kind of behavior the market is currently exhibiting. Why then do almost all speculators lose money? They lose because successful speculation requires that we consistently do that which is psychologically uncomfortable and unnatural.

Why are mean reversion trading models psychologically uncomfortable to implement? In Figure 3, we see that on Friday, March 6, 2009, the E-Mini S&P 500 futures are not only in a clearly defined bear trend, but that they have once again made new contract lows. What the chart cannot show is how overwhelmingly bearish market sentiment was on that day. On Fridays, after finishing my market analysis for the day, I turn off the computer and turn on the financial news, as it is usually entertaining. On this particular Friday, the market had just closed and they were interviewing two market pundits. They will typically have one interviewee advocating the bear argument while their counterpart is bullish. Our first analyst's forecast was 5,000 on the Dow Jones Industrial Average and 500 in the S&P 500 Index. As soon as the words "five hundred" left his lips, the other interrupted, "You are out of your mind." I thought, "Ah, here's the bullish argument." The other analyst then proceeded to berate our bearish forecaster by telling him he was out of his mind because the Dow was going to 2,000 and the S&P 500 to 200. I glanced at the bottom of the screen just to make certain that I had not lost my mind░...░no, the E-Mini S&P futures had in fact closed at 687.75. Next thought, "When the market is at 687.75 and the bullish analyst is calling for it to drop to 500, this has got to be the bottom." Sure enough, the 2009 stock market bottom occurred on Friday, March 6, 2009 (see Figure 4). The trader using a mean reversion model has to consistently buy in to that type of overwhelmingly bearish sentiment or sell in to a 1630s-era tulip—or 2005 housing—bubble-like bullish environment.

Figure 3: March 2009 E-Mini S&P 500 Futures Contract Makes New Lows with Relative Strength Index Oscillator at Oversold Levels


Source: CQG, Inc. © 2010. All rights reserved worldwide.

Figure 4: Rolling Front-Month Weekly E-Mini S&P 500 Futures Contract Showing Close Below Lower Bollinger Band and Oversold Reading on Relative Strength Index


Source: CQG, Inc. © 2010. All rights reserved worldwide.

For both mean reversion as well as trend-following traders, the profitable trade is the one that is almost impossible to execute. Or as I like to say, "If it feels good, don't do it." If it feels awful, like a guaranteed loss—more often than anyone could imagine—that is the profitable trade. If, on the other hand, the trade feels like easy money, run the other way. Now that we have examined the strengths of positive expectancy models derived from mathematical technical indicators as well as their weaknesses and tools to offset such weaknesses, we will briefly review turning these models into mechanical trading systems. Mechanical trading systems based on mathematical technical indicators help us determine the following:

  • Does this model enjoy positive expectancy?
  • What kinds of weaknesses—maximum consecutive losses, worst peak-to-valley equity drawdowns, percentage of winning trades, average trade duration, and so forth—did this model experience in the past?
  • Am I willing to endure these weaknesses in my real-time trading account or do I need a model better suited to my individual psychological profile as a trader?
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Article copyright 2011 by Richard L. Weissman. Reprinted and adapted from Trade Like a Casino: Find Your Edge, Manage Risk, and Win Like the House with permission from John Wiley & Sons, Inc. The statements and opinions expressed in this article are those of the author. Fidelity Investments® cannot guarantee the accuracy or completeness of any statements or data. This reprint and the materials delivered with it should not be construed as an offer to sell or a solicitation of an offer to buy shares of any funds mentioned in this reprint.
The data and analysis contained herein are provided "as is" and without warranty of any kind, either expressed or implied. Fidelity is not adopting, making a recommendation for or endorsing any trading or investment strategy or particular security. All opinions expressed herein are subject to change without notice, and you should always obtain current information and perform due diligence before trading. Consider that the provider may modify the methods it uses to evaluate investment opportunities from time to time, that model results may not impute or show the compounded adverse effect of transaction costs or management fees or reflect actual investment results, and that investment models are necessarily constructed with the benefit of hindsight. For this and for many other reasons, model results are not a guarantee of future results. The securities mentioned in this document may not be eligible for sale in some states or countries, nor be suitable for all types of investors; their value and the income they produce may fluctuate and/or be adversely affected by exchange rates, interest rates or other factors.
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