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No technique, except restricting the model to the currencies, improved results enough to overcome transaction costs in the out-of-sample period. Of course, many techniques and combinations were not tested (e.g., the long-only restriction was tested only with the volatility breakout and not with the HHLL breakout, a better out-of-sample performer), although they might have been effective. In both samples, all models evidenced deterioration over time that cannot be attributed to overoptimization. Breakout models of the kind studied here no longer work, even though they once may have. This accords with the belief that there are fewer and fewer good trends to ride. Traders complain the markets are getting noisier and more countertrending, making it harder to succeed with trend-following methods. No wonder the countertrend limit entry works best! Overall, simple breakout models follow the aforementioned pattern and do not work very well in today s efficient markets. However, with the right combination of model, entry order, and markets, breakouts can yield at least moderate profits. There are many variations on breakout models, many trend filters beyond the ADX, and many additional ways to improve trend-following systems that have not been examined. Hopefully, however, we have provided you with a good overview of popular breakout techniques and a solid foundation on which to begin your own investigations.
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If possible, use a limit order to enter the market. The markets are noisy and usually give the patient trader an opportunity to enter at a better price; this is the single most important thing one can do to improve a system s profitability. Controlling transaction costs with limit orders can make a huge difference in the performance of a breakout model. Even an unsophisticated limit entry, such as the one used in the tests, can greatly improve trading results. A more sophisticated limit entry strategy could undoubtedly provide some very substantial benefits to this kind of trading system. . Focus on support and resistance, fundamental verities of technical analysis that are unlikely to be traded away. The highest-high/lowestlow breakout held up better in the tests than other models, even though
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it did not always produce the greatest returns. Stay away from popular volatility breakouts unless they implement some special twist that enables them to hold up, despite wide use. . Choose trendy markets to trade when using such trend-following models as breakouts. In the world of commodities, the currencies traditionally are good for trend-following systems. The tests suggest that the oils and Coffee are also amenable to breakout trading. Do not rely on indicators like the ADX for trendiness determination. . Use something better than the standard exit to close open positions. Better exit strategies are available, as will be demonstrated in Part III. A good exit can go a long way toward making a trading system profitable.
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Moving averages are included in many technical analysis software packages and written about in many publications. So popular arc moving averages that in 1998, 5 of the 12 issues of Technical Analysis of Stocks and Commodities contained artcles about them. Newspapers often show a 50-day moving average on stock charts, and a 20.day moving average on commodities charts.
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To help understand moving averages, it is first necessary to discuss time series, i.e., series of data points that are chronologically ordered. The daily closing prices for a commodity are one example: They form a string of data points or bars that follow one another in time. In a given series, a sample of consecutive data points may be referred to as a time window. If the data points (e.g., closing prices) in a given time window were added together, and the sum divided by the number of data points in the sample, an average would result. A moving average is when this averaging process is repeated over and over as the sampling period is advanced, one data point at a time, through the series. The averages themselves form a new time series, a set of values ordered by time. The new series is referred to as the moving average of the original or underlying time series (in this case, the moving average of the close). The type of moving average just described is known as a simple moving average, since the average was computed by simply summing the data points in the time window, giving each point equal weight, and then dividing by the number of data points summed.
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PURPOSE OF A MOVING AVERAGE A moving average is used to reduce unwanted noise in a time series so that the underlying behavior, unmasked by interference, can be more clearly perceived; it serves as a data smoother. As a smoothing agent, a moving average is a rudimentary low-passJ%er, i.e., a filter that permits low frequency activity to pass through unimpeded while blocking higher frequency activity. In the time domain, high frequency activity appears as rapid up-and-down jiggles, i.e., noise, and low frequency activity appears as more gradual trends or undulations. Ehlers (1989) discusses the relationship between moving averages and low-pass filters. He provides equations and compares several formal low-pass filters with various moving averages for their usefulness. Moving averages may be used to smooth any time series, not just prices. THE ISSUE OF LAG Besides their ability to decrease the amount of noise in a time series, moving averages are versatile, easy to understand, and readily calculated. However, as with any well-damped low-pass filter or real-time data smoothing procedure, reduced noise comes at a cost: lag. Although smoothed data may contain less noise and, therefore, be easier to analyze, there will be a delay, or lag, before events in the original time series appear in the smoothed series. Such delay can be a problem when a speedy response to events is essential, as is the case for traders. Sometimes lag is not an issue, e.g., when a moving average of one time series is predictive of another series. This occurs when the predictor series leads the series to be predicted enough to compensate for the lag engendered by the moving average. It is then possible to benefit from noise reduction without the cost of delay. Such a scenario occurs when analyzing solar phenomena and seasonal tendencies. Also, lag may not be a serious problem in models that enter when prices cross a moving average line: In fact, the price must lead the moving average for such models to work. Lag is more problematic with models that use the slope or turning points in the average to make trading decisions. In such cases, lag means a delayed response, which, in turn, will probably lead to unprofitable trades. A variety of adaptive moving averages and other sophisticated smoothing techniques have been developed in an effort to minimize lag without giving up much noise reduction. One such technique is based on standard time series forecasting methods to improve moving averages. To eliminate lag, Mulloy (1994) implements a linear, recursive scheme involving multiple moving averages. When the rate of movement in the market is appropriate to the filter, lag is eliminated; however, the filters tend to overshoot (an example of insufficient damping) and deteriorate when market behavior deviates from filter design specifications. Chande ( 1992) took a nonlinear approach, and developed a moving average that adapts to the market on the basis of volatility. Sometimes lag can be controlled or
eliminated by combining several moving averages to create a band-pass filter. Band-pass filters can have effectively zero lag for signals with periodicities near the center of the pass-band; the smoothed signal can be coincident with the original, noisy signal when there is cyclic activity and when the frequency (or periodicity) of the cyclic activity is close to the frequency maximally passed by the filter.
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