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Entries based on seasonality may be effected in at least three ways: with stops, limits, or market orders. While a particular entry option may work especially well with a particular model, any entry may be used with any model. Entry orders themselves have their own advantages and disadvantages. The advantage of a market or&r is that no signaled entry will be missed. Stop orders ensure that no significant trends (in a trend-following model) will elude the trader, and that entry will not occur without confirmation by movement in favor of the trade (possibly beneficial in some countertrend models). The disadvantages are greater slippage and less favorable entry prices, A limit order gets the trader the best price and minimizes transaction costs. However, important trends may be missed while waiting indefinitely for a retracement to the limit price. In countertrend models, a limit order may occasionally worsen the entry price. The entry order may be tilled at the limit price, rather than at a price determined by the negative slippage that sometimes occurs when the market is moving against the trade at the time of entry!
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The data sample used throughout the tests of seasonal entry methods extends from August 1, 1985, to December 31, 1994 (the in-sample period), and from January 1, 1995, through February 1, 1999 (the out-of-sample period). For seasonality studies, an in-sample period of only 10 years means there is a shortage of data on which to develop the model. When generating seasonal entries was discussed, mention was made about calculating seasonal momentum (or average price behavior) based on equivalent dates in previous years. Because of the data shortage, when considering the in-sample data, calculations will be based not only on past years, but also on future years. This is accomplished using fhe jackknife. Target dates are run throughout the series, bar after bar. If only past years are included, for the early data points, there are very few past years or none at all. Since it takes at least 6 years of past data to get a reasonable seasonal average, there would be no seasonality calculations for most of the in-sample period, which itself is only 10 years. Consequently, there is very little data on which to optimize essential parameters or determine how seasonal models perform on the in-sample data. The jackknife, a well-known statistical technique (also known as the leaveone-out method ), helps solve the data shortage problem Suppose a seasonality estimate for June 1, 1987, is being calculated. If only past data from the in-sample period is used, the estimate would be based on data from 1986 and 1985. If, however, the jackknife is applied, not only would data from the past be available, but so would data from the future, i.e., the other years of the in-sample period (1988 through 1994). If the year (1987) that serves as the target bar for the calculations is removed from the in-sample period, the seasonality estimate for that bar would now be based on 9 years of data, a reasonably adequate sample. The jackknife procedure is justified because the data being examined to make the prediction is fairly independent of the data actually being predicted by the seasonality estimate. Since the data used in generating the prediction is at least 1 year away from the target date, it is not contaminated with current market behavior. The process provides an effectively larger sample than could otherwise be obtained, and does so without seriously compromising the available number of degrees of freedom. For bars in the out-of-sample period, all past years are used to generate the seasonal&y estimates. For example, to calculate the seasonality for January 14, 1999, the &past-years technique is used: Data from 1998 all the way back to 1985 are included in the analysis. In this way, no calculation for the out-of-sample period is ever based on any future or current data. All the tests that follow are performed using seasonal entries to trade a diversified portfolio of commodities. The exits are the standard ones, used throughout this book to study entry models. Trades are closed out either when an entry in the opposing direction occurs or when the standard exit closes the trade, whichever comes first. The test platform is also standard. Here is the code for the seasonal trading tests:
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