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Performance of Seasonal Entry Models Broken Down by Model, Order, and Sample
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In earlier chapters, other types of entry models were tested and the limit order was usually found to perform best. In the case of seasonality, the stop order had a dramatic, beneficial impact on performance, despite the additional transaction costs. Previously it appeared that countertrend principles may perform better when combined with some trend-following or confirming element, such as an entry on a stop. lt seems that, with seasonality, the kind of confirmation achieved by using something like a stop is important, perhaps even more so than the kind achieved using the Fast %K. In other words, if, based on seasonal patterns, the market is expected to rise, confirmation that it is indeed rising should be obtained before a trade is entered. Overall, it seems that there is something important about seasonality: It has a real influence on the markets, as evidenced by the seasonality-based system with the stop performing better, or at least on par, with the best of the entry models. This was one of the few profitable entry models tested. Seasonal phenomena seem to have fairly strong effects on the markets, making such models definitely worth further exploration. It would be interesting to test a restriction of the seasonality model to markets on which it performed best or to markets that, for fundamental reasons, would be expected to have strong seasonal behavior. From an examination of the marketby-market analysis (discussed earlier in the context of individual tests), there is quite a bit ofprimafacie evidence that certain markets are highly amenable to seasonal trading. When breakout models were restricted to the currencies, dramatic benefits resulted. Perhaps restricting seasonal models to appropriate markets would be equally beneficial. When looking over all the tests and counting the number of instances in which there were significant, positive returns, an impression can be obtained of the markets that lend themselves to various forms of seasonal trading. Across all tests, Coffee had one of the highest number of positive returns in-sample and no losses-true also for its out-of-sample performance. Coffee, therefore, appears to be a good market to trade with a seasonal model, which makes sense since Coffee is subject to weather damage during frost seasons, causing shortages and thus major increases in price. For Coffee, I1 out of the 12 tests in-sample and 8 out of 12 tests out-of-sample were profitable. Unleaded Gasoline was another commodity that had a large number of positive returns across all tests in both samples: in-sample 8 and out-of-sample 11. Light Crude had 8 in-sample but only 4 out-of-sample positive returns. Live Hogs was another market showing a large number of positive returns in both samples. Figure 8-l illustrates equity growth broken down by type of order and averaged across all models. As can be seen, the stop order performed best, while entry at open performed worst, and the limit order was in between the two. Figure 8-2 shows equity growth broken down by model, with averaging done over orders. The crossover-with-confirmation model had the overall best performance, especially in the later half of the testing periods. The basic crossover model
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started out the best, but after 1990, performance deteriorated. However, the equity trend seemed to reverse to the upside after 1995 in the out-of-sample period, a time when the other models tended to have declining equity when considered across all order types. CONCLUSION These explorations into seasonality have demonstrated that there are significant seasonal effects to be found in the markets. Decisions about how to trade can be made based on an examination of the behavior of the market at nearby dates for a number of years in the past. The information contained on the same date (or a date before or a date after) for a number of years in the past is useful in making a determination about what the market will do in the near future. Although the seasonal effect is not sufficient to be really tradable on the whole portfolio, it is sufficient to overcome transaction costs leading to some profits. For specific markets, however, even the simple models tested might be worth trading. In other words, seasonal phenomena appear to be real and able to provide useful information. There are times of the year when a market rises and times of the year when a market falls,
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