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Table 15-7 shows the same market-by-market breakdown as in Table 15-6, but only the short side is represented. More consistency was evident between in-sample and out-of-sample performances for the short side than for the long. Most notably profitable in both samples was the Japanese Yen. Light Crude, Unleaded Gasoline, Feeder Cattle, Live Hogs, Soybean Meal, and Coffee were also profitable in both samples.
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Several important points were demonstrated by the above tests. First, neural networks hold up less well in out-of-sample tests than do genetically evolved rulebased solutions. This is no doubt a result of the greater number of parameters
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Market-by-Market Performance of the Modified Standard Exit with Added Genetically Evolved Rule-based Signal Exit When Tested Using Random Long Trade Entries
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involved in the neural network model, as compared with the rule-based models being used. In other words, the effects of curve-fitting were damaging to the neural network solution. Also discovered was the fact that the addition of a sophisticated signal exit, whether based on a neural net or a set of genetically evolved entry rules, can greatly improve an exit strategy. When the more robust, genetically evolved rules were applied, the performance benefits obtained persisted in out-of-sample evaluations. The neural network and the rule templates (but not the actual rules) that were used in developing the signal exits were originally developed for inclusion in an
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Market-by-Market Performance of the Modified Standard Exit with Added Genetically Evolved Rule-Based Signal Exit When Tested Using Random Short Trade Entries
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entry model. When the rules were used in an entry model, it was acceptable for them to generate rare event trades. In an exit strategy, however, rules that tire more frequently would be more desirable. There is every indication that a set of rule templates (and ways of combining the rules to obtain signals), specifically designed for use in an exit strategy, would provide much better results than those obtained here. The same should be true for neural networks.
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WHAT HAVE WE LEARNED Curve-fitting can be bad not only when building entries, but also when building exits. n Sophisticated technologies, including genetic algorithms, can be effectively used to improve an exit strategy. . Even crude efforts to improve exits, such as those carried out here, can enhance profits by several hundred dollars per trade.
Conclusion
long road has been traveled since beginning the study of entry and exit strategies. Sometimes the trip has been tedious and discouraging; at other times, stimulating and surprising. As usual after extended journeys, the questions What knowledge has been gained and How may that knowledge be applied beg to be answered. The first question will be addressed by a successively more detailed examination of the results: going from discoveries made about the portfolio performance of entire classes of models, to more specific model-order combinations, down to an inspection of individual markets and how they are best traded. The perspectives taken in the following discussions of what has been achieved are analogous to views from an airplane flying at night. At first, the plane is at a very high altitude: All that can be seen when looking down are large patches of darkness (classes of models that are ineffective or lose) and some patches of light (classes of models that, overall, work fairly well or, at least, perform better than chance). This view provides a basic idea of which models are, overall, viable relative to the entire portfolio of tradables. The plane then descends. More detail is seen. It becomes evident that the brightest spots are often formed by clusters of light having various luminosities (model-order combinations that are, to one extent or another, profitable). Occasionally the dark patches also contain small isolated points of brightness (successful model-order combinations amid approaches that usually are ineffective). At this level, a number of dim areas can be seen as well (model-order combinations that are not profitable, but that have better than chance performance that could be enhanced if combined with a good exit).
Finally, landing is imminent. It is possible to look inside the bright spots and see their detail, i.e., the individual markets the various model-order combinations trade best. The second question above can now be addressed: By identifying the consistently profitable (across samples) model-order combinations and the markets best traded by them, a good portfolio trading strategy can be developed. At this time, it will become clear that out of all the studies performed during the long trip, enough has been learned to assemble a lucrative portfolio of systems and tradables. By way of demonstration, such a portfolio will be compiled and run with the standard exit strategy. THE BIG PICTURE For this perspective, each class of entry model (e.g., all trend-following moving average models, all breakouts, all small neural networks) was examined in its entirety. All the tests for each model type were averaged. Out-of-sample and in-sample performances were separately evaluated. By far the best out-of-sample performer was the genetic model: Out of all the models, it was the only one that showed a substantial profit when averaged over all the different tests. The profit per trade was $3,271. Next best, in terms of out-of-sample behavior over all tests, were the small neural networks. The neural network models were broken down into those for small and large nets because curve-fitting appeared to be a serious issue, especially affecting large nets. The breakdown was a natural and easy one to accomplish because, in the tests, each model was tested with one small and one large network. Out-of-sample, all the small neural networks taken together averaged a loss of $860 per trade. This indicates a significantly better than chance entry in that random entries produced an average loss of over $2,000, with a standard deviation of slightly under $400. Going down in quality, the next best overall approach involved seasonal&y. Altogether, all tests of seasonality models showed an average loss of $966 per trade. Three of the moving average models (crossover, slope, and supportIre& tance) followed the performance of seasonality. These models, when averaged across tests, lost around $1,500 per trade, which is close to the $2,100.per-trade loss expected when using random entries. In other words, the moving average models were only marginally better than random. All the remaining models tested provided entries that were very close to random. Cycles were actually worse than random. In-sample, the genetic models ($12,533 per trade), all the neural network models (small, $8,940, and large, $13,082), and the breakouts ($1,537) traded profitably. Out-of-sample, the genetic models continued to be profitable, the nets were better than chance (although there was significant shrinkage in their performance due to curve-fitting), and the breakouts deteriorated to chance (optimization cannot be a factor in this case).
The next best performers in-sample were the support/resistance moving average models ($300 loss per trade) and the seasonality models ($671 loss per trade). Further down the ladder of best-to-worst performers were the lunar and solar models, which lost $1,076 and $1,067, respectively. Losses in the $1,300 to $1,700 range were observed for the moving average models. The oscillator and cycle models exhibited losses of over $2,000 per trade; when taken as a whole, these models were no better than chance. It is interesting that the genetic model and the small neural network models were the ones that held up out of sample. Such models offer great opportunities for curve-fitting and tend to fail in out-of-sample tests and real trading. Seasonality, which is only rarely the topic of articles, also exhibited potential. On the other hand, the most popular methods (e.g., moving average, oscillator, and cycle models) performed the worst, trading badly both in- and out-of-sample. The breakout models are noteworthy in the sense that, taken as a group, they worked well in the past; but, due to increased market efficiency, have since deteriorated to the point where they currently perform no better than chance. Table C-l contains the annualized return-on-account (the first line of each modelorder combination) and average dollars-per-trade (the second line of each model-order combination) data for all the tests (all model and order combinations) conducted for entry models using the standard exit strategy. The data presented arc for portfolio performance as a whole. The model descriptions (leftmost column) are the same as used elsewhere in this book. The last six lines of the table contain data that can be used as a baseline against which the various entry models can be compared. The baseline data are derived from using the random entry strategy with the unmoditied standard exit strategy. Mean ROA% is the average return on account over several sets of random entries; StdLh ROA% is the standard deviation of the return-on-account. Mean $TRD is the average dollars-per-trade over several sets of random entries; and StdDev $TRD is the standard deviation of the dollars-per-trade. Breakout models had the unique characteristic of being consistently profitable in-sample across almost every model-order combination tested. Except for the volatility breakouts, these models performed much better than chance, albeit not profitably, out-of-sample; i.e., the loss per trade was under $1,000, sometimes under $300 (the average loss to be expected was around $2,000 with random entries). In other words, the breakouts, taken together, were better than random. However, outof-sample, the volatility breakouts did much worse than chance: With at-open and on-stop orders, the model lost more than $5,000 per trade, as though the market s current behavior is precisely designed to make these systems costly to trade. The trend-following moving average models (crossover and slope) all performed slightly better than chance in-sample: They all lost rather heavily, but the losses were almost always less than $2,000. None of the systems, however, did very well. Out-of-sample, the picture was somewhat more variable, but had the same flavor: Most of the models were somewhat better than chance, and one or two were much better than chance (but still not profitable).
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