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The portfolio performance of each model was examined for each of the three order types (at open, on limit, and on stop). Out-of-sample and in-sample performances were separately evaluated. The out-of-sample performance was, by far, the best for the long-side genetic models. Entry at open was especially noteworthy, with a 64.2% in-sample return and 41.0% out-of-sample. The same model was also profitable with entry on limit and on stop, yielding very high dollars-per-trade profits, although small numbers of trades (easily increased with more elaborate models of this kind). In terms of out-of-sample performance, the next best specific model-order combination was the seasonal crossover with confirmation using entry on stop. Like the long genetic models, this one was significantly profitable in both sampling periods: in-sample, $846 per trade, with a 7.4% return; out-of-sample, $1,677 per trade, with a 9.5% return. Other seasonal models also did okay, with out-of-sample profits being made using the simple seasonal crossover model, Next down the list was the short turning-point model that used the small 1610-1 network. This model was profitable in both samples across all orders: out-ofsample, at open, a 9.3% annualized return, with $580 per trade; in-sample, a 35.2% return and $8,448 per trade profit. While still on the subject of neural networks, the reverse Slow %K model was a profitable performer, especially when a stop order was used. Out-of-sample, the annualized return was 6.1%, with $362 per trade. In-sample, there was a 22.5% return, with a $6,764per-trade profit. Note the large shrinkage from in-sample to out-of-sample for these models: While this is evidence of curve-fitting, enough real curves were caught for profits to be made in the verification sample. Another model that produced profits in both samples was the MACD divergence model, especially with entry on limit. This model had a 6.1% annualized return, out-of-sample, and a $9X5-per-trade profit. In-sample, the figures were a 6.7% return and a $1,250 profit per trade.
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Finally, among the models that were profitable in both samples was the simple moving-average suppotVresistance model with entry on stop: It took 6.4% out of the market, with $482 per trade, out-of-sample; and $5.8%, with $227 per trade, in-sample. Almost all other models lost money out-of-sample, and often in-sample, as well, The only exception was the volatility breakout model restricted to the currencies, which performed fairly well. Out-of-sample, it had an 8.5% return and made $2,106 per trade. In sample, it had a 12.4% return, with $3,977 profit per trade.
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Even though most of the other model-order combinations lost out-of-sample, in many cases, the losses were much less than would be expected with a totally random entry. In a number of instances, however, they were worse. It seems evident that there are a number of models that, although not ideal and in need of further development, do yield profitable trading that holds up in a verification sample and yields reasonable statistics.
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Until this point, only tests and models that operate on the whole portfolio have been discussed. In the course of the tests, many observations were made regarding the performance of specific models when trading individual markets. A recurrent observation was that certain models seem to trade certain markets well, while other models trade them poorly. Some markets just seem to be difficult to trade, regardless of model. There is no doubt that by selecting a number of the better models, and then selecting markets that these models trade well, a portfolio of systems to trade a portfolio of markets could be assembled. Therefore, good systemmarket combinations were selected from among the tests conducted in this book. No optimization of model parameters was performed. A portfolio was assembled on the basis of in-sample statistical significance. The intention was to find one good model-order combination for each of the markets in the portfolio. If there were several potential models for a given market, the additional ones were discarded based on such things as model complexity (the more complex the model, the less it was trusted), mediocre portfolio performance, and other similar factors. The specific model-order combinations spanned the entire spectrum of models and orders tested, with various oscillators, moving averages, lunar and solar models, and seasonal and neural network models being represented; genetic models, however, were not included. In the current tests, the particular genetic models that were evolved only traded rare events. For those insample markets that performed well, there were generally no out-of-sample trades. The profitable out-of-sample behavior was achieved on almost a totally different
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