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Summary of Portfolio for All Entry Models Tested with All Order Types
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The countertrend moving average models were more variable than the trendfollowing ones. Many of them showed much smaller losses or even small profits, in-sample. A similar picture was seen out-of-sample, especially with the simple moving average support/resistance model. Except for the MACD divergence model, which behaved differently from the others, oscillators performed very poorly. There was a lot of variability, but on the whole, these models gave per-trade profits that were worse than expected by chance both in-sample and out-of-sample. The RSI overbought/oversold model was the worst of them all. In both samples, it provided staggering losses that were (statistically) significantly worse than those that would have been achieved with a random entry. The seasonal models, on the whole, were clearly better than chance. Although only one of these models actually provided profits in both samples, two of them were profitable out-of-sample, and several had only very small losses (much less than would be expected by chance using random entries) across samples. The basic lunar model had mixed findings. Most of the in-sample results were slightly positive when compared with chance (the random entry), but not profitable. The basic crossover model, however, was decidedly biased above chance in both samples. Although the solar models performed slightly better than chance in-sample, they were mixed and variable out-of-sample. This was also true for the cycle models. However, the cycle models, when using entry at open or on limit, actually performed significantly worse in recent years than a random entry. As with breakouts, the findings are not due to optimization; significant curve-fitting was only detected with the genetic and neural network models. Because of the tremendous sample involved in the portfolio, the optimization of one or two parameters, necessary for most models (other than the genetic and neural ones), had minimal curve-fitting effect. Surprisingly, the neural network models showed a fairly consistent bias to perform better than chance out-of-sample. In-sample, of course, performance was stellar across all tests. There was shrinkage (evidence of curve-fitting), but the shrinkage was not complete, leaving some predictive utility in the out-of-sample data. The results for the genetically evolved rules were the best. In-sample, performance was excellent, Out-of-sample, performance was exceptional for models involving long positions. Summary Many of the models described as significantly better than chance (i.e., better than what would be produced by a random entry) would likely become profitable if coupled with a better exit strategy. In Part III, it was evident that when tested with ran dom entries, the use of a good exit could bolster profits (or cut losses) by about $1,000 per trade. This means that, with a good exit, some of the entry models that had losses of several hundred dollars could be brought into positive, profitable territory.
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As mentioned above, the journey was a long one, sometimes tedious and discouraging. However, this birds-eye view revealed that a lot of potentially profitable entry models were indeed discovered. There were also a number of surprises: Despite terrible reputations and dangerous tendencies toward curve-fitting, the neural network and genetic models were the best performers when tested with data that was not used in training or evolving. Another surprise was that some of the most popular trading approaches, e.g., moving-average crossovers and oscillator-based strategies, turned out to be among the worst, with few exceptions. The results of the cycle models were also revealing: Because of their theoretical elegance, better-if not ideal-performance was expected. However, perhaps due to their popularity, poor performance was observed even though the implementation was a solid, mathematical one.
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