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than a 12.bar exponential moving average (EMA) of the closes, but is less than a 49-bar EMA of the closes, and the current bar represents a 6.bar new high, then exit the long trade. The rules seem to be searching for a situation in which the longer trend is down, but a short-term retracement against the trend has occurred and has reached a point where completion of the retrxement is likely and the longer-term downward trend will resume-a sensible point to exit a long position. The rules for the short side suggest that an exit should occur if the close on the current bar is greater than the 16-bar EMA of the closes and a 22.bar simple moving average of the closes, and if the MACD is sloping down. The specific MACD used employs a 6-bar EMA for its faster moving average and a lo-bar EMA for its slower moving average. The idea encapsulated in these rules seems to be that it is wise to close out short positions if the market, when smoothed, still appears to be moving down, but the most recent close broke above two moving averages, indicating the market may be starting a new trend up. Results of Rule-Based Exits for Longs and Shorts
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Table 15-5 presents the performance data for best of the top 10 solutions (longs and shorts) for the MSES with the addition of genetically evolved, rule-based signal exits. Trades were entered randomly. The table is broken down into results for long positions and results for short positions. It is further broken down by sample and test. Sample (IN or OUT) and test may be BSLN (when the rules were not used) or RULE (when the mles were. used). On the long side, in-sample, the addition of the genetically evolved rule-based exit substantially reduced the loss on the average trade from a baseline of $688 to $324. The percentage of winning trades increased from 41 to 43%. The annualized risk-to-
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Performance of the Modified Standard Exit Strategy with an Added Genetically Evolved Rule-Based Signal Exit When Trades Are Entered Randomly
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reward ratio improved from -0.35 to -0.17. Out-of-sample, the benefit of the genetically evolved mle-based signal exit was maintained, but not quite as dramatically. The loss on the average trade was cut from $1,135 to $990. The percentage of winning trades increased from 39 to 41%. The risk-to-reward ratio improved slightly from -0.61 to -0.60. Overall, adding the genetically evolved rule-based element to the exit strategy worked. In contrast to the neural exit, the benefit was maintained out-of-mm ple, suggesting that curve-fitting and over-optimization were not major issues. On the short side, similar benefits were observed in both samples. In-sample, the addition of the genetically evolved rule-based element reduced the loss on the average trade from a baseline of $2,084 to $1,645. The percentage of winning trades remained unchanged. Paradoxically, the annualized risk-to-reward ratio worsened somewhat, going from - 1.09 to - 1.15. Out-of-sample, the loss on the average trade dropped substantially, from $1,890 in the baseline test to $1,058, when the rule element was active. The percentage of winning trades rose from 38 to 40%, and the annualized risk-to-reward ratio improved, from - 1.02 to -0.73. Again, the addition of the genetically evolved rule-based signal exit to the MSES worked and continued to work out-of-sample.
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Market-by-Market for Longs Results of Rule-Based Exits
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Table 15-6 contains information regarding the market-by-market performance of the MSES for the long side, with the added rule-based signal exit. Several markets showed profitability in both samples: the NYFE, Light Crude, Unleaded Gasoline, and Live Hogs. Other markets were profitable in-sample, but lost heavily out-ofsample (and vice versa). The consistency between in-sample and out-of-sample results was not high.
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