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Portfolio Performance of the Modified Standard Exit Strategy with an Added Neural Signal Exit Evaluated over a Range of Threshold Parameter Values
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Table 15-3 shows the performance data for the portfolio, broken down by market, for the optimal MSES with the added neural signal exit. The results are for tire composite exit with the best threshold value (54) found in the optimization presented in Table 15-2. Live Hogs was the only market that was substantially profitable in both samples. A number of markets (e.g., the Deutschemark and Japanese Yen) showed strong profitability in-sample that was not evident out-of-sample. On the long side, the NYFE and Unleaded Gasoline were profitable in both samples. This could easily be a statistical artifact since, in-sample, a large number of markets had profitable performance on the long side. TEST METHODOLOGY FOR THE GENETIC EXIT COMPONENT Since it is almost certain that different rules are required for the long side, as opposed to the short side, two tests are rtm. In the first test, random entries are generated for long positions using the standard random entry strategy. Any short trades generated are simply not taken. Rules are genetically evolved for inclusion in the MSES for the long positions. In the second test, only short entries are taken. Any long entries generated by the random entry strategy are ignored. An attempt is made to evolve rules that work well as additional elements to the MSES for the short side.
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Market-by-Market Performance for the Modified Standard Exit with a Neural Signal Addition Using Random Entries
The code above shows the logic of both the entries and exits. The modeltype parameter controls whether the longs or shorts are tested. Parameters ptlim and mmsfp are for the profit target and stop (respectively). They are fixed at the same optimal values used in the neural network test earlier, Each of the three rules is calculated as a series of TRUE/FALSE values, and if all three rules are TRUE, a rulebased exit signal (exitrig) is generated. In the exit code, if clauses have been added. For example, an exit at the close is generated ifan exit signal is produced by all three rules being TRUE (exitsig = TRUE). The evolution of rules for the long and short sides follow the same steps as in the chapter on genetics, in which similar rules were evolved for use in entries. The same 12 parameter chromosomes, broken into three genes (each specifying a rule), are employed, and there is no change in that logic in the current test, Rules for the short side and for the long side are produced by allowing 2,500 generations to pass (2,500 runs using OptEvolve). The top 10 solutions for the longs and for the shorts are then tested on both the in-sample and out-of-sample data.
Top 10 Solutions with Baseline Exit Table 15-4 shows the top 10 solutions found for the long side and for the short side. In the table, LINE = the line or generation number; PROB = the probability or statistical significance (the decimal point is omitted but implied in the formatting of these numbers); $TRD = the average dollars-per-trade; TRDS = the total number of trades taken; PFAC = the profit factor; and AROA = the annualized return-on-account. The best solution for the longs was discovered in the 845 generation of the evolutionary process. For the short side, it was in the 1,253 generation. ln contrast to the situation when rules were evolved for use in an entry model, none of the solutions were profitable. However, Table 15-5 shows that when genetically evolved rule-based exits were added, substantial improvement over the baseline was achieved. The rules in Table 15.4 were translated into plain language. The rules for exiting a long position were as follows: If the close on the current bar is greater
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