Even though the training data spanned more than 55 years, I'm not too surprised a genetic program strategy with so many inputs could show good results after training.
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So with six independent models trained on the same data, I ran a test on an out-of-sample period of 1238 input instances for 1957-12-26 through 1962-11-23. Independent means the random number generator used for the training has a different sequence. When 3 or more of the top 7 rules for any of the models had a signal, the simulated number of trades was 266 with 125 wins (a win is greater than 1.6560 percent gain) and had a mean gain of 100 * exp(1.2726 / 100) - 100 == 1.2807 percent.
For this test period, the mean gain for trades taken on all days was 100 * exp(0.519945877769271 / 100) - 100 == 0.5213 percent. The test results were significantly better than the mean result for the entire test period. So maybe this type of system is almost usable.