Pretests for genetic-programming evolved trading programs: “zero-intelligence” strategies and lottery trading - Part 2
Automated Trader Magazine
Part 2 of Pretests for genetic-programming evolved trading programs: “zero-intelligence” strategies and lottery trading bootstrap paper. By Shu-Heng Chen and Nicolas Navet
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3
What does the pretest tell us?
The outcomes of the pretests provide us with answers to the following two questions: is there something essential to learn on the training data that can be of interest for the out-of-sample period ? Does the GP implementation show some evidence of effectiveness in that task ? Clearly, before actually trading with GP evolved programs, these two questions must be answered with reasonable certainty; the rest of this section explains how pretests may help in that regard.
3.1 Question 1: is there something
to learn?
The null hypothesis fl4,0 corresponding to pretest 4 has been presented in Section 2.2. We introduce pretest 5 that will be used in conjunction with pretest 4.
Pretest 5: equivalent intensity random search with training and validation versus lottery trading.
Here, we compare lottery trading to a random search with training and
validation, and a search intensity equivalent to the one used for GP in pretest 4. The
null hypothesis fl5,0 is that the equivalent intensity random does not
outperform lottery trading on the out-of-sample data. Depending on the validity of fl4,0
and fl5,0, we can
draw the conclusions that are summarized
in Table 1.
...