New Project: Multi-class Classification & Trading Strategies

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The reading of the paper "Trading Strategy Mining with Gene Expression Programming" [Huang et al. Proceedings of the 2013 International Conference on Applied Mathematics and Computational Methods in Engineering] sparkled a series of ideas that can be implemented quite easily in GeneXproTools in order to assist in the creation of better trading rules:

  1. Create built-in math functions and linking functions of 2 arguments with 3 discrete outputs (-1, 0, +1 or/and 0, 1, 2) such as the BUY-SELL-WAIT function described in the paper.
  2. We can also create more complex functions with 4-6 discrete outputs for more complex trading decisions.
  3. All the 2-argument n-output functions with a neutral gene (as implemented in GeneXproTools) can be used in a manner as described in the paper, that is, using 2 genes, the first for the BUY-tree and the second for the SELL-tree.
  4. Functions without a neutral gene or functions with more than 2 arguments (I'm still talking about functions with 3-6 discrete outputs) can play a similar role in the creation of trading rules when used in single-gene structures. In this case, when the function is at the root of the tree, the left branch determines the BUY signal and the left the SELL signal.
  5. We can also implement new genetic operators in order to have more control over the root position in the trees. This is not essential for evolving trading rules using the strategy outlined in 4, but is a nice touch and has applications in other domains. The genetic operators I'm thinking about are Fixed-Root Mutation and Conservative Fixed-Root Mutation.
  6. Implications for Classification: These new tools can be used in multi-class classification, at least for simple problems, as more complex problems require decomposing the multi-class problem into n different binary classification problems. This new algorithm will be implemented in the Regression Framework using the fitness functions and visualization/analytics tools available for regression. Particularly interesting are the Hits-based fitness functions as they also give access to the Hits/Outliers statistics that can also be used for model selection.

There are a few minor spin-offs of this project, but we'll get to them one at a time.

I hope you traders out there get inspired by the paper too and contribute to this project with your own ideas for implementing even more powerful tools.

So please tune in!



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