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I recently started to teach myself OOP techniques for Matlab. To start with I looked for free online resources and found some good links:

• link 1
• link 2
• link 3

Following a specialized textbook on the subject has its own benefits and so I’ve decided to buy the following book:

It covers pretty much everything you need to know about Matlab OOP:

A Guide to MATLAB Object-Oriented Programming is the first book to deliver broad coverage of the documented and undocumented object-oriented features of MATLAB®. Unlike the typical approach of other resources, this guide explains why each feature is important, demonstrates how each feature is used, and promotes an understanding of the interactions between features. Assuming an intermediate level of MATLAB programming knowledge, the book not only concentrates on MATLAB coding techniques but also discusses topics critical to general software development. It introduces fundamentals first before integrating these concepts into example applications. In the first section, the book discusses eight basic functions: constructor, subsref, subsasgn, display, struct, fieldnames, get, and set. Building on the previous section, it explores inheritance topics and presents the Class Wizard, a powerful MATLAB class generation tool. The final section delves into advanced strategies, including containers, static variables, and function fronts. With more than 20 years of experience designing and implementing object-oriented software, the expert author has developed an accessible and comprehensive book that aids readers in creating effective object-oriented software using MATLAB.

Hopefully the concepts discussed in the book will come in handy when I decide to create my own Matlab toolboxes.

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The main idea in this presentation is that of co-evolving 2-branch type trees, where one branch represents buy rules and the other represents sell rules for trading. The intuition is that when the branches are evolved together, your final genetic program ends up with buy and sell rules that are duals of each other. Hence an optimum buy rule can be paired with an optimum sell rule - which forms a dual. This is unlike the approach where a sell rule is triggered only if certain criteria for a buy are not met (which may not necessarily be optimum condition for the sell). Although the results in the presentation are somewhat ambiguous, the author points out that it is possible to discover profitable trading rules in the presence of transaction costs under a statistical arbitrage framework. The author also cites a few papers which have applied GP techniques to Statistical Arbitrage. I shall dig up these papers to investigate the approach taken.

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