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Algorithms
Special Issues
Algorithms and Financial Optimization
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Algorithms and Financial Optimization

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 April 2013) | Viewed by 12058

Special Issue Information

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Published Papers (1 paper)

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Research

401 KiB  
Article
An Open-Source Implementation of the Critical-Line Algorithm for Portfolio Optimization
by David H. Bailey and Marcos López de Prado
Algorithms 2013, 6(1), 169-196; https://doi.org/10.3390/a6010169 - 22 Mar 2013
Cited by 18 | Viewed by 11568
Abstract
Portfolio optimization is one of the problems most frequently encountered by financial practitioners. The main goal of this paper is to fill a gap in the literature by providing a well-documented, step-by-step open-source implementation of Critical Line Algorithm (CLA) in scientific language. The [...] Read more.
Portfolio optimization is one of the problems most frequently encountered by financial practitioners. The main goal of this paper is to fill a gap in the literature by providing a well-documented, step-by-step open-source implementation of Critical Line Algorithm (CLA) in scientific language. The code is implemented as a Python class object, which allows it to be imported like any other Python module, and integrated seamlessly with pre-existing code. We discuss the logic behind CLA following the algorithm’s decision flow. In addition, we developed several utilities that support finding answers to recurrent practical problems. We believe this publication will offer a better alternative to financial practitioners, many of whom are currently relying on generic-purpose optimizers which often deliver suboptimal solutions. The source code discussed in this paper can be downloaded at the authors’ websites (see Appendix). Full article
(This article belongs to the Special Issue Algorithms and Financial Optimization)
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