Quantocracy: This is a curated mashup of quantitative trading links.
Portfolio Selection via Topological Data Analysis – Petr Sokerin, Kristian Kuznetsov, Elizaveta Makhneva, Alexey Zaytsev
Portfolio management is an essential part of investment decision-making. However, traditional methods often fail to deliver reasonable performance. This problem stems from the inability of these methods to account for the unique characteristics of multivariate time series data from stock markets. We present a two-stage method for constructing an investment portfolio of common stocks. The method involves the generation of time series representations followed by their subsequent clustering. Our approach utilizes features based on Topological Data Analysis (TDA) for the generation of representations, allowing us to elucidate the topological structure within the data. Experimental results show that our proposed system outperforms other methods. This superior performance is consistent over different time frames, suggesting the viability of TDA as a powerful tool for portfolio selection.
EvoTorch: Scalable Evolutionary Computation in Python – Nihat Engin Toklu, Timothy Atkinson, Vojtěch Micka, Paweł Liskowski, Rupesh Kumar Srivastava
Evolutionary computation is an important component within various fields such as artificial intelligence research, reinforcement learning, robotics, industrial automation and/or optimization, engineering design, etc. Considering the increasing computational demands and the dimensionalities of modern optimization problems, the requirement for scalable, re-usable, and practical evolutionary algorithm implementations has been growing. To address this requirement, we present EvoTorch: an evolutionary computation library designed to work with high-dimensional optimization problems, with GPU support and with high parallelization capabilities. EvoTorch is based on and seamlessly works with the PyTorch library, and therefore, allows the users to define their optimization problems using a well-known API.
Code and documentation.
Awesome Quant (Wilson Feitas) – A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance).
Tax-Loss Harvesting: A Primer – Harry Mamaysky
Investors have access to a multitude of personalized indexing products, a key benefit of which is to allow for tax-loss harvesting (TLH). Using a simple example, we demonstrate many features of TLH and establish an invariance result which highlights circumstances where TLH does not matter. Using simulations, we analyze the seasoning effect of TLH strategies and show that, absent investment inflows, the majority of TLH benefits accrue in the first few years of the strategy. Lower return correlations and a larger difference between high intermediate and lower terminal capital gains tax rates increase the benefit of TLH.
