by GCBC Ventures | Nov 11, 2021 | Finance, Machine Learning
Introduction In this article, we cluster stock price time series with hierarchical clustering and Euclidean, correlation, and Jensen-Shannon distances to answer two questions regarding portfolio diversification. How diversified is a given portfolio? How can a...
by GCBC Ventures | Nov 4, 2021 | Finance, Machine Learning
Introduction We use the K-means algorithm to answer two questions regarding portfolio diversification. How diversified is a given portfolio? How can a diversified portfolio be constructed? Additionally, we use the multidimensional scaling (MDS) algorithm to visualize...
by GCBC Ventures | Nov 20, 2020 | Machine Learning, Papers
In this paper the authors present a Python version of the Self-Organizing Map (SOM) algorithm that can run in parallel on CPUs and GPUs (via CuPy). Additionally, they speed up the algorithm by: heavily exploiting higher-dimensional operations (e.g., matrix-matrix) to...
by GCBC Ventures | Nov 17, 2020 | Finance, Machine Learning
Finding Similar Stocks Results Code 1. Finding Similar Stocks There are many ways to find stocks with similar behavior based on how one defines similarity and the data used. In this article we use a 12 period channel where, for each period, we have (current adjusted...
by GCBC Ventures | Nov 12, 2020 | Machine Learning
Faiss MNIST KNN Faiss Gotchas 1. Faiss Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code...
by GCBC Ventures | Aug 26, 2020 | Machine Learning
Hyperparameter Search With GPyOpt: Part 1 – Scikit-learn Classification and Ensembling Hyperparameter Search With GPyOpt: Part 2 – XGBoost Classification and Ensembling GPyOpt Python Package Using GPyOpt Best Result and Ensembling Results 1. GPyOpt Python Package...
