This is a summary of links featured on Quantocracy on Friday, 04/08/2022. To see our most recent links, visit the Quant Mashup. Read on readers!
-
A Guide to Obtaining Time Series Datasets in Python (h/t @PyQuantNews) [Machine Learning Mastery]Datasets from real-world scenarios are important for building and testing machine learning models. You may just want to have some data to experiment with an algorithm. You may also want to evaluate your model by setting up a benchmark or determining its weaknesses using different sets of data. Sometimes, you may also want to create synthetic datasets, where you can test your algorithms under
-
Is Sector-neutrality in Factor Investing a Mistake? [Alpha Architect]Firm characteristics such as size, book-to-market ratio, profitability, and momentum have been found to be correlated with expected returns. The predictive power of these characteristics may stem from their industry component, their firm-specific component, or both. For example, while the study Do Industries Explain Momentum, found that momentum in stocks stems from the industry component,
-
Simple Machine Learning Models on OrderBook/PositionBook Features [Dekalog Blog]This post is about using OrderBook/PositionBook features as input to simple machine learning models after previous investigation into the relevance of such features. Due to the amount of training data available I decided to look only at a linear model and small neural networks (NN) with a single hidden layer with up to 6 hidden neurons. This choice was motivated by an academic paper I read online