Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
10 documents
This implementation explains how a bivariate Gaussian copula represents dependence between two variables after their observations have been converted to uniform pseudo-observations. It estimates the dependence parameter by mapping those observations through…
This document is a historical price table for a broad set of country and regional exchange-traded funds. It lists dates alongside one price series for each ETF, with examples spanning markets such as Japan, Brazil, Germany, India, and the United Kingdom. The…
This document describes a bivariate Frank copula implementation for modeling dependence between two uniform variables. It provides methods to sample paired observations, calculate the copula density and cumulative distribution, and evaluate a conditional…
This document explains copula-based measures for comparing financial return series by separating marginal distributions from dependence. It presents Spearman’s rho as a rank-based dependence measure and contrasts it with Pearson correlation, which captures…
This document extends a cointegration-based spread strategy from pairs to three or more assets. It forms a weighted combination of log prices using a cointegration vector, then derives a spread return from the same weights. Under stated stationarity…
This experiment runner configures repeated, rolling train-and-test evaluations for LSTM and Temporal Fusion Transformer models on a multi-asset Quandl dataset. It offers variants with different input sequence lengths and optional changepoint feature…
This documentation describes tools for measuring relationships among asset-return series. A dependence matrix computes pairwise codependence using alternatives such as mutual information, variation of information, distance correlation, Spearman rank…
This document outlines a daily strategy for trading a set of assets using a cointegration vector estimated with the Johansen method on training data. It applies the vector to log prices to form a combined process, then sums its recent changes to determine…
The document explains an optimal-transport approach to measuring dependence between asset return series. It first transforms observations into empirical copula coordinates using normalized ranks, which removes marginal scales while retaining dependence…
This reference explains several ways to measure dependence among asset returns and distance between correlation structures. Distance correlation can detect nonlinear dependence and is zero exactly when variables are independent, unlike Pearson correlation,…