Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
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Updated
May 28, 2024 - Jupyter Notebook
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
I will update this repository to learn Machine learning with python with statistics content and materials
Covers the basics of mixed models, mostly using @lme4
Display and analyze ROC curves in R and S+
Functions for the construction of risk-based portfolios
Mixed models @lme4 + custom covariances + parameter constraints
Project Page of Combining 3D Morphable Models: A Large scale Face-and-Head Model - [CVPR 2019]
Variography for the GeoStats.jl framework
Lightweight robust covariance estimation in Julia
Fast & numerically stable statistical analysis
Kriging estimators for the GeoStats.jl framework
This repository contains simple statistical R codes used to describe a dataset. These scripts provide a summarized and easy way of estimating the mean, median, mode, skewness and kurtosis of data. It also provides codes for calculating the covariance.
An R package to explore and quality check data
Machine learning functions written in goLang:
Online statistics
General purpose correlation and covariance estimation
Really simplified animal kingdom hierarchy
Gaussian process regression
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