Transparent and Efficient Financial Analysis

:crown: Multivariate exploratory data analysis in Python — PCA, CA, MCA, MFA, FAMD, GPA

GPU-accelerated Factors analysis library and Backtester

an R package for structural equation modeling and more

Multi-Omics Factor Analysis

An workflow in factor-based equity trading, including factor analysis and factor modeling. For well-established factor models, I implement APT model, BARRA's risk model and dynamic multi-factor model in this project.

多因子指数增强策略/多因子全流程实现

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

psychometrics package, including MIRT(multidimension item response theory), IRT(item response theory),GRM(grade response theory),CAT(computerized adaptive testing), CDM(cognitive diagnostic model), FA(factor analysis), SEM(Structural Equation Modeling) .

Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. **Superseded by the models-by-example repo**.

A Java library for classical test theory, item response theory, factor analysis, and other measurement techniques. It provide tools commonly used in psychometrics and operational testing programs.

An R package for Bayesian structural equation modeling

Object-oriented diagram plots with ggplot2

Application and data for analyzing and structuring portfolios for climate investing.

Market Mix Modelling for an eCommerce firm to estimate the impact of various marketing levers on sales

Fast, linear version of CorEx for covariance estimation, dimensionality reduction, and subspace clustering with very under-sampled, high-dimensional data

factor models for omics data

Scalable Ultra-Sparse Bayesian PCA

Descriptive probabilistic marker gene approach to single-cell pseudotime inference

Open-source investment analytics platform bridging academic research and retail finance. Features include portfolio risk decomposition [Fama-French Five Factor Model], retirement sustainability modeling [Block Bootstrap Monte Carlo], max drawdown/CVaR dashboards, and risk-return optimisation [Markowitz, Ledoit-Wolf] via an intuitive user interface.

R package that performs sparse factor analysis and differential gene expression discovery simultaneously on single-cell CRISPR screening data.

Deep learning-based estimation and inference for item response theory models.

Alpha研究平台

From the given database Find out the personality using this personality traits. Applications in psychology Factor analysis has been used in the study of human intelligence and human personality as a method for comparing the outcomes of (hopefully) objective tests and to construct matrices to define correlations between these outcomes, as well as finding the factors for these results. The field of psychology that measures human intelligence using quantitative testing in this way is known as psychometrics (psycho=mental, metrics=measurement). Advantages 1)Offers a much more objective method of testing traits such as intelligence in humans 2)Allows for a satisfactory comparison between the results of intelligence tests 3)Provides support for theories that would be difficult to prove otherwise

Factor Analysis Kit

Several examples of multivariate techniques implemented in R, Python, and SAS. Multivariate concrete dataset retrieved from https://archive.ics.uci.edu/ml/datasets/Concrete+Slump+Test. Credit to Professor I-Cheng Yeh.

本项目是一个完整的量化投资因子分析系统，专注于中国股票市场的因子研究和指数增强策略。系统从原始数据获取开始，经过因子生成、预处理、单因子测试，最终实现因子合成和正交化，提供指数增强模型的构建。整个系统采用模块化设计，各个组件之间有明确的数据流转关系，形成了一个完整的量化投资研究框架。

Unsupervised learning coupled with applied factor analysis to the five-factor model (FFM), a taxonomy for personality traits used to describe the human personality and psyche, via descriptors of common language and not on neuropsychological experiments. Used kmeans clustering and feature scaling (min-max normalization).

Codebase for Cross-Spectral Factor Analysis (Gallagher et al., 2017)

Inference for Gaussian copula factor models and its application to causal discovery.