📈This repo contains detailed notes and multiple projects implemented in Python related to AI and Finance. Follow the blog here: https://purvasingh.medium.com

Interactive Brokers Fundamental data for humans

Implements different approaches to tactical and strategic asset allocation

Portfolio Construction Functions under the Basic Mean_Variance Model, the Factor Model and the Black_Litterman Model.

Work with trained factor models in Python

R package for fitting high-dimensional multivariate linear mixed effect models

Repository for the AugmentedPCA Python package.

DRIP Asset Allocation is a collection of model libraries for MPT framework, Black Litterman Strategy Incorporator, Holdings Constraint, and Transaction Costs.

Implementation of the FNETS methodology proposed in Barigozzi, Cho and Owens (2024) for network estimation and forecasting of high-dimensional time series

An R package for Factor Model Asset Pricing

Julia package for simulating and estimating multi-level/hierarchical dynamic factor models (HDFMs).

R codes and dataset for the estimation of the high-dimensional state space model proposed in the paper "A dynamic factor model approach to incorporate Big Data in state space models for official statistics" with Franz Palm, Stephan Smeekes and Jan van den Brakel.

A toolkit for asset pricing research

Code for implementing Factor Analysis with BLEssing of dimensionality (FABLE).

Jupyter notebooks implementing Finance projects

A repo to explore quantitative finance models, libraries and tooling.

An empirical analysis of European markets. This thesis compares the perceived dependence of stock and market returns, as measured by the frequency of comovement following Ungeheuer and Weber (2020), with the traditional interpretation of market dependency measured by Sharpe’s beta (1964).

Data, R code, Stan models, and supplementary materials associated with the paper: "A Unified Framework for Psychometrics in Experimental Psychology: The Standardized Generalized Hierarchical Factor Model".

sparseGFM implements sparse generalized factor models for dimension reduction and variable selection in high-dimensional continuous, count, and binary data. Stable release available on CRAN (https://cran.r-project.org/package=sparseGFM); development version hosted on GitHub.

Using publicly available daily factor and a panel of stock returns, estimate the time-varying betas to the selected factor using either DCC-GARCH.

Implemented a statistical factor model using Asymptotic Principal Component Analysis (APCA) and various weighting strategies to improve the performance of a basket of Italian stocks relative to a benchmark (FTSEMIB)

End-to-End Python implementation of Mo et al.'s (2025) ACT-Tensor methodology; a tensor completion framework for financial dataset imputation. Implements cluster-based CP decomposition, HOSVD factor extraction, temporal smoothing (CMA/EMA/Kalman), and downstream asset pricing evaluation. Transforms sparse data into dense machine readable data.

Index and Factor Construction with Implied Covariance Process

Estimation functions for the SRV four factor commodity pricing model

Estimation and inference for factor models in Asset Pricing.

matrix-valued time series methods

📊 Explore AI applications in finance through course materials from NYU Stern, featuring lectures and resources by Professor Arpit Gupta.

Applies Principal Component Analysis (PCA) to daily returns of 20 US equities (2015–2025) to uncover hidden risk factors. Explores variance explained, scree, loadings, factor returns, covariance reconstruction, and Varimax rotation. Results show 3–5 PCs capture ~75% of portfolio risk.

End-to-End Python implementation of Massacci et al.'s (2025) novel Randomized Alpha Test for high-dimensional factor models. Features robust OLS estimation, Extreme Value Theory-based inference, Monte Carlo simulation engine, and rolling-window empirical analysis. Handles N>T panels with non-Gaussian, heteroskedastic returns.

End-to-end portfolio optimization engine with robust numerical methods, DuckDB analytics, and real-time visualization for equity and crypto assets