factor-models

📈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

Systematic ETF relative-strength research, overlapping-sleeve portfolio construction, LEAN implementation, and factor attribution.

Production-quality multi-asset portfolio optimisation and rolling backtesting in Python

Implements different approaches to tactical and strategic asset allocation

Work with trained factor models in Python

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

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

factorlasso is a Python library for sparse multi-output factor-model estimation with sign constraints, prior-centred shrinkage, data-driven grouped penalties, and consistent factor covariance assembly.

Repository for the AugmentedPCA Python package.

Robin: session-native agentic quant research for factor discovery, portfolio backtesting, and strategy promotion.

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

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

An R package for Factor Model Asset Pricing

Cross-asset price impact is set-identified, not point-identified: the confounding gap has rank at most K + rank(B). A diagonal truth still yields a dense cross-impact matrix, and the trades immune to the resulting cost error form a large but measure-zero subspace. Preregistered and reproducible.

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.

Deep-learning factor models for Korean equity asset pricing — autoencoder-style conditional beta networks (Gu, Kelly, Xiu 2021) applied to KR market data.

A toolkit for asset pricing research

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