Python for Finance (O'Reilly)

PyTrendFollow - systematic futures trading using trend following

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

python implementation of the parquet columnar file format.

1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.

FastKAN: Very Fast Implementation of Kolmogorov-Arnold Networks (KAN)

🐝 Tiny CLI to post simultaneously to Mastodon and Bluesky

A Python package for time series classification

A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.

Genetic Programming in Python, with a scikit-learn inspired API

The Vasicek model is a specific application of the Ornstein-Uhlenbeck process in the context of interest rate modeling. I calculated zero-coupon bond prices with 3 different methods in Vasicek mode…

Conjuntos de dados abertos para tarefas de ML

Comparative analysis of pairwise interactions in multivariate time series.

The tensorflow implementation of KANs

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports comp…

SIGKDD'2019: DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks

Distributed hyperparameter optimization made easy

Natural Gradient Boosting for Probabilistic Prediction

A Tree based feature selection tool which combines both the Boruta feature selection algorithm with shapley values.

Python implementations of the Boruta all-relevant feature selection method.

Python implementation of multiple-criteria decision-making algorithms

Python toolkit for quantitative finance

Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

A scikit-learn-compatible module to estimate prediction intervals and control risks based on conformal predictions.

👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.

Lightweight, useful implementation of conformal prediction on real data.

A short introduction to Conformal Prediction methods, with a few examples for classification and regression from the Astrophysical domain, and slides.

STUMPY is a powerful and scalable Python library for modern time series analysis

Implementation of GraphKan with torch geometrics and its application on signal classification

A JAX-based implementation of Kolmogorov-Arnold Networks