Awesome GitHub RepositoriesTime Series Resampling

Pandas is a high-performance data analysis library that provides a comprehensive framework for manipulating, cleaning, and transforming structured datasets. It centers on labeled one-dimensional and two-dimensional data structures, allowing users to construct, filter, and reshape tabular information while performing complex arithmetic and logical operations. The library distinguishes itself through a sophisticated indexing engine that enables automatic data alignment during calculations and relational merges. By utilizing a block-based memory layout, it optimizes cache locality for vectorized

AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc

Quantaxis is a quantitative trading framework designed for building, backtesting, and executing automated strategies across global equities, futures, and cryptocurrencies. It integrates an event-driven backtesting engine, a multi-market execution gateway for order routing, and a quantitative data pipeline for ingesting and storing multi-asset market data. The system features a Rust-accelerated financial library that utilizes Apache Arrow for high-performance technical indicator calculation and zero-copy data processing. It provides a containerized infrastructure model designed for orchestrati

This project is an educational resource and a collection of instructional materials for performing data manipulation and statistical analysis using Python. It provides a comprehensive set of guides and code examples for using the Pandas, NumPy, and Matplotlib libraries to analyze structured data. The resource includes a dedicated guide for reshaping, cleaning, and aggregating tabular data and time series via Pandas, alongside a reference for high-performance vectorized operations and linear algebra using NumPy. It also features tutorials for creating publication-quality charts, distribution p

Este proyecto es un tutorial completo de análisis de datos de pandas y guía de instrucción diseñada para aprender la manipulación y el análisis de datos. Sirve como una guía de procesamiento de datos tabulares y un manual para el análisis de series temporales, proporcionando un enfoque estructurado para limpiar, fusionar y transformar conjuntos de datos. El repositorio funciona como un curso de ingeniería de características de datos, proporcionando tutoriales sobre la construcción y selección de características de conjuntos de datos para mejorar el rendimiento del modelo de aprendizaje automático. También incluye una guía de operaciones de datos vectorizadas para realizar cálculos matemáticos elemento a elemento y manipulaciones de matrices. El material cubre una amplia gama de capacidades, incluyendo flujos de trabajo de limpieza de datos, tareas de integración de datos y análisis de datos tabulares. Proporciona orientación sobre el procesamiento de información textual, el manejo de datos categóricos y la optimización de la velocidad de ejecución para grandes conjuntos de datos. El proyecto se entrega como una serie de Jupyter Notebooks que contienen ejercicios prácticos y problemas de práctica específicos.

pyalgotrade es una librería de trading algorítmico en Python diseñada para desarrollar, realizar backtesting y ejecutar estrategias de trading automatizadas. Proporciona un framework integral para backtesting de estrategias financieras, una librería de análisis técnico para calcular indicadores matemáticos y conectores para la integración con exchanges de criptomonedas. El proyecto se distingue por soportar trading basado en sentimiento mediante la integración de feeds de redes sociales en tiempo real y flujos de palabras clave. Incluye una herramienta de visualización de trading cuantitativo para graficar la acción del precio y curvas de capital de la cartera, junto con optimización de parámetros en paralelo para identificar la configuración de estrategia más efectiva. La librería cubre un amplio rango de capacidades, incluyendo la construcción de pipelines de datos financieros para la ingesta y remuestreo de datos de mercado, gestión del ciclo de vida de órdenes tanto para trading real como en papel (paper trading), y análisis de rendimiento cuantitativo para calcular retornos ajustados al riesgo y drawdowns. También incluye herramientas para análisis técnico, como bandas de volatilidad e indicadores de momentum, así como simulaciones que consideran el deslizamiento de órdenes (slippage) y comisiones de transacción.

This is a containerized algorithmic trading system that connects to Interactive Brokers to execute high-frequency pairs trading strategies on forex instruments. The project implements a mean-reversion model that maintains long-short position pairs, continuously recalculating a beta hedge ratio to profit from temporary divergences in correlated price spreads. The system processes each incoming market tick through a signal pipeline that immediately evaluates indicators and triggers market orders without batching or aggregation. It includes an irregular tick resampling engine that converts inhom

This project is a Python quantitative trading framework and library designed for developing, backtesting, and deploying automated financial strategies. It serves as both an algorithmic trading backtester for evaluating historical performance and an event-driven trading engine for executing trades based on quantitative rules. The framework functions as an educational toolkit, providing guided lessons and resources for quantitative finance learning and the application of mathematical models to market data. The system provides capabilities for algorithmic trading automation and financial strate

Bar chart race is a Python data visualization library that transforms ordered tabular time-series data into animated bar and line chart races. It operates as an extension for rendering dynamic charts that illustrate how rankings and values change over time. The library interpolates wide-format chronological tables into densely sampled frame sequences, calculating intermediate numeric values to produce fluid motion animations. It orchestrates iterative canvas redraws through a plotting backend while supporting external multimedia encoders to export compressed standard video files. Generated a