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

This project is a comprehensive pandas data analysis tutorial and instructional guide designed for learning data manipulation and analysis. It serves as a tabular data processing guide and a manual for time series analysis, providing a structured approach to cleaning, merging, and transforming datasets. The repository functions as a data feature engineering course, providing tutorials on constructing and selecting dataset features to improve machine learning model performance. It also includes a vectorized data operations guide for performing element-wise mathematical computations and matrix

pyalgotrade ist eine Python-Bibliothek für algorithmischen Handel, die für die Entwicklung, das Backtesting und die Ausführung automatisierter Handelsstrategien konzipiert ist. Sie bietet ein umfassendes Framework für das Backtesting von Finanzstrategien, eine Bibliothek für technische Analyse zur Berechnung mathematischer Indikatoren sowie Konnektoren für die Integration von Kryptobörsen. Das Projekt zeichnet sich durch die Unterstützung von sentimentbasiertem Handel durch die Integration von Social-Media-Feeds und Keyword-Streams in Echtzeit aus. Es bietet ein Visualisierungstool für quantitativen Handel zum Plotten von Preisverläufen und Portfolio-Equity-Kurven sowie parallele Parameteroptimierung zur Identifizierung der effektivsten Strategieeinstellungen. Die Bibliothek deckt ein breites Spektrum an Funktionen ab, darunter den Aufbau von Finanzdaten-Pipelines für die Aufnahme und Resampling von Marktdaten, das Order-Lifecycle-Management für Live- und Paper-Trading sowie quantitative Performance-Analysen zur Berechnung risikoadjustierter Renditen und Drawdowns. Sie enthält zudem Tools für technische Analysen, wie Volatilitätsbänder und Momentum-Indikatoren, sowie Simulationen, die Order-Slippage und Transaktionsgebühren berücksichtigen.

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