Projects sharing features with Pandas Datareader

OpenBBTerminal is a Python financial data platform and command line interface designed for aggregating and analyzing market data from diverse APIs. It serves as a quantitative analysis tool for processing stock, crypto, and derivative datasets to identify market trends and build investment strategies. The project utilizes a pluggable financial API framework with an adapter-based architecture, allowing external financial data providers to be integrated as independent modules. This system standardizes information from public and proprietary sources into a unified layer to support cross-asset an

FundamentalAnalysis is a comprehensive financial analysis library, quantitative finance framework, and macroeconomic data integrator. It provides tools for computing financial ratios, executing corporate health metrics, and pricing derivatives and bonds using mathematical models. The project integrates diverse data streams, including global economic indicators, real-time market quotes, and standardized corporate financial statements. It features a technical analysis engine for generating momentum and volatility indicators, as well as a portfolio performance analyzer for tracking risk-adjusted

Tushare is a financial data library for the Python programming environment that provides access to historical and real-time market information. It functions as a data interface for retrieving stock trading records, corporate financial statements, and macroeconomic indicators to support quantitative analysis and research. The library distinguishes itself by automatically transforming raw API responses into tabular data structures, allowing for direct integration with data analysis workflows. It manages access to these datasets through token-based authentication and utilizes schema-mapped parsi

Real time stock and option data.

Python API for accessing Lake high frequency tick trades & order book data

:chartwithupwards_trend: A live cryptocurrency historical trade data blotter. Download live historical trade data from any cryptoexchange, be it for machine learning, backtesting/visualizing trading strategies or for Quantopian/Zipline.

Financial Market Intelligence MCP Server — stock quotes, technical analysis, crypto data, and portfolio insights for AI agents

This is the official documentation for Quandl's Python Package. The package can be used to interact with the latest version of the Quandl RESTful API. This package is compatible with python v2.7.x and v3.x+.

Python module to get stock data from Yahoo! Finance

Cryptocurrency Exchange Websocket Data Feed Handler

Gekko Trading Bot dataset dumps. Ready to use and download history files in SQLite format.

Financial Data Extraction from Investing.com with Python

RQAlpha is a Python-native quantitative trading backtesting framework and live trading execution system. It provides an event-driven engine for simulating trading strategies against historical market data, with realistic transaction costs, slippage models, and corporate action handling. The platform supports multi-asset class trading including stocks, futures, options, and REITs, with separate sub-accounts for different asset types and configurable margin requirements. The framework distinguishes itself through a plugin-based extensible architecture that allows users to swap out core componen

pybroker is a Python algorithmic trading framework and quantitative technical analysis library designed for developing, testing, and optimizing trading strategies using historical market data. It functions as a trading strategy backtester and a financial performance evaluator, providing a structured environment to simulate trading rules and analyze their statistical reliability. The framework distinguishes itself through a market data integration layer that handles the fetching and caching of historical price data from external providers. It incorporates an event-driven backtesting engine and

Backtrader is a Python backtesting framework and algorithmic trading platform. It provides a toolkit for developing automated trading rules and simulating investment strategies using historical financial time-series data. The system functions as a quantitative analysis tool, combining a simulation engine for testing trading rules with a financial data visualizer that generates price action charts. It allows for the calculation of technical indicators and the evaluation of portfolio performance through risk-adjusted returns. The platform covers live trading integration via brokerage APIs and

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

Cryptocurrency exchange API client for Julia

Currency conversion library for Julia

Python client for Alpaca's trade API

Javascript SDK for trading on cryptocurrency exchanges like FTX, OKX, Bybit, and more. Supports API data feeds and WebSocket. Built by the Compendium Team.

Official DexPaprika Python SDK: Pythonic access to cross-chain DEX data, prices, and pools with type hints and caching.

This library provides convenient way to use Coinpaprika.com API in Python.

Fetch stock quote data from Yahoo Finance

Python wrapper for Yahoo! Finance API.

A python wrapper around the https://coinmarketcap.com API.

python-magic is a C-binding wrapper that provides a Python interface for the libmagic system library. It functions as a file signature analyzer and MIME type detector, identifying file formats by comparing header bytes against a database of known binary signatures. The library enables the identification of file types from both file paths and raw data buffers. It supports custom file signature matching through the injection of user-provided magic databases, allowing for the detection of specialized or proprietary formats. The project covers binary data analysis and MIME type mapping to transl