Open-source alternatives to A Stock Data

easyquotation is a Python library that provides access to Chinese stock market data, including real-time quotes, historical daily candlestick prices, exchange-traded fund details, and a stock code database sync utility. It retrieves live trading data from Chinese exchanges, A-shares, and Hong Kong listed stocks without requiring manual API key configuration, offering a unified interface to multiple public data feeds. The library combines several market data providers behind a single query interface, using asynchronous I/O to handle parallel requests and a polling engine that delivers sub-seco

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

efinance is a Python financial data library and programmatic interface designed to automate the acquisition of market data for quantitative trading and analysis. It serves as a toolkit for retrieving real-time and historical information across various asset classes to support the development of backtesting systems and trading strategies. The library provides a multi-asset toolkit for monitoring diverse financial instruments, including stocks, funds, bonds, and futures. It allows for the extraction of specific data points such as shareholder counts, corporate index memberships, and net asset v

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

The FinanceToolkit is an open-source Python library for quantitative finance that provides a unified framework for financial analysis, asset valuation, and risk management. It serves as a comprehensive platform for computing over 200 financial metrics and ratios, with capabilities spanning financial ratio analysis, fixed income analytics, macroeconomic data aggregation, options pricing, and portfolio risk management. The toolkit distinguishes itself through a modular architecture that separates data retrieval from computation, with stateless engines for financial models like Black-Scholes, GA

FinRobot is an AI-powered financial analysis framework that coordinates multiple specialized agents to automate equity research, financial analysis, and investment risk assessment. At its core, it functions as a multi-agent orchestration system where a director and task manager allocate financial tasks to the most suitable large language models based on performance metrics and task requirements. The framework distinguishes itself through its ability to execute complex multi-step financial workflows by routing tasks through perception, reasoning, and action modules. It generates professional e

aiquanttrade is an AI-driven quantitative trading platform that enables the development, backtesting, and deployment of trading strategies powered by machine learning and artificial intelligence. It provides a complete local environment for quantitative research, simulation, and automated live trading through brokerage APIs, supporting both historical backtesting and real-time paper trading without capital risk. The platform distinguishes itself through a modular, event-driven architecture that separates strategy logic from execution, allowing rule-based and machine learning models to be co

This project is a quantitative trading platform and algorithmic trading bot designed for market data aggregation, strategy backtesting, and trade execution. It functions as a comprehensive system for collecting financial data via APIs and web sources, simulating investment strategies against historical records, and programmatically managing investment positions through brokerage interfaces. The platform distinguishes itself through institutional sentiment analysis and market intelligence tools. It monitors institutional fund activity, tracks corporate actions like equity pledges, and crawls f

QuantMuse is an algorithmic trading platform and quantitative trading framework that integrates large language models with mathematical analysis to automate market insights and trading strategies. It functions as a system for building, backtesting, and executing strategies using both historical and real-time market data. The framework is distinguished by its use of large language models for financial analysis and sentiment extraction from news and social media. It utilizes autonomous agents with chain-of-thought reasoning to generate market intelligence and strategic reports, while employing

StockSharp is an algorithmic trading platform and quantitative framework used for developing and deploying trading robots across stock, forex, and cryptocurrency markets. It functions as a multi-asset trading gateway and a dedicated development environment for building, debugging, and scheduling automated strategies. The platform includes a visual strategy workflow editor that maps logic blocks to executable code and a simulation engine that replays historical tick data to validate trading logic. It utilizes a plugin-based broker integration system to normalize diverse exchange protocols into

This is an unofficial client library that provides programmatic access to TradingView chart data, technical indicators, and real-time market prices. It is designed to support automated trading workflows by enabling direct interaction with TradingView’s data and analysis capabilities through code. The library offers a set of tools for working with market data and technical analysis. It includes a historical data extractor for querying past price ranges and indicator values, a real-time market data streamer that uses WebSockets to deliver live price updates and indicator outputs, and a strategy

FinRL-Trading is a modular framework designed for the development, training, and deployment of quantitative trading strategies using reinforcement learning and autonomous agent workflows. It provides a comprehensive infrastructure for managing the entire lifecycle of financial models, from data ingestion and strategy generation to live market execution. The platform distinguishes itself through a multi-agent architecture that coordinates specialized tasks such as sentiment analysis, risk assessment, and collaborative research. By utilizing a standardized environment abstraction, it allows rei

zvt is a quantitative trading framework designed for building, backtesting, and executing algorithmic trading strategies. It functions as a modular system that integrates a financial data pipeline for market data collection, an algorithmic backtesting engine for strategy evaluation, and an event-driven trading system to automate market executions. The project distinguishes itself through a hybrid approach to signal management, using a dynamic tagging system that combines automated quantitative logic with human intervention. It includes a quantitative analysis dashboard for visualizing researc

OpenStock is a stock market analysis platform designed for tracking real-time prices, analyzing market sentiment, and managing personalized financial watchlists. It serves as a financial portfolio tracker that allows users to monitor asset performance through technical indicators and candlestick charts. The platform distinguishes itself by aggregating sentiment data from social media, news sources, and prediction markets to visualize overall investor mood. It also features a specialized onboarding workflow that collects risk tolerance and financial objectives to tailor the tracking experience

Hikyuu is a quantitative trading framework designed for developing, backtesting, and executing systematic trading strategies. It functions as a high-speed system that combines a financial time-series library, a multi-factor analysis tool, and a quantitative backtesting engine to support comprehensive trading research. The framework is distinguished by its high-speed computing core, which utilizes multi-threaded execution to process large volumes of market data for technical indicator generation. It supports a modular strategy composition model where signal, risk, and fund management component

FinGPT is a suite of specialized financial tools and a framework for adapting large language models to the financial domain. It provides a set of pipelines for financial entity extraction, sentiment analysis, and retrieval-augmented generation to improve the accuracy of financial information systems. The project distinguishes itself through efficient training workflows, utilizing low-rank adaptation and quantized low-rank adaptation to fine-tune models on consumer-grade hardware. It employs market-labeled datasets and reinforcement learning that uses actual stock price movements as reward sig

AkShare is a Python financial data library and programmatic interface designed for fetching real-time and historical stock, currency, and economic market data. It serves as a quantitative data acquisition tool for gathering the large-scale financial datasets required for economic research and quantitative analysis. The library provides a unified interface to retrieve datasets from various official and commercial providers, removing the need to write custom scrapers for individual financial sources. It maps standardized function calls to diverse third-party sources to normalize varying respons

FinceptTerminal is a quantitative finance platform and financial engineering library designed for asset valuation, risk management, and fixed-income analytics. It provides a comprehensive suite for algorithmic trading and investment strategy automation, integrating specialized language model agents and node-based workflows to automate market research and alpha generation. The project distinguishes itself with a dedicated game theory analysis engine for calculating Nash equilibria and simulating strategic interactions in competitive markets. It also features a specialized credit risk modeling

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

This project is a Python library designed for the programmatic retrieval and analysis of diverse financial datasets. It functions as a comprehensive toolkit for quantitative research, providing a unified interface to fetch historical and real-time market data across asset classes including equities, futures, bonds, cryptocurrencies, and foreign exchange. By abstracting complex network requests into simple, parameter-driven functions, it enables users to integrate financial data into research workflows and automated trading systems. The library distinguishes itself through its scraper-based ag

Cointop is a terminal-based cryptocurrency dashboard that displays real-time market data, prices, and portfolio values in a text-based interface. It aggregates data from multiple cryptocurrency exchange APIs and presents it in an interactive terminal user interface with vim-inspired keyboard shortcuts for navigation and control. The application distinguishes itself by embedding an SSH server that allows remote access to the dashboard from any device, with persistent client configuration tied to individual SSH keys. It includes a fuzzy search system for quickly finding coins by name, desktop n

Rate.sx is a terminal-based financial utility that provides real-time currency conversion and market data access through standard HTTP requests. It functions as a RESTful service designed to deliver financial information directly to command-line environments, allowing users to retrieve exchange rates and perform calculations without leaving their terminal. The service distinguishes itself by offering text-based visualizations of historical exchange rate trends and automated currency conversion through simple query parameters. By mapping numerical data to character-based grid layouts, it enabl

Ashare is a market data aggregator and financial time-series table generator designed to provide a stable stream of price and volume data for quantitative analysis. It functions as a multi-provider data proxy that converts raw asset price feeds into structured tables for immediate processing. The system ensures high availability for data feeds through a failover mechanism that automatically switches between primary and backup market data sources. This provider-agnostic layer allows the tool to maintain continuous data availability without altering the underlying analysis logic. The project c

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

This project is a comprehensive framework for engineering financial data pipelines, designed to automate the collection, cleaning, and synchronization of large-scale market datasets. It functions as a quantitative trading data engine, providing the infrastructure necessary to manage historical and real-time asset pricing information for research and machine learning workflows. The system distinguishes itself through a configuration-driven approach to orchestration, allowing users to manage complex data acquisition tasks across multiple financial providers. It features resilient middleware tha

VectorBT is a vectorized trading strategy backtesting framework that simulates thousands of strategy configurations in a single pass over historical price data. It operates as a parameter optimization engine, a portfolio performance analyzer, a technical indicator calculator, and a financial data fetcher, all built around a DataFrame-centric data model that uses NumPy broadcasting for signal alignment and compiled code acceleration for performance. The framework distinguishes itself through its ability to run large-scale parameter sweeps by constructing every combination of strategy parameter