Projects sharing features with OpenBBTerminal

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

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

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

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

Python library to download market data via Bloomberg, Eikon, Quandl, Yahoo etc.

Valuecell is an artificial intelligence financial trading platform and market analysis engine. It functions as a multi-exchange trading bot and financial data orchestrator, designed to analyze market data and execute automated trades across global financial exchanges. The system utilizes a modular agent plugin framework that allows for the integration of third-party tools and agents through a shared community registry. It incorporates a retrieval-augmented generation approach to analyze fundamental financial documents and historical patterns, grounding AI responses in factual data. The platf

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

TradingAgents-CN is a multi-agent framework designed for autonomous financial market analysis and automated trading execution. It functions as a containerized orchestrator that leverages large language models to perform complex reasoning, research, and decision-making tasks within financial environments. The platform distinguishes itself through a modular architecture that integrates diverse artificial intelligence providers and financial data sources into a unified pipeline. It provides granular control over agent behavior through prompt-driven logic configuration and multi-model orchestrati

Abu is an algorithmic trading framework designed for the development, backtesting, and optimization of automated trading strategies. It functions as a quantitative financial analysis library that processes time-series data to identify market trends, volatility patterns, and key price levels. The platform distinguishes itself through a modular architecture that integrates diverse financial data sources and a rule-based engine for automated risk management. It enables users to construct complex trading signals by layering technical indicators and machine learning models, while simultaneously en

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

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

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

Real time stock and option data.

Extract data from a wide range of Internet sources into a pandas DataFrame.

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

This project is an algorithmic trading platform designed to automate financial market analysis and the execution of investment strategies. It provides an end-to-end environment for processing real-time market data through automated decision models, allowing for the triggering of financial transactions based on predefined quantitative signals and risk parameters without manual intervention. The platform distinguishes itself through a modular pipeline architecture that decouples data ingestion, signal generation, and trade execution, facilitating the iterative refinement of investment models. I

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

Awesome-quant is a curated directory of open-source software libraries and tools designed for quantitative finance, algorithmic trading, and financial data analysis. It serves as a central hub for discovering resources that support the entire lifecycle of financial modeling, from raw data ingestion to complex statistical research. The repository organizes specialized tools into categorized collections, enabling users to identify solutions for high-performance numerical computing, technical indicator calculation, and derivative pricing. It highlights frameworks that facilitate the construction

This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms. The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing larg

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

This project is a Python financial analytics framework and quantitative trading library. It provides a suite of mathematical tools for asset pricing, statistical market analysis, and the development of algorithmic trading strategies. The library is distinguished by its focus on currency and commodity correlation modeling, using regression and normalization to identify exchange rate drivers. It features a specialized portfolio optimization engine that applies graph theory, such as clique centrality and degeneracy ordering, alongside quadratic programming to balance risk-adjusted returns. The

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

This library provides a unified interface for interacting with hundreds of global cryptocurrency exchanges. It serves as a standardized framework for building automated trading systems, allowing developers to fetch real-time market data, manage account balances, and execute orders across multiple financial platforms through a single, predictable set of methods. The project distinguishes itself by abstracting the complexities of diverse exchange-specific application programming interfaces into a consistent internal schema. It includes a modular authentication layer that automatically handles c

This project is an algorithmic trading engine designed for the automated execution of cryptocurrency strategies. It provides a modular execution core that connects to multiple centralized and decentralized exchanges, allowing users to deploy rule-based trading logic across various spot and futures markets. The platform serves as a comprehensive environment for the entire trading lifecycle, from initial strategy development to live market operations. What distinguishes this platform is its integrated suite for quantitative analysis and predictive modeling. It features a robust backtesting engi

Eiten is an AI-powered market analysis platform and quantitative toolset designed to translate statistical market data and options flow into investment strategies. It provides a suite of specialized financial tools, including an analysis platform driven by large language models, a quantitative portfolio optimizer, and a trading strategy backtester. The project distinguishes itself through the use of random matrix theory to filter covariance noise and mathematical algorithms for portfolio optimization. It integrates these capabilities with a financial data bot for delivery of real-time researc

Thunder Client is a REST API client extension for VS Code that functions as an HTTP request manager, testing tool, and mocking workspace. It allows users to send requests, organize them into collections, and manage API configurations directly within the editor. The project distinguishes itself through a command-line interface for executing automated test suites in CI/CD pipelines and a Git-based synchronization system for sharing request collections and environment configurations across teams. It also incorporates artificial intelligence to automate the conversion of API scripts during migrat

hledger is a plain text accounting tool and double-entry ledger manager that stores financial transactions in human-readable text files. It functions as a financial reporting engine for generating balance sheets and income statements, and as a multi-currency investment tracker for managing commodity lots and capital gains. The project distinguishes itself by providing multi-interface data access, allowing users to interact with their financial data via a command line interface, a terminal user interface, and a web server. It features a market-price valuation system to calculate the current va