Projects sharing features with FundamentalAnalysis

This project is a quantitative finance library providing implementations of numerical methods for financial engineering. It focuses on derivative pricing, portfolio optimization, stochastic simulation, and volatility calibration. The library includes tools for calculating option values using Monte Carlo simulations, binomial trees, and Fourier inversion. It provides a framework for fitting volatility smiles to market data and a simulation engine for generating asset price paths via geometric Brownian motion and jump-diffusion models. The codebase covers broader numerical analysis capabilitie

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

This project is a comprehensive market data toolkit and financial analysis system specifically designed for China A-shares. It serves as a data pipeline for retrieving real-time quotes, aggregating corporate financial statements, and automating equity research. The system distinguishes itself through specialized monitors for institutional capital movements, including Northbound fund flows, margin trading balances, and large block transactions. It also features a dedicated options Greeks calculator for ETF derivatives and tools to gauge market sentiment via retail popularity rankings and trend

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

tqsdk-python is a quantitative trading SDK and framework designed for developing automated strategies for futures, options, and stocks using Python. It functions as an algorithmic trading engine and financial market data API, providing the tools necessary to backtest strategies, analyze historical data, and execute live trades across multiple brokerage accounts. The project distinguishes itself through a specialized option analytics library that calculates Greeks, implied volatility, and volatility surfaces using the Black-Scholes model. It further supports complex order execution patterns, s

pyalgotrade is a Python algorithmic trading library designed for developing, backtesting, and executing automated trading strategies. It provides a comprehensive framework for financial strategy backtesting, a technical analysis library for computing mathematical indicators, and connectors for cryptocurrency exchange integration. The project distinguishes itself by supporting sentiment-based trading through the integration of real-time social media feeds and keyword streams. It features a quantitative trading visualization tool for plotting price action and portfolio equity curves, along with

This is a quantitative finance library built on TensorFlow for financial engineering, asset pricing, and risk management. It serves as a financial derivative pricing engine, a model calibration tool, and a hardware-accelerated math library for numerical tasks. The library provides specialized capabilities for pricing financial assets using standard models and American option logic, as well as calibrating pricing models to market data through local volatility. It includes tools for constructing yield curves via bootstrapping algorithms and monotone convex interpolation. The framework covers a

FinanceDatabase is a system of data repositories and interfaces providing a corporate fundamental database, a financial market data API, and an SEC filings aggregator. It functions as a financial valuation engine and a macroeconomic indicator feed, offering a programmatic way to access market quotes, corporate fundamentals, and official regulatory disclosures. The project distinguishes itself through an institutional ownership tracker that monitors fund holdings, insider trading activity, and political financial disclosures. It also includes a dedicated tool for extracting and analyzing offic

LLM-Trading-Lab is a trading framework designed to execute equity trades and manage portfolios using large language models while adhering to strict investment constraints. The system distinguishes itself by integrating an algorithmic trading auditor that logs the reasoning behind model-driven decisions for retrospective analysis. It also includes a quantitative research reporter that transforms experimental results into portable reports and weekly summaries for long-term archiving. The framework covers several core functional areas, including automated risk management to enforce stop-loss ac

QuantStats is an open-source Python library that calculates risk and return metrics from a portfolio return series and generates comprehensive HTML tear sheets. It computes dozens of financial statistics—including Sharpe ratio, drawdown, and volatility—in a single pass over the input data, using vectorized pandas operations for efficiency. The library distinguishes itself by combining portfolio performance analysis with Monte Carlo simulation, which models thousands of random return paths to estimate the probability of reaching financial targets or hitting loss thresholds. It produces self-co

Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading strategies. It provides a comprehensive environment for quantitative finance, allowing users to simulate trading logic against historical market data or connect directly to brokerage platforms for automated real-time trading. The project distinguishes itself through a unified event-driven architecture that treats backtesting and live trading with the same API. This consistency is supported by a flexible data-feed abstraction layer that normalizes diverse financial sources, ena

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

Sequoia-X is a quantitative stock screening system designed to scan financial markets for stocks that match specific technical patterns and quantitative criteria after the trading day ends. It functions as a technical analysis scanner that automatically detects price breakouts and volume spikes using historical and daily market data. The system integrates a market data cache to fetch public stock API data into a local database for faster retrieval and analysis. It further operates as a Feishu notification bot, utilizing a webhook-based alerting mechanism to push filtered stock selection resul

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

PyPortfolioOpt is a Python library for financial portfolio optimization that implements mean-variance optimization, Black-Litterman models, and Hierarchical Risk Parity methods. It provides a complete toolkit for constructing risk-adjusted asset portfolios by combining expected return estimation, covariance modeling, constraint handling, and discrete allocation into a single optimization framework. The library distinguishes itself through its integration of multiple optimization approaches within a unified interface. It includes a Black-Litterman Bayesian framework that blends market equilibr

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

Zenbot is an automated cryptocurrency trading bot designed to execute trades on exchanges based on technical analysis and predefined risk parameters. It functions as a technical analysis engine that processes market data through mathematical indicators to generate actionable trade signals. The system includes a genetic algorithm strategy optimizer to automatically discover the most profitable parameter configurations. It provides multiple simulation environments, including a trading strategy backtester for replaying historical data and a paper trading simulator for testing strategies against

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

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

This project provides technical documentation and reference guides for spot trading, including specifications for REST, WebSocket, and FIX protocols. It serves as a comprehensive resource for integrating with spot trading endpoints to execute trades, query account data, and fetch market statistics. The project distinguishes itself by supporting institutional-grade connectivity through the Financial Information eXchange standard and simple binary encoding to reduce latency and payload size. It also includes a dedicated sandbox environment for validating trading logic and strategies without fin

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

Rotki is a local-first financial management suite designed for cryptocurrency portfolio tracking, tax accounting, and decentralized finance analytics. It functions as a self-hosted application that secures all financial records, transaction history, and user credentials within an encrypted local database, ensuring that sensitive data remains entirely under user control. The platform distinguishes itself through a privacy-preserving architecture that eliminates reliance on centralized cloud storage or third-party data providers. Users maintain full control over their connectivity by configurin

IronCalc is an XLSX spreadsheet engine and formula evaluator designed to compute numerical expressions and manage workbook structures. It utilizes a logic engine compatible with industry standards to evaluate formulas and manage cell dependencies. The project provides a comprehensive suite of specialized toolkits, including a financial calculation library for bond pricing and net present value, and an engineering math toolkit for complex number arithmetic and Bessel functions. It also features a web-based spreadsheet interface for creating and formatting workbooks. The engine covers a broad

This project is a Python quantitative finance library designed for gathering, manipulating, and analyzing stock market data. It provides a suite of tools for quantitative stock analysis, including an equity screening framework for filtering stocks based on technical and fundamental criteria. The library features a machine learning price predictor for classifying stock movements and forecasting future price directions. It also includes a financial technical analysis tool to calculate indicators such as Bollinger Bands, RSI, and MACD, alongside an algorithmic trading simulator for testing portf

TradeMaster is a reinforcement learning trading framework and algorithmic trading simulator designed for designing and testing quantitative trading strategies. The system provides a platform for developing reinforcement learning agents, managing quantitative portfolios, and optimizing trade execution using financial market data. The project features specialized components for multi-modality data preprocessing, a high-fidelity market environment simulation for strategy backtesting, and a quantitative portfolio manager for capital reallocation across multiple assets. It includes a trade executi

QuantLib is a quantitative finance library and analysis engine built in C++ for executing complex financial calculations and simulations. It serves as a framework for quantitative finance modeling and trading risk management, providing the tools necessary to calculate fair values and risk metrics for diverse financial assets. The project focuses on financial instrument modeling and the evaluation of potential losses and exposure levels to inform portfolio management decisions. It provides a system for modeling financial instruments and managing trading risk through quantitative mathematical m

This project is a Python wrapper for the TA-Lib C library, serving as a financial technical analysis library and quantitative trading tool. It provides a collection of mathematical functions designed to analyze market price movements, identify trading signals, and recognize candlestick patterns within financial data. The library focuses on the computation of trend, momentum, and volume metrics. It includes specialized tools for candlestick pattern recognition to detect recurring price action shapes in both historical and real-time data. The system integrates with NumPy arrays to process cont