Projects sharing features with Tf Quant Finance

A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.

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

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

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

gs-quant is a quantitative finance library and financial data analytics toolkit. It serves as a framework for analyzing financial data, developing systematic trading strategies, and managing risk exposure for derivative products in global markets. The project provides tools for quantitative financial analysis, quantitative portfolio modeling, and the development of systematic trading strategies. It enables the calculation of risk for derivative products to structure and hedge positions across markets.

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

This library is a Python-based tool for retrieving historical and real-time financial market data from public sources. It functions as a programmatic interface for downloading stock prices, dividends, financial statements, and corporate calendars, allowing users to perform automated research and analysis on various market assets. The project distinguishes itself by structuring retrieved financial time series directly into tabular data frames, which facilitates mathematical analysis and manipulation of market metrics. It supports efficient data retrieval through multi-threaded batch downloadin

Cython QuantLib wrappers

quant-wiki is a comprehensive knowledge base and structured reference for quantitative finance, financial engineering, and algorithmic trading. It serves as a centralized library of documentation covering mathematical models, financial instruments, and systematic trading strategies. The project integrates AI-driven capabilities through a modular retrieval-augmented generation framework that extracts structured data from research papers and news. It features a multi-agent workflow engine designed to discover and validate predictive alpha factors, alongside tools for local large language model

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 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

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

This project is a numerical computing library designed for scientific and engineering mathematical operations. It functions as a comprehensive linear algebra framework, a statistical analysis library, and a toolkit for mathematical optimization and numerical integration. The library is distinguished by its provider-based native acceleration, which allows managed code to be swapped for platform-native binary libraries to increase the performance of computationally intensive routines. It also supports a hybrid approach to matrix storage, implementing separate strategies for dense and sparse mat

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 a suite of machine learning and statistical tools designed for stock price prediction, financial time series forecasting, and the execution of algorithmic trading strategies. It provides a collection of deep learning and statistical models used to forecast asset prices and market trends. The system includes a market scenario simulator that uses Monte Carlo sampling to generate potential price paths and estimate financial risk. It further features a portfolio optimization tool for calculating asset distributions to maximize returns based on historical volatility, as well as a m

ml5-library is a JavaScript machine learning library that functions as a browser-based inference engine. It provides a high-level wrapper for implementing neural networks and data models, allowing users to execute machine learning predictions directly on the client side. The library simplifies the integration of machine learning into web applications and creative coding projects by removing the requirement for deep mathematical expertise. It specifically enables web-based image classification through the use of pretrained deep learning models to identify and label objects within images. The

ConvNetJS is a JavaScript deep learning library and neural network training engine designed for client-side machine learning. It functions as a framework for building, training, and running convolutional neural networks directly within a web browser without the need for a backend server. The library specializes in image recognition and pattern analysis using convolutional and pooling layers. It enables the creation of models for classification and regression tasks, as well as the development of reinforcement learning agents that optimize behavior through trial and error in simulated environme

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

Zipline is a Python-based algorithmic trading library designed for the development and backtesting of investment strategies. It functions as a quantitative finance engine that processes historical market data to simulate trading interactions and evaluate strategy performance through custom metrics. The platform provides a modular, event-driven framework that manages portfolio state transitions based on time-series data streams. Beyond its core trading capabilities, the system includes a comprehensive financial data analysis toolkit for manipulating large-scale market datasets to support syste

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 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

Portfolio and risk analytics in Python

Riskfolio-Lib is a Python portfolio optimization library and convex risk management tool. It provides a framework for calculating optimal asset allocations using convex risk measures and mathematical programming solvers, supporting linear, quadratic, and semidefinite programming. The library features a hierarchical risk parity framework and financial asset clustering tools to group similar instruments and improve diversification. It includes a portfolio backtesting engine for simulating investment strategies using historical data and cross-validation. The system covers a broad range of quant

Quantlib implementation in pure Julia

GPU-accelerated Factors analysis library and Backtester

Common financial technical indicators implemented in Pandas.