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.
Les fonctionnalités principales de lballabio/quantlib sont : Quantitative Finance & Trading, Financial Instruments and Pricing, Trading Risk Analysis, Financial Calendar Logic, Financial Instrument Definitions, Quantitative Analysis Engines, Derivative Risk Frameworks, Compile-Time Type Dispatch.
Les alternatives open-source à lballabio/quantlib incluent : goldmansachs/gs-quant — gs-quant is a quantitative finance library and financial data analytics toolkit. It serves as a framework for… llmquant/quant-wiki — quant-wiki is a comprehensive knowledge base and structured reference for quantitative finance, financial engineering,… google/tf-quant-finance — This is a quantitative finance library built on TensorFlow for financial engineering, asset pricing, and risk… mementum/backtrader — Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading… wuye9036/cpptemplatetutorial — This project is a C++ template tutorial and metaprogramming guide. It provides instructional content on using… enthought/pyql — Cython QuantLib wrappers.
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.
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
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
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