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QuantResearch is a quantitative research framework and specialized toolkit for algorithmic simulation, financial time-series analysis, and systematic trading. It provides an event-driven backtesting environment for validating strategies against historical tick and bar data, alongside a dedicated portfolio optimization engine for calculating asset weights and risk metrics. The project distinguishes itself through a machine learning finance toolkit that implements recurrent neural networks for price prediction and reinforcement learning for derivative pricing. It also features advanced statisti
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
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
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.
Risk tools for commodities trading and finance
The main features of bbcho/risktools-dev are: Portfolio Optimization.
Projects with overlapping indexed features include: letianzj/quantresearch — QuantResearch is a quantitative research framework and specialized toolkit for algorithmic simulation, financial… pyportfolio/pyportfolioopt — PyPortfolioOpt is a Python library for financial portfolio optimization that implements mean-variance optimization,… cantaro86/financial-models-numerical-methods — This project is a quantitative finance library providing implementations of numerical methods for financial… cvxpy/cvxpy — CVXPY is a Python-embedded domain-specific language for modeling and solving convex optimization problems using… goldmansachs/gs-quant — gs-quant is a quantitative finance library and financial data analytics toolkit. It serves as a framework for… braverock/performanceanalytics — Status](https://travis-ci.org/braverock/PerformanceAnalytics.svg?branch=master)](https://travis-ci.org/braverock/Perfor…