Volatility 3.0 development

A complete set of volatility estimators based on Euan Sinclair's Volatility Trading

AgentEval is the comprehensive .NET toolkit for AI agent evaluation—tool usage validation, RAG quality metrics, stochastic evaluation, and model comparison—built first for Microsoft Agent Framework (MAF) and Microsoft.Extensions.AI. What RAGAS, PromptFoo and DeepEval do for Python, AgentEval does for .NET

PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

Retrieve official Federal Reserve economic series for GDP, inflation, rates, employment, money supply, and volatility research.

High-performance quantitative finance in Rust — 120+ stochastic processes, option pricing, calibration, fixed income, risk & copulas, with SIMD/GPU acceleration and Python bindings.

Screen equities with systematic value, momentum, quality, volatility, size, and growth factor frameworks.

Python implementation of pricing analytics and Monte Carlo simulations for stochastic volatility models including log-normal SV model, Heston

The highfrequency package contains an extensive toolkit for the use of highfrequency financial data in R. It contains functionality to manage, clean and match highfrequency trades and quotes data. Furthermore, it enables users to: calculate easily various liquidity measures, estimate and forecast volatility, and investigate microstructure noise and intraday periodicity.

Volatility trading using Long and Short Straddle options strategies on Interactive Broker using Yahoo Finance and TWS API

Option pricing based on Black-Scholes processes, Monte-Carlo simulations with Geometric Brownian Motion, historical volatility, implied volatility, Greeks hedging

GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management