foureng is a Python library for quantitative finance: Fourier and characteristic-function based option pricing. It exposes unified dispatchers (`price_strip` and `price`) over a registry of characteristic-function models (BSM, Heston, VG, Kou, Bates, CGMY, NIG, 3/2, rough Heston, etc.) and numerical engines (COS, Carr-Madan FFT, FRFT, Lewis, Hilbert, PROJ, CTMC, SINC, SWIFT, contour, plus Monte Carlo baselines). The product layer covers European vanillas, Bermudan/American, barriers, Asians, lookbacks, cliquets, faders, variance/credit instruments, and multi-asset payoffs. It is a library, not a service or CLI: usage is through the Python API and Jupyter notebooks.
Jesse is a mature, full-stack algorithmic trading framework designed for cryptocurrency backtesting, live/paper trading, hyperparameter optimization, Monte Carlo simulation, and rule significance testing. It combines a Python library with a FastAPI web server and a Nuxt.js dashboard, offering a comprehensive environment for developing and validating trading strategies. The repository also includes an MCP (Model Context Protocol) server for AI-assistant integration, over 180 technical indicators (many with Rust-accelerated backends), and a research API for programmatic strategy analysis. Architecture is modular with distinct layers: CLI entrypoint → web controllers / MCP tools → service layer → simulation engine (backtest/optimize/live) → exchange adapters. State management uses global singletons for in-memory simulation state, while persistent data is stored via Peewee ORM (PostgreSQL, optional TimescaleDB). The indicator library showcases a clean pattern with sequential/non-sequential modes and Rust fallback. However, the codebase shows signs of organic growth with some tightly coupled modules (global config, store singletons) and a large static frontend bundle that is opaque to analysis. Key strengths include the breadth of supported exchanges (10+), robust backtesting with realistic fee/margin/leverage modeling, and a well-designed MCP tool layer for AI-assisted development. Weaknesses include exchange driver duplication, a complex Rust subprocess for coordination, and dependency on many external APIs that require ongoing maintenance.
This repository provides a header-only C++ library with Python bindings for solving partial differential equations (PDEs) using Monte Carlo methods, specifically Walk on Spheres (WoS) and Walk on Stars (WoS★) algorithms. It supports Laplace, Poisson, and screened Poisson equations with Dirichlet, Neumann, and Robin boundary conditions in 2D and 3D. The library includes variance reduction techniques (Boundary Value Caching, Reverse Walk on Stars) and handles exterior problems via the Kelvin transform. It is designed for grid-free PDE solving, relying on geometric queries (via FCPW BVH or SDF grids) rather than volume meshing. The codebase is research-oriented, with clear reference implementations and citations to foundational papers, but includes many TODO comments and incomplete optimizations. The architecture is modular with template-based dimension and value type abstraction, making it adaptable but also complex. Demo applications in C++ and Python illustrate usage on model problems with JSON configuration and image output.