{"repo":"NVIDIA-AI-Blueprints/portfolio-optimization","free":true,"listed":false,"github":"https://github.com/NVIDIA-AI-Blueprints/portfolio-optimization","clone":"git clone https://github.com/NVIDIA-AI-Blueprints/portfolio-optimization.git","description":"Powered by NVIDIA cuOpt: a GPU-accelerated portfolio optimization toolkit for building, backtesting, and scaling Mean-CVaR and Mean-Variance investment workflows with CUDA-X Data Science.","language":"Jupyter Notebook","stars":477,"topics":["portfolio-optimization","quantitative-finance","algorithmic-trading","cuopt","gpu-acceleration","cufolio"],"license":"Apache-2.0","category":"trading","readme_excerpt":"Portfolio Optimization Powered by NVIDIA cuOpt Disclaimer This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use. --- Overview This portfolio optimization developer example addresses the financial industry's trade-off between computational speed and model complexity . By leveraging NVIDIA accelerated computing — NVIDIA cuOpt for GPU-accelerated portfolio solves, and RAPIDS cuML for GPU scenario generation — this solution transforms robust analysis (e.g., Mean-CVaR, large-scale simulations) from slow batch processing into a fast, iterative workflow for dynamic decision-making. Accelerated Architecture The end-to-end pipeline connects market data ingestion to optimal strategy backtesting using the NVIDIA CUDA ecosystem: 1. Data Science & Scenario Generation Technology: CUDA-X Data Science — RAPIDS cuML for GPU KDE scenario generation Function: Accelerates data preprocessing and the learning/sampling of return distributions. Performance: Achieves speedups of up to 100x when generating scenarios. 2. Mean-CVaR Optimization Technology: NVIDIA cuOpt open-source solvers. Function: Efficiently solves complex, scenario-based Mean-CVaR portfolio optimization problems. Performance: Consistently outperforms state-of-the-art CPU-based solvers, with up to 160x speedups in large-scale problems. 3. Strategy Backtesting & Refinement Technology: CUDA-X Data Science and HPC SDK . Function: Rig","default_branch":null,"files":null,"tree":[],"storefront":"/r/NVIDIA-AI-Blueprints","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/NVIDIA-AI-Blueprints/portfolio-optimization/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}