1.9 KiB
| name | description |
|---|---|
| dgx-spark-portfolio-optimization | GPU-Accelerated portfolio optimization using cuOpt and cuML — on NVIDIA DGX Spark. Use when setting up portfolio-optimization on Spark hardware. |
Portfolio Optimization
GPU-Accelerated portfolio optimization using cuOpt and cuML
This playbook demonstrates an end-to-end GPU-accelerated workflow using NVIDIA cuOpt and NVIDIA cuML to solve large-scale portfolio optimization problems, using the Mean-CVaR (Conditional Value-at-Risk) model, in near real-time.
Portfolio Optimization (PO) involves solving high-dimensional, non-linear numerical optimization problems to balance risk and return. Modern portfolios often contain thousands of assets, making traditional CPU-based solvers too slow for advanced workflows. By moving the computational heavy lifting to the GPU, this solution dramatically reduces computation time.
Outcome: You will implement a pipeline that provides tools for performance evaluation, strategy backtesting, benchmarking, and visualization. The workflow includes:
- GPU-Accelerated Optimization: Leveraging NVIDIA cuOpt LP/MILP solvers
- Data-Driven Risk Modeling: Implementing CVaR as a scenario-based risk measure that models tail risks without making assumptions about asset return distributions.
- Scenario Generation: Using GPU-accelerated Kernel Density Estimation (KDE) via NVIDIA cuML to model return distributions.
- Real-World Constraint Management: Implementing constraints including concentration limits, leverage constraints, turnover limits, and cardinality constraints.
- Comprehensive Backtesting: Evaluating portfolio performance with specific tools for testing rebalancing strategies.
Full playbook: /home/runner/work/dgx-spark-playbooks/dgx-spark-playbooks/nvidia/portfolio-optimization/README.md