cufolio

What problem does it solve?

The cufolio skill solves the problem of efficiently building, optimizing, backtesting, rebalancing, or analyzing stock portfolios using GPU-accelerated Mean-CVaR and Mean-Variance optimization.

Core Features & Use Cases

• GPU-accelerated Mean-CVaR: Compute returns, generate KDE scenarios for CVaR, and solve complex optimization problems with cuOpt GPU solver.
• Mean-Variance Optimization: Solve variance-cap Markowitz allocations as SOCP/QCQP problems with the cuOpt GPU solver.
• Efficient Frontier: Plot or inspect an efficient frontier for a portfolio universe.
• Backtesting & Rebalancing: Rigorously test the trading strategies and provide insights into strategy fine-tuning, including backtesting against benchmarks and rebalancing workflows.
• Use Case: Imagine you need to optimize a stock portfolio with a specific risk aversion level and CVaR constraint. Use cufolio to compute returns, generate scenarios for CVaR, and solve the optimization problem with cuOpt.

Quick Start

Use the cufolio skill to build and backtest an optimized portfolio based on the S&P 500 dataset with a risk aversion level of 1.0 and a confidence level of 0.95.