domains / families / business / portfolio-optimization

portfolio-optimization

separate

Use when a user asks to build, optimize, backtest, rebalance, or analyze a stock portfolio with Mean-CVaR, Mean-Variance/SOCP variance caps, efficient frontiers, scenario generation, or NVIDIA cuOpt.

When to use

• Build and analyze quantitative portfolios with NVIDIA-accelerated Mean-CVaR and Mean-Variance optimization. Use the `portfolio_optimization` package to compute returns, generate KDE scenarios for CVaR, solve variance-cap Markowitz allocati…
• Build or optimize a Mean-CVaR portfolio from stock prices.
• Allocate weights across tickers while controlling downside CVaR risk.
• Solve Mean-Variance or Markowitz allocations with a hard variance or volatility cap using cuOpt SOCP/QCQP support.
• Plot or inspect an efficient frontier for a portfolio universe.
• Produce a weights-by-risk-aversion table.
• Backtest an optimized portfolio against benchmarks.
• Rebalance a portfolio on a schedule or drift trigger.

When not to use

• Requires an NVIDIA GPU with cuOpt and cuML; CPU solvers are intentionally disallowed.
• Mean-Variance SOCP variance caps require cuOpt QCQP/SOCP support. Use the 26.06 line or newer when installing CUDA extras.
• `cuda13-socp` intentionally installs cuOpt without cuML because `cuml-cu13` 26.06 is not published yet; use it for direct SOCP/QCQP validation, not GPU KDE CVaR workflows.
• Cardinality plus SOCP is treated as unsupported unless the package exposes explicit mixed-integer conic support.
• CPU-only eval containers can still validate routing, data handling, and reporting behavior, but they cannot produce a valid cuOpt solve. In that case, report the missing GPU/cuOpt runtime explicitly.
• Default price data is a historical snapshot and may omit current constituents.
• First-run dataset download depends on network access unless the user supplies a CSV.

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