cufolio

# cuFOLIO Skill <!-- SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. SPDX-License-Identifier: Apache-2.0 --> ## Purpose Build and analyze quantitative portfolios with NVIDIA-accelerated Mean-CVaR and Mean-Variance optimization. Use cuFOLIO to compute returns, generate KDE scenarios for CVaR, solve variance-cap Markowitz allocations as SOCP/QCQP problems with the cuOpt GPU solver, trace an efficient frontier, backtest portfolios, and run rebalancing workflows from price data. ## When to Use Use this skill when the task is to: - 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. - Run workflows on an S&P 500, S&P 100, Dow 30, or user-supplied price dataset. Common trigger phrases include "optimize my portfolio", "build a CVaR portfolio", "use cuFOLIO on these tickers", "solve with cuOpt", "plot the efficient frontier", "show weights by risk aversion", "backtest this allocation", "rebalance monthly", "analyze my holdings with CVaR", "compare allocations", "reduce downside risk", "construct an allocation", "assess allocation options", "stress-test my holdings", "evaluate downside-risk exposure", "review my holdings under weight caps", "compare benchmark portfolios", "simulate CVaR scenarios", "screen portfolio risk", "optimize holdings under constraints", "solve a variance-cap portfolio", "use SOCP", "set a volatility cap", and "find a lower-risk allocation". Do not use it for generic finance summaries, price forecasting, neural-network training, vehicle routing, or non-portfolio optimization. ## Prerequisites - Python environment with the installed `cufolio` package. - NVIDIA GPU runtime with cuOpt and cuML installed. Mean-Variance SOCP workflows require a cuOpt build with QCQP/SOCP support, such as the 26.06 line or newer. - CUDA extra matching the host and workflow: `uv sync --extra cuda12` for full cuOpt/cuML 26.06 on CUDA 12, `uv sync --extra cuda13` for the current full CUDA 13 stack, or `uv sync --extra cuda13-socp` for CUDA 13 SOCP-only validation with cuOpt 26.06. - `cvxpy` exposing `cp.CUOPT`. - Network access on first run if the default price CSV must be downloaded. ## Setup This skill drives the installed `cufolio` package. A ready environment can come from the Brev launchable or from `NVIDIA-AI-Blueprints/cuFOLIO` after installing the matching CUDA extra. In packaged agent/eval sandboxes, `cufolio` may be available through `PYTHONPATH` rather than as a separately published wheel. Verify the local package with `python -c "import cufolio"` before declaring it missing. Do not `pip install cufolio`, do not reimplement cuFOLIO workflows from scratch, and do not replace the package APIs with generic pandas/scipy/cvxpy portfolio code. For concrete implementation details, use `references/workflows/agent_recipes.md` as the source of truth. It contains exact working shapes for loading prices, preparing returns, solving with cuOpt, building a 25-point frontier, backtesting against equal weight, and calling the rebalancer. The default dataset is `data/stock_data/sp500.csv`. It is gitignored. Before a first-run download, tell the user this fetches public market data through the cuFOLIO/yfinance data helper and ask them to confirm: ```python import cvxpy as cp from cufolio.cvar_parameters import CvarParameters from cufolio.utils import download_data download_data("data/stock_data", datasets=["sp500"]) CVAR_SOLVER_SETTINGS = {"solver": cp.CUOPT, "verbose": False, "solver_method": "PDLP"} cvar_params = CvarParameters( w_min=0.0, w_max=1.0, c_min=0.0, c_max=0.0, risk_aversion=1.0, confidence=0.95, ) ``` ## Instructions Briefly state the defaults being applied before execution, then use these guardrails: 1. Load `data/stock_data/sp500.csv`; if it is missing, ask before downloading `sp500` with `cufolio.utils.download_data`. Do not glob, substitute, or fabricate price data. 2. Validate user CSVs before solving: require a date-like index or first date column, numeric ticker columns, at least 60 rows after date filtering, and at least one requested ticker. If the user gives start/end dates, slice the price DataFrame before returns computation and report the retained date range. Filter tickers on the price DataFrame before returns are computed. `regime_dict` does not take a ticker field. 3. Compute LOG returns with `utils.calculate_returns(...)`. 4. For Mean-CVaR tasks, generate scenarios with `cvar_utils.generate_cvar_data(...)`, KDE, and `KDESettings(device="GPU")`. For Mean-Variance SOCP variance-cap tasks, do not generate CVaR scenarios; use the `returns_dict` directly after LOG return computation. 5. For ordinary Mean-CVaR portfolio requests, define `CvarParameters` with explicit `w_min` and `w_max`, and set `c_min=0.0` and `c_max=0.0` so the result is fully invested instead of 100 percent cash. 6. For variance-cap, volatility-cap, Markowitz, SOCP, or QCQP requests, define `MeanVarianceParameters` with `var_limit` set to a positive variance bound, `c_min=0.0`, `c_max=0.0`, and `L_tar=1.0` for long-only fully invested allocations. If the user gives a volatility cap, square it before assigning `var_limit`. 7. Build `cvar_optimizer.CVaR(returns_dict, cvar_params)` for Mean-CVaR tasks. Build `mean_variance_optimizer.MeanVariance(returns_dict, mean_variance_params, api_settings=ApiSettings(api="cuopt_python"))` for direct cuOpt Mean-Variance SOCP tasks. 8. Solve with NVIDIA cuOpt only. For CVaR, verify `hasattr(cp, "CUOPT")` and `str(cp.CUOPT) in {str(s) for s in cp.installed_solvers()}`, then pass `CVAR_SOLVER_SETTINGS` to every single-shot solve or looped frontier solve. For direct Mean-Variance SOCP, verify the `cuopt` Python package is importable and call the optimizer with `api="cuopt_python"`; cuOpt auto-selects the barrier method for quadratic constraints. Never fall back to CLARABEL, SCS, ECOS, or another CPU solver. If cuOpt is absent, finish validation/setup and report that the GPU/cuOpt runtime is missing instead of fabricating a CPU result. 9. For custom constraints, map user requests to the appropriate parameter model: CVaR risk controls to `CvarParameters`, variance or volatility caps to `MeanVarianceParameters.var_limit`, weight caps to `w_min`/`w_max`, risk appetite to `risk_aversion`, confidence level to `confidence`, and cash allowance to `c_max`. Treat cardinality plus SOCP as unsupported unless the package exposes explicit mixed-integer conic support. 10. If the user omits a benchmark for backtesting, use an equal-weight portfolio over the same tickers. If the user omits a constraint, keep the defaults table values and briefly restate consequential assumptions before solving. 11. Deliver weights sorted by allocation, cash weight, expected return, solver label (`cuOpt GPU`), and the risk metric used: CVaR for Mean-CVaR or realized variance plus `var_limit` for SOCP. Include any requested frontier figure, weights table, backtest metrics, or rebalancing schedule. For tables, include tickers as columns or rows with decimal weights and percentages; for plots, preserve the returned cuFOLIO figure instead of redrawing from scratch. 12. For report-grade answers, include evidence that the requested workflow actually ran. For an efficient frontier, state `len(results_df)` and use the requested `ra_num` (25 unless the user specifies otherwise). For a variance-cap SOCP solve, report `result_row["solver"]`, realized variance, the requested `var_limit`, and confirm realized variance is at or below the cap. For a weights table, expand `results_df["weights"]` into ticker columns and include `cash` plus `risk_aversion`. For a backtest, include `mean portfolio return`, `sharpe`, `sortino`, and `max drawdown` for both optimized and benchmark portfolios. For rebalancing, include `results_dataframe`, `re_optimize_dates`, and the tail of `cumulative_portfolio_value`. ## Canonical Workflow Skeleton Start positive cuFOLIO tasks from this shape and adapt only the requested output. For complete copyable functions, read `references/workflows/agent_recipes.md` before writing custom code. ### Mean-CVaR workflow ```python import cvxpy as cp import pandas as pd from cufolio import backtest, cvar_optimizer, cvar_utils, rebalance, utils from cufolio.cvar_parameters import CvarParameters from cufolio.portfolio import Portfolio from cufolio.settings import KDESettings, ReturnsComputeSettings, ScenarioGenerationSettings if not hasattr(cp, "CUOPT") or str(cp.CUOPT) not in {str(s) for s in cp.installed_solvers()}: raise RuntimeError("cuOpt GPU solver is required; do not substitute a CPU solver.") CVAR_SOLVER_SETTINGS = {"solver": cp.CUOPT, "verbose": False, "solver_method": "PDLP"} prices = utils.get_input_data("data/stock_data/sp500.csv") returns_dict = utils.calculate_returns( prices, regime_dict=None, returns_compute_settings=ReturnsComputeSettings(return_type="LOG"), ) returns_dict = cvar_utils.generate_cvar_data( returns_dict, ScenarioGenerationSettings( fit_type="kde", kde_settings=KDESettings(device="GPU"), ), ) cvar_params = CvarParameters( w_min=0.0, w_max=1.0, c_min=0.0, c_max=0.0, risk_aversion=1.0, confidence=0.95, ) optimizer = cvar_optimizer.CVaR(returns_dict, cvar_params) result, optimal_portfolio = optimizer.solve_optimization_problem( solver_settings=CVAR_SOLVER_SETTINGS, print_results=False, ) ``` ### Mean-Variance SOCP workflow ```python import importlib.util import numpy as np from cufolio import mean_variance_optimizer, utils from cufolio.mean_variance_parameters import MeanVarianceParameters from cufolio.settings import ApiSettings, ReturnsComputeSettings if importlib.util.find_spec("cuopt") is None: raise RuntimeError("cuOpt Python API is required; do not substitute a CPU solver.") prices = utils.get_input_data("data/stock_data/sp500.csv") returns_dict = utils.calculate_returns( prices, regime_dict=None, returns_compute_settings=ReturnsComputeSettings(return_type="LOG"), ) weights = np.ones(len(returns_dict["tickers"])) / len(returns_dict["tickers"]) var_limit = float(weights @ returns_dict["covariance"] @ weights) * 1.05 mean_variance_params = MeanVarianceParameters( w_min=0.0, w_max=1.0, c_min=0.0, c_max=0.0, L_tar=1.0, var_limit=var_limit, ) optimizer = mean_variance_optimizer.MeanVariance( returns_dict, mean_variance_params, api_settings=ApiSettings(api="cuopt_python"), ) result, optimal_portfolio = optimizer.solve_optimization_problem(print_results=False) realized_variance = float( optimal_portfolio.weights @ returns_dict["covariance"] @ optimal_portfolio.weights ) ``` For an efficient frontier or weights table, call: ```python results_df, fig, ax = cvar_utils.create_efficient_frontier( returns_dict, cvar_params, CVAR_SOLVER_SETTINGS, ra_num=25, show_plot=False, show_discretized_portfolios=False, benchmark_portfolios=False, print_portfolio_results=False, ) weights_table = pd.DataFrame(results_df["weights"].tolist(), index=results_df.index) ``` For a benchmark backtest, wrap the solved allocation in `Portfolio(name="cuOpt Optimal", tickers=returns_dict["tickers"], weights=optimal_portfolio.weights, cash=optimal_portfolio.cash)`, create an equal-weight `Portfolio` over the same `returns_dict["tickers"]`, then use `backtest.portfolio_backtester(..., test_method="historical").backtest_against_benchmarks(...)`. The backtester returns `(backtest_results, ax)`. For monthly rebalancing, write the price DataFrame to a CSV path first. Instantiate `rebalance.rebalance_portfolio(dataset_directory=<csv_path>, ...)` with `re_optimize_criteria={"type": "drift_from_optimal", "threshold": 0, "norm": 1}` and call `re_optimize(transaction_cost_factor=..., plot_title="Monthly Rebalancing")`. The rebalancer returns `(results_dataframe, re_optimize_dates, cumulative_portfolio_value)`. ## Data and Defaults | Setting | Default | |---|---| | Dataset | `data/stock_data/sp500.csv` | | Date range | Full available range | | Portfolio type | Long-only | | Max weight | None unless specified | | Risk aversion | `1.0` | | Confidence | `0.95` | | Scenario method | KDE on GPU | | Solver | CVaR: cuOpt GPU with PDLP; Mean-Variance SOCP: direct cuOpt Python API with barrier auto-selected | | Rebalancing | None unless requested | The default S&P 500 file is a historical snapshot and can omit current constituents. User-supplied CSVs should be date-indexed price tables with ticker columns, compatible with `utils.get_input_data`. If requested tickers are absent, drop them, report the omissions, and continue with available columns unless the user explicitly asks you to fetch other data. ## Key APIs Use the package APIs instead of reimplementing portfolio math or simulation loops. cuFOLIO helpers return flat objects: `returns_dict` has keys such as `returns`, `mean`, `covariance`, and `tickers`; do not index it as `returns_dict["regime_1"]`. `solve_optimization_problem(...)` returns `(result_row, portfolio)`, not a nested result dictionary. - Returns: `utils.calculate_returns(input_dataset, regime_dict, returns_compute_settings)`. - Regime filter: `regime_dict` is `None` or `{"name": "...", "range": ("YYYY-MM-DD", "YYYY-MM-DD")}`; it is not keyed by regime name and does not contain tickers. - Scenarios: `cvar_utils.generate_cvar_data(returns_dict, scenario_generation_settings)` for Mean-CVaR only. - CVaR optimizer: `cvar_optimizer.CVaR(returns_dict, cvar_params)`. - Mean-Variance SOCP optimizer: `mean_variance_optimizer.MeanVariance(returns_dict, mean_variance_params, api_settings=ApiSettings(api="cuopt_python"))`. - CVaR solve: `result_row, portfolio = cvar_problem.solve_optimization_problem(solver_settings=CVAR_SOLVER_SETTINGS, print_results=False)`. - SOCP solve: `result_row, portfolio = mean_variance_problem.solve_optimization_problem(print_results=False)`. - Efficient frontier: `cvar_utils.create_efficient_frontier(returns_dict, cvar_params, solver_settings=CVAR_SOLVER_SETTINGS, ra_num=25)`. The returned `results_df` includes metrics, a `weights` dict column, and `cash`. - Portfolio: `Portfolio(name="", tickers=None, weights=None, cash=0.0, time_range=None)`; pass tickers and a flat array-like `weights` aligned to those tickers. - Backtest: create `portfolio.Portfolio` objects for the optimized allocation and each benchmark; for an equal-weight benchmark, use weights of `1 / len(tickers)` and `cash=0.0`, then call `backtest.portfolio_backtester(test_portfolio, returns_dict, risk_free_rate=0.0, test_method="historical", benchmark_portfolios=[...]).backtest_against_benchmarks(...)`. - Rebalance: `rebalance.rebalance_portfolio(...)` requires `dataset_directory` to be a CSV path, not a DataFrame. Call `re_optimize(...)`; it returns `(results_dataframe, re_optimize_dates, cumulative_portfolio_value)`. - Settings models: `ReturnsComputeSettings`, `ScenarioGenerationSettings`, `KDESettings`, `ApiSettings`, `CvarParameters`, and `MeanVarianceParameters`. ## Examples - "Build the optimal portfolio from the S&P 500": load prices, compute LOG returns, generate GPU KDE scenarios, set long-only fully invested `CvarParameters`, solve with cuOpt, and report diversified weights plus return/CVaR. - "Solve a variance-cap portfolio with SOCP": load prices, compute LOG returns, set `MeanVarianceParameters(var_limit=...)`, solve with direct `api="cuopt_python"`, and report expected return, realized variance, `var_limit`, and weights. - "Plot the efficient frontier": call `create_efficient_frontier(...)`, return `results_df`, and show or save the figure as requested. - "Give me weights by risk aversion": expand `results_df["weights"]` into a per-asset table. - "Backtest against equal weight": build the optimized and equal-weight `Portfolio` objects, then use the cuFOLIO backtester and report Sharpe, Sortino, and max drawdown. - "Backtest monthly rebalancing": configure `rebalance_portfolio` with the drift trigger above and run `re_optimize(transaction_cost_factor=...)`. ## Limitations