riskfolio-lib

What it is and what it does

Riskfolio-Lib is a portfolio optimization library built on CVXPY that lets you construct investment portfolios using mathematically rigorous models without deep solver expertise. It integrates directly with Pandas DataFrames and supports a large range of optimization objectives (minimum risk, maximum return, maximum Sharpe ratio, risk parity) combined with 26+ risk measures spanning dispersion, downside, and drawdown categories. You can apply constraints on leverage, turnover, cardinality, tracking error, and graph-based relationships between assets, and use advanced techniques like hierarchical risk parity, Black-Litterman views, risk factor models, and entropy pooling.

The library is designed for students, academics, and practitioners who need to move from theory to working portfolios quickly. It handles the convex optimization setup internally, letting you focus on portfolio design rather than solver configuration. It includes tools for building efficient frontiers, calculating risk contributions, estimating uncertainty sets, clustering assets by codependence, and generating reports in Jupyter or Excel. You can also plug in commercial solvers (MOSEK, GUROBI) for large-scale problems.

Use it for:

• Build mean-variance efficient frontiers and find optimal allocations for a given risk tolerance or return target.
• Construct risk parity or equal-weight portfolios with constraints on maximum position size, leverage, or sector concentration.
• Incorporate expert views or market factors into portfolio construction using Black-Litterman or risk factor models.
• Optimize hierarchical or nested clustered portfolios to reduce estimation error and improve out-of-sample stability.
• Backtest portfolio rebalancing strategies with turnover and cardinality constraints to manage transaction costs.
• Calculate and visualize risk contributions per asset or risk factor to understand portfolio exposure.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Riskfolio-Lib builds optimized investment portfolios using mathematical models—mean-variance, risk parity, hierarchical clustering, and Black-Litterman approaches—with support for 26+ risk measures and constraints on leverage, turnover, and cardinality.

Yes, if you need to solve real portfolio optimization problems beyond simple equal-weight or market-cap allocation. The library is actively maintained, has no known vulnerabilities, runs on modern Python versions, and offers a mature API covering academic and practitioner use cases. Install friction is moderate due to numerical dependencies, but prebuilt wheels are available. The permissive BSD license poses no compliance risk. Start with the tutorial if you are new to portfolio optimization.

Install

riskfolio-lib on PyPI

pip install riskfolio-lib

uv add riskfolio-lib

poetry add riskfolio-lib

Installing riskfolio-lib

Before you install

Medium install friction: 15 runtime dependencies including numerical (numpy, scipy, pandas), optimization (cvxpy, clarabel, SCS), and statistical libraries (scikit-learn, statsmodels, arch). Prebuilt wheels available for Python 3.10–3.14 on macOS, Linux, and Windows. Active maintenance with recent releases.

License in practice

BSD 3-clause permissive license allows commercial use, modification, and redistribution with minimal restrictions—suitable for both proprietary and open-source projects.

Quickstart

import pandas as pd
import riskfolio as rp

returns = pd.read_csv('returns.csv', index_col=0)
port = rp.Portfolio(returns=returns)
port.optimize('MinRisk')
weights = port.weights

Requires Python >=3.10; cvxpy and its solver dependencies (clarabel or SCS) must resolve successfully; large-scale problems may benefit from commercial solvers (MOSEK, GUROBI) configured separately.

Verify before relying

• Performance characteristics and scalability limits for portfolio sizes beyond typical institutional use.
• Solver selection guidance and trade-offs between clarabel, SCS, and commercial alternatives for specific problem classes.
• Numerical stability and convergence guarantees under extreme market conditions or edge-case covariance matrices.

Package facts

Evidence: riskfolio_lib-7.3.0-cp310-cp310-macosx_10_9_universal2.whl; riskfolio_lib-7.3.0-cp310-cp310-macosx_10_9_x86_64.whl; riskfolio_lib-7.3.0-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; riskfolio_lib-7.3.0-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; riskfolio_lib-7.3.0-cp310-cp310-win_amd64.whl; riskfolio_lib-7.3.0-cp311-cp311-macosx_10_9_universal2.whl; riskfolio_lib-7.3.0-cp311-cp311-macosx_10_9_x86_64.whl; riskfolio_lib-7.3.0-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; riskfolio_lib-7.3.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; riskfolio_lib-7.3.0-cp311-cp311-win_amd64.whl; riskfolio_lib-7.3.0-cp312-cp312-macosx_10_13_universal2.whl; riskfolio_lib-7.3.0-cp312-cp312-macosx_10_13_x86_64.whl; riskfolio_lib-7.3.0-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; riskfolio_lib-7.3.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; riskfolio_lib-7.3.0-cp312-cp312-win_amd64.whl; riskfolio_lib-7.3.0-cp313-cp313-macosx_10_13_universal2.whl; riskfolio_lib-7.3.0-cp313-cp313-macosx_10_13_x86_64.whl; riskfolio_lib-7.3.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; riskfolio_lib-7.3.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; riskfolio_lib-7.3.0-cp313-cp313-win_amd64.whl

Keywords: finance, portfolio, optimization, quant, asset allocation, investing

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