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riskfolio-lib

Business
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Portfolio risk and optimization: mean-variance, risk parity, CVaR, CDaR, worst-case, and robust optimization. Factor models, Black-Litterman, NCO. Supports plotting and interactive dashboards.

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How to use this skill

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Source SKILL.md: https://github.com/mkurman/zorai/blob/HEAD/skills/scientific-skills/riskfolio-lib/SKILL.md

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Overview

Riskfolio-Lib provides portfolio optimization beyond mean-variance: risk parity, CVaR, CDaR, worst-case, robust optimization, NCO (Network Clustering), and hierarchical methods. Includes factor models, Black-Litterman, and built-in plotting for efficient frontiers.

Installation

uv pip install riskfolio-lib

Mean-Variance Optimization

import riskfolio as rp
import yfinance as yf

prices = yf.download(["AAPL", "MSFT", "GOOGL", "AMZN", "NVDA"], start="2022-01-01")["Close"]
returns = prices.pct_change().dropna()

port = rp.Portfolio(returns=returns)
port.assets_stats(method_mu="hist", method_cov="hist")

# Max Sharpe
w = port.optimization(model="Classic", rm="MV", obj="Sharpe", hist=True)
print("Optimal weights:", w.to_dict())

# Risk parity
w_rp = port.optimization(model="Classic", rm="MV", obj="MinRisk", hist=True)

References