| name | riskfolio-lib |
| description | 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. |
| tags | ["portfolio-optimization","risk-parity","cvar","factor-models","risk-management","quant-finance","zorai"] |
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")
w = port.optimization(model="Classic", rm="MV", obj="Sharpe", hist=True)
print("Optimal weights:", w.to_dict())
w_rp = port.optimization(model="Classic", rm="MV", obj="MinRisk", hist=True)
References
