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pyportfolioopt

Business
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Portfolio optimization library: mean-variance, Black-Litterman, CVaR optimization, risk parity, Hierarchical Risk Parity (HRP), and CLA. Factor models, shrinkage estimators, and ex-ante risk analysis.

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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/pyportfolioopt/SKILL.md

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Overview

PyPortfolioOpt implements mean-variance optimization, Black-Litterman, CVaR optimization, risk parity, Hierarchical Risk Parity (HRP), and CLA. Handles asset allocation with factor models and ex-ante risk decomposition.

Installation

uv pip install PyPortfolioOpt

Max Sharpe Portfolio

import yfinance as yf
from pypfopt import EfficientFrontier, risk_models, expected_returns

prices = yf.download(["AAPL", "MSFT", "GOOGL"], start="2022-01-01")["Close"]
mu = expected_returns.mean_historical_return(prices)
S = risk_models.sample_cov(prices)

ef = EfficientFrontier(mu, S)
weights = ef.max_sharpe()
print(ef.clean_weights())
perf = ef.portfolio_performance()
print(f"Return: {perf[0]:.2%}, Vol: {perf[1]:.2%}, Sharpe: {perf[2]:.2f}")

HRP

from pypfopt import HRPOpt
returns = prices.pct_change().dropna()
hrp = HRPOpt(returns)
weights = hrp.optimize()

References