Portfolio Optimization with PyPortfolioOpt

PyPortfolioOpt provides portfolio optimization methods including mean-variance, Black-Litterman, and HRP. ```bash pip install PyPortfolioOpt pandas yfinance

Prepare price data:

```python
import yfinance as yf
tickers = ["AAPL", "MSFT", "GOOGL", "AMZN", "TSLA"]
prices = yf.download(tickers, start="2022-01-01", end="2024-06-01")["Close"]
returns = prices.pct_change().dropna()

Find the optimal risk-return tradeoff using Markowitz optimization. ```python from pypfopt import EfficientFrontier, risk_models, expected_returns mu = expected_returns.mean_historical_return(prices) S = risk_models.sample_cov(prices) ef = EfficientFrontier(mu, S) weights = ef.max_sharpe() cleaned_weights = ef.clean_weights() for ticker, weight in cleaned_weights.items(): if weight > 0.01: print(f"{ticker}: {weight:.4f}") ef.portfolio_performance(verbose=True)

Use `ef.min_volatility()` for minimum variance or `ef.efficient_return(0.2)` for target return.

HRP uses clustering for diversification without matrix inversion. ```python from pypfopt import HRPOpt hrp = HRPOpt(returns) hrp_weights = hrp.optimize() hrp.portfolio_performance(verbose=True) from pypfopt import plotting import matplotlib.pyplot as plt plotting.plot_dendrogram(hrp) plt.show()

HRP produces more stable allocations than mean-variance for large asset universes.