Implement simulation-based portfolio optimizer in Python

Q: Implement simulation-based portfolio optimizer in Python

This question evaluates skills in simulation-based portfolio optimization, numerical and statistical computation with NumPy/Pandas, robust time-series data handling, constrained random weight generation, and reproducible experiments.

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Question

Given a pandas DataFrame 'returns' of daily asset returns (index: dates; columns: tickers) and an annualized risk‑free rate r_f, implement a simulation‑based portfolio optimizer in Python:

Generate N random long‑only portfolios (weights ≥ 0, sum to 1), with an optional max‑weight constraint per asset and a random seed for reproducibility.
For each portfolio, compute annualized expected return, annualized volatility, and Sharpe ratio; handle missing values and differing asset histories robustly.
Identify the portfolio with the highest Sharpe and return: best weights, expected return, volatility, Sharpe; also return a DataFrame of all simulations sorted by Sharpe.
Use NumPy/Pandas vectorization (avoid Python loops where possible) and include clear function signatures, docstrings, and brief time/space complexity notes.

Implement simulation-based portfolio optimizer in Python

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