I'd seen Monte Carlo portfolio stuff before but never had to code it under pressure.
Start by clarifying the problem: we need to find the maximum Sharpe ratio portfolio by randomly simulating weight vectors. Outline the steps: generate random weights (e.g., Dirichlet distribution), compute portfolio return and volatility using the provided price-return data, calculate Sharpe ratio (assuming risk-free rate), and track the best weights. Emphasize that this is a Monte Carlo simulation, not an optimization, and discuss trade-offs like computational cost and the possibility of using more efficient methods.
Pro tip: Mention that while random simulation is simple, it may not find the true optimum; you could use optimization techniques like quadratic programming or gradient-based methods for better results. Also, note that the Sharpe ratio calculation should use the sample mean and covariance of returns, and consider annualization if needed.
Confirm the DataFrame structure (e.g., rows as time steps, columns as assets) and whether returns are already computed. Ask about risk-free rate and if short-selling is allowed.
Simulate random weight vectors that sum to 1 (e.g., using Dirichlet distribution or normalizing random numbers). Ensure weights are non-negative if long-only.
For each weight vector, calculate expected return (weighted average of asset means) and volatility (sqrt of w^T Σ w, where Σ is covariance matrix). Then compute Sharpe ratio = (return - risk_free) / volatility.
Keep track of the weight vector with the highest Sharpe ratio. After simulation, return that vector.
Acknowledge that random simulation may miss the optimal portfolio and mention optimization methods (e.g., scipy.optimize) or analytical solutions for mean-variance optimization.
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