Crypto Portfolio Optimization with Python

Running multiple algorithmic trading python strategies across multiple assets creates a portfolio management challenge: how do you allocate capital across them? Modern Portfolio Theory (MPT) and its variants provide a rigorous framework.

The Efficient Frontier

The efficient frontier plots the set of portfolios with the maximum return for a given level of risk. Using Python's scipy.optimize you can find the maximum Sharpe ratio portfolio:

from scipy.optimize import minimize\ndef neg_sharpe(weights):\n    ret = np.dot(weights, mean_returns)\n    std = np.sqrt(weights @ cov_matrix @ weights)\n    return -ret / std\nresult = minimize(neg_sharpe, initial_weights, constraints=constraints)

Risk Parity

Risk parity allocates capital so each asset contributes equally to portfolio volatility. This is more robust than mean-variance optimization in crypto algo trading because it doesn't rely on return estimates, which are notoriously unstable.