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AdvancedPortfolio ManagementPython

Run this module

cd "Portfolio Optimizer"
python "portfolio_tutorial.py"

View source on GitHub

Portfolio Optimizer (Mean-Variance)

This utility helps you find the best mix of assets for a portfolio, balancing risk and return using the foundation of Modern Portfolio Theory (MPT).

What is Portfolio Optimization?

  • It's choosing how much of each asset (e.g., stock, fund) to hold, to get the highest return for a given risk—or lowest risk for a given return.
  • The mean-variance method by Markowitz is the classic blueprint. It led to a Nobel prize!

What Does This Module Do?

  • Finds the portfolio with the highest Sharpe ratio (best risk-adjusted return)
  • Uses asset expected returns & covariance matrix (how assets move together)

How to Use

  1. Enter expected annual returns as a numpy array (one for each asset).
  2. Enter the covariance matrix (numpy array), describing how assets co-move.
  3. Optionally, add a risk-free rate.
  4. Call mean_variance_optimizer() for weights of the optimal portfolio.

Example

from optimizer import mean_variance_optimizer
import numpy as np
means = np.array([0.08, 0.10, 0.12])
cov = np.array([[0.04, 0.01, 0.01], [0.01, 0.09, 0.02], [0.01, 0.02, 0.16]])
w = mean_variance_optimizer(means, cov, risk_free_rate=0.03)
print('Optimal Weights:', w)

Why It Matters

  • Used by real portfolio managers, CFA takers, bankers, and academics
  • Shows how diversification lowers risk
  • Great stepping stone to deeper finance and investing topics

See other UTILS modules for more finance learning and analysis!

Continue in Portfolio Management

  • Monte Carlo Portfolio Simulator

    This utility helps you forecast possible futures for a portfolio using random simulations—a key idea in finance, risk management, and statistics!

  • Portfolio Management

    This folder contains utilities for portfolio management, risk analysis, and investment optimization.

  • Portfolio Management - Black Litterman

    The Black-Litterman (1990) model addresses the instability of mean-variance optimization by blending market equilibrium returns with investor views using Bayesian updating.

  • Portfolio Management - Risk Parity

    Risk parity builds a portfolio where every asset contributes the same amount of risk to the total — not the same amount of capital. A naive 60/40 stock/bond portfolio is ~90% equity risk despite being only 60% equity capital; risk parity fixes that imbalance.

  • Portfolio Tracker

    This utility uses the yfinance API to fetch current prices automatically. All other calculations and data are managed locally for learning and experimentation.

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