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AdvancedPortfolio ManagementPython

Run this module

cd "Portfolio Management"
python "portfolio_utils.py"

View source on GitHub

Portfolio Management Utilities

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

Available Utilities

Portfolio Management (portfolio_utils.py)

  • Portfolio valuation and allocation analysis
  • Rebalancing calculations
  • Diversification metrics
  • Position sizing
  • Portfolio turnover analysis

Risk Analysis (risk_utils.py)

  • Value at Risk (VaR) calculations
  • Maximum drawdown analysis
  • Volatility calculations
  • Sharpe and Sortino ratios
  • Correlation analysis
  • Stress testing

Usage

# Portfolio operations
from portfolio_utils import calculate_portfolio_value, rebalance_portfolio
from risk_utils import calculate_var, calculate_sharpe_ratio, calculate_max_drawdown

# Portfolio analysis
portfolio_value = calculate_portfolio_value(holdings, prices)
allocation = calculate_portfolio_allocation(holdings, prices)

# Risk management
var_95 = calculate_var(returns, 0.95)
sharpe = calculate_sharpe_ratio(returns)
max_dd = calculate_max_drawdown(prices)

# Rebalancing
trades = rebalance_portfolio(target_allocation, holdings, prices, portfolio_value)

Installation

Requires numpy for statistical calculations:

pip install numpy

Testing

Run each utility directly to see demonstrations:

python portfolio_utils.py
python risk_utils.py

Common Use Cases

  • Portfolio Analysis: Evaluate portfolio performance and risk
  • Risk Management: Calculate risk metrics and set risk limits
  • Rebalancing: Maintain target asset allocation
  • Performance Tracking: Monitor portfolio returns and metrics
  • Investment Optimization: Optimize portfolio composition
  • Compliance: Generate risk reports for regulatory requirements

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 - 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 Optimizer

    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).

  • 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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