riskoptima 1.1.0

The RiskOptima toolkit is a comprehensive Python solution designed to assist investors in evaluating, managing, and optimizing the risk of their investment portfolios. This package implements advanced financial metrics and models to compute key risk indicators, including Value at Risk (VaR), Conditional Value at Risk (CVaR), and volatility assessment

RiskOptima

RiskOptima is a comprehensive Python toolkit for evaluating, managing, and optimizing investment portfolios. This package is designed to empower investors and data scientists by combining financial risk analysis, backtesting, mean-variance optimization, and machine learning capabilities into a single, cohesive package.

Key Features

• Portfolio Optimization: Includes mean-variance optimization, efficient frontier calculation, and maximum Sharpe ratio portfolio construction.
• Risk Management: Compute key financial risk metrics such as Value at Risk (VaR), Conditional Value at Risk (CVaR), volatility, and drawdowns.
• Backtesting Framework: Simulate historical performance of investment strategies and analyze portfolio dynamics over time.
• Machine Learning Integration: Future-ready for implementing machine learning models for predictive analytics and advanced portfolio insights.
• Monte Carlo Simulations: Perform extensive simulations to analyze potential portfolio outcomes.
• Comprehensive Financial Metrics: Calculate returns, Sharpe ratios, covariance matrices, and more.

Installation

pip install riskoptima

Usage

Example 1: Efficient Frontier

from riskoptima import RiskOptima
import pandas as pd

# Download market data
data = RiskOptima.download_data_yfinance(['AAPL', 'MSFT', 'GOOG'], '2022-01-01', '2022-12-31')
daily_returns, cov_matrix = RiskOptima.calculate_statistics(data)

# Calculate Efficient Frontier
mean_returns = daily_returns.mean()
vols, rets, weights = RiskOptima.efficient_frontier(mean_returns, cov_matrix)

# Plot Efficient Frontier
RiskOptima.plot_ef_ax(50, mean_returns, cov_matrix)

Example 2: Monte Carlo Simulation

simulated_portfolios, weights_record = RiskOptima.run_monte_carlo_simulation(daily_returns, cov_matrix)

Example 3: Macaulay Duration

Navigate to -> https://github.com/JordiCorbilla/portfolio_risk_kit/blob/main/portfolio_risk_kit.ipynb

Documentation

For complete documentation and usage examples, visit the GitHub repository:

RiskOptima GitHub

Contributing

We welcome contributions! If you'd like to improve the package or report issues, please visit the GitHub repository.

License

RiskOptima is licensed under the MIT License.