# 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.
## Stats
https://pypistats.org/packages/riskoptima
## 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. See example here https://github.com/JordiCorbilla/efficient-frontier-monte-carlo-portfolio-optimization
- Comprehensive Financial Metrics: Calculate returns, Sharpe ratios, covariance matrices, and more.
## Installation
See the project here: https://pypi.org/project/riskoptima/
```
pip install riskoptima
```
## Usage
Example 1: Efficient Frontier
```python
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
```python
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](https://github.com/JordiCorbilla/RiskOptima)
## 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.
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"description": "# RiskOptima\n\n\n\n\nRiskOptima 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.\n\n## Stats\nhttps://pypistats.org/packages/riskoptima\n\n## Key Features\n\n- Portfolio Optimization: Includes mean-variance optimization, efficient frontier calculation, and maximum Sharpe ratio portfolio construction.\n- Risk Management: Compute key financial risk metrics such as Value at Risk (VaR), Conditional Value at Risk (CVaR), volatility, and drawdowns.\n- Backtesting Framework: Simulate historical performance of investment strategies and analyze portfolio dynamics over time.\n- Machine Learning Integration: Future-ready for implementing machine learning models for predictive analytics and advanced portfolio insights.\n- Monte Carlo Simulations: Perform extensive simulations to analyze potential portfolio outcomes. See example here https://github.com/JordiCorbilla/efficient-frontier-monte-carlo-portfolio-optimization\n- Comprehensive Financial Metrics: Calculate returns, Sharpe ratios, covariance matrices, and more.\n\n## Installation\n\nSee the project here: https://pypi.org/project/riskoptima/\n\n```\npip install riskoptima\n```\n## Usage\n\nExample 1: Efficient Frontier\n```python\nfrom riskoptima import RiskOptima\nimport pandas as pd\n\n# Download market data\ndata = RiskOptima.download_data_yfinance(['AAPL', 'MSFT', 'GOOG'], '2022-01-01', '2022-12-31')\ndaily_returns, cov_matrix = RiskOptima.calculate_statistics(data)\n\n# Calculate Efficient Frontier\nmean_returns = daily_returns.mean()\nvols, rets, weights = RiskOptima.efficient_frontier(mean_returns, cov_matrix)\n\n# Plot Efficient Frontier\nRiskOptima.plot_ef_ax(50, mean_returns, cov_matrix)\n```\nExample 2: Monte Carlo Simulation\n```python\nsimulated_portfolios, weights_record = RiskOptima.run_monte_carlo_simulation(daily_returns, cov_matrix)\n```\n\nExample 3: Macaulay Duration\n```\nNavigate to -> https://github.com/JordiCorbilla/portfolio_risk_kit/blob/main/portfolio_risk_kit.ipynb\n```\n\n## Documentation\n\nFor complete documentation and usage examples, visit the GitHub repository:\n\n[RiskOptima GitHub](https://github.com/JordiCorbilla/RiskOptima)\n\n## Contributing\n\nWe welcome contributions! If you'd like to improve the package or report issues, please visit the GitHub repository.\n\n## License\n\nRiskOptima is licensed under the MIT License.\n\n",
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