Metadata-Version: 2.1
Name: riskoptima
Version: 1.17.0
Summary: 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
Home-page: https://github.com/JordiCorbilla/RiskOptima
License: MIT
Keywords: portfolio,risk,optimization,VaR,backtesting,monte-carlo,machine-learning,random-forest,linear-regression,gradient-boosting,mean-variance,black-litterman
Author: Jordi Corbilla
Author-email: jordi.coll.corbilla@gmail.com
Requires-Python: >=3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Dist: matplotlib (>=3.8.4)
Requires-Dist: numpy (>=1.26.4)
Requires-Dist: pandas (>=2.1.4)
Requires-Dist: scikit-learn (>=1.5.1)
Requires-Dist: scipy (>=1.13.1)
Requires-Dist: seaborn (>=0.13.2)
Requires-Dist: squarify (>=0.4.4)
Requires-Dist: statsmodels (>=0.14.2)
Requires-Dist: xgboost (>=2.1.3)
Requires-Dist: yfinance (>=0.2.51)
Project-URL: Repository, https://github.com/JordiCorbilla/RiskOptima
Description-Content-Type: text/markdown

# RiskOptima

![image](https://github.com/user-attachments/assets/b9bc3bd0-d8fa-4f01-97e6-44bf4b886bcb)


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 - Monte Carlo Portfolio Optimization
```python
import pandas as pd
from riskoptima import RiskOptima

# Define your current porfolio with your weights and company names
asset_data = [
    {"Asset": "MO",    "Weight": 0.04, "Label": "Altria Group Inc.",       "MarketCap": 110.0e9},
    {"Asset": "NWN",   "Weight": 0.14, "Label": "Northwest Natural Gas",   "MarketCap": 1.8e9},
    {"Asset": "BKH",   "Weight": 0.01, "Label": "Black Hills Corp.",         "MarketCap": 4.5e9},
    {"Asset": "ED",    "Weight": 0.01, "Label": "Con Edison",                "MarketCap": 30.0e9},
    {"Asset": "PEP",   "Weight": 0.09, "Label": "PepsiCo Inc.",              "MarketCap": 255.0e9},
    {"Asset": "NFG",   "Weight": 0.16, "Label": "National Fuel Gas",         "MarketCap": 5.6e9},
    {"Asset": "KO",    "Weight": 0.06, "Label": "Coca-Cola Company",         "MarketCap": 275.0e9},
    {"Asset": "FRT",   "Weight": 0.28, "Label": "Federal Realty Inv. Trust", "MarketCap": 9.8e9},
    {"Asset": "GPC",   "Weight": 0.16, "Label": "Genuine Parts Co.",         "MarketCap": 25.3e9},
    {"Asset": "MSEX",  "Weight": 0.05, "Label": "Middlesex Water Co.",       "MarketCap": 2.4e9}
]
asset_table = pd.DataFrame(asset_data)

capital = 100_000

asset_table['Portfolio'] = asset_table['Weight'] * capital

start_date = '2024-01-01'
end_date = RiskOptima.get_previous_working_day()

RiskOptima.plot_efficient_frontier_monte_carlo(
    asset_table,
    start_date=start_date,
    end_date=end_date,
    risk_free_rate=0.05,
    num_portfolios=10000,
    market_benchmark='SPY',
    set_ticks=False,
    x_pos_table=1.15,    # Position for the weight table on the plot
    y_pos_table=0.52,    # Position for the weight table on the plot
    title=f'Efficient Frontier - Monte Carlo Simulation {start_date} to {end_date}'
)
```
![efficient_frontier_monter_carlo_20250203_205339](https://github.com/user-attachments/assets/f48f9f44-38cd-4d4c-96f2-48e767d7316e)

### Example 2: Portfolio Optimization using Mean Variance and Machine Learning
```python
RiskOptima.run_portfolio_optimization_mv_ml(
    asset_table=asset_table,
    training_start_date='2022-01-01',
    training_end_date='2023-11-27',
    model_type='Linear Regression',    
    risk_free_rate=0.05,
    num_portfolios=100000,
    market_benchmark=['SPY'],
    max_volatility=0.15,
    min_weight=0.03,
    max_weight=0.2
)
```
![machine_learning_optimization_20250203_210953](https://github.com/user-attachments/assets/0fae24a6-8d1d-45e7-b3d2-16939a1aadf7)

### Example 3: Portfolio Optimization using Probability Analysis
```python
ANALYSIS_START_DATE = RiskOptima.get_previous_year_date(RiskOptima.get_previous_working_day(), 1)
ANALYSIS_END_DATE   = RiskOptima.get_previous_working_day()
BENCHMARK_INDEX     = 'SPY'
RISK_FREE_RATE      = 0.05
NUMBER_OF_WEIGHTS   = 10_000
NUMBER_OF_MC_RUNS   = 1_000

RiskOptima.run_portfolio_probability_analysis(
    asset_table=asset_table,
    analysis_start_date=ANALYSIS_START_DATE,
    analysis_end_date=ANALYSIS_END_DATE,
    benchmark_index=BENCHMARK_INDEX,
    risk_free_rate=RISK_FREE_RATE,
    number_of_portfolio_weights=NUMBER_OF_WEIGHTS,
    trading_days_per_year=RiskOptima.get_trading_days(),
    number_of_monte_carlo_runs=NUMBER_OF_MC_RUNS
)
```
![probability_distributions_of_final_fund_returns20250205_212501](https://github.com/user-attachments/assets/8ea20d1f-e74f-4559-b66f-41ee657dd63b)

### Example 4: Macaulay Duration
```
from riskoptima import RiskOptima
cf = RiskOptima.bond_cash_flows_v2(4, 1000, 0.06, 2)  # 2 years, semi-annual, hence 4 periods
md_2 = RiskOptima.macaulay_duration_v3(cf, 0.05, 2)
md_2
```
![image](https://github.com/user-attachments/assets/8bf54461-7256-4162-9230-f29aeeef4a10)

## 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.

### Support me

<a href="https://www.buymeacoffee.com/jordicorbilla" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a>

