actrisk 0.1.0.0

Of the Actuary, By the Actuary, For the Actuary: A Python package for actuarial risk modeling and simulation

ActRisk

A Python package for actuarial risk modeling and simulation.

Features

• Risk Modeling: Advanced tools for actuarial risk analysis
• Monte Carlo Simulations: High-performance simulation capabilities
• Parallel Processing: Optimized for large-scale computations
• Configuration Management: Flexible YAML-based configuration system
• Statistical Analysis: Comprehensive statistical tools for risk assessment

Installation

From PyPI (recommended)

pip install actrisk

From Source

git clone https://github.com/jzhng105/actrisk.git
cd actrisk
pip install -e .

Development Installation

git clone https://github.com/jzhng105/actrisk.git
cd actrisk
pip install -e .[dev]

Quick Start

from actrisk.core import actfitter
from actstats import actuarial as act

# Load configuration
config = Config('config.yaml')

sev_data = act.lognormal(0.5,0.2).rvs(size=10000)

#############################
###### Fit Severity #########
#############################
# User specifies distributions and metrics 
distribution_names = config.distributions['severity']
metrics = config.metrics

sev_fitter = actfitter(sev_data, distributions=distribution_names, metrics=metrics)
sev_fitter.fit()
sev_fitter.best_fits
sev_fitter.selected_fit

Documentation

• User Guide - Getting started and basic usage
• API Reference - Detailed API documentation
• Examples - Code examples and tutorials
• Development - Contributing and development guidelines

Features in Detail

Configuration Management

# Initialize fitter with config file
config = utils.Config('code/config.yaml')

Fit Severity

sev_data = act.lognormal(0.5,0.2).rvs(size=10000)

#############################
###### Fit Severity #########
#############################
# User specifies distributions and metrics 
distribution_names = config.distributions['severity']
metrics = config.metrics

sev_fitter = DistributionFitter(sev_data, distributions=distribution_names, metrics=metrics)
sev_fitter.fit()
sev_fitter.best_fits
sev_fitter.selected_fit
sev_fitter.get_selected_dist()
# Selecting a distribution manually
sev_fitter.select_distribution('uniform')
selected_fit = sev_fitter.selected_fit

print("Selected fitting distribution:", selected_fit['name'])
print("Parameters:", selected_fit['params'])
print("AIC:", selected_fit['aic'])
print("BIC:", selected_fit['bic'])

# Calculating statistics
sev_fitter.calculate_statistics().to_csv('outputs/statistics.csv')

# Plotting predictions
sev_fitter.plot_predictions()

# Produce summary
sev_fitter.summary().to_csv('outputs/summary.csv')

# Generating samples
samples = sev_fitter.sample(size=10)
print("Generated samples:", samples)

samples = sev_fitter.sample_mixed(0.1, 0.1, size=10)

Fit Frequency

#############################
###### Fit frequency ########
#############################
distribution_names = config.distributions['frequency']
metrics = config.metrics

freq_fitter = DistributionFitter(freq_data, distributions=distribution_names, metrics=metrics)
freq_fitter.distributions
freq_fitter.fit()
freq_fitter.best_fits
freq_fitter.selected_fit

Stochastic Simulation

#####################################
###### Stochastic Simulation ########
#####################################
freq_dist = freq_fitter.selected_fit['name']
freq_params = freq_fitter.get_selected_params()
sev_dist = sev_fitter.selected_fit['name']
sev_params = sev_fitter.get_selected_params()


simulator = stk.StochasticSimulator(freq_dist, freq_params, sev_dist, sev_params, 100, True, 1234, 0.6, 'frank', 0.6)
simulator = stk.StochasticSimulator(freq_dist, freq_params, sev_dist, sev_params, 100, True, 1234, 0.6)
simulator = stk.StochasticSimulator(freq_dist, freq_params, sev_dist, sev_params, 100, True, 1234)

simulations = simulator.gen_agg_simulations()
simulator.all_simulations
simulator.calc_agg_percentile(99.2)
simulator.plot_distribution()
simulator.results.mean()
simulator.plot_correlated_variables()
simulator.all_simulations
print(pd.DataFrame(simulator.analyze_results()))

##### Generate correlated mutivariate distribution
corr_matrix_file = 'code/utils/corr_matrix.csv'
dist_list_file = 'code/utils/dist_list.json'
simulator = stk.StochasticSimulator(freq_dist, freq_params, sev_dist, sev_params, 10000, True, 1034, 0.6)
simulator.gen_multivariate_corr_simulations(corr_matrix_file, dist_list_file, True)
simulator._all_simulations_data
data = pd.DataFrame(simulator._all_simulations_data)
data_t = data.transpose()
# Compute correlation matrix
correlation_matrix = data_t.corr()
print(correlation_matrix)

Development

Setting up Development Environment

# Clone the repository
git clone https://github.com/jzhng105/actrisk.git
cd actrisk

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for new functionality
5. Ensure all tests pass (pytest)
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use ActRisk in your research, please cite:

@software{actrisk2025,
  title={ActRisk: A Python package for actuarial risk modeling and simulation},
  author={Juntao Zhang},
  year={2025},
  url={https://github.com/jzhng105/actrisk}
}

Support

• Documentation: docs/
• Issues: GitHub Issues
• Discussions: GitHub Discussions

Changelog

See CHANGELOG.md for a list of changes and version history.