actrisk 0.1.0.2

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 import load_config, DistributionFitter
from actstats import actuarial as act

# Load configuration
config = load_config()

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 = load_config()

Fit Distributions

# ---------------------------------------------
# Import required modules
# ---------------------------------------------
from actrisk import load_config, DistributionFitter
from actstats import actuarial as act

# ---------------------------------------------
# 1. Generate Example Data
# ---------------------------------------------
# Severity data: Using lognormal distribution with mean=0.5 and sigma=0.2
sev_data = act.lognormal(0.5, 0.2).rvs(size=10000)

# Frequency data: Using Poisson distribution with λ=10
freq_data = act.poisson.rvs(10, 1000)

# ---------------------------------------------
# 2. Load Configuration
# ---------------------------------------------
# This loads distribution lists and metrics from the actrisk config file
config = load_config()

# ---------------------------------------------
# 3. Fit Severity Distributions
# ---------------------------------------------
# Get severity distributions and metrics from config
distribution_names = config.distributions['severity']
metrics = config.metrics

# Initialize severity fitter
sev_fitter = DistributionFitter(sev_data, distributions=distribution_names, metrics=metrics)

# Perform fitting
sev_fitter.fit()

# View best fits and selected distribution
print("Best fits:", sev_fitter.best_fits)
print("Selected fit:", sev_fitter.selected_fit)
print("Selected distribution object:", sev_fitter.get_selected_dist())

# Manually selecting a distribution (example: 'uniform')
sev_fitter.select_distribution('uniform')
selected_fit = sev_fitter.selected_fit

# Print details of the selected fit
print("Selected fitting distribution:", selected_fit['name'])
print("Parameters:", selected_fit['params'])
print("AIC:", selected_fit['aic'])
print("BIC:", selected_fit['bic'])

# Calculate statistics for severity
sev_fitter.calculate_statistics()

# Plot predictions
sev_fitter.plot_predictions()

# Print summary report
sev_fitter.summary()

# ---------------------------------------------
# 4. Generate Samples from Severity Fit
# ---------------------------------------------
samples = sev_fitter.sample(size=10)
print("Generated samples:", samples)

# Generate mixed samples (e.g., weighted combinations)
samples = sev_fitter.sample_mixed(0.1, 0.1, size=10)
print("Generated samples:", samples)

# ---------------------------------------------
# 5. Fit Frequency Distributions
# ---------------------------------------------
distribution_names = config.distributions['frequency']
metrics = config.metrics

# Initialize frequency fitter
freq_fitter = DistributionFitter(freq_data, distributions=distribution_names, metrics=metrics)

# Show available frequency distributions
print("Frequency distributions:", freq_fitter.distributions)

# Perform fitting
freq_fitter.fit()

# View best fits and summary
print("Frequency best fits:", freq_fitter.best_fits)
print("Frequency selected fit:", freq_fitter.selected_fit)
freq_fitter.summary()

Stochastic Simulation

#####################################
###### Stochastic Simulation ########
#####################################
# ---------------------------------------------
# 1. Import Required Modules
# ---------------------------------------------
from actrisk import StochasticSimulator
from actstats import actuarial as act

# ---------------------------------------------
# 2. Define Frequency and Severity Distributions
# ---------------------------------------------
# Frequency distribution: Poisson with λ=10
freq_dist = 'poisson'
freq_params = (10,)

# Severity distribution: Lognormal with meanlog=10, sigma=0.5
sev_dist = 'lognormal'
sev_params = (10, 0.5)

# Preview quantile (e.g., 80th percentile of Poisson)
quantile_80 = act.poisson.ppf(0.8, 10)
print("80th percentile of Poisson(10):", quantile_80)

# ---------------------------------------------
# 3. Initialize Simulator with Different Levels of Complexity
# ---------------------------------------------

# With copula and correlation settings
simulator = StochasticSimulator(freq_dist, freq_params, sev_dist, sev_params, 10000, True, 1234, 0.6, 'frank', 0.6)

# Without specifying copula_type and theta (defaults apply)
simulator = StochasticSimulator(freq_dist, freq_params, sev_dist, sev_params, 10000, True, 1234, 0.6)

# Without using copula at all
simulator = StochasticSimulator(freq_dist, freq_params, sev_dist, sev_params, 10000, True, 1234)

# ---------------------------------------------
# 4. Generate Simulated Aggregate Losses
# ---------------------------------------------
simulations = simulator.gen_agg_simulations()

# Access full simulation DataFrame
print("All simulations preview:")
print(simulator.all_simulations.head())

# ---------------------------------------------
# 5. Analyze Simulation Results
# ---------------------------------------------

# Calculate aggregate percentile (e.g., 99.2%)
percentile_99_2 = simulator.calc_agg_percentile(99.2)
print("99.2% Aggregate Loss Percentile:", percentile_99_2)

# Plot loss distribution histogram
simulator.plot_distribution()

# Show simulation mean
print("Mean simulated loss:", simulator.results.mean())

# If copula is used, plot frequency-severity correlation structure
simulator.plot_correlated_variables()

# Summary statistics and shape diagnostics
simulator.analyze_results()

# ---------------------------------------------
# 6. Apply Deductibles and Limits
# ---------------------------------------------
# Apply per occurrence deductible of 1,000
# Occurrence limit of 10,000
# Annual aggregate deductible of 100,000
# Annual aggregate limit of 300,000
gross_loss = simulator.apply_deductible_and_limit(1000, 10000, 100000, 300000)


# Assign processed loss to expected structure for reporting
gross_loss['amount'] = gross_loss['gross_loss']

# Re-analyze results based on capped/layered gross loss
simulator.analyze_results(all_simulations=gross_loss)

# ---------------------------------------------
# 7. Export Simulated Data to CSV
# ---------------------------------------------
simulator.all_simulations

Correlated Mutivariate Distribution Simulation

import pandas as pd
from actrisk import StochasticSimulator

##### Generate correlated mutivariate distribution
corr_matrix_file = 'examples/correlated_sim/corr_matrix.csv'
dist_list_file = 'examples/correlated_sim/dist_list.json'
simulator = StochasticSimulator("normal", [1,0], "normal",[1,0], 100000, True, 1234) # placeholder parameters for the simulator
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)

Synthetic Claim Simulation

##########################################
###### Synthetic Claim Simulation ########
##########################################
import pandas as pd
from actrisk import ClaimSimulator

# Simulate policy characteristics
policies = pd.DataFrame({
    'policy_id': range(1, 101),
    'freq_dist': 'poisson',
    'freq_params': list(zip(np.random.uniform(0.6, 0.8, 100).round(2),)), 
    'sev_dist': 'lognormal',
    'sev_params': list(zip(np.random.uniform(0.8, 1.2, 100).round(2), np.random.uniform(0.3, 0.7, 100).round(2))),
    'start_date': pd.Timestamp('2023-01-01'),
    'end_date': pd.Timestamp('2023-12-31'),
})

# Instantiate the ClaimSimulator with input policies and np random seed 42
claim_sim = ClaimSimulator(policies, 42)

# Access the processed policy DataFrame
claim_sim.policies

# Run the claim simulation (frequency × severity) for all policy groups
claim_sim.simulate_claims()

# Access the resulting simulated claim records
claim_sim.claim_data

# Set parameters for the non-homogeneous Poisson process (NHPP) for date simulation
lambda0 = 10     # Baseline intensity
alpha = 0.5      # Seasonality amplitude
phase = 0        # Phase shift of the seasonality
T = 1            # Duration of the exposure in years

# Simulate claim occurrence dates using a seasonal NHPP
claim_sim.simulate_dates_nhpp(lambda0, alpha, phase, T)

# Shift claim dates so that the simulation aligns with calendar year starting from 2023
start_year = 2023
claim_sim.apply_shifted_dates(start_year)

# Define base loss development factors (LDFs) by development month
base_LDFs = {
    0: 2,     # Initial LDF at 0 months
    3: 1.5,   # LDF at 3 months
    6: 1.2,
    9: 1.1,
    12: 1.05,
    15: 1.02,
    18: 1.00  # Ultimate LDF at 18 months
}

volatility = 0.1      # Standard deviation for stochastic fluctuation in LDFs
tail_factor = 1.0     # No additional tail development (fully developed at 18 months)

# Simulate the claim development triangles based on LDFs and apply stochastic volatility
claim_sim.simulate_claim_development(base_LDFs, volatility, tail_factor)

# Access the simulated claim development triangle or long-format development data
claim_sim.claim_development

# Access updated policies (could include mappings to simulated claims)
claim_sim.policies

# Save the simulated claim development data to a file (replace with actual path)
claim_sim.save_claim_development('sample_file_path')

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.