Welcome! The chainladder package was built to be able to handle all of your actuarial needs in python. It consists of popular actuarial tools, such as triangle data manipulation, link ratios calculation, and IBNR estimates with both deterministic and stochastic models. We build this package so you no longer have to rely on outdated softwares and tools when performing actuarial pricing or reserving indications.
This package strives to be minimalistic in needing its own API. The syntax mimics popular packages pandas for data manipulation and scikit-learn for model construction. An actuary that is already familiar with these tools will be able to pick up this package with ease. You will be able to save your mental energy for actual actuarial work.
Chainladder is built by a group of volunteers, and we need YOUR help!
There are two ways to install the chainladder package, using pip or conda:
- Using pip:
pip install chainladder
- Using conda:
conda install -c conda-forge chainladder
If you would like to try pre-release features, install the package directly from GitHub.
pip install git+https://github.com/casact/chainladder-python/
The package comes pre-loaded with sample insurance datasets that are publicly available. We have also drafted tutorials that use the chainladder package on these datasets to demonstrate some of the commonly used functionalities that the package offers.
Once you have the package installed, we recommend that you follow the starter tutorial and work alongside with the pre-loaded datasets.
Note that a lot of the examples shown might not be applicable in a real world scenario, and is only meant to demonstrate some of the functionalities included in the package. The user should always follow all applicable laws, the Code of Professional Conduct, applicable Actuarial Standards of Practice, and exercise their best actuarial judgement.
Please visit the documentation page for examples, how-tos, and source code documentation.
Do you have a question, a new idea, or a feature request? Join the discussions on GitHub. Your question is more likely to get answered here than on Stack Overflow. We are always happy to answer any usage questions or hear ideas on how to make chainladder better.
We welcome volunteers for all aspects of the project. Whether you are new to actuarial reserving, new to python, or both; feedback, questions, suggestions and, of course, contributions are all welcomed. We can all learn from each other, together.
Check out our contributing guidelines.
This package is released under Mozilla Public License 2.0.