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https://github.com/RatulMaharaj/predictable
A framework for actuarial modelling.
https://github.com/RatulMaharaj/predictable
actuarial cashflows modelling-framework numpy pandas python
Last synced: 3 months ago
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A framework for actuarial modelling.
- Host: GitHub
- URL: https://github.com/RatulMaharaj/predictable
- Owner: RatulMaharaj
- License: mit
- Created: 2022-08-13T22:07:46.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-09-29T02:42:38.000Z (over 1 year ago)
- Last Synced: 2024-12-01T01:50:45.603Z (3 months ago)
- Topics: actuarial, cashflows, modelling-framework, numpy, pandas, python
- Language: Python
- Homepage: https://predictable.readthedocs.io/
- Size: 96.7 KB
- Stars: 5
- Watchers: 2
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: docs/contributing.md
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - RatulMaharaj/predictable - A framework for actuarial modelling. (Python)
README
# Predictable
[](https://github.com/pypa/hatch)
[](https://github.com/charliermarsh/ruff)
[](https://opensource.org/licenses/MIT)
[](https://github.com/RatulMaharaj/predictable/actions/workflows/pytest.yaml)
[](https://github.com/RatulMaharaj/predictable/actions/workflows/build.yaml)
[](https://predictable.readthedocs.io/en/latest/?badge=latest)
## What is it?
A framework for actuarial modelling.
## Installation
```sh
pip install predictable
```
## Quick start example
A `model.py` file will be used to house the modelling logic which will be applied to each modelpoint.
```python
# import the library
from predictable import CashFlow, DiscountFactors, Model, StaticCashFlow
# Create new model instance
model = Model()
# Add a premium component
model.add_component(
CashFlow(
input_array=[100], formula=lambda prev: prev * 1.05, label="premium"
)
)
# Add a sum assured component
model.add_component(CashFlow(label="cover", input_array=[1_000_000]))
# Add an expense component
model.add_component(
StaticCashFlow(
input_array=[10, 10, 10, 10, 10],
label="expense",
)
)
# Add discounting component
model.add_component(DiscountFactors(interest_rate=0.05, label="V"))
# Project cashflows over term
# Results return a pandas df object
df = model.project(term=10)
# Perform linear combination style manipulations
# Discounting the components
components = ["premium", "cover", "expense"]
for component in components:
df[f"V_{component}"] = df[component] * df["V"]
# Define reserving relationship
df["Reserve"] = df["V_cover"] + df["V_expense"] - df["V_premium"]
# Results get returned as a pandas dataframe
print(df)
```
## Documentation
This project is documented using sphinx and the full documentation can be found at [predictable.readthedocs.io](https://predictable.readthedocs.io/en/latest/).
## Development & Contibutions
The following steps can be followed to set up a development environment.
1. Clone the project:
```sh
git clone https://github.com/RatulMaharaj/predictable.git
cd predictable
```
2. Install [hatch](https://hatch.pypa.io/latest/)
```sh
pipx install hatch
```
3. Enter the default environment (this will activate the default virtual environment and install the project in editable mode).
```sh
hatch shell default
```
### Testing
This project uses `pytest` for testing purposes. The tests can be found in the `tests` directory. Tests will run after every commit (locally) and on every push (using github actions) but can also be run manually using:
```sh
hatch run test
```
### Linting
This project is linted using `ruff` and formatted with `black`. The linting and formatting can be run manually using:
```sh
hatch run lint
```
```sh
hatch run format
```
### Editing the docs
The documentation for this project can be found in the `docs` directory. The documentation is created using mkdocs and can be viewed locally using:
```sh
hatch run docs:serve
```
The docs can also be built for deployment using:
```sh
hatch run docs:build
```
## License
[MIT](https://github.com/RatulMaharaj/predictable/blob/main/LICENSE)