Metadata-Version: 2.1
Name: pyloans
Version: 0.1.2b0
Summary: A package to generate, analyze and work with simple installment loans.
License: MIT
Author: Sandeep
Author-email: sandeep.kavadi.uc@gmail.com
Requires-Python: >=3.7,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: numpy-financial (>=1.0.0,<2.0.0)
Requires-Dist: pandas (>=1.5.2,<2.0.0)
Description-Content-Type: text/markdown

![tests](https://github.com/sandeepkavadi/pyloans/actions/workflows/test.yml/badge.svg)
![docs build](https://github.com/sandeepkavadi/pyloans/actions/workflows/docs.yml/badge.svg)

# `pyloans`: A simulator for installment based financial obligations

## Introduction

`pyloans` is a python based package to simplify, analyze and work with
installment based loan obligations. The installments are generally a fixed
amount that the borrower pays to the lender at equally spaced intervals
over the life of the loan.

The pyloans package is written with both the borrowers (end-consumer) and
lenders (financial institutions) in mind.

From a borrower's perspective the package offers the following functionality:
1. Original schedule of Cashflows
2. Modified schedule of Cashflows, in case of additional payments or full
   pre-payment
3. Updated date of maturity based on additional payments made
4. Annual Percentage Rate (APR)

[comment]: <> (5. Compare offers and consolidate multiple financial
   obligations)

From a lenders perspective the package offers the following functionality:
1. Weighted average life of a loan (WAL)

[comment]: <> (2. Consolidate multiple loan objects into a portfolio)
[comment]: <> (3. Simulate various loan structure to quantify impact to
lender's profitability)
[comment]: <> (4. Simulate an unsecured lending portfolio by creating multiple
   instances of loan objects with random initial parameters based on
   historical distributions for each parameter.)
[comment]: <> (5. Systematic way to understand portfolio profitability based on
   historical distributions of prepayments, charge-offs and loan structures.)

Please see [Quickstart guide](https://sandeepkavadi.github.io/pyloans/quickstart/) for basic functionality of the
package.

