Lending Club Optimizer

Predicts the default rate of loans and automatically order the model's favorite currently available loans. Likely to increase ROI from 7.5% (Lending Club Average) to 13.5%. This project identifies investment stratagies that fit custom investment profiles. The user specifies the amount of capital she is willing to invest and either her desired expected return or her maximum risk tolerance.

Overview

• The app directory has two major entry points.
  • execute_orders.py will order the model's favorite loans. process_api supports this process.
  • main.py renders a webpage that allows the user to view currently available loans and its expected rate of return.

• scripts contains code used in developing the model
• notebooks contains Jupyter Notebooks that tell a story of the process I followed

Replication

Getting the data

The full data is available to Lending Club members. The data can be downloaded here. It is critical to first log in as FICO scores are only available once logged in.

Access to the Lending Club API

You need to be registered on the Lending Club site in order to use the lending club API. I recommend that you store the API credentials as an environemntal variable for security reasons. My app expects the name of the environmental variable to be LENDING_CLUB_API.

Starting the dashboard app

bokeh serve --show app

Notes on relative imports

It seems like it is going to be a better idea to handle things

Restructuring this app

The API here was somewhat ill-planned. This should be turned into a library that accesses multiple things and has a nice API that will perform various operations (orders, show current notes, and

Refresh TImes

2AM, 6AM, 10AM and 2 PM