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Prepayment-Mortgage-Trading-Analysis-and-Prediction

Welcome to the Mortgage Trading Analysis and Prediction project! This repository contains the code and resources for a comprehensive case study on mortgage trading, designed to help you understand the financial system, sharpen your data modeling, DAX, and financial analysis skills, and experience the dynamic environment of a mortgage trading desk.

Project Overview

As a newly hired junior trader on a trading desk for a mortgage originator, you will:

  • Identify a population of mortgages to trade.
  • Evaluate each mortgage and each bid received from prospective buyers.
  • Execute trades based on your analysis.

This project aims to provide a unique perspective on the banking industry and improve your technical skills in data modeling, DAX, and financial analysis.

Project Structure

The repository is organized as follows:

  • data/: Contains the dataset used for analysis and prediction.
  • notebooks/: Jupyter notebooks with step-by-step analysis and modeling.
  • scripts/: Python scripts for data preprocessing, modeling, and evaluation.
  • reports/: Generated reports and visualizations.
  • README.md: This readme file.

Dataset

The dataset used in this project is a synthetic mortgage dataset. It includes information on mortgage loans and their performance, such as loan amounts, interest rates, borrower credit scores, loan-to-value ratios, remaining terms, and whether the loan has defaulted.

Dataset Information:

  • loan_id: Unique identifier for each loan
  • origination_date: Date when the loan was originated
  • loan_amount: Amount of the loan
  • interest_rate: Interest rate of the loan
  • credit_score: Borrower's credit score at the time of origination
  • loan_to_value: Loan-to-value ratio at the time of origination
  • remaining_term: Remaining term of the loan in months
  • monthly_payment: Monthly payment amount
  • default: Indicator of whether the loan has defaulted (binary: 0 for no default, 1 for default)
  • bid_price: Price bid by prospective buyers (for simulation purposes)

Installation

To run the project locally, follow these steps:

  1. Clone the repository:

    git clone https://github.com/Technocolabs100/Mortgage-Prepayment-Analysis-and-Prediction.git
cd Mortgage-Prepayment-Analysis-and-Prediction

  2. Create and activate a virtual environment:

    python3 -m venv venv
source venv/bin/activate   # On Windows, use `venv\Scripts\activate`

  3. Install the required dependencies:

    pip install -r requirements.txt

Usage

Data Preprocessing

Run the preprocessing script to clean and prepare the data:

python scripts/preprocess_data.py

Exploratory Data Analysis (EDA)

Open the Jupyter notebooks in the notebooks/ directory to perform exploratory data analysis:

jupyter notebook notebooks/eda.ipynb

Modeling and Evaluation

Run the modeling script to train and evaluate the machine learning models:

python scripts/train_model.py

Interactive Dashboard

To explore the interactive dashboard, use a BI tool like Power BI or Tableau and connect it to the processed data. The dashboard will help you visualize the mortgage portfolio, bids, and analysis results.

Reporting

Generate reports and visualizations using the notebooks and scripts provided. The reports will summarize the trading activity, including the mortgages traded, bids received, and financial outcomes.

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your changes.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions or feedback, please contact technocollabs@gmail.com

Enjoy the fast-paced and challenging environment of a mortgage trading desk as you delve into this comprehensive case study!