Time-series Returns Forecasting

Welcome to the returns-forecasting repository! This project is aimed at forecasting timeseries returns for S&P500 stocks and Bloomberg Barclays US Aggregate Bond Index. This README file will guide you through the process of setting up and running the project on your local machine.

Table of Contents

• Pre-requisites
• Installation
• Usage

Pre-requisites

• Python: Make sure you have Python installed on your machine. You can download the latest version of Python from the official Python website: python.org.

• Package Manager: Install a package manager like pip or conda. Pip is the default package manager for Python and is usually pre-installed with Python. Conda is an alternative package manager that is often used for managing Python environments. You can install conda by downloading Anaconda or Miniconda from their respective websites.

• Virtual Environment (optional): It is recommended to set up a virtual environment for your Python project. Virtual environments allow you to isolate project dependencies and avoid conflicts between different projects. You can create a virtual environment using tools like venv, virtualenv, or conda.

Installation

Follow these steps in your terminal/command prompt to install this repo, or you can directly clone using Github Desktop using the below url

1. Clone this repository

git clone https://github.com/george-dominic/returns-forecasting.git

2. Change into the project's directory:

cd returns-forecasting

3. Install the required dependencies by running the following command:

pip install -r requirements.txt

Usage

1. The Jupyter notebooks should be ready to run now, so you can just click and open the .ipynb files in your code editor or jupyterlab
2. Once opened you can hit the Run All button to execute the notebook

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Thank you! 😄