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Adaptable Individualized Investment Recommendation Engine (AIRE)

Overview

This repository contains the code and documentation for our Bachelor Thesis project, "Adaptable Individualized Investment Recommendation Engine (AIRE)." The project was completed in May 2021 and aimed to design a system that provides personalized stock portfolio recommendations based on user interests and risk tolerance.

Project Details

  • Title: Adaptable Individualized Investment Recommendation Engine (AIRE)
  • Completion Date: May 2021
  • Degree: Bachelor Thesis

Features

  • Utilized clustering techniques to assess user risk tolerance and preferences.
  • Made use of sentimental analysis over twitter to under stock mentality
  • Made a ensemble machine learning model to weigh the individual's user interest.
  • Deployed the entire system as an end-to-end CI/CD pipeline using Jenkins across Amazon EC2 instance.

How It Works

The Personalized Stock Recommender uses clustering algorithms to analyze user-provided data, such as historical investment behavior, financial goals, and risk preferences. Based on this information and daily sentiment of stocks on twitter, the system assesses the user's risk tolerance level and identifies their interests in different types of stocks.

Using the collected data, the recommender system generates a personalized stock portfolio that aligns with the user's risk profile and investment preferences.

Deployment

The entire system has been deployed as an end-to-end CI/CD pipeline, allowing for seamless updates and maintenance. The deployment utilizes Jenkins for continuous integration and continuous deployment (CI/CD) across an Amazon EC2 instance.

Project Structure

The repository is structured as follows:

  1. /.ebextensions -> The '/.ebextensions/00-packages.config' contains configuration files for yum and git.

  2. /static -> The /static directory contains all the assets, additional components like images and fonts and data for the project which supports '/templates'.

  3. /templates -> The '/templates' directory contains HTML pages or frontend related to the project, such as the sign-in, signup, dashboard, user info etc.

  4. /tests

  5. README.md

  6. application.py -> The 'application.py' contains scripts to get the data using twitter API, Cleaning the data, sentiment analysis, recommendation and setup files with variables like API keys or database connections.

  7. forest.txt

  8. requirement.txt -> The 'requirement.txt' includes all the necessary packages used in this project.

Getting Started

Will be updated

Feedback and Contributions

Feedback and contributions are welcome! If you have any suggestions, improvements, or bug reports, please open an issue or submit a pull request.

Thank you for your interest in the Personalized Stock Recommender project. If you have any questions or need further information, feel free to contact me.

Author: Vishaal Saravanan, Roobesh Balaji & Mohamed Arshad