Financial Analyst Assistant Portal

The Financial Analyst Assistant Portal is a robust tool designed for equity research analysts to enhance their workflow by analyzing annual reports, answering questions using NLP and Deep Learning, and fetching the latest financial news. This platform aims to streamline financial analysis and decision-making processes.

Table of Contents

• Features
• Tech Stack
• Installation
• Usage
• Project Structure
• Contributing
• License
• Contributors
• Contact

Features

• Document Upload: Easily upload and manage company annual reports.
• Q&A Platform: Get answers to specific queries related to uploaded documents using NLP.
• Data Extraction: Extract financial data using Python libraries like Regex and Pandas.
• News Fetching: Retrieve the latest financial news through web scraping techniques.
• Dynamic PDF Access: Access PDFs dynamically from notebooks for quick reference.
• User Authentication: Secure login and profile management for users.

Tech Stack

• Frontend: HTML, CSS, JavaScript, Bootstrap
• Backend: Flask (Python)
• Database: SQL / phpMyAdmin
• NLP Library: NLTK
• Machine Learning: Deep Learning models for question-answering
• Other Libraries: Pandas, Regex

Installation

Follow these steps to set up the project on your local machine:

1. Clone the repository:

   git clone https://github.com/deepkorat/financial_analyst_assistant_portal.git
   cd financial_analyst_assistant_portal

2. Create a virtual environment:

   python -m venv env
   source env/bin/activate  # On Windows use `env\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the application:

   flask run

5. Access the app:

   Open your browser and navigate to http://127.0.0.1:5000/.

Usage

1. Upload Documents: Log in and upload financial documents you want to analyze.
2. Ask Questions: Use the Q&A section to ask specific questions about the uploaded reports.
3. View News: Check the latest financial news directly within the portal.

Project Structure

• app/ - Contains the main application logic, routes, and views.
• notebooks/ - Jupyter notebooks for code trials and data analysis.
• static/ - Stores static files such as CSS, JavaScript, and images.
• temp/ - Temporary files.
• uploads/ - Stores uploaded files.

Contributing

Contributions are welcome! If you have suggestions or improvements, please:

1. Fork the repository.

2. Create a new branch:

   git checkout -b feature-branch

3. Make your changes and commit them:

   git commit -m 'Add new feature'

4. Push to the branch:

   git push origin feature-branch

5. Open a pull request.

License

This project is licensed.

Contributors

Contact

For questions or suggestions, please contact:

• Deep Korat - Email

• Ajay Tingare - Email