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FinancialPy

FinancialPy is an interactive web application that simulates a trading platform, allowing users to invest in a virtual environment using a paper account. This application is designed to provide an educational and realistic experience, making it an ideal tool for those who want to learn and practice stock market investing.

Features

  • Stock Search & Exploration: Users can search for and explore various stocks.
  • Dedicated Stock Pages: Each stock has a dedicated page displaying:
    • A price trend chart.
    • A table with current prices.
    • Personalized predictions and recommendations from the integrated bot.
  • Virtual Portfolio: Users can track the performance of purchased stocks and view profit margins.
  • Transaction Management:
    • Buy and sell stocks using a virtual balance.
    • The account balance updates automatically after each transaction.

Technologies Used

  • Backend: Flask, SQLite3
  • Frontend: Jinja, HTML, CSS
  • API Integration: AlpacaAPI

How to Use the Application

Setup & Running the Application

  1. Update the path to the virtual environment.
  2. Run the run_all.py script to initialize the database and launch the application.
  3. Access the application at: http://127.0.0.1:5000/

Usage Guide

  1. Search for a Stock: Enter the stock name or symbol in the search bar and press Enter.
  2. View Stock Details: Click the blue View button to access the stock’s dedicated page.
  3. Analyze Data:
    • Examine the price trend chart and current price table.
    • Get stock predictions and recommendations from the bot.
  4. Buy Stocks:
    • Select a quantity (within your available virtual balance).
    • Confirm the purchase to be redirected to your portfolio.
  5. Sell Stocks:
    • Navigate to the portfolio page.
    • Go back to the stock's dedicated page.
    • Use the Sell option under the buy section.
    • Your balance updates automatically after selling.

Contributors

  • Bănilă Rareș - Developed the stock portfolio and backend.
  • Rizea Eduard - Developed the bot and contributed to the database.
  • Manda Ștefan - Implemented the frontend.

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