File | Date | Author | Commit |
---|---|---|---|
.github | 2024-07-26 |
![]() |
[659b6d] Update deploy.yml |
__pycache__ | 2024-07-26 |
![]() |
[c08263] Your descriptive message about the changes |
templates | 2024-07-26 |
![]() |
[c08263] Your descriptive message about the changes |
venv | 2024-07-26 |
![]() |
[921760] Initial commit of Flask application |
README.md | 2024-05-28 |
![]() |
[89f0a8] automated commit by submit50 |
app.py | 2024-05-28 |
![]() |
[4ef239] automated commit by submit50 |
config.ini | 2024-05-28 |
![]() |
[4ef239] automated commit by submit50 |
mydatabase.db | 2024-05-28 |
![]() |
[4ef239] automated commit by submit50 |
requirements.txt | 2024-07-26 |
![]() |
[810958] Update torch version to 2.3.0 |
stock_data.db | 2024-05-28 |
![]() |
[4ef239] automated commit by submit50 |
This project is a web application that displays historical performance of prices of two stocks. It fetches stock price data from the Alpha Vantage API (free version), calculates annual growth and returns, and visualizes the results with a graph and table. However, this does not take into account adjustments like dividend and split. The data needed for adjustment we noticed are not available in Alpha Vantage free version of API.
Features:
Select stock exchange (NASDAQ, NYSE, AMEX)
Enter two stock symbols for comparison
Fetch historical stock data using the Alpha Vantage API
Display comparison results through graphs and tables
Fetching and Displaying Data: Upon user submission of stock symbols, the application initiates a process to fetch historical data from the Alpha Vantage API.
The fetched data includes daily stock prices and other relevant information for the specified symbols.
Utilizing the Pandas library, the application organizes the retrieved data into structured dataframes for efficient manipulation and analysis.
The historical data is then displayed on the user interface in graphical and tabular formats for easy interpretation.
Through this seamless process of data retrieval and presentation, users gain insights into the performance of their selected stocks over time.
Incorporating Adjustments: Due to limitations with free API access, adjustments related to dividends and stock splits have not been incorporated. These adjustments are crucial for a more accurate depiction of returns and should be considered for future development to make the application more practical for real-life usage.
Conclusion: With continuous development and innovation, this project aims to provide users with a comprehensive tool for making informed investment decisions in an ever-evolving market landscape, while acknowledging its current status as a learning demonstration rather than a fully practical application.
Key Features:
Files:
app.py
: Contains the main Flask application logic, routing, data fetching, and calculations.index.html
: The main page with the stock symbol input form.result.html
: The page that displays the comparison results (graph, table, etc.).Design Choices:
Potential Improvements:
How to Run:
pip install Flask pandas matplotlib requests
app.py
.flask run
http://127.0.0.1:5000/