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Stock_Dashboard

Stock Dashboard Using Streamlit in Pyhton

README file for the provided code:

# Stock Dashboard

This Stock Dashboard is a web application built using Streamlit, which allows users to analyze stock data, view pricing movements, fundamental data, news, and technical indicators for a given stock ticker.

## Installation

To run this application, make sure you have the following dependencies installed:

- streamlit
- pandas
- numpy
- yfinance
- plotly.express
- plotly.graph_objects
- alpha_vantage
- stocknews
- pandas_ta

You can install these dependencies by running the following command:

```shell
pip install streamlit pandas numpy yfinance plotly alpha_vantage stocknews pandas_ta

Usage

  1. Run the application by executing the following command in your terminal:
streamlit run stock_dashboard.py
  1. Once the application is running, you will see a sidebar on the left side where you can enter the stock ticker and select the start and end dates for the data.

  2. The dashboard consists of several tabs:

    • Line Chart: Displays the line chart of the adjusted close prices for the given stock ticker.
    • Candle Chart: Displays the candlestick chart for the given stock ticker.
    • Pricing Data: Shows the pricing movements, annual return, standard deviation, and risk-adjusted return for the selected stock.
    • Fundamental Data: Provides the balance sheet, income statement, and cash flow statement for the selected stock.
    • Top 10 News: Displays the top 10 news articles related to the selected stock, including their published date, title, summary, and sentiment.
    • Technical Analysis: Allows users to select and view various technical indicators for the selected stock.
  3. Uncomment the code related to the OpenAI ChatGPT integration if you have a valid session token for the ChatGPT API. This will provide additional information on reasons to buy/sell the stock and SWOT analysis.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please feel free to submit a pull request.

License

This project is licensed under the MIT License.