Stock Data Visualization with MongoDB 📈

This repository contains a Python script to fetch stock data from an API and store it in a MongoDB database.
Additionally, it includes Jupyter Notebook files for data analysis and visualization, which will help you explore and understand the stock market trends.

📌 Pre-requisites

Python 3
Jupyter Notebook
MongoDB

You can install Python packages using pip:

pip install -r requirements.txt

Getting Started✨

Clone the repository to your local machine:

git clone https://github.com/yourusername/stock-data-visualization.git
cd stock-data-visualization

Create a MongoDB database to store the stock data. Make sure to update the MongoDB connection settings in the script if necessary.
Run the Python script fetch_stock_data.py to fetch stock data from the API and store it in the MongoDB database:

python fetch_stock_data.py

Data Visualization 📊

Line charts to track stock prices over time :

This chart shows the opening, closing, high, and low prices of a stock over a period of time.
It can be used to identify trends in the stock market.

Candlestick charts for a detailed view of stock price movements :

This chart is similar to a line chart, but it also includes information about the volume of stock traded during each period.
Candlestick charts are often used by technical analysts to identify patterns in stock price movements.

Moving averages to identify trends :

A moving average is a line that is plotted on a stock chart to show the average price of the stock over a period of time.
It can be used to identify trends in the stock market and to generate buy and sell signals.

Volume analysis to determine trading activity :

Volume analysis is the study of the volume of stock traded during a period of time.
It can be used to identify trading activity and to predict future stock price movements.

Feel free to modify and customize these visualizations to suit your needs.

📌 Links to other resources

If you would like to deep dive more, please refer these resources :

A Beginner's Guide to Stock Data Analysis-->https://www.investopedia.com/articles/07/stock-data-analysis.asp
Stock Data Visualization with Python-->https://www.packtpub.com/product/stock-data-visualization-with-python/9781789801747
Jupyter Notebook Tutorial-->https://jupyter.org/tutorial/
Magic of Matplotlib-->https://matplotlib.org/stable/contents.html
NumPy - an essesntial Tool-->https://numpy.org/doc/stable/

Contributing 🚀

If you would like to contribute to this project, please follow these steps:

Fork the repository.
Create a new branch for your changes: git checkout -b feature/your-feature.
Commit your changes: git commit -m 'Add your feature'.
Push the branch to your fork: git push origin feature/your-feature.
Create a pull request.