Analyze historical stock market data using pandas
to identify trends and patterns. Use matplotlib
to visualize stock price movements over time, and pandas
to calculate financial metrics such as moving averages, volatility, and returns. You can also explore financial reporting practices by comparing the financial statements of different companies in the same industry.
- Fetch historical stock market data using
yfinance
. - Utilize
pandas
for data manipulation and analysis. - Visualize stock price movements over time using
matplotlib
. - Calculate financial metrics such as moving averages, volatility, and returns.
- Compare financial statements of companies within the same industry.
To get started with the project, follow these steps:
First, clone the repository to your local machine using the following command:
git clone https://github.com/AnujSaha0111/StockVisualization.git
Navigate to the project directory and install the required Python dependencies using the command:
cd StockVisualization
pip install -r requirements.txt
To start the Jupyter Notebook and run the data_analysis.ipynb notebook, use the following command:
jupyter notebook
Once Jupyter opens in your web browser, navigate to the data_analysis.ipynb file and open it. Run the cells to visualize stock data.
Modify the code in data_analysis.ipynb
to analyze data for different time periods or companies.
Experiment with different financial metrics and visualizations to gain insights into stock market trends.
-
I have done my financial reporting in the
Stock_Visualization
file by taking Google, Microsoft, and Apple as my companies for Technology Industries. You can use any company by simply changing the ticker. -
In the
Financial_Reporting
section, you can change the name of the companies whose financial reports you want to analyze. You will need to edit the loading of the financial statement, as different companies have different formats. Get the financial reports of your desired company and experiment with different financial metrics and visualizations to gain insights into stock market trends.