Stock Analysis Notebook

This Jupyter Notebook provides a comprehensive analysis of stock data. It includes various techniques for data processing, visualization, and statistical analysis to help understand stock market trends and performance.

Features:

Data Import and Preprocessing: Load stock data from CSV files, clean and preprocess the data for analysis. Descriptive Statistics: Calculate and display key statistics such as mean, median, standard deviation, etc. Visualization: Generate various plots including line charts, bar charts, histograms, and more to visualize stock performance over time. Correlation Analysis: Analyze the correlation between different stocks and market indices. Technical Indicators: Calculate and visualize common technical indicators like moving averages, Bollinger Bands, and RSI. Portfolio Analysis: Assess the performance of a stock portfolio using metrics such as cumulative returns and Sharpe ratio.

Requirements:

To run this notebook, you will need the following Python libraries:

numpy pandas matplotlib seaborn plotly yfinance