<aside> 💡 Tip: This assignment is designed to help you apply basic data analysis techniques to financial time series data. Focus on understanding the data and practicing your Python and Pandas skills rather than trying to predict stock movements. Try using minimal LLMs to ensure proper understanding.

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Tasks

1. Data Collection and Preparation

• Choose a stock from a major exchange (e.g., NYSE, NASDAQ).
• Download at least 5 years of historical daily price data for your chosen stock from Yahoo Finance.
• Load the data into a Pandas DataFrame.

2. Exploratory Data Analysis (EDA)

• Inspect the data for missing values, data types, and basic statistics.
• Create a line plot of the closing prices over time.
• Plot the trading volume over time.
• Calculate and plot a 30-day moving average for the closing price.

3. Time Series Analysis

• Identify any visible trends or patterns in the stock price and volume.
• Check for seasonality by comparing yearly data.
• Calculate the percentage change in closing price day-over-day.

4. Reporting

• Write a brief report (100-200 words) summarizing your findings from the EDA and time series analysis. Write this report either in the .ipynb file.
• Include your observations about trends, seasonality, and any other interesting patterns you've noticed.

5. Code Submission

• Prepare a Jupyter notebook or Python script with your code, including comments explaining each step.