Downloaded and Analyzed Stock Data with yfinance

📅 2024-01-15 — Session: Downloaded and Analyzed Stock Data with yfinance

🕒 20:50–21:05
🏷️ Labels: Yfinance, Stock Data, Python, Dataframe, Financial Analysis
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The goal of this session was to download, process, and analyze stock data using Python’s yfinance library.

Key Activities

• Downloading Stock Data: Utilized the yfinance library to download the last 60 days of stock data for a list of tickers. The script included error handling and data concatenation.
• Data Concatenation: Combined stock data into a single DataFrame, ensuring robust error handling for any missing data points.
• Fetching and Comparing Prices: Retrieved current stock prices and compared them with historical prices from two months ago, leveraging yfinance.
• Ranking Stock Prices: Implemented a method to rank closing prices within each ticker group using pandas’ groupby and rank functions.

Achievements

• Successfully downloaded and processed stock data, creating a comprehensive DataFrame for analysis.
• Implemented a robust error handling mechanism to manage missing data.
• Developed a method to rank stock prices effectively within the dataset.

Pending Tasks

• Further analysis of ranked stock data to identify trends or insights.
• Integration of additional financial metrics for a more comprehensive analysis.