Financial Data Transformation with Python Automation - Google Colab

Here we delve into data parsing, web scraping, and in-depth financial evaluation, using Python automation tools.

Google Colab:
Accompanying Google Colab can be accessed here:

In this tutorial, we guide you through the step-by-step process of importing the FT bank fines dataset into Google Colab and parsing same using the pandas and automating analysis with yfinance, yahooFinancials, Beautiful Soup, requests and Plotly python libraries.

Introduction to Pandas

Introduction to Python Visualization

Here, we set out how to deploy in Google Colab web scraping, where we harness the potential of Beautiful Soup and Requests. We extract some curated data from Wikipedia and the Federal Reserve, streamlining the data retrieval process. By applying automation, we gain access to critical insights for our analysis in a timely fashion. This introduces some key efficiencies in terms of retrieving tables and lists of top banks by assets available from credible sources.

We also employ the yfinance and yahoofinancials Python libraries alongside pandas. This helps produce streamlined extraction and analysis of financial data from Yahoo Finance, and this permits analysts to automate standard Financial/Accounting ratio analysis. These insights help to furnish a comprehensive overview of the financial health and performance of major US banks - the used case set out here.

Data visualization tools like Plotly can be deployed in creating dynamic charts that illustrate stock price trends in an interactive fashion. Here we track the stock price time series over past two decades for leading US banks. From this, analysts can conceivable discern patterns and correlations that offer deeper insights into market dynamics and help formulate potential hypotheses as to what impacts bank fines exerted on banks in the US.

In the analysis we observe the cumulative returns over a 20-year period for the same banks. while considering the potential implications of financial penalties on stock market performance.

Overall Benefits of Automation
Automating data extraction with pandas, Beautiful Soup, and Requests offers several advantages:

Efficiency: Automation eliminates the need for manual data extraction, saving time and effort.

Consistency: The process ensures consistent data retrieval, reducing human error.

Scalability: The same script can be used to scrape multiple webpages, enabling scalability.

Repeatability: Automating the process allows for repeatable and reproducible results.