Stock Market Analysis with Python: Data Collection and Backtesting

Testing your investment ideas should not rely on guesswork. By leveraging Python, you can collect real historical stock data and run simulations to see how your strategies would have performed in the past. This text-based course guides you through the fundamentals of financial data analysis, showing you how to retrieve, clean, and analyze market data step by step. You will learn how to write clean Python code to fetch market data, organize it using modern data libraries, and set up basic backtesting frameworks to verify your market hypotheses. Through written explanations and practical code snippets, you will build the skills needed to make data-driven decisions. What you'll learn: - Understand core stock market concepts and the principles of historical backtesting - Collect real-time and historical financial data using modern Python libraries - Clean and structure market data using modern pandas techniques for analysis - Formulate testable investment hypotheses and translate them into code - Build a basic backtesting simulation to evaluate strategy performance - Apply clean coding practices and type hints to financial scripts The course begins with key terminology, basic market concepts, and foundational definitions before progressing to practical data collection. You will then move through written walk-throughs to build your first simulation framework from scratch. This course is designed for beginners interested in finance and programming, with no prior experience in financial data analysis or Python required. Start reading today to build your own data-driven stock market simulations.