Types of Financial Data: OHLCV, Fundamentals, and Sentiment Explained

Learn the essential types of financial data used in stock market analysis, including OHLCV price data, company fundamentals, and sentiment indicators. This video demonstrates how to load, reshape, and analyze real stock data using Python, with practical examples for both beginners and professionals. You will discover best practices for handling multi-ticker data, identifying missing values, and combining price history with business metrics for comprehensive stock analysis. Follow along as we walk through data cleaning, error handling, and merging techniques to prepare your data for modeling and visualization. By the end, you will be able to compare companies, benchmark sectors, and build a basic portfolio analysis using real-world financial datasets. This guide is ideal for anyone looking to strengthen their data skills in finance and investment analytics. 00:00 Introduction to financial data types 00:28 Setting up the Python environment 01:13 Core data concepts explained 01:53 Understanding OHLCV and fundamentals 03:07 Loading OHLCV data for multiple stocks 04:12 Reshaping and previewing OHLCV data 05:27 Pivoting data for comparison 05:35 Filtering for single stock analysis 06:37 Loading and structuring company fundamentals 08:32 Sorting and comparing by P/E ratio 09:26 Grouping and benchmarking by sector 10:11 Handling missing values in fundamentals 11:02 Aggregating profit margins by sector 11:53 Screening companies by earnings per share 12:46 Merging company lists with fundamentals 14:01 Ranking market leaders by sector 14:50 Visualizing financial metrics 16:08 Building a sample portfolio analysis 17:52 Common data handling mistakes 19:00 Best practices for multi-ticker data 20:01 Managing missing and mismatched columns 21:02 Checking and converting data types 21:56 Merging price and fundamental data 22:56 Sector benchmarking and summary 23:46 Lesson recap and next steps #Finance #StockMarket #PythonAnalysis