Introduction to Technical Analysis for Finance and Stock Market Analytics

Learn how to perform technical analysis using Python with real stock market data. This step-by-step tutorial covers importing financial data, calculating moving averages, visualizing trends, generating trading signals, and handling missing values. You will see practical examples for both beginners and advanced users, including portfolio analysis and building modular code for scalable analytics. By following this guide, you will understand how to apply technical indicators like simple moving averages and rolling volatility, visualize buy and sell signals, and compare strategies such as buy-and-hold versus moving average crossovers. The workflow is designed to help you build a robust foundation for analyzing and interpreting stock price trends using Python. 00:00 Introduction to technical analysis 00:41 Setting up the Python environment 01:49 Understanding the core data 02:31 Loading and inspecting stock data 03:46 Filtering by ticker and date 04:55 Calculating a simple moving average 06:08 Visualizing price and moving average 07:38 Comparing multiple moving averages 08:24 Generating trading signals 09:53 Visual signal annotation 10:29 Applying to multiple tickers 11:08 Building a rolling volatility indicator 12:33 Pivoting price data for portfolio analysis 13:46 Handling missing values in financial data 14:07 Common patterns and best practices 15:41 Modular code for technical analysis 16:56 End-to-end technical trading example 18:13 Next steps and further learning #Python #TechnicalAnalysis #StockMarket