Retrieving data is the heart of SQL. In Part II of our beginner-friendly tutorial series, we’ll dive into the four essential techniques that let you extract, filter, summarize, and refine datasets: Basic SELECT Queries Advanced Filtering and Expressions Aggregation and Grouping Subqueries and Derived Tables Mastering these topics will empower you to answer real-world questions, from listing customer orders to calculating monthly sales trends. Basic SELECT Queries The SELECT statement is your gateway to any relational database. You’ll learn how to: Specify columns and use aliases ( SELECT first_name AS fname ) Retrieve all fields with SELECT * for quick previews Limit result sets ( LIMIT 10 , TOP 5 ) to speed up testing Sort data with ORDER BY (ascending/descending) Example: sql SELECT id, first_name, last_name FROM customers ORDER BY last_name ASC; This simple query fetches a clean, ordered list of customer names in seconds. Advanced Filtering and Expressions Once you can pull r...
Turning Fibonacci from a visual crutch into a robust decision framework Fibonacci retracement is seductive: clean lines, iconic ratios, and the satisfying sense that price “respects” geometry. But markets are not Euclidean—they are noisy, adaptive, and reflexive. If you use Fibonacci as a drawing tool, you’ll get drawing-level results. If you use it as a framework—with standardized swing logic, probabilistic validation, confluence rules, and risk-first execution—you can turn those lines into a disciplined edge. This chapter dives very deep into where Fibonacci fails, why it fails, and how to engineer best practices that hold up in real trading and systematic testing. 6.1 Common pitfalls Subjectivity: swing points vary by timeframe and analyst Fibonacci analysis begins with picking a swing high and swing low, but that’s already a minefield. Ambiguous impulses: In real markets, you’ll often see overlapping swings, nested corrections, and failed breakouts. Your “obvious” swing might be a...