Moving averages are among the most foundational tools in quantitative finance, yet most practitioners only ever use two or three variants. This article — the third in a four-part series — pushes beyond the classics (SMA, EMA) into more sophisticated smoothing techniques that better handle noise, lag, and non-stationary price dynamics. Understanding a wider toolkit gives you more precise control over signal generation and trend detection across different market regimes. In this installment, we implement several advanced moving average methods in Python using yfinance for data retrieval, pandas and numpy for computation, and matplotlib for visualization. Each method is applied to real equity price data so you can immediately compare behavior on live time series. By the end, you will have a modular, reusable codebase that slots directly into a backtesting pipeline. Most algo trading content gives you theory. This gives you the code. 3 Python strategies. Fully backtested. Colab notebook included.…