Most traders default to simple or exponential moving averages and stop there. But the landscape of moving average design is far richer — engineers, statisticians, and professional traders have developed dozens of specialized smoothing techniques, each making a different tradeoff between lag, noise rejection, and responsiveness. Understanding these alternatives gives you a more complete toolkit for signal construction, trend filtering, and regime detection. This article — the final part of a four-part series — covers eight niche but powerful moving averages: the Jurik Moving Average (JMA), End Point Moving Average (EPMA), Chande Moving Average (CMA), Harmonic Moving Average, McGinley Dynamic, Anchored Moving Average, Filtered Moving Average, and the Ichimoku Kijun-sen. Each is implemented from scratch in Python using pandas , numpy , and yfinance , plotted against real price data so you can see exactly how they behave in practice. Most algo trading content gives you theory. This gives you the code.…