Moving averages are the load-bearing walls of technical analysis. Before you build regime filters, momentum signals, or adaptive trend-following systems, you need a rigorous understanding of how price smoothing actually works — not just the textbook SMA, but the full spectrum of weighted, adaptive, and statistically grounded variants that professional quants use in production. This four-part series covers 36 distinct moving average methods, each implemented in Python with real market data. In this first installment, we implement nine foundational methods: Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA), Double EMA (DEMA), Triple EMA (TEMA), Hull Moving Average (HMA), Kaufman Adaptive Moving Average (KAMA), VIDYA, and the Arnaud Legoux Moving Average (ALMA). Each method is coded from scratch or validated against known formulas, plotted on SPY closing prices, and compared for lag and noise characteristics. Most algo trading content gives you theory.…