A tutorial adapting an Indirect Kalman Filtering (IKF) pipeline — originally designed for sports biomechanics — to analyze comparative financial trends between Gold and Bitcoin. The four-stage pipeline covers: Kalman filtering to extract latent trends from noisy log returns, high-pass filtering to remove low-frequency drift, range normalization to a 0–100 scale for comparability, and directional divergence detection with shaded visualization. Full R code is provided using tidyquant, KFAS, zoo, scales, and ggbraid packages.
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