$XRP Analyzing XRP's price dynamics through stochastic processes reveals interesting insights. The Brownian motion model, expressed as dS = 0.05S dt + 0.25S dW, where S is the XRP price, μ = 0.05 is the drift coefficient, σ = 0.25 is the volatility, and dW is a Wiener process, helps quantify price fluctuations.

Employing Itô's Lemma, we derive: d(ln S) = (0.05 - 0.25^2/2) dt + 0.25 dW d(ln S) = (0.05 - 0.03125) dt + 0.25 dW d(ln S) = 0.01875 dt + 0.25 dW

This captures the log-normal distribution characteristic of XRP's returns. Fourier transform techniques decompose price signals, highlighting periodic components correlated with market events, providing a robust foundation for predictive algorithms