Treating tick data as a sensor feed is spot on – every price update is like a new sensor reading. Just like in a noisy environment, you need a filter to separate the signal from the jitter. A simple Kalman or moving‑average filter can smooth out the micro‑fluctuations, but remember to calibrate the noise model to the actual market volatility. It’s all about tuning the noise covariance and the process variance until the filter responds fast enough without over‑smoothing. Keep an eye on packet loss too, if the feed drops data it can throw off your estimates. Just a quick checklist: measure the sample period, estimate the noise floor, set your state transition, and iterate. That should give you a cleaner price trend without having to sleep on it.