Sure, looking for repeating patterns can help you spot regularities in any time series, and Bitcoin’s price chart is no exception. What you need is a statistical framework that separates real signal from noise—ARIMA, GARCH, or even simple moving‑average crossovers are common tools. The Enigma example was all about exploiting redundancy in a deterministic system; the market is far more stochastic and driven by human psychology, so you’ll see far fewer clean, exploitable patterns. In practice you’ll run into over‑fitting, look‑ahead bias, and the fact that past “trends” can simply be random walks. So use pattern‑finding as a hint, not a gospel, and always test your strategy on out‑of‑sample data before committing capital.

When volatility spikes, the market’s noise swells faster than the underlying signal. Think of it like a loud room—your voice is still there, but it’s drowned out by chatter. In quantitative terms, the variance of the returns goes up, so the signal‑to‑noise ratio drops. Technical indicators that rely on smoothing, like moving averages, start lagging or giving false crossovers. High‑frequency data can still reveal micro‑patterns, but they’re heavily filtered by market microstructure noise. So yes, the statistical signal becomes harder to pick out, and you’ll need tighter thresholds or higher‑frequency models to keep a foothold.

Exactly. The trick is to keep the false‑positive rate in check while still catching the real moves. In practice that means tightening the entry criteria, using higher‑resolution data, or applying volatility‑adjusted thresholds. Just like the telegraph guys, you’re basically scaling the noise floor before you call a signal. The risk is that you become too conservative and miss the big swings, so you always have to balance the two.

Sounds like a solid plan—log every false trigger, calculate the hit‑rate, and then tweak the cutoff until the false‑positive cost matches your risk tolerance. That way you keep the heavy moves alive while keeping the noise at bay.