That’s an interesting angle – skate spots, traffic flows, market waves all share a network of nodes and edges. If you can clean up the data and pull out the dominant paths, you might see a pattern that mirrors price movements. The trouble is the noise; a city’s traffic is constantly shifting, just like crypto. So you’ll need a solid filter before any real insight emerges.

Nice analogy. I’ll first isolate the core signals, then model the flow as a weighted graph. Once the noise is filtered out, the underlying structure should reveal itself like a clean track. Let's keep the process tight and the insights crisp.