When market making, cross asset correlations are your friend. What's funny though is you can footgun yourself here if you assume markets are too efficient. Suppose you're on a secondary exchange making BTC, and some ALTCOIN, using something like the binance feeds as your source of truth. Your mm engine sees BTC jump 2bips and immediately reprices ALTCOIN, canceling your inner ask. But then nothing happens to ALTCOIN for the next couple of ticks, so your system forgets about the BTC jump and requotes ALTCOIN at the level implied by your feed + some basis adjustment. A few milliseconds later ALTCOIN does jump and you get picked off. So you had the right idea but the wrong execution. The market for ALTCOIN was actually less efficient than you assumed, and you backed off the signal too quickly. What's the solution? Modeling. You need to analyze tick data and model the correlations temporally. The precise impulse response function is something to be discovered empirically, but it's well motivated by the fact that there's an easily observable statistical relation: ALT_return ~ beta * BTC_return + epsilon (Or more generally ALT ~ beta_i * f_i + epsilon where f_i are factors.) For most coins. This leads to all kinds of modeling ideas.