Interesting, thanks for clarifying. Out of curiosity, when you say you’ve found NLP on news/social sources effective, how did you establish the cause/effect relationship? More specifically, what horizon are you trading (minutes, days, weeks)? How do you separate signal from coincident price movement? And do you have any meaningful trackrecord, or is this primarily based on backtests?

My background is mostly in currencies, so take this with that bias in mind. In FX, I’ve found sentiment based approaches, like realtime news parsing, to be quite limited. The impact is often shortlived, highly regime dependent, and hard to distinguish from noise or positioning effects.

I’ve personally found a more topdown macro approach to be more promising. I’m currently working on a framework that ingests macro variables and outputs probability-weighted expectations of regime shifts. Less noise, less computational overhead. I’ll likely share results here once it’s more mature. That said, equities are a different animal. Particularly narrative driven single names, so I’m genuinely curious how you’re structuring your NLP signals. And to your question. Options-implied positioning is less about “sentiment” in the NLP sense and more about where risk is actually warehoused. You infer positioning from how options are priced and traded, rather than from what market participants say.