The Volfefe Index

I would say it's a PR piece, because they named it "Volfefe" and because the Wikipedia article reads like it was written by a PR firm for JPM. (and if not that, it at least quotes a lot of PR articles)

These indices are officially published once a day, so technically JPMorgan doesn't have to publish real-time data of the index. There are no exchange-traded product on top of this, so if JPMorgan sells you a swap that settles on some value of the index, they also don't need to provide a live value for trading.

Also the computation needs to be transparent and reproducible. I didn't read the spec, but I doubt that this is a complex NLP model.

Technically, some people are already using super fast twitter api for sentiment analysis, so they probably do it the way you say, but they're on the buy-side, not the bank side.

EDIT: basically the purpose of this index is to hedge your vol exposure to Trump's tweets. JPMorgan will sell you a swap that will pay more or less depending on what Trump says. They'll have a fairly large bid/ask spread cause this is in fact quite random, but statistically, they found that this product may make sense for some people.

So this article is only describing the fact that JPM constructed an index that shows how tweets affect the US rates markets. The index is not tradeable afaik.

One of the markers JPM uses is likes and retweets, so I don't even think this is calculated with low latency.

Disclaimer: not a quant but I've built a similar system.

>some low latency twitter API that you can pay big money to access

Yes. The twitter firehose is the big one, where you get a stream of _all_ tweets. Twitter doesn't publish the price, and one source claims $360k/y for half of the hose. https://econsultancy.com/want-your-own-twitter-firehose-you-...

>And then I guess that goes to some array of GPUs to run the NLP model as quickly as possible?

General speaking, trained models usually aren't so big that you need a cluster of GPUs, or even a single one. Sometimes running it on a CPU makes more sense because if it's small enough it's just easier and cheaper.

In particular, basic NLP and sentiment analysis is pretty easy, especially like in this case when you're dealing with a very limited vocabulary. A free CPU based library like https://github.com/cjhutto/vaderSentiment should be good enough.

> And the NLP results get traded on by placing orders with an exchange?

Yes. Doing this quickly is pretty tricky, but financial institutions like JPMorgan Chase would have access to an extremely fast and low cost (per trade) API to do this. And then they probably already have the tech to strategically place big orders, so the other bots don't realise what you're doing. Or just use a private exchange, aka a dark pool: https://en.wikipedia.org/wiki/Dark_pool

I think some quants even skip the NLP part, and just trade based on the immediate reaction. ie. They detect a trump tweet. Entire stock index dips. they sell everything. or vice versa. why do the NLP work when thousands of other quants have already done it, and have just as good a latency as you?

Well companies like JPM only publish this stuff after the rest of the world discovers it and it stops making money. They don’t publish actively profitable strategies.

Vol - for volatility as it usually results in a price change. Timings of tweets seem to coincide with market openings\closings.

edit:spelling

Why would you need this for a music app at all?

I believe it means volatility of covfefe.