The Volfefe Index(en.wikipedia.org)
en.wikipedia.org
The Volfefe Index
https://en.wikipedia.org/wiki/Volfefe_index
24 comments
Given that JPM is publishing this I think we can draw one of two conclusions. Either this strategy used to work, but no longer works due to it becoming too popular. Or they never managed to actually get this strategy to work, so they're publishing it as a fun PR piece.
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)
I'm sure there are some quants reading this thread, so here's a real question: Can somebody explain the technical pipeline from tweet->execution?
I assume there's some low latency twitter API that you can pay big money to access? And then I guess that goes to some array of GPUs to run the NLP model as quickly as possible? And the NLP results get traded on by placing orders with an exchange?
Are there companies that roll up many low latency data feeds for purposes like this?
I assume there's some low latency twitter API that you can pay big money to access? And then I guess that goes to some array of GPUs to run the NLP model as quickly as possible? And the NLP results get traded on by placing orders with an exchange?
Are there companies that roll up many low latency data feeds for purposes like this?
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
>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
Disclaimer: arguably a quant but definitely not this kind.
In this specific case you only need access to Trump his tweets, which you can get without parsing and evaluating the firehose (expensive to get and process).
> In particular, basic NLP and sentiment analysis is pretty easy, especially like in this case when you're dealing with a very limited vocabulary.
Exactly. With Trump's tweets you can probably find easy correlations between sentiment (good or bad) and a select range of words as well as tweet times, whether he is in office or not, whether it was tweeted from his android[1] or not. Statistically speaking you can also look at his overall sentiment. Eg. simply always short the market whenever he tweets might be the profitable move 80% of the time.
[1]: http://varianceexplained.org/r/trump-tweets/
In this specific case you only need access to Trump his tweets, which you can get without parsing and evaluating the firehose (expensive to get and process).
> In particular, basic NLP and sentiment analysis is pretty easy, especially like in this case when you're dealing with a very limited vocabulary.
Exactly. With Trump's tweets you can probably find easy correlations between sentiment (good or bad) and a select range of words as well as tweet times, whether he is in office or not, whether it was tweeted from his android[1] or not. Statistically speaking you can also look at his overall sentiment. Eg. simply always short the market whenever he tweets might be the profitable move 80% of the time.
[1]: http://varianceexplained.org/r/trump-tweets/
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.
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.
One of the markers JPM uses is likes and retweets, so I don't even think this is calculated with low latency.
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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?
Carl Quintanilla (cnbc) had a thread with some of the charts on this:
https://twitter.com/carlquintanilla/status/11706934775769169...
Geeks were far faster to think of that:
https://github.com/maxbbraun/trump2cash
https://github.com/maxbbraun/trump2cash
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.
I have traversed through every new article & none of them actually link to the mysterious report. Just copying each other blindly. All I found was the twitter thread with plots.
r/Wallstreetbets expertise discussion about this market index:
https://www.reddit.com/r/wallstreetbets/comments/d1edm4/jpmo...
https://www.reddit.com/r/wallstreetbets/comments/d1edm4/jpmo...
Similar to this, I think some people trade off the back of "woke" twitter storms. They buy stock at the peak of the outrage cycle and sell when everyone has moved on to the next.
EG Buy Papa John's Pizza on July 11th (when the CEO got fired for using the N-word). It's up $6 since then.
EG Buy Papa John's Pizza on July 11th (when the CEO got fired for using the N-word). It's up $6 since then.
Most 'research' articles are really just published for the entertainment of traders. JPM are probably just trying to drum up some notoriety by publishing this index.
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Twitter-driven economy.