Correlating financial time series with micro-blogging activity (2012) [pdf]

I remember the firehose and $<Ticker> symbols, good times. Though it didn't last. If I remember correctly it lost its predictive value around 2014.

Bloomberg came out with a sentiment product right around then and nlp libraries got good enough that anyone could do it in under a month of work, heck now you see weekend projects almost daily on reddit of people who are still trying to do sentiment analysis on twitter, mostly for crypto to trade off of.

There was a time a few years ago when you could use the Robinhood hot list to trade from, well technically you'd trade against it as what was hot on Robinhood inevitably crashed as those types of trades have no conviction or fortitude to hold and they're always pulling money to put it into the next shiny thing, ex GME of course, that one seems to have sticking power!!

I’m always curious what the cost and feasibility would be to implement these simulated trading strategies. Seems to me you would need a couple more things to actually do this: 1. Consistent access to near-real-time tweets or other scraped blog content at very low cost via an API, and 2. A service that allows you to make very frequent automated trades at very low cost, also via an API. Or a better modeling strat that knows how to find the features that correlate with a steady increase in price over time to reduce required trading volume.

I assume this is basically what high freq trading is, but the cost of access to those two services is probably so large that you need your base capital to be in the tens of millions, or to have a ridiculously performant model (like +20% vs indices) to actually make a profit.