The paper I was talking about is

"Dow Jones Trading with Deep Learning: The Unreasonable Effectiveness of Recurrent Neural Networks"

to be presented at

http://insticc.org/node/TechnicalProgram/data/presentationDetails/69221 The paper is not publicly available, but you can ask the authors for copy. I have a copy and can send it to people interested, but I won't post it here publicly. PM me if you want a copy.

Couple comments on various things previously mentioned now that the paper is semi-public:

- The paper predicts daily close of DJIA from daily open value + opens of previous n days (2-10).

- The trading algorithm is simply buy if predicted close > open and sell otherwise. If you cannot buy (already have position), then hold. If you cannot sell (already hold cash), then hold cash.

- Authors use training data from 01/01/2000-06/30/2009 and test data from 07/01/2009 and 12/31/2017. This somewhat answers the critique that training is from bull market: it's not. Testing is not completely from bull market either.

- Authors use pretty much vanilla LSTM, so IMO the critique that "

*1000s of academics looking for signals and the winners publish a paper*" or that they have tweaked/over-fitted the model until it worked does not seem to apply. (It's possible that they messed up somehow and used testing data in training, but they seem to be careful, so it doesn't seem very likely). This is really vanilla IMO without much tweaking at all. Which makes the results surprising BTW.

- I have some other comments, but I'd rather discuss this further with people who have read the paper, so I won't post them now.

As I mentioned, I spent a bunch of time reimplementing what these guys presumably implemented.

I do not get their results. My results are pretty much at level of random guessing, i.e. the accuracy is around 48-52% while they get up to 80% accuracy.

It's quite possible I am not doing something the same way they did.

It's possible that their implementation or testing is messed up somehow too. But it's hard to prove that. Maybe they'll opensource their implementation sometime in the future.

If anyone is interested to get together (online through some tools?) and go through the paper and/or my implementation, we can do it. PM me and we'll try to figure out what would work best.