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Using a Keras Long Short-Term Memory Model to Predict Stock Prices

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Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#151

Post by AndrewKemendo »

modeless wrote: post #148
Loughla wrote: post #131

Earlier quoted context omitted.

As someone outside the tech sphere on either coast, that's all ML seems to be. What I've seen from companies marketing to Higher Education is - we have a lot of data, you set arbitrary flags to the data that you believe indicate 'x' (or even better, they have pre-built data expectations) and you will get 'y' outcome. And none of it is actually based on anything real. It's all anecdotal applied to extreme amounts of a…

> that's all ML seems to be. You have to be very selective about what you consider "ML" to come to that conclusion. There has been a constant parade of incredible, mind-blowing results out of ML over the past decade, advancing the state of the art by leaps and bounds both in research and in real applications. Do you not remember how terrible speech recognition and speech synthesis were just a few short years ago? Did…

I concur totally, however think we should add in a caveat that is relevant for this audience: ...only the major consumer tech companies are actually reaping the benefit of applied ML in a way that is profitable.

That's because they have the platforms and applications that people are using at scale. So ML is a force multiplier if you already have a consistent and strong user base for a good product.

If you're trying to get a product or company started, unless you're a pure ML research company like Clarifai (and arguably even then), ML is probably going to cost you more than you gain.

Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#152

Post by hendzen »

jwiley wrote: post #102
goodside wrote: post #93

This is pure nonsense. This isn’t even the right way to begin thinking about this as a forecasting task — the target series should be log-normal returns, not raw asset price. The performance of this model is laughably bad, which is probably why he spends zero time evaluating its effectiveness. You could trivially get better forecasts than this by naively repeating the last-observed price . This isn’t ML. It’s cargo-c…

Can you clarify why "the target series should be log-normal returns" is important or provide a pointer for more information?

https://en.wikipedia.org/wiki/Stationary_process

Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#155

Post by ipsa »

i_phish_cats wrote: post #136

Let's take it easy on the author... LSTM's are cool especially when you first grok them, and stock data is the most abundant time series data out there.

It's the webdev equivalent of creating a log-in form tutorial and putting the password in a JavaScript variable.

Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#156

Post by [deleted] »

throwawaymath wrote: post #81
DevX101 wrote: post #26

I'm not a purist believer in the efficient market hypothesis. BUT, I doubt there's much alpha to be gained simply from looking at price data which is widely and publicly available. Also keep in mind that markets are dynamic feedback loops so even if this model had an edge, the act of publishing this article would work against you to neutralize that edge in the future. There's a good reason the most successful 'quanti…

I'm pretty sure that lifetime NDA is a myth. It gets repeated very often (like a lot of the Renaissance mythos) but I haven't seen evidence for it, and on its face it sounds flagrantly unenforceable. It could be that the compensation alone substantially reduces turnover. Likewise once they've hit "their number", employees may decide to go into tech or philanthropy instead of a competing firm. For what it's worth, Ren…

[deleted]

Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#157

Post by [deleted] »

[deleted] wrote: post #156
throwawaymath wrote: post #81

Earlier quoted context omitted.

I'm pretty sure that lifetime NDA is a myth. It gets repeated very often (like a lot of the Renaissance mythos) but I haven't seen evidence for it, and on its face it sounds flagrantly unenforceable. It could be that the compensation alone substantially reduces turnover. Likewise once they've hit "their number", employees may decide to go into tech or philanthropy instead of a competing firm. For what it's worth, Ren…

[deleted]

[deleted]

Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#159

Post by chunsj »

No, this is a bad example of prediction; if you bet money on the direction of prediction, you'll lose much. Drawing prices and making it looking as similar is not correct prediction. At least you should show >50% correct direction prediction if it is considered to be "well processed" machine learning.

Re: Using a Keras Long Short-Term Memory Model to Predict Stock Prices

#160

Post by theothermkn »

goodside wrote: post #141
ak39 wrote: post #129

Earlier quoted context omitted.

Would it be the same (valid) with percentage returns?

Less so. Log-normal returns are better because they have the property that a summation of log-normal returns over contiguous intervals is equal to the log-normal returns of the combined interval. In other words: Losing 5% and then gaining 5% doesn’t put you back at exactly 100%, and log-normal fixes that.

In the extreme, two successive trades, where the first gains 110% and the second loses 100%, “average” out to a 5% return. However, you don’t want to make that pair of trades.
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