51–60 of 178 posts
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…
How does a lifetime non-compete work?
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…
A lifetime noncompete really I assume they pay you for life for this noncompete.
On the flip side, employees are compensated very well, and the turnover is very low.
It just lags the current price. If there is no alpha in the price signal, then the rational thing to do is to just stay at the current price, which is what the algo seems to have learned to do.
Before clicking I thought "I bet they do stacked LSTM to predict the _next_ time step" :|
Total noob question in this space (algotrading with DL algorithms). What DL is good at, is to automate tasks that are easy for humans (e.g. telling cat from dog, understand a sentence from sound wave, translation, etc) but hard for machines without DL. Now, if a task is even hard/borderline impossible for humans to achieve (e.g. predicting stock market with higher accuracy and consistency), why would we believe DL co…
I think relating DL with human skills is confusing you. DL already beats humans in recognizing dog breeds. DL could always do a better job, because it looks at more patterns than humanly possible.
-- Yogi Berra
https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Se...
Except Jakob explains the folly of the method.
Whereas the author of this piece says the following:
From the plot we can see that the real stock price went up while our model also predicted that the price of the stock will go up. This clearly shows how powerful LSTMs are for analyzing time series and sequential data.
Earlier quoted context omitted.
DL isn’t just good at automating human tasks (see AlphaZero and AlphaGo). It can be used to recognize patterns and train to solve problems better than humans. Though in this case since the market is people making predictions about a prediction I’m not sure how much it’d help. There’s probably something about humans using a rough estimate of what the stock has been historically to inform where it could go though so it…
By DL, i meant generic DL techniques like CNN, LSTM (used in this article), which relies on large amount of labeled data to train, and predict on similar data, thus my comment about automation. Alpha Go (Zero) is very specialized for the game of go, not sure how much of its specialized algorithm could transfer to other generic use cases.
AlphaZero is a generalized successor, and it does just that:
It's tough to make predictions, especially about the future. -- Yogi Berra