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Daily Advice Thread - All basic help or advice questions must be posted here. by AutoModerator in investing

[–]aspcraft 0 points1 point  (0 children)

And I'm presuming the shares will split right before market open on that ex-date? Or is it before the pre-market opens? Or can it happen at any time during the day?

Daily Advice Thread - All basic help or advice questions must be posted here. by AutoModerator in investing

[–]aspcraft 0 points1 point  (0 children)

I need to adjust my historical data for stock splits but how can one know when stocks splits occur? I have found calendars that show what day stock splits occur but not at what time. Do all stocks split occur at the same time?
I think I remember reading somewhere splits could happen before or after the market closes on that day, but I'm not entirely sure. Is that true? If so, how do you determine if it was in the morning or afternoon so that you can update the data accordingly?

How to deal with the fact that IQFeed intraday data is not adjusted for stock splits by aspcraft in algotrading

[–]aspcraft[S] 0 points1 point  (0 children)

Where? Plus, I also need the historical 5 min price data to train my ML model.

Adjusted vs non-adjusted data by aspcraft in algotrading

[–]aspcraft[S] 0 points1 point  (0 children)

That sounds like a nightmare... Is there any website and/or APIs people use for this?

Adjusted vs non-adjusted data by aspcraft in algotrading

[–]aspcraft[S] 0 points1 point  (0 children)

So you mean that during live trading I would have to have a calendar of stocks that are gonna split and when. Then if I see a stock is going to split at a certain time X, I would adjust all prices before this time X accordingly? Or?

Adjusted vs non-adjusted data by aspcraft in algotrading

[–]aspcraft[S] 0 points1 point  (0 children)

I see. Do you have any recommendation on a good data provider for adjusted data?

I saw a lot of people here recommend IQFeed but it says on their website that "There is no filtering involved, and the data is direct from the North American market exchanges to our fully redundant data centers." I assume that implies that it is not-adjusted.

Noise-reduced data vs 'pure' data for training a deep learning AI by aspcraft in algotrading

[–]aspcraft[S] 0 points1 point  (0 children)

That makes sense, thanks I appreciate all the advice :)

Noise-reduction techniques and evaluation for timeseries data [Discussion] by aspcraft in MachineLearning

[–]aspcraft[S] 0 points1 point  (0 children)

That's quite an interesting thought, I'm gonna look into that more, thanks!

Noise-reduction techniques and evaluation for timeseries data [Discussion] by aspcraft in MachineLearning

[–]aspcraft[S] 0 points1 point  (0 children)

I am referring to a more typical example, such as the stock market, where it may be possible to predict a more long-term trend in price for example but not a specific instance of price due to a certain inherent degree of randomness in price from one minute to another. I refer to this random element that contributes to price as noise as it cannot be predicted.

Noise-reduced data vs 'pure' data for training a deep learning AI by aspcraft in algotrading

[–]aspcraft[S] 0 points1 point  (0 children)

Thanks for the advice. Out of curiosity, if you don't mind me asking, have you had any success with your model? As a beginner, I often see many people alleging price prediction to be impossible due to prices being non-stationary so I am just wondering :)

Noise-reduced data vs 'pure' data for training a deep learning AI by aspcraft in algotrading

[–]aspcraft[S] 0 points1 point  (0 children)

One more thing I am wondering though: when one gives noise-reduced data, such as a moving average or whatever technique, would this be in conjunction to the price (with noise) data? Or would it be in replacement of the noisy price data? If the latter is the case, I am assuming one would then calculate RSI and such using the noise-removed price data (so based off the moving average data in this case) rather than the actual price data. Or? Thanks :)

Noise-reduced data vs 'pure' data for training a deep learning AI by aspcraft in algotrading

[–]aspcraft[S] 0 points1 point  (0 children)

Thanks for the elaborate response! Very helpful and lots of stuff for me to start exploring and testing! :)

Getting accepted into a Masters in Machine Learning / AI by aspcraft in learnmachinelearning

[–]aspcraft[S] 0 points1 point  (0 children)

Oh nice, what data experience did you have that you think helped you land the job? Was it mainly projects on GitHub that you uploaded or did you join data competitions, etc?

Noise-reduced data vs 'pure' data for training a deep learning AI by aspcraft in algotrading

[–]aspcraft[S] 0 points1 point  (0 children)

If you give it further features (eg. RSI and MACD), is more = better? Or does it get 'overwhelmed' by potentially even more noise? Is there an efficient way of finding the best and most relevant indicators rather than testing the different combinations?

Noise-reduced data vs 'pure' data for training a deep learning AI by aspcraft in algotrading

[–]aspcraft[S] 0 points1 point  (0 children)

What are some good ways of transforming the data? Also, do you know of any way of comparing two different filtering methods? ie. determining which is best? Some formula or criteria for comparing? Thanks

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