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Perhaps someone can provide guidance on the best way to implement the binning described in "3. For example, starting with minute-level data, I'd like to create 15 minute wide bins, compute the average for each bin, and store the result in a vector. Additionally, it would be nice to store a corresponding datetime stamp centered on each bin, but this is not absolutely necessary.
Does Pandas have a convenient way of doing this, so that I could keep the data returned by the batch transform in its native format? I'm interested in pattern recognition in time series, and as discussed in the paper, the binning is the first step in coding the time series. Lin, Jessica, et al. Rolling statistics with a window could do the trick. I'll give it a try when I get the chance. This can be done using pandas up- and downsampling. To use the up- and down-sampling, it appears that I'd need to get the Quantopian data into a Pandas time series structure, correct?
If you know how to whip together an example, I'd appreciate it. Otherwise, I'll fiddle around with it at some point. The data passed to the batch transform is a pandas Series so you can simply call the resample method. Here's a quick backtest to demonstrate, be sure to run it on minutely data! The resample method sorta works. For the attached backtest, I obtained this log output I switched to a 5-minute period:. First, when a manually take the mean for the first five minutes, I obtain The resample method gives Also, the time stamp for the first entry in the downsampled data is Shouldn't it be This is due to how the resample method closes the bin interval, you can set it to left or right.
The difference is shown in the output of the attached backtest:. For frequencies that evenly subdivide 1 day, by default resample uses the first of the aggregated intervals. This behaviour can be changed using the base keyword, for example, for '5min' frequency, base could be 0 through 4.
Seems more appropriate for a streaming algorithm, since the datetime stamps automatically correspond to the instant the information is available. From the application description in the first post, I thought resample was more appropriate: You're correct that the resample method is directly appropriate for the application I describe above.
I need to do a little homework, but I think that the statistics should work out the same for the algorithm I have in mind. Sorry, something went wrong.
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Might be some guidance here: This can be done using pandas up- and downsampling df. Thanks Aidan, To use the up- and down-sampling, it appears that I'd need to get the Quantopian data into a Pandas time series structure, correct? There was an error loading this backtest. Backtest from to with initial capital. Returns 1 Month 3 Month 6 Month 12 Month. Alpha 1 Month 3 Month 6 Month 12 Month. Beta 1 Month 3 Month 6 Month 12 Month. Sharpe 1 Month 3 Month 6 Month 12 Month. Sortino 1 Month 3 Month 6 Month 12 Month.
Volatility 1 Month 3 Month 6 Month 12 Month. This backtest was created using an older version of the backtester. Please re-run this backtest to see results using the latest backtester. Learn more about the recent changes. There was a runtime error. Sorry for the inconvenience. Try using the built-in debugger to analyze your code. If you would like help, send us an email. Thanks Aidan, The resample method sorta works. For the attached backtest, I obtained this log output I switched to a 5-minute period: The difference is shown in the output of the attached backtest: Hi Aidan, I added a line: A 0 1 2 3 4 5 6 7 8 9 In : A NaN 0.
Thanks Aidan, You're correct that the resample method is directly appropriate for the application I describe above. Please sign in or join Quantopian to post a reply. Already a Quantopian member? Algorithm Backtest Live Algorithm Notebook. Sorry, research is currently undergoing maintenance. Please check back shortly. If the maintenance period lasts longer than expected, you can find updates on status. Sorry, something went wrong on our end. Please try again or contact Quantopian support.
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