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Hello,
I am trying to deploy a live algorithm, but it keeps failing as one of my History() calls takes longer than 20 seconds to return. Is there an easy way to split this call to History() across multiple calls and then combine the data so that none of them time out?
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Jing Wu
Once you get over about 120K data points you'll run into timeout issues. Splitting the history calls and concatenating them will take more time as the timeout issue happens for a single loop. You need to trim the results down to a smaller history request.
KILLC
Thank you for the advice!
I'm trying to get minutely close data on around 8 symbols for the last 20ish days. To do so, I'm using the following code:
hist = self.History(self.stocks, 17*390, Resolution.Minute)['close'] prices = hist.unstack(level=0)How can I trim my results down if I use all of this data? I'm calculating minutely returns, and from there using those to get a standard deviation and mean of these returns.
KILLC
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.
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