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What is the time limitation for Machine Learning api

From doc, I see that there is a longer time limitation for Machine Learning method, self.Train().

I would like to ask if I do something like this:

def CoarseSelect(self, coarse):
self.Train(MyFunc())
# other things
return [My selection]

CoarseSelect is called daily, is it mean that I have 30 min limit daily for the call of CoarseSelect() ? Is there a difference between live and backtest? 

Thank you very much.

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Hi Sulfred,

Using self.Train just means the function passed has 30 minutes to run, and it is to make allow OnData to run longer, but coarse filters don’t have time limits. As for the difference between self.Train in backtesting and live, in backtesting, self.Train is synchronous, which means the algorithm stops until the function finishes running, while in live, self.Train is asynchronous, which means it is called, but the algorithm keeps running the rest of the code, which means we usually need a boolean flag to alert us if our model is ready.

Best,
Shile Wen

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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.


Thank you

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Update Backtest





0

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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