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Pandas/Numpy in Lean

Hi,

I'm looking at Lean as a possible home backtesting framework. From the framework I've seen, they fall into 2 categories - either they use pandas/numpy (and associated libraries) or they code everything themselves. I come from a Matlab background and at the moment I use pandas/numpy for backtesting.

From what I can see Lean falls into the second category. Is it possible however, to use pandas/numpy etc. when writing backtests in python? Do you have any examples?

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It is possible, yes.
Moreover, when you make a historical data request, the method return a pandas.DataFrame:

# Get history in pandas.DataFrame format, use list of string
df = self.History(["EURUSD"], timedelta(7))
df = self.History(["EURUSD"], timedelta(7), Resolution.Minute)
df = self.History(["EURUSD"], 14)
df = self.History(["EURUSD"], 14, Resolution.Minute)
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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.


Alexandre,

Do you recommend then that we just get a history dataframe at initialiation and then add to it as the data comes in?   It seems that is the only way to get a true pandas dataframe. 

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We recommend not making historical data requests on every data event (every time step).
You can make one historical request at Initialize and add to is as data comes in if you need an updated data frame on every time step. However, if you just need an updated data frame once a day, for example, when the market opens, it may be simpler to just make the history when you need it.

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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.


Yes, that makes sense. It’s what I’m attempting to do with QuoteBars. I’ve got it working but without dateindex. I’m not sure I need that. It’s a bit of a hack IMHO the way I did it. If you guys could provide a ideal example it would be useful. Also, if you had versions of indicators which would accept a dataframe it might be nice.
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Hi Richard,

While we don't have versions of indicators that accept dataframes, it is possible to create custom indicators that would accept a dataframe and can mimic the same behavior as QuantConnect indicators. You can find documentation for custom indicators in C# (https://www.quantconnect.com/lean/documentation/topic15.html) and in Python here.

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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.


Update Backtest





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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.


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