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I run code in research jupyter notebook:
minutes=len(df_price)
ema = qb.Indicator(ExponentialMovingAverage(750), syl, minutes, Resolution.Minute)
close = qb.Indicator(SimpleMovingAverage(1), syl, minutes, Resolution.Minute)
close = close.drop('simplemovingaverage', axis = 1)
close_over_ema = IndicatorExtensions.Over(close, ema)
the output is :
InvalidCastException: cannot convert object to target type
at Python.Runtime.PyObject.AsManagedObject (System.Type t) [0x00011] in <5699e871b2d343059e1ba5b428b465a0>:0
at QuantConnect.Indicators.IndicatorExtensions.Over (Python.Runtime.PyObject left, Python.Runtime.PyObject right, System.String name) [0x00008] in <03b550573bc44002ba53a1f597f9e9c4>:0
at (wrapper managed-to-native) System.Reflection.MonoMethod.InternalInvoke(System.Reflection.MonoMethod,object,object[],System.Exception&)
at System.Reflection.MonoMethod.Invoke (System.Object obj, System.Reflection.BindingFlags invokeAttr, System.Reflection.Binder binder, System.Object[] parameters, System.Globalization.CultureInfo culture) [0x00032] in <b0e1ad7573a24fd5a9f2af9595e677e7>:0
Link Liang
Hi Li,
The reason is, the Indicator created by qb.Indicator() is actually a panda dataframe, not a QC Indicator class type. If you try to print type(ema) for example, you would see `pandas.core.frame.DataFrame
`. Therefore, IndicatorExtensions.Over cannot take those dataframes as arguments. It only works in backtesting algorithms.
Meanwhile, I would suggest to manually divide two value. Here is information for accessing value in panda dataframe. Hope it helps!
Li li
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