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Hello,
Is it possible to get historical indicators in the algorithm environment? In the research environment the following works:
import datetime as dt
import QuantConnect.Indicators as qci
start_date = dt.datetime(2019, 6, 1)
end_date = dt.datetime(2019, 12, 31)
qb = QuantBook()
spy = qb.AddEquity("SPY")
history = qb.History(qb.Securities.Keys, 360, Resolution.Daily)
bb = qci.BollingerBands(30, 2)
bb_df = qb.Indicator(bb, spy.Symbol, start_date, end_date, Resolution.Daily)
bb_df.drop('standarddeviation', 1).plot()I would now like to get historical indicators in a pandas dataframe during backtest initialisation. In the algo environment I set:
qb = selfAnd do the same as above (without the plot), however I get the following error:
During the algorithm initialization, the following exception has occurred: AttributeError : 'MyAlgo' object has no attribute 'Indicator'
at Initialize in main.py:line 54
at calc_indicators in main.py:line 92
AttributeError : 'MyAlgo' object has no attribute 'Indicator'
Any help would be appreciated. Thanks!
Alexandre Catarino
Hey Dr Damage ,
You are getting that error because in the algorithm backtesting environment, QCAlgorithm doesn't have an Indicator method. qb.Indicator is a helper method in the research environment for plotting purposes. Instead, to create an Indicator, you must find the specific indicator constructor for each indicator. These can be found in the indicator reference table in the documentation.
In the algorithm backtesting environment, you can create a BB indicator and then subscribe to daily resolution SPY data. This will automatically update the indicator as historical data is fed into the algorithm:
bbIndicator = self.BB(spy.Symbol, 30, 2, Resolution.Daily)It is also possible to create a "blank" indicator and then populate it your own data with the Update method. Learn more about indicators here.
Here is an example of using rolling windows to store historical indicator values.
Dr Damage
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