I'm fairly new to Python as well as QuantConnect (previously a Javascript dev), and I'm needing help on this one.

I'm trying to get to where I have a List of Numpy arrays where each Numpy array has 20 rows of Historical data and indicators for each row. However, each subsequent array I want to have it's previous 19 bars of historical data for that bar. So kind of like this:

 

From data like this:

Datetime          SMA     EMA     MACD     RSI    OpenPrice     NextOpenPrice

9/3/19 12:55        35         34         8           3          34.17               35.01

9/3/19 12:50        34          33         9          2          33.18               34.17

9/3/19 12:45        32          33          8          3          32.48             33.18

9/3/19 12:40         32          32         9          3          32.12              32.48

 

I want the Numpy arrays to look like this:

 

print(resultset)

resultset[

array( [ [ 35,  34,   8,  3,  34.17,   35.01],

[ 34, 33, 9, 2, 33.18, 34.17], [ 32, 33, 8, 3, 32.48, 33.18], [ . . . ] [ ---total of 20 rows-- ], [ . . . ], [ 27, 28, 5, 1, 26.48, 26.89] ] ),

array( [ [ 34,  33,  9,  2,  33.18,   34.17],   <----Notice how this next array starts on the second row of the first array

[ 32, 33, 8, 3, 32.48, 33.18], [ 32, 32, 9, 3, 32.12, 32.48], [ . . . ] [ ---total of 20 rows-- ], [ . . . ], [ 26, 27, 4, 1, 26.13, 26.48] ] ), . . . ---nparrays for each group of bars back 240 bars--- ---meaning a rolling group of 20 bars for each 240 bars--- . . . ] I'm having trouble adding the indicators to the historical data as well as figuring out how to get those rolling 20 bars into each array. You'll also notice I have the times in increments of 5 minutes. I'm also having trouble getting both history data and indicators matched up correctly on increments of 5 minutes. I know I'm supposed to use a Consolidator, but I can't figure out how to get that to work for history and indicators and the times be matched up correctly. I hope this makes sense and someone can help me Thank you

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