I found the answer in another post. When I'm looping through my data, I rename the dataframe columns to fit the BT convention, then I convert that into a data feed and apply the name.
I'll share my Norgate Data Code here for future reference.
# Obtain a Pandas dataframe for a given security from Norgate Data
priceadjust = norgatedata.StockPriceAdjustmentType.TOTALRETURN
padding_setting = norgatedata.PaddingType.NONE
start_date = '2019-05-01'
#end_date = '2011-12-31'
timeseriesformat = 'pandas-dataframe'
universe = ['AAPL','TSLA']#,'GE','AMD','SPY']
for ticker in universe:
pricedata_dataframe = norgatedata.price_timeseries(
ticker,
stock_price_adjustment_setting = priceadjust,
padding_setting = padding_setting,
start_date = start_date,
#end_date = end_date,
format=timeseriesformat,
)
# Rename columns to suit Backtrader
pricedata_dataframe.rename(
columns={ 'Open':'open', 'High':'high', 'Low':'low', 'Close':'close', 'Volume':'volume', 'Open Interest':'open interest'},
inplace=True)
data = bt.feeds.PandasData(dataname=pricedata_dataframe)
data._name = ticker
cerebro.adddata(data)
Anyways, thanks for creating and sharing such an amazing project.
