One can apply a TALIB function to multiple securities by first grouping by security (using the groupby method ) and then applying a function to each group (using the apply method).

First, one needs to change the results of the get_pricing method into a dataframe. The default is a pandas panel. The to_frame method works nicely. Once we have a dataframe then group by security and apply our talib function. The one 'gotcha' is the apply method passes a dataframe to the function. The TALIB functions expect series. A straightforward workaround is to use a helper function which takes the dataframe as an input then calls the TALIB function with the appropriate columns. Like this

def my_CDLENGULFING(df):  
    """  
    Map the appropriate columns from the get_pricing dataframe to the TALIB function  
    Return the latest value of CDLENGULFING  
    """  
    return talib.CDLENGULFING(  
        open=df.open_price,  
        high=df.high,  
        low=df.low,  
        close=df.close_price,  
        )[-1]

Putting it all together, something like this will work to get the last (most recent) value of the 'talib.CDLENGULFING' function for multiple securities. The result is a series indexed by security. The values of the series are the latest CDLENGULFING values.

# Import the talib library  
import talib

# Set the start and end dates  
start = '2018-08-01'  
end = '2019-08-30'

# Set the securites (assets) we want to check  
assets = ['SSO', 'SDS']

# Fetch the OHLCV data for the securities  
data_panel = get_pricing(assets, start_date=start, end_date=end, frequency='daily')  
data_df = data_panel.to_frame()

# Now apply our talib helper function to each security  
# Level 1 are the securities  
cdlengulfing_latest = data_df.groupby(level=1).apply(my_CDLENGULFING)

See the attached notebook. It goes into more detail first passing a single security to a TALIB function and then finally multiple securities as above. Good question!

Wonderful and many thanks!