Suppose that i have i=1,..,n assets (close price only), and for each asset i I have j=1,..,k macro indicators.

I want to make a strategy that gives a score to each of the n assets. The score is a function of the value of the k indicators.

I have tried to add all asset closes and macro indicators at once using bt.feeds.PandasData but im not sure if this is the best way.

One obvious issue with my approach is that i have to know the index of each self.datas which is cumberstone.

Is there an easier way to build my data feed and use it inside my strategy?

import pandas as pd
import os
import backtrader as bt

class DataFeed(bt.feeds.PandasData):

    params = (
        ('datetime', None),
        ('open', None),
        ('high', None),
        ('low', None),
        ('close', 'Close'),
        ('openinterest', None),
    )

class Teste(bt.Strategy):
    def __init__(self):
        teste=1
        
if __name__ == '__main__':
    # initialize cerebro
    cerebro = bt.Cerebro()

    # add strategy
    cerebro.addstrategy(Teste)

    # add target assets to cerebro
    target_asset_df = pd.read_excel(os.path.join(os.getcwd(), 'data', 'inputs_swap.xlsx'), sheet_name='target_assets').dropna()
    target_asset_df = target_asset_df.set_index('Date')

    for asset in target_asset_df.columns:
        loop_df = target_asset_df[[asset]]
        loop_df.columns = ['Close']
        df_tot_feed = DataFeed(dataname=loop_df)
        cerebro.adddata(df_tot_feed, name=asset)

    # add inputs
    inputs_df = pd.read_excel(os.path.join(os.getcwd(), 'data', 'inputs_swap.xlsx'), sheet_name='inputs').dropna()
    inputs_df = inputs_df.set_index('Date')

    for input in inputs_df.columns:
        loop_df = inputs_df[[input]]
        loop_df.columns = ['Close']
        df_tot_feed = DataFeed(dataname=loop_df)
        cerebro.adddata(df_tot_feed, name=input)

    # run cerebro
    cerebro.run()