Performance benchmark: 10000 data feed back testing

[bicepjai]

I loaded 10405 data feed for backtesting with fundamental data. I am trying to have access to all data feed at the same time so that I can update/change my stock portfolio in the strategy part of the code.

I filtered the data to include just the fundamental data SEC release date and not daily data as shown below

selected_daily_columns       = ['open', 'high', 'low', 'close', 'volume']
selected_fundamental_columns = ["debt","cashneq","equity","de"]

custom_params = [('datetime', "date"), ('openinterest',None)]
custom_params += [(name, name) for name in selected_daily_columns]
custom_params += [(name, name) for name in selected_fundamental_columns]

class PandasCustomData(bt.feeds.PandasData):
    lines = tuple(selected_fundamental_columns)
    params = tuple(custom_params)

I have powerful machine (like 1.5 TB RAM and 56 core).
I am trying to run an empty strategy code to if

class TestStrategy(bt.Strategy):

    def log(self, txt, dt=None):
        ''' Logging function for this strategy'''
        dt = dt or self.datas[0].datetime.date(0)
#         dt = dt or self.datetime.date()
        print('%s, %s' % (dt.isoformat(), txt))

    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        pass


    def next(self):
        # Simply log the closing price of the series from the reference
        msg = ""
        for tickeri,ticker in ticker_data_feedi.items():
            msg += f' ;{ticker} Close:{self.datas[tickeri].close[0]:.2f}'
            msg += "" if pd.isna(self.datas[tickeri].equity[0]) else f' Equity:{self.datas[tickeri].equity[0]}'
            msg += "" if pd.isna(self.datas[tickeri].de[0]) else f' DE:{round(self.datas[tickeri].de[0], 6)}'

#         self.log( msg )

I set up the benchmark for cerebro runs on log2 data feed sizes.
How can make this faster. I am using all cpus

benchmark = pd.DataFrame({'nfeeds' : [], "time_taken":[]})

for nfeed in [2**i for i in range(15)]:
    
    bd = {"nfeed" :  nfeed}
    start_time = time.time()
    
    try:
        del cerebro
    except NameError:
        pass

    cerebro = bt.Cerebro(maxcpus=42)

    cerebro.addstrategy(TestStrategy)
    cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='mysharpe')

    ticker_data_feedi = {}
    for i, (feed_name, pd_data_feed) in enumerate(pd_data_feeds):
        cerebro.adddata(pd_data_feed)
        ticker_data_feedi[i] = feed_name
        if i == nfeed:
            break

    # cerebro.addsizer(ProportionalSizer)
    cerebro.broker.setcash(10000.0)
    cerebro.broker.setcommission(commission=0.001)
    strategies = cerebro.run()
    
    end_time = time.time()
    bd["time_taken"] = end_time - start_time
    print(nfeed, end_time - start_time)
    
    benchmark.append(bd, ignore_index=True)
    del ticker_data_feedi; gc.collect()

For 10k tickers with dataframe of size 471656, it took around 21 minutes.
I cannot see all my cores active when i use the maxcpus option as above.
Is there anything that needs to be set to get all cores active ?