Looping Backtest for several assets; for some results in 0 trades

[mauriciolopol]

I've been backtesting a strategy for a lot of assets with the same parameters.
For this reason I created a loop "for". In each iterarion of the loop, I run the backtest with an asset whoose OHLC data were taken from a Excel file. The issue is that for some assets, the backtest is resulting in 0 trades done.
When I run the same strategy, with the same parameters from Python iteractive prmpt, the result makes sense with a number of trades compatible with the date from where I started the backtest!
I don't know what happening! Basically, I make a loop for and in each iteration I run the backtest with a specific asset with the same parameters for all assets. In my case, I have OHLC data from 121 asset, so the backtest is done 121 times in a row.
Please, anyine may help me to solve this issue! The code is below:

def testa_model (dire: str, plan: str, startDate: str, **kwargs):
  Files = [x for x in listdir(dire) if x.upper().endswith(".XLSX")]
      for file in tqdm(Files):
          flData = join(dire, file)
          asset = file.upper().replace('.XLSX','')
          DADO = pd.read_excel(flData, sheet_name=0,
                          parse_dates=True, index_col="Data", decimal=",")
          DADO = DADO.sort_index()
          DADO = DADO[DADO.index > startDate]
          DataInicial =f"{DADO.index[0]:%d/%m/%Y}"
          DADO = DADO.dropna(axis=0)
          RSI2.v_ifrLow = kwargs['IFR']
          RSI2.max = kwargs['MAX']
          RSI2.ndays = kwargs['DAYS']
          RSI2.Loss = kwargs['LOSS']
          RSI2.Type = kwargs['TIPO']
          RSI2.pATR = kwargs['ATR'] 
          Param = f"IFR={kwargs['IFR']} MAX={kwargs['MAX']} DAYS={kwargs['DAYS']} " \
          f"LOSS={kwargs['LOSS']} ATR={kwargs['ATR']}"
          bt = Backtest(DADO, RSI2, cash=50000, commission=0.0, trade_on_close=True)
          stats = bt.run()
          T = stats._trades       
          Payoff = T[T['PnL']>0]['PnL'].mean() / abs(T[T['PnL']<0]['PnL'].mean())
          initialCash = stats['Equity Final [$]']/(1+stats['Return [%]']/100)
          bestOp = initialCash*(1+(stats['Best Trade [%]']/100))
          worstOp = initialCash*(1+(stats['Worst Trade [%]']/100))
          nOpGain = round(stats['Win Rate [%]']/100*stats['# Trades'])
          nOpLoss = stats['# Trades'] - nOpGain
          listAppend = [
                          asset,
                          stats['Profit Factor'],
                          stats['Win Rate [%]'],
                          Payoff,
                          stats['Sharpe Ratio'],
                          stats['# Trades'],
                          bestOp,
                          worstOp,
                          stats['Avg. Trade Duration'],
                          stats['Equity Final [$]'],
                          stats['Equity Peak [$]'],
                          stats['Return [%]'],
                          nOpGain,
                          nOpLoss,
                          stats['Max. Drawdown [%]'],
                          stats['Avg. Drawdown [%]'],
                          Param,
                          DataInicial
                          ]
          otim = pd.read_excel(plan)
          otim.loc[len(otim)] = listAppend
          otim.to_excel(plan,index=False)