class VWAPWalkForwardStrategy(VWAPBounceStrategy):
 def next(self):
 month = (
 pd.Series(pd.DatetimeIndex(self.data.df.index).month)
 .diff()
 .abs().gt(0)
 .cumsum()
 .fillna(0)
 )
 
 if month.iloc[-1] < N_TRAIN:
 return
 
 if month.iloc[-1] == month.iloc[-2]:
 return super().next()
 
 data = self.data.df.reset_index()[month.gt(month.iloc[-1] - N_TRAIN - 1)]
 data = data.set_index("date")[:-1]
 support_rejection = [True, False]
 resistance_rejection = [True, False]
 combo_choices = list(
 itertools.product(
 *[
 support_rejection,
 resistance_rejection,
 ]
 )
 )
 
 rets = {}
 print(data.iloc[0])
 print(data.iloc[-1])
 for option in combo_choices:
 bt = Backtest(data, VWAPBounceStrategy, commission=0, cash=30000)
 output = bt.run(
 support_rejection=option[0],
 resistance_rejection=option[1],
 )
 _target = output["Win Rate [%]"] * output["Avg. Trade WL Ratio"]
 rets[option] = _target
 rets = pd.Series(rets)
 best = rets.idxmax()
 print(best)
 
 # use best result
 self.support_rejection = best[0]
 self.resistance_rejection = best[1]
 (
 self.longs,
 self.shorts,
 self.longXs,
 self.shortXs,
 self.ma_mid,
 self.rsi,
 self.eod,
 ) = self.I(
 self.vwapbounce_signal,
 self.data.df,
 plot=False,
 )
 super().next()