Looking for help on Bresserts DSS Indicator

overcash

Hi, I'm attempting to build out a Bresserts DSS (Double Smoothed Stochastic) indicator. I have a working version both in TV and Pandas, but I'm failing to get it to work in BT.

When I pass the second stochastic into a bt.ind.EMA it fails to produce a line, or a value, only Nan's. I've run this with the EMA as a separate indicator, passing the stochastic as input, and still nothing. Thanks in advance for the help.

class DSS (bt.Indicator):

    lines = ('percent_K', 'pre_Calc','percent_K','stoch_triple', 'smoother','xDSS','stoch2','stochastic',)
    plotlines = dict(percent_K=dict(_name='%K', _plotskip=True,),
                        pre_Calc=dict(_plotskip=True),
                        stoch_triple =dict(_plotskip = True),
                        stochastic =dict(_plotskip = False),
                        xDSS = dict(_plotskip=True),)

    params = dict(
        period=10,
        ema_length = 9,
        trigger_length = 5
    )

    def __init__(self):


        self.lines.pre_Calc = btind.EMA(bt.talib.STOCH(self.data.close, self.data.high, self.data.low, period = self.p.period ), period = self.p.ema_length)
        # self.lines.pre_Calc = bt.talib.STOCH(self.data.close, self.data.high, self.data.low, period = self.p.period ) 

        self.lines.stochastic = btind.EMA(bt.talib.STOCH(self.lines.pre_Calc, self.lines.pre_Calc, self.lines.pre_Calc, period = self.p.period ),period = self.p.ema_length)

Tradingview code

'''
study(title="DSS Bressert (Double Smoothed Stochastic)", shorttitle="DSS Bressert")
PDS = input(10, minval=1)
EMAlen = input(9, minval=1)
TriggerLen = input(5, minval=1)

xPreCalc = ema(stoch(close, high, low, PDS), EMAlen)
xDSS = ema(stoch(xPreCalc, xPreCalc, xPreCalc, PDS), EMAlen)
xTrigger = ema(xDSS, TriggerLen)

'''

Pandas code

    def DSS(self, High, Low, Close):
        self.df_xfer = pd.DataFrame()

        self.period = 10 #PDS
        self.ema_len = 9 #
        self.trigger_len = 5
 
        # Set minimum low and maximum high of the k stoch
        self.df_xfer['High_Max'] = High.rolling(self.period).max()
        self.df_xfer['Low_Min'] = Low.rolling(self.period).min()
        
        #Calculate the Stochastic
        self.df_xfer['%K'] = 100 * (Close - self.df_xfer['Low_Min']) / (self.df_xfer['High_Max'] - self.df_xfer['Low_Min'])


        self.df_xfer['preCalc'] = self.df_xfer['%K'].ewm(span = self.ema_len, min_periods=1 ).mean()  
        self.df_xfer['xDSS_3'] = self.stochastics(self.df_xfer['preCalc'],self.df_xfer['preCalc'],self.df_xfer['preCalc']).ewm(span = self.ema_len, min_periods=1).mean()     
        self.df_xfer['Trigger'] = self.df_xfer['xDSS_3'].ewm(span = self.trigger_len, min_periods=1).mean()

        return (self.df_xfer['xDSS_3'], self.df_xfer['Trigger'])