Validating my implementation of a k's envelopes strategy

[pranavlal]

Hi,
I am trying to back-test a strategy using k's envelopes. The 800 period moving average of the highs and lows is taken. These averages act as resistance and support levels respectively..

I am following the article at the below link.

Here is my code. Basically, have I implemented this strategy as the article describes? What got me confused is the indexing of the variables so I want to be sure that I am not making a basic programming error.

The signal function from the article is below.

def signal(Data, close, ma_high, ma_low, buy, sell):
    
    for i in range(len(Data)):
        if Data[i, close] < Data[i, ma_high] and Data[i, close] > Data[i, ma_low] and Data[i - 1, close] > Data[i, ma_high] and Data[i - 2, close] > Data[i, ma_high]:
                Data[i, buy] = 1
        
        if Data[i, close] < Data[i, ma_high] and Data[i, close] > Data[i, ma_low] and Data[i - 1, close] < Data[i, ma_low] and Data[i - 2, close] < Data[i, ma_low]:
                Data[i, sell] = -1

My code follows.

	stop_loss_factor=2
	take_profit_factor=3
	l_ema_lookback=800
	def init(self):
		super().init()
				self.high_ma=self.I(talib.MA,self.data.df["High"].to_numpy(),timeperiod=self.l_ema_lookback)
		self.low_ma=self.I(talib.MA,self.data.df["Low"].to_numpy(),timeperiod=self.l_ema_lookback)
		self.set_trailing_sl(self.stop_loss_factor)
	def next(self):
		super().next() 
		limit_order_target=(self.data.High-self.data.Low)+self.data.High
		stop_loss=self.data.Close-self.stop_loss_factor*self.atr
		take_profit=self.data.Close+self.take_profit_factor*self.atr
		
		if  self.data.Close>self.low_ma and self.data.Close<self.high_ma and self.data.Close[-2]>self.high_ma[-2] and self.data.Close[-3]>self.high_ma[-3]:
			
			self.buy(sl=stop_loss,tp=take_profit)
		elif ((self.data.Close>self.low_ma and self.data.Close<self.high_ma and self.data.Close[-2]<self.low_ma[-2] and self.data.Close[-3]<self.low_ma[-3])):

			self.position.close()    
df=pd.read_csv("./ohlc/hourly/SBIN.csv",parse_dates=['date'], index_col=['date'],date_parser=dateparse)
print("length of data frame=",len(df))

df["Open"]=df["open"]
df["High"]=df["high"]
df["Low"]=df["low"]
df["Close"]=df["close"]


bt = Backtest(df, candlestickAdx, commission=.002,exclusive_orders=True,cash=30000)

opt=bt.optimize(take_profit_factor=range(2,3),stop_loss_factor=range(1,3),l_ema_lookback=range(10,900),maximize='Equity Final [$]',random_state=0,max_tries=300)
o=bt.run()
print(o)
#print(opt._strategy)
#bt.plot(filename="strategy.html")