Testing For Specifically the Tenkan

[TraderCode99]

I have been trying to specifically test for the Tenkan part of the Ichimoku indicator but am running into some trouble. I am not able to pull the specific column as I have previously done for other indicators. This is my code:

def indicator(data, tenkan_length = 30, kijun_length = 26, senkou_length = 52):
    ichimoku = ta.overlap.ichimoku(high=df.High, low=df.Low, close=df.Close, tenkan=tenkan_length, kijun=kijun_length, senkou=senkou_length, include_chikou=False)
    if ichimoku is None:
        ichimoku = np.ichimoku(len(data))
    else:
        ichimoku = ichimoku[:len(data)]  # Truncate to match data length
        ichimoku = np.nan_to_num(ichimoku)  # Replace NaN values with zeros
    return ichimoku

class TENKAN(Strategy):
    tenkan_length = 30
    def init(self):
        self.tenkan = self.I(indicator, self.data, self.tenkan_length)
        self.Buy_position_open = False
        self.Sell_position_open = False
    def next(self):
        if (crossover(self.data.Close, self.tenkan[2])):
            self.buy(size=0.5)
            self.Buy_position_open = True
        elif (crossover(self.tenkan[2], self.data.Close)):
            self.sell(size=0.5)
            self.Sell_position_open = True 
        if (self.position.pl_pct < -0.003 and self.Buy_position_open) or (self.position.pl_pct > 0.015 and self.Buy_position_open):
            self.position.close()
            self.Buy_position_open = False
        elif (self.position.pl_pct < -0.015 and self.Sell_position_open) or (self.position.pl_pct > 0.003 and self.Sell_position_open): 
            self.position.close()
            self.Sell_position_open = False

RuntimeError: Indicator "indicator(30)" errored with exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.

Not sure what this error means. I've tried using chat gpt but that hasn't helped. Is there any other way to do this?

[twopirllc]

Hello @azimgiant,

Would be great if you can provide more info, such as Pandas TA version, and other relevant details. Also it would be great if you could clean up your code so it is more readable.

Kind Regards,
KJ