AlphaTrend Strategy about freqtrade-strategies HOT 3 CLOSED

    alphatrend = self.generateAlphaTrend(dataframe)

    dataframe['k1'] = alphatrend['k1']
    dataframe['k2'] = alphatrend['k2']
    dataframe['signal'] = alphatrend['signal']

    # for elis leverage
    if self.can_use_elis_leverage == True:         
        dataframe['elis'] = alphatrend['elis']

    return dataframe

    dataframe.loc[
        (   
            # (qtpylib.crossed_above(dataframe['k1'] , dataframe['k2']))
            (dataframe['k1'] > dataframe['k2']) &
            (dataframe['signal'].shift(1) == 'sell')
        ),
        'enter_long',
    ] = 1

    dataframe.loc[
        (
            # (qtpylib.crossed_below(dataframe['k1'] , dataframe['k2']))
            (dataframe['k1'] < dataframe['k2']) &
            (dataframe['signal'].shift(1) == 'buy')
        ),
        "enter_short",
    ] = 1

    return dataframe

    atr = ta.ATR(df['high'], df['low'], df['close'], timeperiod=ap)

    at = 'k1'
    at2 = 'k2'

    noVolumeData = False
    upt = []
    downT = []
    AlphaTrend = [0.0]
    rsi = ta.RSI(df['close'], timeperiod=ap)
    
    k1 = []
    k2 = []
    
    mfi = ta.MFI(df['high'], df['low'], df['close'], df['volume'], timeperiod=ap)


    # print(at)
    for i in range(len(df['low'])):
        if pd.isna(atr[i]):
            upt.append(0)
        else:
            upt.append(df['low'][i] - (atr[i] * coeff))

    for i in range(len(df['high'])):
        if pd.isna(atr[i]):
           downT.append(0)
        else:
          downT.append(df['high'][i] + (atr[i] * coeff))

    for i in range(1, len(df['close'])):
        if noVolumeData is True and rsi[i] >= 50:
            if upt[i] < AlphaTrend[i - 1]:
                AlphaTrend.append(AlphaTrend[i - 1])
            else:
               AlphaTrend.append(upt[i])

        elif noVolumeData is False and mfi[i] >= 50:
            if upt[i] < AlphaTrend[i - 1]:
               AlphaTrend.append(AlphaTrend[i - 1])
            else:
               AlphaTrend.append(upt[i])
        else:
            if downT[i] > AlphaTrend[i - 1]:
                AlphaTrend.append(AlphaTrend[i - 1])
            else:
                AlphaTrend.append(downT[i])

    for i in range(len(AlphaTrend)):
        if i < 2:
            k2.append(0)
            k1.append(AlphaTrend[i])
        else:
            k2.append(AlphaTrend[i - 2])
            k1.append(AlphaTrend[i])



    df[at] = pd.DataFrame(data=k1, columns=['k1'])
    df[at2] = pd.DataFrame(data=k2, columns=['k2'])

    conditions  = [ df[at] > df[at2], (df[at] == df[at2]), df[at] < df[at2] ]
    choices     = [ "buy", 'same', 'sell' ]
    df["basic_signal"] = np.select(conditions, choices, default=np.nan)


    previous = "same"
    for i in range(len(df)):
      if df.loc[i,"k1"] == df.loc[i,"k2"]:
        comment = previous
      else:
        comment = "buy" if df.loc[i,"k1"] > df.loc[i,"k2"] else "sell"
      df.loc[i,"signal"] = comment
      previous = comment

    if self.can_use_elis_leverage == True:
        elisGenerate = self.elis(dataframe)
        df['elis'] = elisGenerate['elis']
    
    df.fillna(0, inplace=True)
    # df.to_csv('df_at.csv', index = False, header=True)


    
    return df
####### calculate AlphaTrend #######


`