Super Trend back testing with python(Part-2)

Complete Python Code for Super Trend:

def TrueRange(data):
    data = data.copy()
    data["TR"] = np.nan
    for i in range(1,len(data)):
        h = data.loc[i,"high"]
        l = data.loc[i,"low"]
        pc = data.loc[i-1,"close"]    
        x = h-l
        y = abs(h-pc)
        z = abs(l-pc)
        TR = max(x,y,z)
        data.loc[i,"TR"] = TR
    return data

def average_true_range(data, period=10, drop_tr=True, smoothing="SMA"):
    data = data.copy()
    if smoothing == "RMA":
        data['atr_' + str(period) + '_' + str(smoothing)] = data['TR'].ewm(com=period - 1,
                                                                           min_periods=period).mean()
    elif smoothing == "SMA":
        data['atr_' + str(period) + '_' + str(smoothing)] = data['TR'].rolling(window=period).mean()
    elif smoothing == "EMA":
        data['atr_' + str(period) + '_' + str(smoothing)] = data['TR'].ewm(span=period, adjust=False).mean()
    if drop_tr:
        data.drop(['TR'], inplace=True, axis=1)
    data = data.round(decimals=2)
    return data
def SuperTrend(data):
    ### Time frame changing
    import time
    start = time.time()
    TR1 = TrueRange(data)
    ATR_df = average_true_range(data = TR1, period=10, drop_tr=True, smoothing="SMA")
    f=2
    n=11
    df2 = ATR_df.copy()
    #Calculation of SuperTrend
    df2['Upper Basic']=(df2['high']+df2['low'])/2+(f*df2['atr_10_SMA'])
    df2['Lower Basic']=(df2['high']+df2['low'])/2-(f*df2['atr_10_SMA'])
    df2['Upper Band']=df2['Upper Basic']
    df2['Lower Band']=df2['Lower Basic']
    for i in range(n,len(df2)):
        if df2['close'][i-1]<=df2['Upper Band'][i-1]:
            df2['Upper Band'][i]=min(df2['Upper Basic'][i],df2['Upper Band'][i-1])
        else:
            df2['Upper Band'][i]=df2['Upper Basic'][i]    
    for i in range(n,len(df2)):
        if df2['close'][i-1]>=df2['Lower Band'][i-1]:
            df2['Lower Band'][i]=max(df2['Lower Basic'][i],df2['Lower Band'][i-1])
        else:
            df2['Lower Band'][i]=df2['Lower Basic'][i]   
            df2['SuperTrend']=np.nan
    for i in df2['SuperTrend']:
        if df2['close'][n-1]<=df2['Upper Band'][n-1]:
            df2['SuperTrend'][n-1]=df2['Upper Band'][n-1]
        elif df2['close'][n-1]>df2['Upper Band'][i]:
            df2['SuperTrend'][n-1]=df2['Lower Band'][n-1]
    for i in range(n,len(df2)):
        if df2['SuperTrend'][i-1]==df2['Upper Band'][i-1] and df2['close'][i]<=df2['Upper Band'][i]:
            df2['SuperTrend'][i]=df2['Upper Band'][i]
        elif df2['SuperTrend'][i-1]==df2['Upper Band'][i-1] and df2['close'][i]>=df2['Upper Band'][i]:
            df2['SuperTrend'][i]=df2['Lower Band'][i]
        elif df2['SuperTrend'][i-1]==df2['Lower Band'][i-1] and df2['close'][i]>=df2['Lower Band'][i]:
            df2['SuperTrend'][i]=df2['Lower Band'][i]
        elif df2['SuperTrend'][i-1]==df2['Lower Band'][i-1] and df2['close'][i]<=df2['Lower Band'][i]:
            df2['SuperTrend'][i]=df2['Upper Band'][i]
    end = time.time()
    print('Time taken for SuperTrend Calculation:',(end - start)/60,'minutes')
    return df2

For more about code please follow my Git link -

EDA/SuperTrend.ipynb at main · vamsikrishna00466/EDA