New version of PMAX and Supertrend

[TomPer]

I propose a new version for PMAX and Supertrend with loop. Same result but really faster

def PMAX(dataframe, period, multiplier,length):
start_time = time.time()
df = dataframe.copy()
last_row = dataframe.tail(1).index.item()

mavalue = 'MA_' + str(length)
df[mavalue] = ta.EMA(df , length)
df['ATR'] = ta.ATR(df, period)

pm = 'PM_' + str(period) + '_' + str(multiplier)
pmx = 'PMX_' + str(period) + '_' + str(multiplier)

# Compute basic upper and lower bands
BASIC_UB = (df[mavalue] + multiplier * df['ATR']).values
BASIC_LB = (df[mavalue] - multiplier * df['ATR']).values
FINAL_UB = np.zeros(last_row + 1)
FINAL_LB = np.zeros(last_row + 1)
PM = np.zeros(last_row + 1)
MAVALUE = df[mavalue].values

# Compute final upper and lower bands
for i in range(period, last_row):
    FINAL_UB[i] = BASIC_UB[i] if BASIC_UB[i] < FINAL_UB[i - 1] or MAVALUE[i - 1] > FINAL_UB[i - 1] else FINAL_UB[i - 1]
    FINAL_LB[i] = BASIC_LB[i] if BASIC_LB[i] > FINAL_LB[i - 1] or MAVALUE[i - 1] < FINAL_LB[i - 1] else FINAL_LB[i - 1]

# Set the Supertrend value
for i in range(period, last_row):
    PM[i] = FINAL_UB[i] if PM[i - 1] == FINAL_UB[i - 1] and MAVALUE[i] <= FINAL_UB[i] else \
            FINAL_LB[i] if PM[i - 1] == FINAL_UB[i - 1] and MAVALUE[i] >  FINAL_UB[i] else \
            FINAL_LB[i] if PM[i - 1] == FINAL_LB[i - 1] and MAVALUE[i] >= FINAL_LB[i] else \
            FINAL_UB[i] if PM[i - 1] == FINAL_LB[i - 1] and MAVALUE[i] <  FINAL_LB[i] else 0.00
df_PM = pd.DataFrame(PM, columns=[pm])
df = pd.concat([df, df_PM],axis=1)

# Mark the trend direction up/down
df[pmx] = np.where((df[pm] > 0.00), np.where((df['close'] < df[pm]), 'down',  'up'), np.NaN)

df.fillna(0, inplace=True)

end_time = time.time()
# print("total time taken this loop: ", end_time - start_time)

return df

---

    def supertrend(self, dataframe: DataFrame, multiplier, period):
    start_time = time.time()

    df = dataframe.copy()
    last_row = dataframe.tail(1).index.item()

    df['TR'] = ta.TRANGE(df)
    df['ATR'] = ta.SMA(df['TR'], period)

    st = 'ST_' + str(period) + '_' + str(multiplier)
    stx = 'STX_' + str(period) + '_' + str(multiplier)

    # Compute basic upper and lower bands
    BASIC_UB = ((df['high'] + df['low']) / 2 + multiplier * df['ATR']).values
    BASIC_LB = ((df['high'] + df['low']) / 2 - multiplier * df['ATR']).values
    FINAL_UB = np.zeros(last_row + 1)
    FINAL_LB = np.zeros(last_row + 1)
    ST = np.zeros(last_row + 1)
    CLOSE = df['close'].values

    # Compute final upper and lower bands
    for i in range(period, last_row):
        FINAL_UB[i] = BASIC_UB[i] if BASIC_UB[i] < FINAL_UB[i - 1] or CLOSE[i - 1] > FINAL_UB[i - 1] else FINAL_UB[i - 1]
        FINAL_LB[i] = BASIC_LB[i] if BASIC_LB[i] > FINAL_LB[i - 1] or CLOSE[i - 1] < FINAL_LB[i - 1] else FINAL_LB[i - 1]

    # Set the Supertrend value
    for i in range(period, last_row):
        ST[i] = FINAL_UB[i] if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] <= FINAL_UB[i] else \
                FINAL_LB[i] if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] >  FINAL_UB[i] else \
                FINAL_LB[i] if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] >= FINAL_LB[i] else \
                FINAL_UB[i] if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] <  FINAL_LB[i] else 0.00
    df_ST = pd.DataFrame(ST, columns=[st])
    df = pd.concat([df, df_ST],axis=1)

    # Mark the trend direction up/down
    df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down',  'up'), np.NaN)

    df.fillna(0, inplace=True)

    end_time = time.time()
    # print("total time taken this loop: ", end_time - start_time)

    return DataFrame(index=df.index, data={
        'ST' : df[st],
        'STX' : df[stx]
    })

[Matthias]

Please provide code contributions as Pull request - not as text in an issue.

That way we can review and merge the changes directly, without the need of manual work from our side.
Thanks.

[Matthias]

Closing this for the reasons above.

Feel free to submit this as Pull request however.