SuperTrend code using pandas python

Question

I am trying to code the following algorithm for SuperTrend indicator in python using pandas.

BASIC UPPERBAND = (HIGH + LOW) / 2 + Multiplier * ATR
BASIC LOWERBAND = (HIGH + LOW) / 2 - Multiplier * ATR

FINAL UPPERBAND = IF( (Current BASICUPPERBAND < Previous FINAL UPPERBAND) or (Previous Close > Previous FINAL UPPERBAND))
                    THEN (Current BASIC UPPERBAND) ELSE Previous FINALUPPERBAND)
FINAL LOWERBAND = IF( (Current BASIC LOWERBAND > Previous FINAL LOWERBAND) or (Previous Close < Previous FINAL LOWERBAND)) 
                    THEN (Current BASIC LOWERBAND) ELSE Previous FINAL LOWERBAND)

SUPERTREND = IF((Previous SUPERTREND = Previous FINAL UPPERBAND) and (Current Close <= Current FINAL UPPERBAND)) THEN
                Current FINAL UPPERBAND
            ELSE
                IF((Previous SUPERTREND = Previous FINAL UPPERBAND) and (Current Close > Current FINAL UPPERBAND)) THEN
                    Current FINAL LOWERBAND
                ELSE
                    IF((Previous SUPERTREND = Previous FINAL LOWERBAND) and (Current Close >= Current FINAL LOWERBAND)) THEN
                        Current FINAL LOWERBAND
                    ELSE
                        IF((Previous SUPERTREND = Previous FINAL LOWERBAND) and (Current Close < Current FINAL LOWERBAND)) THEN
                            Current FINAL UPPERBAND

Here is the code that I wrote and tested:

# Compute basic upper and lower bands
df['basic_ub'] = (df['high'] + df['low']) / 2 + multiplier * df[atr]
df['basic_lb'] = (df['high'] + df['low']) / 2 - multiplier * df[atr]

# Compute final upper and lower bands
for i in range(0, len(df)):
    if i < period:
        df.set_value(i, 'basic_ub', 0.00)
        df.set_value(i, 'basic_lb', 0.00)
        df.set_value(i, 'final_ub', 0.00)
        df.set_value(i, 'final_lb', 0.00)
    else:
        df.set_value(i, 'final_ub', (df.get_value(i, 'basic_ub') 
                                     if df.get_value(i, 'basic_ub') < df.get_value(i-1, 'final_ub') or df.get_value(i-1, 'close') > df.get_value(i-1, 'final_ub') 
                                     else df.get_value(i-1, 'final_ub')))
        df.set_value(i, 'final_lb', (df.get_value(i, 'basic_lb') 
                                     if df.get_value(i, 'basic_lb') > df.get_value(i-1, 'final_lb') or df.get_value(i-1, 'close') < df.get_value(i-1, 'final_lb') 
                                     else df.get_value(i-1, 'final_lb')))

# Set the Supertrend value
for i in range(0, len(df)):
    if i < period:
        df.set_value(i, st, 0.00)
    else:
        df.set_value(i, 'st', (df.get_value(i, 'final_ub')
                             if ((df.get_value(i-1, 'st') == df.get_value(i-1, 'final_ub')) and (df.get_value(i, 'close') <= df.get_value(i, 'final_ub')))
                             else (df.get_value(i, 'final_lb')
                                   if ((df.get_value(i-1, 'st') == df.get_value(i-1, 'final_ub')) and (df.get_value(i, 'close') > df.get_value(i, 'final_ub')))
                                   else (df.get_value(i, 'final_lb')
                                         if ((df.get_value(i-1, 'st') == df.get_value(i-1, 'final_lb')) and (df.get_value(i, 'close') >= df.get_value(i, 'final_lb')))
                                         else (df.get_value(i, 'final_ub')
                                               if((df.get_value(i-1, 'st') == df.get_value(i-1, 'final_lb')) and (df.get_value(i, 'close') < df.get_value(i, 'final_lb')))
                                               else 0.00
                                              )
                                        )
                                  ) 
                            )
                    )


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

I works, but I am not happy with the for loop. Can anyone help optimise it?

You can find the released code on Github!

Answer 1

SuperTrend Indicator is included in pandas_ta where you can simply:

import pandas_ta as ta

sti = ta.supertrend(df['High'], df['Low'], df['Close'], length=7, multiplier=3)

Given that df is a pandas DataFrame with OHLC prices, the result sti is a DataFrame with 4 columns:

1. trend
2. direction
3. long
4. short

where the trend is a concatenation of the long and short lines. Note that column captions are dynamic and contain the length and multiplier parameter values.