Comments (2)
^^ formatted
from technical.
hi i add ATR type in the code..and update it.
def SuperTrend(dataframe, period = 10, multiplier = 3, atrtype=1):
import talib.abstract as ta
df = dataframe.copy()
atr = 'ATR_' + str(period)
df[atr]=ta.ATR(df , timeperiod = period)
st = 'ST_' + str(period) + '_' + str(multiplier)
stx = 'STX_' + str(period) + '_' + str(multiplier)
# Compute basic upper and lower bands
if atrtype==1:
df['basic_ub'] = (df["high"] + df["low"]) / 2 + multiplier * df[atr]
df['basic_lb'] = (df["high"] + df["low"]) / 2 - multiplier * df[atr]
elif atrtype==2:
df['basic_ub'] = (df["high"] + df["low"] + df["close"]) / 3 + multiplier * df[atr]
df['basic_lb'] = (df["high"] + df["low"] + df["close"]) / 3 - multiplier * df[atr]
elif atrtype==3:
df['basic_ub'] = (df["high"] + df["low"]+df["open"] + df["close"]) / 4 + multiplier * df[atr]
df['basic_lb'] = (df["high"] + df["low"]+df["open"] + df["close"]) / 4 - multiplier * df[atr]
# Compute final upper and lower bands
df['final_ub'] = 0.00
df['final_lb'] = 0.00
for i in range(period, len(df)):
df['final_ub'].iat[i] = df['basic_ub'].iat[i] if df['basic_ub'].iat[i] < df['final_ub'].iat[i - 1] or df['close'].iat[i - 1] > df['final_ub'].iat[i - 1] else df['final_ub'].iat[i - 1]
df['final_lb'].iat[i] = df['basic_lb'].iat[i] if df['basic_lb'].iat[i] > df['final_lb'].iat[i - 1] or df['close'].iat[i - 1] < df['final_lb'].iat[i - 1] else df['final_lb'].iat[i - 1]
# Set the Supertrend value
df[st] = 0.00
for i in range(period, len(df)):
df[st].iat[i] = df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] <= df['final_ub'].iat[i] else \
df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] > df['final_ub'].iat[i] else \
df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] >= df['final_lb'].iat[i] else \
df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] < df['final_lb'].iat[i] 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)
# Remove basic and final bands from the columns
df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1)
df.fillna(0, inplace=True)
return df
from technical.
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from technical.