Create SuperTrend.py

indicators/SuperTrend.py

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+import pandas as pd
+import numpy as np
+import talib
+
+def SuperTrend(dataframe, st_mode=1, atr_period=24, atr_multiplication=3):
+    df = dataframe.copy()
+
+    # Calculate ATR
+    df['atr'] = talib.ATR(df['high'], df['low'], df['close'], timeperiod=atr_period)
+
+    # Initialize indicator buffer
+    ind_buffer = [0.0] * len(df)
+
+    for i in range(len(df)):
+        if st_mode == 1:
+            atr_value = df['atr'][i]
+            upper_level = (df['high'][i] + df['low'][i]) / 2 + atr_multiplication * atr_value
+            lower_level = (df['high'][i] + df['low'][i]) / 2 - atr_multiplication * atr_value
+
+            if df['close'][i] > ind_buffer[i - 1] and df['close'][i - 1] <= ind_buffer[i - 1]:
+                ind_buffer[i] = lower_level
+            elif df['close'][i] < ind_buffer[i - 1] and df['close'][i - 1] >= ind_buffer[i - 1]:
+                ind_buffer[i] = upper_level
+            elif ind_buffer[i - 1] < lower_level:
+                ind_buffer[i] = lower_level
+            elif ind_buffer[i - 1] > upper_level:
+                ind_buffer[i] = upper_level
+            else:
+                ind_buffer[i] = ind_buffer[i - 1]
+
+    df['sq_super_trend'] = ind_buffer
+    return df['sq_super_trend']
+
+# Example usage:
+# Replace df with your pandas DataFrame containing columns ['open', 'high', 'low', 'close', 'volume']
+# Call the function with your desired parameters
+# Example: super_trend_values = SuperTrend(df, st_mode=1, atr_period=24, atr_multiplication=3)