Create Fractals.py

indicators/Fractals.py

@@ -0,0 +1,55 @@
+import pandas as pd
+import numpy as np
+
+def Fractals(dataframe, fractal=3):
+    """
+    Calculate fractal indicators for high and low prices based on the input parameters.
+
+    Parameters:
+    dataframe (DataFrame): Input DataFrame with columns ["high", "low"].
+    fractal (int): Number of bars to consider for fractal calculations (default is 3).
+
+    Returns:
+    DataFrame: A new DataFrame with fractal up and down values as "Fractal_Up" and "Fractal_Down" columns.
+    """
+    df = dataframe.copy()
+
+    if (fractal - 1) / 2 <= 0:
+        fractal_used = 3
+        print(f"Incorrect value for input variable fractal={fractal}. Using the default value={fractal_used} for calculations.")
+    else:
+        fractal_used = fractal
+
+    ext_up_fractals_buffer = np.zeros(len(df))
+    ext_down_fractals_buffer = np.zeros(len(df))
+
+    for i in range(len(df)):
+        if i <= fractal_used - 1:
+            ext_up_fractals_buffer[i] = 0
+            ext_down_fractals_buffer[i] = 0
+            continue
+
+        middle_bar = i - (fractal_used - 1) // 2 - 1
+        current_high = df["high"].iloc[middle_bar]
+        current_low = df["low"].iloc[middle_bar]
+        found_high = True
+        found_low = True
+
+        for a in range(i - fractal_used, i):
+            if a == middle_bar:
+                continue
+
+            # Fractals up
+            if df["high"].iloc[a] >= current_high:
+                found_high = False
+            # Fractals down
+            if df["low"].iloc[a] <= current_low:
+                found_low = False
+
+        ext_up_fractals_buffer[i] = current_high if found_high else 0
+        ext_down_fractals_buffer[i] = current_low if found_low else 0
+
+    df["Fractal_Up"] = ext_up_fractals_buffer
+    df["Fractal_Down"] = ext_down_fractals_buffer
+
+    return df