Create Reflex.py

indicators/Reflex.py

@@ -0,0 +1,81 @@
+import numpy as np
+import pandas as pd
+
+def Reflex(dataframe, reflex_period=24):
+    class ReflexIndicator:
+        def __init__(self):
+            self.val = []
+            self.valc = []
+            self.c1 = 1
+            self.c2 = 1
+            self.c3 = 1
+            self.multi = 1
+            self.m_array = []
+            self.m_arraySize = -1
+            self.m_period = 0
+
+        def cosine(self, a):
+            return np.cos(a * np.pi / 180.0)
+
+        def initialize(self, period):
+            self.m_period = period if period > 1 else 1
+            a1 = np.exp(-1.414 * np.pi / (self.m_period * 0.5))
+            b1 = 2.0 * a1 * self.cosine(np.cos(1.414 * 180 / (self.m_period * 0.5)))
+            self.c2 = b1
+            self.c3 = -a1 * a1
+            self.c1 = 1.0 - self.c2 - self.c3
+            self.multi = 1 + sum(range(1, self.m_period))
+
+        def calculate(self, value, i, bars):
+            if self.m_arraySize < bars:
+                self.m_arraySize = bars + 500
+                self.m_array = [0] * self.m_arraySize
+
+            self.m_array[i]['value'] = value
+
+            if i > 1:
+                self.m_array[i]['ssm'] = self.c1 * (self.m_array[i]['value'] + self.m_array[i-1]['value']) / 2.0 + \
+                                          self.c2 * self.m_array[i-1]['ssm'] + \
+                                          self.c3 * self.m_array[i-2]['ssm']
+            else:
+                self.m_array[i]['ssm'] = value
+
+            if i > self.m_period:
+                self.m_array[i]['sum'] = self.m_array[i-1]['sum'] + self.m_array[i]['ssm'] - self.m_array[i-self.m_period]['ssm']
+            else:
+                self.m_array[i]['sum'] = self.m_array[i]['ssm']
+                for k in range(1, self.m_period):
+                    if i - k >= 0:
+                        self.m_array[i]['sum'] += self.m_array[i-k]['ssm']
+
+            tslope = (self.m_array[i-self.m_period]['ssm'] - self.m_array[i]['ssm']) / self.m_period if i >= self.m_period else 0
+
+            sum_val = 0
+            if i > inpReflexPeriod:
+                for a in range(1, inpReflexPeriod + 1):
+                    sum_val = sum_val + self.m_array[i]['ssm'] + a * tslope - self.m_array[i-a]['ssm']
+                sum_val = sum_val / inpReflexPeriod
+
+            self.m_array[i]['ms'] = 0.04 * sum_val * sum_val + 0.96 * self.m_array[i-1]['ms'] if i > 0 else 0
+
+            return sum_val / np.sqrt(self.m_array[i]['ms']) if self.m_array[i]['ms'] != 0 else 0
+
+    df = dataframe.copy()
+    reflex = ReflexIndicator()
+    reflex.initialize(reflex_period)
+
+    val = []
+    valc = []
+
+    for i in range(len(df)):
+        value = reflex.calculate(df['close'][i], i, len(df))
+        val.append(value)
+        valc.append(0 if i == 0 or val[i] == val[i-1] else (1 if val[i] < val[i-1] else 0))
+
+    df['Reflex'] = val
+    return df['Reflex']
+
+# Example usage:
+# Replace df with your pandas DataFrame containing columns ['close']
+# Call the function with your desired reflex_period
+# Example: reflex_values = Reflex(df, reflex_period=24)