From ba504653df0b9e3cbd0aa8f4ed8f13f957568769 Mon Sep 17 00:00:00 2001
From: jim geovedi <jim@geovedi.com>
Date: Wed, 8 Nov 2023 06:45:02 +0000
Subject: [PATCH] Create QQE.py

---
 indicators/QQE.py | 71 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 71 insertions(+)
 create mode 100644 indicators/QQE.py

diff --git a/indicators/QQE.py b/indicators/QQE.py
new file mode 100644
index 0000000..cfb64ed
--- /dev/null
+++ b/indicators/QQE.py
@@ -0,0 +1,71 @@
+import numpy as np
+import pandas as pd
+import talib
+
+def QQE(dataframe, rsi_period=14, sf=5, wf=4.236):
+    df = dataframe.copy()
+    
+    # Calculate the Wilders Period
+    wilders_period = rsi_period * 2 - 1
+    start_bar = sf if wilders_period < sf else wilders_period
+    
+    rsi_handle = talib.RSI(df["close"].values, timeperiod=rsi_period)
+    
+    # Initialize indicator buffers
+    rsi_ma = np.zeros(len(df))
+    tr_level_slow = np.zeros(len(df))
+    rsi_values = np.zeros(len(df))
+    atr_rsi = np.zeros(len(df))
+    ma_atr_rsi = np.zeros(len(df))
+    ma_atr_rsi_wp = np.zeros(len(df))
+    
+    for i in range(1, len(df)):
+        rsi_values[i] = rsi_handle[i]
+        rsi_ma[i] = rsi_values[i]
+    
+    for i in range(1, len(df)):
+        rsi_ma[i] = rsi_ma[i] * (2.0 / (1 + sf)) + (1 - (2.0 / (1 + sf))) * rsi_ma[i - 1]
+        atr_rsi[i] = abs(rsi_ma[i - 1] - rsi_ma[i])
+        ma_atr_rsi[i] = atr_rsi[i]
+    
+    for i in range(1, len(df)):
+        ma_atr_rsi[i] = ma_atr_rsi[i] * (2.0 / (1 + wilders_period)) + (1 - (2.0 / (1 + wilders_period))) * ma_atr_rsi[i - 1]
+        ma_atr_rsi_wp[i] = ma_atr_rsi[i]
+    
+    i = len(df) - 1
+    tr = tr_level_slow[i - 1]
+    rsi1 = rsi_ma[i - 1]
+    
+    while i >= 1:
+        rsi0 = rsi_ma[i]
+        ma_atr_rsi_wp[i] = ma_atr_rsi_wp[i] * (2.0 / (1 + wilders_period)) + (1 - (2.0 / (1 + wilders_period))) * ma_atr_rsi_wp[i - 1]
+        dar = ma_atr_rsi_wp[i] * wf
+        
+        dv = tr
+        if rsi0 < tr:
+            tr = rsi0 + dar
+            if rsi1 < dv:
+                if tr > dv:
+                    tr = dv
+        elif rsi0 > tr:
+            tr = rsi0 - dar
+            if rsi1 > dv:
+                if tr < dv:
+                    tr = dv
+        tr_level_slow[i] = tr
+        rsi1 = rsi0
+        i -= 1
+    
+    rsi_ma_series = pd.Series(rsi_ma, name="rsi_ma")
+    tr_level_slow_series = pd.Series(tr_level_slow, name="tr_level_slow")
+    rsi_values_series = pd.Series(rsi_values, name="rsi")
+    atr_rsi_series = pd.Series(atr_rsi, name="atr_rsi")
+    ma_atr_rsi_series = pd.Series(ma_atr_rsi, name="ma_atr_rsi")
+    ma_atr_rsi_wp_series = pd.Series(ma_atr_rsi_wp, name="ma_atr_rsi_wp")
+    
+    return rsi_ma_series, tr_level_slow_series, rsi_values_series, atr_rsi_series, ma_atr_rsi_series, ma_atr_rsi_wp_series
+
+# Example usage:
+# Replace df with your pandas DataFrame containing columns ['close']
+# Call the function with your desired parameters
+#rsi_ma, tr_level_slow, rsi_values, atr_rsi, ma_atr_rsi, ma_atr_rsi_wp = QQE(df)