Create jma.py

Jurik indicator - JMA
2026-08-04 13:03:59 +08:00 · 2021-07-16 17:13:19 -07:00
parent 1deb7559f6
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+# -*- coding: utf-8 -*-
+from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
+import numpy as np
+from numpy import nan as npNaN
+import math
+
+def jma(close, length=None, phase=0, offset=None, **kwargs):
+    """
+    Indicator: Jurik Moving Average (JMA)
+    Implementation of: https://c.mql5.com/forextsd/forum/164/jurik_1.pdf
+    Jurik Volty from: https://www.prorealcode.com/prorealtime-indicators/jurik-volatility-bands/
+    """
+    # Validate Arguments
+    length = int(length) if length and length > 0 else 7
+    close = verify_series(close, length)
+    offset = get_offset(offset)
+    if close is None: return
+
+    # Calculate base variables
+    jma = Volty = vsum = np.zeros_like(close)
+    det0 = det1 = ma2 = bsmax = bsmin = 0.0
+    jma[0] = ma1 = close[0]
+    len1 = max(((math.log(math.sqrt(0.5*(length-1)))/math.log(2.0))+2),0)
+    len2 = math.sqrt(0.5*(length-1))*len1
+    pow1 = max(len1-2.0,0.5)
+    PR = 0.5 if phase<-100 else (2.5 if phase>100 else phase/100+1.5)
+    beta = 0.45*(length-1)/(0.45*(length-1)+2)
+
+    # Iterate through the dataset, calculate Volty and jma
+    for i in range(1, close.shape[0]):
+      hprice = np.amax(close[max(i-length,0):i])
+      lprice = np.amin(close[max(i-length,0):i])
+      del1 = hprice - bsmax
+      del2 = lprice - bsmin
+      Volty[i] = abs(del1) if del1>del2 else abs(del2) if del1<del2 else 0
+      vsum[i] = vsum[i-1] + 0.1 * (Volty[i]-Volty[i-min(i,10)])
+      avgVolty = np.mean(vsum[i-min(i,65):i])
+      dVolty = max(min(math.exp((1/pow1)*math.log(len1)),Volty[i]/avgVolty),1)
+      pow2 = math.exp(pow1*math.log(dVolty))
+      Kv = math.exp(math.sqrt(pow2)*math.log(len2/(len2+1)))
+      bsmax = hprice if del1>0 else hprice - Kv*del1
+      bsmin = lprice if del2<0 else lprice - Kv*del2
+      rVolty = max(min(math.pow(len1,1/pow1), Volty[i]/avgVolty),1)
+      power = math.pow(rVolty,pow1)
+      alpha = math.pow(beta, power)
+      ma1 = (1 - alpha) * close[i] + alpha * ma1
+      det0 = (close[i] - ma1) * (1 - beta) + beta * det0
+      ma2 = ma1 + PR * det0 
+      det1 = (ma2 - jma[i-1]) * math.pow(1-alpha,2) + math.pow(alpha,2) * det1
+      jma[i] = jma[i-1] + det1
+
+    # Remove initial lookback data and convert to pandas frame
+    jma[0:length-1] = npNaN
+    jma = Series(jma, index=close.index)
+
+    # Offset
+    if offset != 0:
+        jma = jma.shift(offset)
+
+    # Handle fills
+    if "fillna" in kwargs:
+        jma.fillna(kwargs["fillna"], inplace=True)
+    if "fill_method" in kwargs:
+        jma.fillna(method=kwargs["fill_method"], inplace=True)
+
+    # Name & Category
+    jma.name = f"JMA_{length}"
+    jma.category = "overlap"
+
+    return jma
+
+
+jma.__doc__ = \
+"""Volume Weighted Moving Average (VWMA)
+
+Jurik Moving Average
+
+Sources:
+    Implementation of: https://c.mql5.com/forextsd/forum/164/jurik_1.pdf
+    Jurik volatility: https://www.prorealcode.com/prorealtime-indicators/jurik-volatility-bands/
+
+Calculation:
+    Default Inputs:
+        length=7
+        phase=0
+
+Args:
+    close (pd.Series): Series of 'close's
+    length (int): Period of calculation. Default: 7
+    phase (float): how heavy/light the average is [-100, 100] Default: 0
+    offset (int): How many lengths to offset the result. Default: 0
+
+Kwargs:
+    fillna (value, optional): pd.DataFrame.fillna(value)
+    fill_method (value, optional): Type of fill method
+
+Returns:
+    pd.Series: New feature generated.
+"""