diff --git a/pandas_ta/overlap/jma.py b/pandas_ta/overlap/jma.py
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+++ b/pandas_ta/overlap/jma.py
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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
+
+    # Define base variables
+    jma = np.zeros_like(close)
+    Volty = np.zeros_like(close)
+    vSum = np.zeros_like(close)
+    Kv = det0 = det1 = ma2 = 0.0
+    jma[0] = ma1 = uBand = lBand = close[0]
+    # Static variables
+    SumLen = 10
+    len = 0.5*(length-1)
+    PR = 0.5 if phase<-100 else 2.5 if phase>100 else phase*0.01+1.5
+    len1 = max((math.log(math.sqrt(len))/math.log(2.0))+2.0, 0)
+    pow1 = max(len1-2.0, 0.5)
+    len2 = math.sqrt(len)*len1
+    bet = len2/(len2+1)
+    beta = 0.45*(length-1)/(0.45*(length-1)+2.0)
+    for i in range(1, close.shape[0]):
+        price = close[i]
+        # Price volatility
+        del1 = price-uBand
+        del2 = price-lBand
+        Volty[i] = max(abs(del1),abs(del2)) if abs(del1)!=abs(del2) else 0
+        # Relative price volatility factor
+        vSum[i] = vSum[i-1] + (Volty[i]-Volty[max(i-SumLen,0)])/SumLen
+        avgVolty = np.average(vSum[max(i-65,0):i+1])
+        dVolty = 0 if avgVolty==0 else Volty[i]/avgVolty
+        rVolty = max(1.0, min(math.pow(len1, 1/pow1), dVolty))
+        # Jurik volatility bands
+        pow2 = math.pow(rVolty, pow1)
+        Kv = math.pow(bet, math.sqrt(pow2))
+        uBand = price if (del1 > 0) else price - (Kv*del1)
+        lBand = price if (del2 < 0) else price - (Kv*del2)
+        # Jurik Dynamic Factor
+        power = math.pow(rVolty, pow1)
+        alpha = math.pow(beta, power)
+        # 1st stage - prelimimary smoothing by adaptive EMA
+        ma1 = ((1-alpha)*price)+(alpha*ma1) #
+        # 2nd stage - one more prelimimary smoothing by Kalman filter
+        det0 = ((price-ma1)*(1-beta))+(beta*det0)
+        ma2 = ma1+PR*det0
+        # 3rd stage - final smoothing by unique Jurik adaptive filter
+        det1 = ((ma2-jma[i-1])*(1-alpha)*(1-alpha))+(alpha*alpha*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__ = \
+""" Jurik Moving Average Average (JMA)
+
+Sources:
+    Implementation of: https://c.mql5.com/forextsd/forum/164/jurik_1.pdf
+
+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.
+"""