diff --git a/pandas_ta/volume/__init__.py b/pandas_ta/volume/__init__.py
index 95e5a2a..b6c6346 100644
--- a/pandas_ta/volume/__init__.py
+++ b/pandas_ta/volume/__init__.py
@@ -5,6 +5,7 @@ from .aobv import aobv
 from .cmf import cmf
 from .efi import efi
 from .eom import eom
+from .kvo import kvo
 from .mfi import mfi
 from .nvi import nvi
 from .obv import obv
diff --git a/pandas_ta/volume/kvo.py b/pandas_ta/volume/kvo.py
new file mode 100644
index 0000000..e777892
--- /dev/null
+++ b/pandas_ta/volume/kvo.py
@@ -0,0 +1,109 @@
+# -*- coding: utf-8 -*-
+from numpy import where as npWhere
+from pandas import DataFrame
+from pandas_ta.utils import get_offset, verify_series
+
+
+def kvo(high, low, close, volume, fast=None, slow=None, length_sig=None, offset=None, **kwargs):
+    """Indicator: Klinger Volume Oscillator (KVO)"""
+    # Validate arguments
+    fast = int(fast) if fast and fast > 0 else 34
+    slow = int(slow) if slow and slow > 0 else 55
+    length_sig = int(length_sig) if length_sig and length_sig > 0 else 13
+    high = verify_series(high, max(fast, slow) + length_sig)
+    low = verify_series(low, max(fast, slow) + length_sig)
+    close = verify_series(close, max(fast, slow) + length_sig)
+    volume = verify_series(volume, max(fast, slow) + length_sig)
+    offset = get_offset(offset)
+
+    if high is None or low is None or close is None or volume is None: return
+
+    # Calculate Result
+    mom = (high + low + close).diff(1)
+    trend = npWhere(mom > 0, 1, 0) + npWhere(mom < 0, -1, 0)
+    dm = high - low
+
+    cm = [0.0] * len(high)
+    for i in range(1, len(high)):
+        cm[i] = (cm[i - 1] + dm[i]) if trend[i] == trend[i - 1] else (dm[i - 1] + dm[i])
+
+    vf = volume * trend * abs(dm / cm * 2 - 1) * 100
+
+    # this is the ma used by the tradingview script
+    def ema(x, n):
+        return x.ewm(alpha=2 / (n + 1), min_periods=n).mean()
+
+    kvo = ema(vf, fast) - ema(vf, slow)
+    kvo_signal = ema(kvo, length_sig)
+
+    # Offset
+    if offset != 0:
+        kvo = kvo.shift(offset)
+        kvo_signal = kvo_signal.shift(offset)
+
+    # Handle fills
+    if "fillna" in kwargs:
+        kvo.fillna(kwargs["fillna"], inplace=True)
+        kvo_signal.fillna(kwargs["fillna"], inplace=True)
+    if "fill_method" in kwargs:
+        kvo.fillna(method=kwargs["fill_method"], inplace=True)
+        kvo_signal.fillna(method=kwargs["fill_method"], inplace=True)
+
+    # Name and Categorize it
+    kvo.name = f"KVO_{fast}_{slow}"
+    kvo_signal.name = f"KVOSig_{length_sig}"
+    kvo.category = kvo_signal.category = "volume"
+
+    # Prepare DataFrame to return
+    data = {kvo.name: kvo, kvo_signal.name: kvo_signal}
+    kvoandsig = DataFrame(data)
+    kvoandsig.name = f"KVO_{fast}_{slow}_{length_sig}"
+    kvoandsig.category = kvo.category
+
+    return kvoandsig
+
+
+kvo.__doc__ = \
+"""Klinger Volume Oscillator (KVO)
+
+This indicator was developed by Stephen J. Klinger. It is designed to predict price reversals in a market 
+by comparing volume to price. 
+
+Sources:
+    https://www.tradingview.com/script/Qnn7ymRK-Klinger-Volume-Oscillator/
+    https://www.daytrading.com/klinger-volume-oscillator
+
+Calculation:
+    Default Inputs:
+        fast = 34, slow = 55, length_sig = 13. 
+    HLC3 = (h + l + c) / 3
+    MOM = HLC3t - HLC3t-1
+    TREND = { 1   if MOM > 0  \
+             -1   if MOM < 0  \
+              0   otherwise
+    DM = h - l
+    CM = { CMt-1 + DMt  if TRENDt == TRENDt-1   \
+           DMt-1 + DMt  otherwise
+
+    vf = 100 * v * TREND * abs(2 * dm / cm - 1) 
+    kvo = ema(vf, fast) - ema(vf, slow)
+    kvo_signal = ema(kvo, length_sig)
+    
+
+Args:
+    high (pd.Series): Series of 'high's
+    low (pd.Series): Series of 'low's
+    close (pd.Series): Series of 'close's
+    volume (pd.Series): Series of 'volume's
+    fast (int): The fast period. Default: 34
+    long (int): The long period. Default: 55
+    length_sig (int): The signal period. Default: 13
+    offset (int): How many periods to offset the result. Default: 0
+
+Kwargs:
+    fillna (value, optional): pd.DataFrame.fillna(value)
+    fill_method (value, optional): Type of fill method
+
+Returns:
+    pd.DataFrame: kvo and kvo_signal columns. 
+"""