ENH #283 vhf indicator TST vhf

README.md

@@ -45,7 +45,7 @@ _Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that lever
     * [Overlap](#overlap-32)
     * [Performance](#performance-3)
     * [Statistics](#statistics-9)
-    * [Trend](#trend-16)
+    * [Trend](#trend-17)
     * [Utility](#utility-5)
     * [Volatility](#volatility-14)
     * [Volume](#volume-15)
@@ -778,7 +778,7 @@ Use parameter: cumulative=**True** for cumulative results.
 | ![Example Z Score](/images/SPY_ZScore.png) |
 <br/>
 
-### **Trend** (16)
+### **Trend** (17)
 
 * _Average Directional Movement Index_: **adx**
     * Also includes **dmp** and **dmn** in the resultant DataFrame.
@@ -798,6 +798,7 @@ Use parameter: cumulative=**True** for cumulative results.
 * _Short Run_: **short_run**
 * _Trend Signals_: **tsignals**
 * _TTM Trend_: **ttm_trend**
+* _Vertical Horizontal Filter_: **vhf**
 * _Vortex_: **vortex**
 
 | _Average Directional Movement Index_ (ADX) |
@@ -947,6 +948,7 @@ trading account, or fund. See: ```help(ta.drawdown)```
 * _Schaff Trend Cycle_ (**stc**) is an evolution of the popular MACD incorportating two
 cascaded stochastic calculations with additional smoothing. See: ```help(ta.stc)```
 * _Tom DeMark's Sequential_ (**td_seq**) attempts to identify a price point where an uptrend or a downtrend exhausts itself and reverses. Currently exlcuded from ```df.ta.strategy()``` for performance reasons. See: ```help(ta.td_seq)```
+* _Vertical Horizontal Filter_ (**vhf**) was created by Adam White to identify trending and ranging markets.. See: ```help(ta.vhf)```
 
 <br/>
 

pandas_ta/__init__.py

@@ -71,7 +71,7 @@ Category = {
     "trend": [
         "adx", "amat", "aroon", "chop", "cksp", "decay", "decreasing", "dpo",
         "increasing", "long_run", "psar", "qstick", "short_run", "tsignals",
-        "ttm_trend", "vortex"
+        "ttm_trend", "vhf", "vortex"
     ],
     # Volatility
     "volatility": [

pandas_ta/core.py

@@ -599,7 +599,7 @@ class AnalysisIndicators(BasePandasObject):
 
         total_indicators = len(ta_indicators)
         header = f"Pandas TA - Technical Analysis Indicators - v{self.version}"
-        s = f"{header}\nTotal Indicators: {total_indicators + len(ALL_PATTERNS)}\n"
+        s = f"{header}\nTotal Indicators & Utilities: {total_indicators + len(ALL_PATTERNS)}\n"
         if total_indicators > 0:
             print(f"{s}Abbreviations:\n    {', '.join(ta_indicators)}\n\nCandle Patterns:\n    {', '.join(ALL_PATTERNS)}")
         else:
@@ -1464,6 +1464,11 @@ class AnalysisIndicators(BasePandasObject):
         result = ttm_trend(high=high, low=low, close=close, length=length, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
+    def vhf(self, length=None, drift=None, offset=None, **kwargs):
+        close = self._get_column(kwargs.pop("close", "close"))
+        result = vhf(close=close, length=length, drift=drift, offset=offset, **kwargs)
+        return self._post_process(result, **kwargs)
+
     def vortex(self, drift=None, offset=None, **kwargs):
         high = self._get_column(kwargs.pop("high", "high"))
         low = self._get_column(kwargs.pop("low", "low"))

pandas_ta/trend/vhf.py

@@ -1,64 +1,66 @@
 # -*- coding: utf-8 -*-
 from numpy import fabs as npFabs
-from pandas_ta.utils import get_offset, verify_series
+from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
-def vhf(source, length=None, offset=None, **kwargs):
+def vhf(close, length=None, drift=None, offset=None, **kwargs):
     """Indicator: Vertical Horizontal Filter (VHF)"""
     # Validate arguments
     length = int(length ) if length and length > 0 else 28
-    source = verify_series(source, length)  # usually close price
+    close = verify_series(close, length)
+    drift = get_offset(drift)
     offset = get_offset(offset)
 
-    if source is None: return
+    if close is None: return
 
     # Calculate Result
-    hcp = source.rolling(length).max()
-    lcp = source.rolling(length).min()
-    diff = npFabs(source - source.shift(1))
-    vhf_ = npFabs(hcp - lcp) / diff.rolling(length).sum()
+    hcp = close.rolling(length).max()
+    lcp = close.rolling(length).min()
+    diff = npFabs(close - close.shift(drift))
+    vhf = npFabs(hcp - lcp) / diff.rolling(length).sum()
 
     # Offset
     if offset != 0:
-        vhf_ = vhf_.shift(offset)
+        vhf = vhf_.shift(offset)
 
     # Handle fills
     if "fillna" in kwargs:
-        vhf_.fillna(kwargs["fillna"], inplace=True)
+        vhf.fillna(kwargs["fillna"], inplace=True)
     if "fill_method" in kwargs:
-        vhf_.fillna(method=kwargs["fill_method"], inplace=True)
+        vhf.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name and Categorize it
-    vhf_.name = f"VHF_{length}"
-    vhf_.category = "trend"
+    vhf.name = f"VHF_{length}"
+    vhf.category = "trend"
 
-    return vhf_
+    return vhf
 
 
-vhf.__doc__ = """Vertical Horizontal Filter (VHF)
-    
-    VHF was created by Adam White to identify trending and ranging markets.   
-    
-    Sources:
-        https://www.incrediblecharts.com/indicators/vertical_horizontal_filter.php
-    
-    Calculation:
-        Default Inputs:
-            source = Close, length = 28
-        HCP = Highest Close Price in Period
-        LCP = Lowest Close Price in Period
-        Change = abs(Ct - Ct-1)
-        VHF = (HCP - LCP) / RollingSum[length] of Change     
-                
-    Args:
-        source (pd.Series): Series of prices (usually close). 
-        length (int): The period length. Default: 28
-        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.Series: New feature generated.
-    """
+vhf.__doc__ = \
+"""Vertical Horizontal Filter (VHF)
+
+VHF was created by Adam White to identify trending and ranging markets.
+
+Sources:
+    https://www.incrediblecharts.com/indicators/vertical_horizontal_filter.php
+
+Calculation:
+    Default Inputs:
+        length = 28
+    HCP = Highest Close Price in Period
+    LCP = Lowest Close Price in Period
+    Change = abs(Ct - Ct-1)
+    VHF = (HCP - LCP) / RollingSum[length] of Change
+
+Args:
+    source (pd.Series): Series of prices (usually close).
+    length (int): The period length. Default: 28
+    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.Series: New feature generated.
+"""

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

setup.py

@@ -18,7 +18,7 @@ setup(
         "pandas_ta.volatility",
         "pandas_ta.volume"
     ],
-    version=".".join(("0", "2", "78")),
+    version=".".join(("0", "2", "78b")),
     description=long_description,
     long_description=long_description,
     author="Kevin Johnson",

tests/test_indicator_trend.py

@@ -180,6 +180,11 @@ class TestTrend(TestCase):
         self.assertIsInstance(result, DataFrame)
         self.assertEqual(result.name, "TTMTREND_6")
 
+    def test_vhf(self):
+        result = pandas_ta.vhf(self.close)
+        self.assertIsInstance(result, Series)
+        self.assertEqual(result.name, "VHF_28")
+
     def test_vortex(self):
         result = pandas_ta.vortex(self.high, self.low, self.close)
         self.assertIsInstance(result, DataFrame)