diff --git a/README.md b/README.md
index 2a454b5..7db68bb 100644
--- a/README.md
+++ b/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.
|  |
-### **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)```
diff --git a/pandas_ta/__init__.py b/pandas_ta/__init__.py
index 29662eb..b6fd4d1 100644
--- a/pandas_ta/__init__.py
+++ b/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": [
diff --git a/pandas_ta/core.py b/pandas_ta/core.py
index e405fc3..2bc6f36 100644
--- a/pandas_ta/core.py
+++ b/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"))
diff --git a/pandas_ta/trend/vhf.py b/pandas_ta/trend/vhf.py
index 1e1797e..1a976ce 100644
--- a/pandas_ta/trend/vhf.py
+++ b/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.
+"""
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/setup.py b/setup.py
index 0f7d459..f6bf63c 100644
--- a/setup.py
+++ b/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",
diff --git a/tests/test_indicator_trend.py b/tests/test_indicator_trend.py
index 9fe064d..43ccd29 100644
--- a/tests/test_indicator_trend.py
+++ b/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)