ENH #214 chop ln addition DOC and TST update

README.md

@@ -97,7 +97,7 @@ $ pip install pandas_ta
 
 Latest Version
 --------------
-Best choice! Version: *0.2.70b*
+Best choice! Version: *0.2.71b*
 ```sh
 $ pip install -U git+https://github.com/twopirllc/pandas-ta
 ```
@@ -923,6 +923,7 @@ cascaded stochastic calculations with additional smoothing. See: ```help(ta.stc)
 * _ADX_ (**adx**): Added ```mamode``` with default "**RMA**" and with the same ```mamode``` options as TradingView. See ```help(ta.adx)```.
 * _Average True Range_ (**atr**): The default ```mamode``` is now "**RMA**" and with the same ```mamode``` options as TradingView. See ```help(ta.atr)```.
 * _Bollinger Bands_ (**bbands**): New argument ```ddoff``` to control the Degrees of Freedom. Default is 0.  See ```help(ta.bbands)```.
+* _Choppiness Index_ (**chop**): New argument ```ln``` to use Natural Logarithm (True) instead of the Standard Logarithm (False). Default is False.  See ```help(ta.chop)```.
 * _Chande Kroll Stop_ (**cksp**): Added ```tvmode``` with default ```True```. When ```tvmode=False```, **cksp** implements “The New Technical Trader” with default values. See ```help(ta.cksp)```.
 * _Decreasing_ (**decreasing**): New argument ```strict``` checks if the series is continuously decreasing over period ```length```. Default: ```False```. See ```help(ta.decreasing)```.
 * _Increasing_ (**increasing**): New argument ```strict``` checks if the series is continuously increasing over period ```length```. Default: ```False```. See ```help(ta.increasing)```.

pandas_ta/trend/chop.py

@@ -1,14 +1,16 @@
 # -*- coding: utf-8 -*-
 from numpy import log10 as npLog10
+from numpy import log as npLn
 from pandas_ta.volatility import atr
 from pandas_ta.utils import get_offset, get_drift, verify_series
 
 
-def chop(high, low, close, length=None, atr_length=None, scalar=None, drift=None, offset=None, **kwargs):
+def chop(high, low, close, length=None, atr_length=None, ln=None, scalar=None, drift=None, offset=None, **kwargs):
     """Indicator: Choppiness Index (CHOP)"""
     # Validate Arguments
     length = int(length) if length and length > 0 else 14
     atr_length = int(atr_length) if atr_length is not None and atr_length > 0 else 1
+    ln = bool(ln) if isinstance(ln, bool) else False
     scalar = float(scalar) if scalar else 100
     high = verify_series(high, length)
     low = verify_series(low, length)
@@ -24,8 +26,11 @@ def chop(high, low, close, length=None, atr_length=None, scalar=None, drift=None
     atr_ = atr(high=high, low=low, close=close, length=atr_length)
     atr_sum = atr_.rolling(length).sum()
 
-    chop = scalar * (npLog10(atr_sum) - npLog10(diff))
-    chop /= npLog10(length)
+    chop = scalar
+    if ln:
+        chop *= (npLn(atr_sum) - npLn(diff)) / npLn(length)
+    else:
+        chop *= (npLog10(atr_sum) - npLog10(diff)) / npLog10(length)
 
     # Offset
     if offset != 0:
@@ -38,7 +43,7 @@ def chop(high, low, close, length=None, atr_length=None, scalar=None, drift=None
         chop.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name and Categorize it
-    chop.name = f"CHOP_{length}_{atr_length}_{scalar}"
+    chop.name = f"CHOP{'ln' if ln else ''}_{length}_{atr_length}_{scalar}"
     chop.category = "trend"
 
     return chop
@@ -73,6 +78,7 @@ Args:
     close (pd.Series): Series of 'close's
     length (int): It's period. Default: 14
     atr_length (int): Length for ATR. Default: 1
+    ln (bool): If True, uses ln otherwise log10. Default: False
     scalar (float): How much to magnify. Default: 100
     drift (int): The difference period. Default: 1
     offset (int): How many periods to offset the result. Default: 0

setup.py

@@ -18,7 +18,7 @@ setup(
         "pandas_ta.volatility",
         "pandas_ta.volume"
     ],
-    version=".".join(("0", "2", "70b")),
+    version=".".join(("0", "2", "71b")),
     description=long_description,
     long_description=long_description,
     author="Kevin Johnson",

tests/test_ext_indicator_trend.py

@@ -34,10 +34,14 @@ class TestTrendExtension(TestCase):
         self.assertEqual(list(self.data.columns[-3:]), ["AROOND_14", "AROONU_14", "AROONOSC_14"])
 
     def test_chop_ext(self):
-        self.data.ta.chop(append=True)
+        self.data.ta.chop(append=True, ln=False)
         self.assertIsInstance(self.data, DataFrame)
         self.assertEqual(self.data.columns[-1], "CHOP_14_1_100")
 
+        self.data.ta.chop(append=True, ln=True)
+        self.assertIsInstance(self.data, DataFrame)
+        self.assertEqual(self.data.columns[-1], "CHOPln_14_1_100")
+
     def test_cksp_ext(self):
         self.data.ta.cksp(tvmode=False, append=True)
         self.assertIsInstance(self.data, DataFrame)

tests/test_indicator_trend.py

@@ -90,10 +90,14 @@ class TestTrend(TestCase):
                 error_analysis(result.iloc[:, 0], CORRELATION, ex)
 
     def test_chop(self):
-        result = pandas_ta.chop(self.high, self.low, self.close)
+        result = pandas_ta.chop(self.high, self.low, self.close, ln=False)
         self.assertIsInstance(result, Series)
         self.assertEqual(result.name, "CHOP_14_1_100")
 
+        result = pandas_ta.chop(self.high, self.low, self.close, ln=True)
+        self.assertIsInstance(result, Series)
+        self.assertEqual(result.name, "CHOPln_14_1_100")
+
     def test_cksp(self):
         result = pandas_ta.cksp(self.high, self.low, self.close, tvmode=False)
         self.assertIsInstance(result, DataFrame)