Merge branch 'ryanrussell-development' into development

DOC update readability

README.md

@@ -28,7 +28,7 @@ Pandas TA - A Technical Analysis Library in Python 3
 
 <br/>
 
-_Pandas Technical Analysis_ (**Pandas TA**) is a free, Open Source, and easy to use Technical Analysis library with a Pandas DataFrame Extension. It has over 200 indicators, utility functions and TA Lib Candlestick Patterns. Beyond TA feature generation, it has a flat libary structure, it's own DataFrame Extension (called ```ta```), Custom Indicator Sets (called a ```Study```) and Custom Directory creation. Lastly, it includes methods to help with Data Acquisition and Stochastic Sampling, Backtesting Support with Signal and Trend methods, and some basic Performance Metrics.
+_Pandas Technical Analysis_ (**Pandas TA**) is a free, Open Source, and easy to use Technical Analysis library with a Pandas DataFrame Extension. It has over 200 indicators, utility functions and TA Lib Candlestick Patterns. Beyond TA feature generation, it has a flat library structure, it's own DataFrame Extension (called ```ta```), Custom Indicator Sets (called a ```Study```) and Custom Directory creation. Lastly, it includes methods to help with Data Acquisition and Stochastic Sampling, Backtesting Support with Signal and Trend methods, and some basic Performance Metrics.
 
 <br/>
 

pandas_ta/momentum/td_seq.py

@@ -18,8 +18,8 @@ def td_seq(
         https://tradetrekker.wordpress.com/tdsequential/
 
     Args:
-        close (pd.Series): Series of 'close's
-        asint (bool): If True, fillnas with 0 and change type to int.
+        close (pd.Series): Series of close's
+        asint (bool): If True, fillna's with 0 and change type to int.
             Default: False
         show_all (bool): Show 1 - 13. If set to False, show 6 - 9.
             Default: True

pandas_ta/statistics/tos_stdevall.py

@@ -35,7 +35,7 @@ def tos_stdevall(
 
     Returns:
         pd.DataFrame: Central LR, Pairs of Lower and Upper LR Lines based on
-            mulitples of the standard deviation. Default: returns 7 columns.
+            multiples of the standard deviation. Default: returns 7 columns.
     """
     # Validate
     _props = f"TOS_STDEVALL"

pandas_ta/trend/cksp.py

@@ -35,7 +35,7 @@ def cksp(
 
     Sources:
         https://www.multicharts.com/discussion/viewtopic.php?t=48914
-        "The New Technical Trader", Wikey 1st ed. ISBN 9780471597803, page 95
+        "The New Technical Trader", Wiley 1st ed. ISBN 9780471597803, page 95
 
     Args:
         close (pd.Series): Series of 'close's

pandas_ta/trend/psar.py

@@ -103,7 +103,7 @@ def psar(
 
         sar = _sar  # Update SAR
 
-        # Seperate long/short sar based on falling
+        # Separate long/short sar based on falling
         if falling:
             short.iloc[row] = sar
         else:

pandas_ta/utils/data/polygon_api.py

@@ -9,7 +9,7 @@ def polygon_api(ticker: str, **kwargs) -> DataFrame:
     r"""
     polygon_api - polygon.io API helper function.
 
-    It returns OCHLV data from polygon (A valid subscription is required).
+    It returns OHCLV data from polygon (A valid subscription is required).
     To install the `polygon library <https://github.com/pssolanki111/polygon>`__ ,
     use ``pip install polygon``.
     You can customize the range of data using kwargs ``from_date``,
@@ -21,7 +21,7 @@ def polygon_api(ticker: str, **kwargs) -> DataFrame:
     the **kwarg** ``kind``, defaulting to ``None`` which doesn't
     pull/display any additional info.
 
-    **The function will always return the OCHLV dataframe no matter what
+    **The function will always return the OHCLV dataframe no matter what
     additional info you ask it to pull.** The additional information is
     used for display only (yet?)
 

pandas_ta/volume/ad.py

@@ -13,7 +13,7 @@ def ad(
     """Accumulation/Distribution (AD)
 
     Accumulation/Distribution indicator utilizes the relative position
-    of the close to it's High-Low range with volume then cummulated.
+    of the close to it's High-Low range with volume then accumulated.
 
     Sources:
         https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/accumulationdistribution-ad/

pandas_ta/volume/adosc.py

@@ -16,7 +16,7 @@ def adosc(
     """Accumulation/Distribution Oscillator or Chaikin Oscillator
 
     Accumulation/Distribution Oscillator indicator utilizes
-    Accumulation/Distribution and treats it similarily to MACD
+    Accumulation/Distribution and treats it similarly to MACD
     or APO.
 
     Sources:

pandas_ta/volume/eom.py

@@ -19,7 +19,7 @@ def eom(
     """Ease of Movement (EOM)
 
     Ease of Movement is a volume based oscillator that is designed to
-    measure the relationship between price and volume flucuating across
+    measure the relationship between price and volume fluctuating across
     a zero line.
 
     Sources:

pandas_ta/volume/kvo.py

@@ -22,7 +22,7 @@ def kvo(
 ) -> DataFrame:
     """Klinger Volume Oscillator (KVO)
 
-    This indicator was developed by Stephen J. Klinger. It attemps to
+    This indicator was developed by Stephen J. Klinger. It attempts to
     predict price reversals in a market by comparing volume to price.
 
     Sources:

setup.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 from distutils.core import setup
 
-long_description = "Pandas Technical Analysis, Pandas TA, is a free, Open Source, and easy to use Technical Analysis library with a Pandas DataFrame Extension. It has over 200 indicators, utility functions and TA Lib Candlestick Patterns. Beyond TA feature generation, it has a flat libary structure, it's own DataFrame Extension (called 'ta'), Custom Indicator Studies and Independent Custom Directory."
+long_description = "Pandas Technical Analysis, Pandas TA, is a free, Open Source, and easy to use Technical Analysis library with a Pandas DataFrame Extension. It has over 200 indicators, utility functions and TA Lib Candlestick Patterns. Beyond TA feature generation, it has a flat library structure, it's own DataFrame Extension (called 'ta'), Custom Indicator Studies and Independent Custom Directory."
 
 setup(
     name="pandas_ta",

tests/test_ext_indicator_trend.py

@@ -131,7 +131,7 @@ class TestTrendExtension(TestCase):
         self.assertIsInstance(self.data, DataFrame)
         self.assertEqual(list(self.data.columns[-1:]), ["VHF_28"])
 
-    def test_vortext_ext(self):
+    def test_vortex_ext(self):
         self.data.ta.vortex(append=True)
         self.assertIsInstance(self.data, DataFrame)
         self.assertEqual(list(self.data.columns[-2:]), ["VTXP_14", "VTXM_14"])

tests/test_indicator_statistics.py

@@ -91,7 +91,7 @@ class TestStatistics(TestCase):
         self.assertIsInstance(result, Series)
         self.assertEqual(result.name, "STDEV_30")
 
-    def test_tos_sdtevall(self):
+    def test_tos_stdevall(self):
         """Statistics: ToS Stdevall"""
         result = pandas_ta.tos_stdevall(self.close)
         self.assertIsInstance(result, DataFrame)