MAINT DOC refactor

README.md

@@ -75,14 +75,14 @@ _Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that lever
 
 Stable
 ------
-The ```pip``` version is the last most stable release.
+The ```pip``` version is the last most stable release. Version: *0.2.23b*
 ```sh
 $ pip install pandas_ta
 ```
 
 Latest Version
 --------------
-Best choice!
+Best choice! Version: *0.2.45b*
 ```sh
 $ pip install -U git+https://github.com/twopirllc/pandas-ta
 ```
@@ -656,7 +656,7 @@ Use parameter: cumulative=**True** for cumulative results.
 
 <br/><br/>
 
-# **Performance Metrics** (BETA)
+# **Performance Metrics** &nbsp; _BETA_
 _Performance Metrics_ are a **new** addition to the package and consequentially are likely unreliable. **Use at your own risk.** These metrics return a _float_ and are _not_ part of the _DataFrame_ Extension. They are called the Standard way. For Example:
 
 ```python
@@ -722,8 +722,4 @@ article in the June, 1994 issue of Technical Analysis of Stocks & Commodities Ma
 <br />
 
 # **Sources**
-* [Original TA-LIB](http://ta-lib.org/)
-* [TradingView](http://www.tradingview.com)
-* [Sierra Chart](https://search.sierrachart.com/?Query=indicators&submitted=true)
-* [FM Labs](https://www.fmlabs.com/reference/default.htm)
-* [User 42](https://user42.tuxfamily.org/chart/manual/index.html)
+[Original TA-LIB](http://ta-lib.org/) | [TradingView](http://www.tradingview.com) | [Sierra Chart](https://search.sierrachart.com/?Query=indicators&submitted=true) | [MQL5](https://www.mql5.com) | [FM Labs](https://www.fmlabs.com/reference/default.htm) | [Pro Real Code](https://www.prorealcode.com/prorealtime-indicators) | [User 42](https://user42.tuxfamily.org/chart/manual/index.html)

pandas_ta/candles/cdl_doji.py

@@ -77,7 +77,8 @@ Args:
 
 Kwargs:
     naive (bool, optional): If True, prefills potential Doji less than
-        the length if less than a percentage of it's high-low range. Default: False
+        the length if less than a percentage of it's high-low range.
+        Default: False
     fillna (value, optional): pd.DataFrame.fillna(value)
     fill_method (value, optional): Type of fill method
 

pandas_ta/cycles/ebsw.py

@@ -36,8 +36,7 @@ def ebsw(close, length=None, bars=None, offset=None, **kwargs):
         c3 = -1 * a1 * a1
         c1 = 1 - c2 - c3
         Filt = c1 * (HP + lastHP) / 2 + c2 * FilterHist[1] + c3 * FilterHist[0]
-        # Filt = float("{:.8f}".format(float(Filt)))        # to fix for small scientific notations, the big ones fail
-        # print('Filt: ', Filt, 'FilterHist (list): ', FilterHist)
+        # Filt = float("{:.8f}".format(float(Filt))) # to fix for small scientific notations, the big ones fail
 
         # 3 Bar average of Wave amplitude and power
         Wave = (Filt + FilterHist[1] + FilterHist[0]) / 3

pandas_ta/momentum/ao.py

@@ -57,9 +57,9 @@ Calculation:
 Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
-    fast (int): The short period.  Default: 5
-    slow (int): The long period.   Default: 34
-    offset (int): How many periods to offset the result.  Default: 0
+    fast (int): The short period. Default: 5
+    slow (int): The long period. Default: 34
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/apo.py

@@ -40,7 +40,7 @@ apo.__doc__ = \
 
 The Absolute Price Oscillator is an indicator used to measure a security's
 momentum.  It is simply the difference of two Exponential Moving Averages
-(EMA) of two different periods.  Note: APO and MACD lines are equivalent.
+(EMA) of two different periods. Note: APO and MACD lines are equivalent.
 
 Sources:
     https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/absolute-price-oscillator-apo/
@@ -53,9 +53,9 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    fast (int): The short period.  Default: 12
-    slow (int): The long period.   Default: 26
-    offset (int): How many periods to offset the result.  Default: 0
+    fast (int): The short period. Default: 12
+    slow (int): The long period. Default: 26
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/bias.py

@@ -50,10 +50,10 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): The period.  Default: 26
-    mamode (str): Options: 'ema', 'hma', 'rma', 'sma', 'wma'.  Default: 'sma'
-    drift (int): The short period.  Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): The period. Default: 26
+    mamode (str): Options: 'ema', 'hma', 'rma', 'sma', 'wma'. Default: 'sma'
+    drift (int): The short period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/brar.py

@@ -85,10 +85,10 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    length (int): The period.  Default: 26
-    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
+    length (int): The period. Default: 26
+    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
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/cci.py

@@ -62,9 +62,9 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 14
-    c (float):  Scaling Constant.  Default: 0.015
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 14
+    c (float): Scaling Constant. Default: 0.015
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/cg.py

@@ -46,8 +46,8 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): The length of the period.  Default: 10
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): The length of the period. Default: 10
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/coppock.py

@@ -58,10 +58,10 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): WMA period.  Default: 10
-    fast (int): Fast ROC period.  Default: 11
-    slow (int): Slow ROC period.  Default: 14
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): WMA period. Default: 10
+    fast (int): Fast ROC period. Default: 11
+    slow (int): Slow ROC period. Default: 14
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/er.py

@@ -79,8 +79,8 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/eri.py

@@ -73,8 +73,8 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 14
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 14
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/fisher.py

@@ -102,9 +102,9 @@ Calculation:
 Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
-    length (int): Fisher period.  Default: 9
-    signal (int): Fisher Signal period.  Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): Fisher period. Default: 9
+    signal (int): Fisher Signal period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/inertia.py

@@ -75,10 +75,10 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 20
-    rvi_length (int): RVI period.  Default: 14
-    drift (int): The difference period.  Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 20
+    rvi_length (int): RVI period. Default: 14
+    drift (int): The difference period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/kdj.py

@@ -86,7 +86,7 @@ Args:
     close (pd.Series): Series of 'close's
     length (int): Default: 9
     signal (int): Default: 3
-    offset (int): How many periods to offset the result.  Default: 0
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/kst.py

@@ -83,17 +83,17 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    roc1 (int): ROC 1 period.  Default: 10
-    roc2 (int): ROC 2 period.  Default: 15
-    roc3 (int): ROC 3 period.  Default: 20
-    roc4 (int): ROC 4 period.  Default: 30
-    sma1 (int): SMA 1 period.  Default: 10
-    sma2 (int): SMA 2 period.  Default: 10
-    sma3 (int): SMA 3 period.  Default: 10
-    sma4 (int): SMA 4 period.  Default: 15
-    signal (int): It's period.  Default: 9
-    drift (int): The difference period.   Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    roc1 (int): ROC 1 period. Default: 10
+    roc2 (int): ROC 2 period. Default: 15
+    roc3 (int): ROC 3 period. Default: 20
+    roc4 (int): ROC 4 period. Default: 30
+    sma1 (int): SMA 1 period. Default: 10
+    sma2 (int): SMA 2 period. Default: 10
+    sma3 (int): SMA 3 period. Default: 10
+    sma4 (int): SMA 4 period. Default: 15
+    signal (int): It's period. Default: 9
+    drift (int): The difference period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/macd.py

@@ -93,7 +93,7 @@ macd.__doc__ = \
 
 The MACD is a popular indicator to that is used to identify a security's trend.
 While APO and MACD are the same calculation, MACD also returns two more series
-called Signal and Histogram.  The Signal is an EMA of MACD and the Histogram is
+called Signal and Histogram. The Signal is an EMA of MACD and the Histogram is
 the difference of MACD and Signal.
 
 Sources:
@@ -109,10 +109,10 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    fast (int): The short period.  Default: 12
-    slow (int): The long period.   Default: 26
-    signal (int): The signal period.   Default: 9
-    offset (int): How many periods to offset the result.  Default: 0
+    fast (int): The short period. Default: 12
+    slow (int): The long period. Default: 26
+    signal (int): The signal period. Default: 9
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/mom.py

@@ -45,8 +45,8 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/pgo.py

@@ -57,8 +57,8 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 14
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 14
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/ppo.py

@@ -78,11 +78,11 @@ Calculation:
 
 Args:
     close(pandas.Series): Series of 'close's
-    fast(int): The short period.  Default: 12
-    slow(int): The long period.   Default: 26
-    signal(int): The signal period.   Default: 9
-    scalar (float): How much to magnify.  Default: 100
-    offset(int): How many periods to offset the result.  Default: 0
+    fast(int): The short period. Default: 12
+    slow(int): The long period. Default: 26
+    signal(int): The signal period. Default: 9
+    scalar (float): How much to magnify. Default: 100
+    offset(int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/psl.py

@@ -71,10 +71,10 @@ Calculation:
 Args:
     close (pd.Series): Series of 'close's
     open_ (pd.Series, optional): Series of 'open's
-    length (int): It's period.  Default: 12
-    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
+    length (int): It's period. Default: 12
+    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
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/pvo.py

@@ -76,11 +76,11 @@ Calculation:
 
 Args:
     volume (pd.Series): Series of 'volume's
-    fast (int): The short period.  Default: 12
-    slow (int): The long period.   Default: 26
-    signal (int): The signal period.   Default: 9
-    scalar (float): How much to magnify.  Default: 100
-    offset (int): How many periods to offset the result.  Default: 0
+    fast (int): The short period. Default: 12
+    slow (int): The long period. Default: 26
+    signal (int): The signal period. Default: 9
+    scalar (float): How much to magnify. Default: 100
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/qqe.py

@@ -131,7 +131,6 @@ Sources:
     https://www.tradingview.com/script/IYfA9R2k-QQE-MT4/
     https://www.tradingpedia.com/forex-trading-indicators/quantitative-qualitative-estimation
     https://www.prorealcode.com/prorealtime-indicators/qqe-quantitative-qualitative-estimation/
-    
 
 Calculation:
     Default Inputs:
@@ -142,7 +141,8 @@ Args:
     length (int): RSI period. Default: 14
     smooth (int): RSI smoothing period. Default: 5
     factor (float): QQE Factor. Default: 4.236
-    mamode (str): Smoothing MA type: "ema", "hma", "rma", "sma" or "wma". Default: "ema"
+    mamode (str): Smoothing MA type: "ema", "hma", "rma", "sma" or "wma".
+        Default: "ema"
     drift (int): The difference period. Default: 1
     offset (int): How many periods to offset the result. Default: 0
 

pandas_ta/momentum/roc.py

@@ -48,8 +48,8 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/rvgi.py

@@ -74,9 +74,9 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 14
-    swma_length (int): It's period.  Default: 4
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 14
+    swma_length (int): It's period. Default: 4
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/slope.py

@@ -39,7 +39,8 @@ def slope( close, length=None, as_angle=None, to_degrees=None, vertical=None, of
 slope.__doc__ = \
 """Slope
 
-Returns the slope of a series of length n.  Can convert the slope to angle. Default: slope.
+Returns the slope of a series of length n. Can convert the slope to angle.
+Default: slope.
 
 Sources: Algebra I
 

pandas_ta/momentum/squeeze.py

@@ -203,7 +203,7 @@ Args:
     mom_length (int): Momentum Period. Default: 12
     mom_smooth (int): Smoothing Period of Momentum. Default: 6
     mamode (str): Only "ema" or "sma". Default: "sma"
-    offset (int): How many periods to offset the result.  Default: 0
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     tr (value, optional): Use True Range for Keltner Channels. Default: True

pandas_ta/momentum/stoch.py

@@ -88,7 +88,7 @@ Args:
     k (int): The Fast %K period. Default: 14
     d (int): The Slow %K period. Default: 3
     smooth_k (int): The Slow %D period. Default: 3
-    offset (int): How many periods to offset the result.  Default: 0
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/stochrsi.py

@@ -90,7 +90,7 @@ Args:
     rsi_length (int): RSI period. Default: 14
     k (int): The Fast %K period. Default: 3
     d (int): The Slow %K period. Default: 3
-    offset (int): How many periods to offset the result.  Default: 0
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/trix.py

@@ -68,11 +68,11 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 18
-    signal (int): It's period.  Default: 9
-    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
+    length (int): It's period. Default: 18
+    signal (int): It's period. Default: 9
+    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
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/tsi.py

@@ -73,7 +73,7 @@ Args:
     slow (int): The long period. Default: 25
     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
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/uo.py

@@ -90,14 +90,14 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    fast (int): The Fast %K period.  Default: 7
-    medium (int): The Slow %K period.  Default: 14
-    slow (int): The Slow %D period.  Default: 28
-    fast_w (float): The Fast %K period.  Default: 4.0
-    medium_w (float): The Slow %K period.  Default: 2.0
-    slow_w (float): The Slow %D period.  Default: 1.0
-    drift (int): The difference period.   Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    fast (int): The Fast %K period. Default: 7
+    medium (int): The Slow %K period. Default: 14
+    slow (int): The Slow %D period. Default: 28
+    fast_w (float): The Fast %K period. Default: 4.0
+    medium_w (float): The Slow %K period. Default: 2.0
+    slow_w (float): The Slow %D period. Default: 1.0
+    drift (int): The difference period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/momentum/willr.py

@@ -56,8 +56,8 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 14
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 14
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/overlap/alma.py

@@ -15,7 +15,7 @@ def alma(close, length=None, sigma=None, distribution_offset=None, offset=None,
     offset = get_offset(offset)
 
     # Pre-Calculations
-    m = (distribution_offset * (length - 1))
+    m = distribution_offset * (length - 1)
     s = length / sigma
     wtd = list(range(length))
     for i in range(0, length):
@@ -31,6 +31,7 @@ def alma(close, length=None, sigma=None, distribution_offset=None, offset=None,
             window_sum = window_sum + wtd[j] * close[i - j]
             cum_sum = cum_sum + wtd[j]
         almean = window_sum / cum_sum
+
         if i == length:
             result.append(npNaN)                                            # additional one bar NaN as pre-roll
         else:
@@ -76,10 +77,9 @@ Args:
     close (pd.Series): Series of 'close's
     length (int): It's period, window size. Default: 10
     sigma (float): Smoothing value. Default 6.0
-    distribution_offset (float):
-        Value to offset the distribution min 0 (smoother),
-        max 1 (more responsive). Default 0.85
-    offset (int): How many periods to offset the result.  Default: 0
+    distribution_offset (float): Value to offset the distribution min 0
+        (smoother), max 1 (more responsive). Default 0.85
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/overlap/dema.py

@@ -46,8 +46,8 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 10
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/overlap/ema.py

@@ -55,8 +55,8 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 10
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     adjust (bool, optional): Default: False

pandas_ta/overlap/fwma.py

@@ -47,9 +47,9 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 10
-    asc (bool): Recent values weigh more.  Default: True
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    asc (bool): Recent values weigh more. Default: True
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/overlap/hilo.py

@@ -113,8 +113,8 @@ Args:
     close (pd.Series): Series of 'close's
     high_length (int): It's period. Default: 13
     low_length (int): It's period. Default: 21
-    mamode (str): Options: 'sma' or 'ema'.  Default: 'sma'
-    offset (int): How many periods to offset the result.  Default: 0
+    mamode (str): Options: 'sma' or 'ema'. Default: 'sma'
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     adjust (bool): Default: True

pandas_ta/overlap/hma.py

@@ -52,8 +52,8 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 10
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/overlap/hwma.py

@@ -7,19 +7,18 @@ def hwma(close, na=None, nb=None, nc=None, offset=None, **kwargs):
     """Indicator: Holt-Winter Moving Average"""
     # Validate Arguments
     close = verify_series(close)
-    na = float(na) if na and na > 0 else 0.2
-    nb = float(nb) if nb and nb > 0 else 0.1
-    nc = float(nc) if nc and nc > 0 else 0.1
+    na = float(na) if na and na > 0 and na < 1 else 0.2
+    nb = float(nb) if nb and nb > 0 and nb < 1 else 0.1
+    nc = float(nc) if nc and nc > 0 and nc < 1 else 0.1
     offset = get_offset(offset)
 
-    # Initialize ..
-    m = close.size
-    last_f = close[0]
-    # last_a, last_v = 0, 0
-    last_a = last_v = 0
-    result = []
 
-    # Calculate ..
+    # Calculate Result
+    last_a = last_v = 0
+    last_f = close[0]
+
+    result = []
+    m = close.size
     for i in range(m):
         F = (1.0 - na) * (last_f + last_v + 0.5 * last_a) + na * close[i]
         V = (1.0 - nb) * (last_v + last_a) + nb * (F - last_f)
@@ -70,9 +69,10 @@ Calculation:
     A[i] = (1-nc) * A[i-1] + nc * (V[i] - V[i-1])
 
 Args:
-    na - parameter of the equation that describes a smoothed series (from 0 to 1)
-    nb - parameter of the equation to assess the trend (from 0 to 1)
-    nc - parameter of the equation to assess seasonality (from 0 to 1)
+    close (pd.Series): Series of 'close's
+    na (float): Smoothed series parameter (from 0 to 1). Default: 0.2
+    nb (float): Trend parameter (from 0 to 1). Default: 0.1
+    nc (float): Seasonality parameter (from 0 to 1). Default: 0.1
     close (pd.Series): Series of 'close's
 
 Kwargs:

pandas_ta/overlap/ichimoku.py

@@ -115,10 +115,10 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    tenkan (int): Tenkan period.  Default: 9
-    kijun (int): Kijun period.  Default: 26
-    senkou (int): Senkou period.  Default: 52
-    offset (int): How many periods to offset the result.  Default: 0
+    tenkan (int): Tenkan period. Default: 9
+    kijun (int): Kijun period. Default: 26
+    senkou (int): Senkou period. Default: 52
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/overlap/kama.py

@@ -66,11 +66,11 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 10
-    fast (int): Fast MA period.  Default: 2
-    slow (int): Slow MA period.  Default: 30
-    drift (int): The difference period.  Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    fast (int): Fast MA period. Default: 2
+    slow (int): Slow MA period. Default: 30
+    drift (int): The difference period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/overlap/linreg.py

@@ -105,12 +105,16 @@ Args:
     offset (int): How many periods to offset the result.  Default: 0
 
 Kwargs:
-    angle (bool, optional): If True, returns the angle of the slope in radians. Default: False.
-    degrees (bool, optional): If True, returns the angle of the slope in degrees. Default: False.
-    intercept (bool, optional): If True, returns the angle of the slope in radians. Default: False.
-    r (bool, optional): If True, returns it's correlation 'r'. Default: False. 
+    angle (bool, optional): If True, returns the angle of the slope in radians.
+        Default: False.
+    degrees (bool, optional): If True, returns the angle of the slope in
+        degrees. Default: False.
+    intercept (bool, optional): If True, returns the angle of the slope in
+        radians. Default: False.
+    r (bool, optional): If True, returns it's correlation 'r'. Default: False.
     slope (bool, optional): If True, returns the slope. Default: False.
-    tsf (bool, optional): If True, returns the Time Series Forecast value. Default: False.
+    tsf (bool, optional): If True, returns the Time Series Forecast value.
+        Default: False.
     fillna (value, optional): pd.DataFrame.fillna(value)
     fill_method (value, optional): Type of fill method
 

pandas_ta/overlap/pwma.py

@@ -29,8 +29,8 @@ def pwma(close, length=None, asc=None, offset=None, **kwargs):
 pwma.__doc__ = \
 """Pascal's Weighted Moving Average (PWMA)
 
-Pascal's Weighted Moving Average is similar to a symmetric triangular
-window except PWMA's weights are based on Pascal's Triangle.
+Pascal's Weighted Moving Average is similar to a symmetric triangular window
+except PWMA's weights are based on Pascal's Triangle.
 
 Source: Kevin Johnson
 
@@ -49,8 +49,8 @@ Calculation:
 Args:
     close (pd.Series): Series of 'close's
     length (int): It's period.  Default: 10
-    asc (bool): Recent values weigh more.  Default: True
-    offset (int): How many periods to offset the result.  Default: 0
+    asc (bool): Recent values weigh more. Default: True
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/overlap/rma.py

@@ -27,7 +27,8 @@ def rma(close, length=None, offset=None, **kwargs):
 rma.__doc__ = \
 """wildeR's Moving Average (RMA)
 
-The WildeR's Moving Average is simply an Exponential Moving Average (EMA) with a modified alpha = 1 / length.
+The WildeR's Moving Average is simply an Exponential Moving Average (EMA) with
+a modified alpha = 1 / length.
 
 Sources:
     https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/V-Z/WildersSmoothing
@@ -42,8 +43,8 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 10
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/overlap/sinwma.py

@@ -32,8 +32,8 @@ def sinwma(close, length=None, offset=None, **kwargs):
 sinwma.__doc__ = \
 """Sine Weighted Moving Average (SWMA)
 
-A weighted average using sine cycles.  The middle term(s) of the average have the highest
-weight(s).
+A weighted average using sine cycles. The middle term(s) of the average have the
+highest weight(s).
 
 Source:
     https://www.tradingview.com/script/6MWFvnPO-Sine-Weighted-Moving-Average/
@@ -54,8 +54,8 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 10
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/overlap/sma.py

@@ -46,8 +46,8 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 10
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     adjust (bool): Default: True

pandas_ta/overlap/supertrend.py

@@ -109,8 +109,9 @@ Args:
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
     length (int) : length for ATR calculation. Default: 7
-    multiplier (float): Coefficient for upper and lower band distance to midrange. Default: 3.0
-    offset (int): How many periods to offset the result.  Default: 0
+    multiplier (float): Coefficient for upper and lower band distance to
+        midrange. Default: 3.0
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/overlap/swma.py

@@ -31,8 +31,9 @@ swma.__doc__ = \
 """Symmetric Weighted Moving Average (SWMA)
 
 Symmetric Weighted Moving Average where weights are based on a symmetric
-triangle.  For example: n=3 -> [1, 2, 1], n=4 -> [1, 2, 2, 1], etc...  This moving
-average has variable length in contrast to TradingView's fixed length of 4.
+triangle.  For example: n=3 -> [1, 2, 1], n=4 -> [1, 2, 2, 1], etc...
+This moving average has variable length in contrast to TradingView's fixed
+length of 4.
 
 Source:
     https://www.tradingview.com/study-script-reference/#fun_swma
@@ -51,9 +52,9 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 10
-    asc (bool): Recent values weigh more.  Default: True
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    asc (bool): Recent values weigh more. Default: True
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/overlap/t3.py

@@ -62,9 +62,9 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 10
-    a (float): 0 < a < 1.  Default: 0.7
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    a (float): 0 < a < 1. Default: 0.7
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     adjust (bool): Default: True

pandas_ta/overlap/tema.py

@@ -46,8 +46,8 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 10
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     adjust (bool): Default: True

pandas_ta/overlap/trima.py

@@ -47,8 +47,8 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 10
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     adjust (bool): Default: True

pandas_ta/overlap/vwma.py

@@ -44,8 +44,8 @@ Calculation:
 Args:
     close (pd.Series): Series of 'close's
     volume (pd.Series): Series of 'volume's
-    length (int): It's period.  Default: 10
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/overlap/wcp.py

@@ -40,7 +40,7 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    offset (int): How many periods to offset the result.  Default: 0
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/overlap/wma.py

@@ -62,9 +62,9 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 10
-    asc (bool): Recent values weigh more.  Default: True
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    asc (bool): Recent values weigh more. Default: True
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/overlap/zlma.py

@@ -60,9 +60,9 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 10
-    mamode (str): Options: 'ema', 'hma', 'sma', 'wma'.  Default: 'ema'
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    mamode (str): Options: 'ema', 'hma', 'sma', 'wma'. Default: 'ema'
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/performance/log_return.py

@@ -50,9 +50,9 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 20
-    cumulative (bool): If True, returns the cumulative returns.  Default: False
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 20
+    cumulative (bool): If True, returns the cumulative returns. Default: False
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/performance/percent_return.py

@@ -43,9 +43,9 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 20
-    cumulative (bool): If True, returns the cumulative returns.  Default: False
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 20
+    cumulative (bool): If True, returns the cumulative returns. Default: False
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/statistics/median.py

@@ -27,7 +27,7 @@ def median(close, length=None, offset=None, **kwargs):
 median.__doc__ = \
 """Rolling Median
 
-Rolling Median of over 'n' periods.  Sibling of a Simple Moving Average.
+Rolling Median of over 'n' periods. Sibling of a Simple Moving Average.
 
 Sources:
     https://www.incrediblecharts.com/indicators/median_price.php

pandas_ta/trend/chop.py

@@ -71,11 +71,11 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 14
-    atr_length (int): Length for ATR.  Default: 1
-    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
+    length (int): It's period. Default: 14
+    atr_length (int): Length for ATR. Default: 1
+    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
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/trend/cksp.py

@@ -75,10 +75,10 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    p (int): ATR and first stop period.  Default: 10
-    x (float): ATR scalar.  Default: 1
-    q (int): Second stop period.  Default: 9
-    offset (int): How many periods to offset the result.  Default: 0
+    p (int): ATR and first stop period. Default: 10
+    x (float): ATR scalar. Default: 1
+    q (int): Second stop period. Default: 9
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/trend/decreasing.py

@@ -46,7 +46,10 @@ def decreasing(close, length=None, strict=None, asint=None, offset=None, **kwarg
 decreasing.__doc__ = \
 """Decreasing
 
-Returns True if the series is decreasing over a period, False otherwise. If the kwarg 'strict' is True, it returns True if it is continuously decreasing over the period. When using the kwarg 'asint', then it returns 1 for True or 0 for False. 
+Returns True if the series is decreasing over a period, False otherwise.
+If the kwarg 'strict' is True, it returns True if it is continuously decreasing
+over the period. When using the kwarg 'asint', then it returns 1 for True
+or 0 for False.
 
 Calculation:
     if strict:

pandas_ta/trend/dpo.py

@@ -58,9 +58,9 @@ Calculation:
 
 Args:
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 1
-    centered (bool): Shift the dpo back by int(0.5 * length) + 1.  Default: True
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 1
+    centered (bool): Shift the dpo back by int(0.5 * length) + 1. Default: True
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/trend/increasing.py

@@ -46,7 +46,10 @@ def increasing(close, length=None, strict=None, asint=None, offset=None, **kwarg
 increasing.__doc__ = \
 """Increasing
 
-Returns True if the series is increasing over a period, False otherwise. If the kwarg 'strict' is True, it returns True if it is continuously increasing over the period. When using the kwarg 'asint', then it returns 1 for True or 0 for False. 
+Returns True if the series is increasing over a period, False otherwise.
+If the kwarg 'strict' is True, it returns True if it is continuously increasing
+over the period. When using the kwarg 'asint', then it returns 1 for True
+or 0 for False. 
 
 Calculation:
     if strict:

pandas_ta/trend/psar.py

@@ -128,9 +128,9 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series, optional): Series of 'close's. Optional
-    af (float): Acceleration Factor.  Default: 0.02
-    max_af (float): Maximum Acceleration Factor.  Default: 0.2
-    offset (int): How many periods to offset the result.  Default: 0
+    af (float): Acceleration Factor. Default: 0.02
+    max_af (float): Maximum Acceleration Factor. Default: 0.2
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/trend/qstick.py

@@ -46,8 +46,8 @@ def qstick(open_, close, length=None, offset=None, **kwargs):
 qstick.__doc__ = \
 """Q Stick
 
-The Q Stick indicator, developed by Tushar Chande, attempts to quantify and identify
-trends in candlestick charts.
+The Q Stick indicator, developed by Tushar Chande, attempts to quantify and
+identify trends in candlestick charts.
 
 Sources:
     https://library.tradingtechnologies.com/trade/chrt-ti-qstick.html
@@ -61,9 +61,9 @@ Calculation:
 Args:
     open (pd.Series): Series of 'open's
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 1
-    ma (str): The type of moving average to use.  Default: None, which is 'sma'
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 1
+    ma (str): The type of moving average to use. Default: None, which is 'sma'
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/trend/ttm_trend.py

@@ -72,8 +72,8 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 6
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 6
+    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

pandas_ta/trend/vortex.py

@@ -78,9 +78,9 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    length (int): ROC 1 period.  Default: 14
-    drift (int): The difference period.   Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): ROC 1 period. Default: 14
+    drift (int): The difference period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volatility/aberration.py

@@ -86,9 +86,9 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    length (int): The short period.  Default: 5
-    atr_length (int): The short period.  Default: 15
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): The short period. Default: 5
+    atr_length (int): The short period. Default: 15
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volatility/accbands.py

@@ -89,11 +89,11 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    length (int): It's period.  Default: 10
-    c (int): Multiplier.  Default: 4
-    mamode (str): Two options: None or 'ema'.  Default: 'ema'
-    drift (int): The difference period.   Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): It's period. Default: 10
+    c (int): Multiplier. Default: 4
+    mamode (str): Two options: None or 'ema'. Default: 'ema'
+    drift (int): The difference period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volatility/atr.py

@@ -43,8 +43,8 @@ def atr(high, low, close, length=None, mamode=None, drift=None, offset=None, **k
 atr.__doc__ = \
 """Average True Range (ATR)
 
-Averge True Range is used to measure volatility, especially
-volatility caused by gaps or limit moves.
+Averge True Range is used to measure volatility, especially volatility caused by
+gaps or limit moves.
 
 Sources:
     https://www.tradingview.com/wiki/Average_True_Range_(ATR)

pandas_ta/volatility/hwc.py

@@ -31,7 +31,6 @@ def hwc(close, na=None, nb=None, nc=None, nd=None, scalar=None, channel_eval=Non
     chan_width = []
     chan_pct_width = []
 
-    print(channel_eval)
     # Calculate ..
     for i in range(m):
         F = (1.0 - na) * (last_f + last_v + 0.5 * last_a) + na * close[i]
@@ -125,10 +124,12 @@ def hwc(close, na=None, nb=None, nc=None, nd=None, scalar=None, channel_eval=Non
 hwc.__doc__ = \
 """HWC (Holt-Winter Channel)
 
-Channel indicator HWC (Holt-Winters Channel) based on HWMA - a three-parameter moving average 
-calculated by the method of Holt-Winters.
-This version has been implemented for Pandas TA by rengel8 based on a publication for MetaTrader 5
-extended by width and percentage price position against width of channel.
+Channel indicator HWC (Holt-Winters Channel) based on HWMA - a three-parameter
+moving average calculated by the method of Holt-Winters.
+
+This version has been implemented for Pandas TA by rengel8 based on a
+publication for MetaTrader 5 extended by width and percentage price position
+against width of channel.
 
 Sources:
     https://www.mql5.com/en/code/20857
@@ -139,7 +140,7 @@ Calculation:
     F[i] = (1-na) * (F[i-1] + V[i-1] + 0.5 * A[i-1]) + na * Price[i]
     V[i] = (1-nb) * (V[i-1] + A[i-1]) + nb * (F[i] - F[i-1])
     A[i] = (1-nc) * A[i-1] + nc * (V[i] - V[i-1])
-    
+
     Top = HWMA + Multiplier * StDt
     Bottom = HWMA - Multiplier * StDt
     where..
@@ -152,7 +153,7 @@ Args:
     nc - parameter of the equation to assess seasonality (from 0 to 1)
     nd - parameter of the channel equation (from 0 to 1)
     scaler - multiplier for the width of the channel calculated
-    channel_eval - boolean to return width and percentage price position against price  
+    channel_eval - boolean to return width and percentage price position against price
     close (pd.Series): Series of 'close's
 
 Kwargs:

pandas_ta/volatility/massi.py

@@ -63,9 +63,9 @@ Calculation:
 Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
-    fast (int): The short period.  Default: 9
-    slow (int): The long period.   Default: 25
-    offset (int): How many periods to offset the result.  Default: 0
+    fast (int): The short period. Default: 9
+    slow (int): The long period. Default: 25
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volatility/natr.py

@@ -39,8 +39,7 @@ def natr(high, low, close, length=None, mamode=None, scalar=None, drift=None, of
 natr.__doc__ = \
 """Normalized Average True Range (NATR)
 
-Normalized Average True Range attempt to normalize the average
-true range.
+Normalized Average True Range attempt to normalize the average true range.
 
 Sources:
     https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/normalized-average-true-range-natr/
@@ -55,9 +54,9 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    length (int): The short period.  Default: 20
-    scalar (float): How much to magnify.  Default: 100
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): The short period. Default: 20
+    scalar (float): How much to magnify. Default: 100
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volatility/pdist.py

@@ -47,8 +47,8 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    drift (int): The difference period.   Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    drift (int): The difference period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volatility/rvi.py

@@ -72,10 +72,9 @@ def rvi(close, high=None, low=None, length=None, scalar=None, refined=None, thir
 rvi.__doc__ = \
 """Relative Volatility Index (RVI)
 
-The Relative Volatility Index (RVI) was created in 1993 and
-revised in 1995. Instead of adding up price changes like RSI
-based on price direction, the RVI adds up standard deviations
-based on price direction.
+The Relative Volatility Index (RVI) was created in 1993 and revised in 1995.
+Instead of adding up price changes like RSI based on price direction, the RVI
+adds up standard deviations based on price direction.
 
 Sources:
     https://www.tradingview.com/wiki/Keltner_Channels_(KC)
@@ -99,12 +98,12 @@ Args:
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
     length (int): The short period. Default: 14
-    scalar (float): A positive float to scale the bands.   Default: 100
+    scalar (float): A positive float to scale the bands. Default: 100
     mamode (str): Options: 'sma' or 'ema'. Default: 'sma'
     refined (bool): Use 'refined' calculation which is the average of
         RVI(high) and RVI(low) instead of RVI(close). Default: False
     thirds (bool): Average of high, low and close. Default: False
-    offset (int): How many periods to offset the result.  Default: 0
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volatility/thermo.py

@@ -34,7 +34,7 @@ def thermo(high, low, length=None, long=None, short=None, mamode=None, drift=Non
 
     # Binary output, useful for signals
     if asint:
-        thermo_long = thermo_long.astype(int)  
+        thermo_long = thermo_long.astype(int)
         thermo_short = thermo_short.astype(int)
 
     # Offset
@@ -66,7 +66,12 @@ def thermo(high, low, length=None, long=None, short=None, mamode=None, drift=Non
     thermo.category = thermo_ma.category = thermo_long.category = thermo_short.category = "volatility"
 
     # Prepare Dataframe to return
-    data = {thermo.name: thermo, thermo_ma.name: thermo_ma, thermo_long.name: thermo_long, thermo_short.name: thermo_short}
+    data = {
+        thermo.name: thermo,
+        thermo_ma.name: thermo_ma,
+        thermo_long.name: thermo_long,
+        thermo_short.name: thermo_short
+    }
     df = DataFrame(data)
     df.name = f"THERMO{_props}"
     df.category = thermo.category

pandas_ta/volatility/true_range.py

@@ -56,8 +56,8 @@ Args:
     high (pd.Series): Series of 'high's
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
-    drift (int): The shift period.   Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    drift (int): The shift period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volume/ad.py

@@ -65,7 +65,7 @@ Args:
     close (pd.Series): Series of 'close's
     volume (pd.Series): Series of 'volume's
     open (pd.Series): Series of 'open's
-    offset (int): How many periods to offset the result.  Default: 0
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volume/adosc.py

@@ -63,9 +63,9 @@ Args:
     close (pd.Series): Series of 'close's
     open (pd.Series): Series of 'open's
     volume (pd.Series): Series of 'volume's
-    fast (int): The short period.  Default: 12
-    slow (int): The long period.   Default: 26
-    offset (int): How many periods to offset the result.  Default: 0
+    fast (int): The short period. Default: 12
+    slow (int): The long period. Default: 26
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volume/cmf.py

@@ -70,8 +70,8 @@ Args:
     close (pd.Series): Series of 'close's
     volume (pd.Series): Series of 'volume's
     open_ (pd.Series): Series of 'open's. Default: None
-    length (int): The short period.  Default: 20
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): The short period. Default: 20
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volume/efi.py

@@ -59,10 +59,10 @@ Calculation:
 Args:
     close (pd.Series): Series of 'close's
     volume (pd.Series): Series of 'volume's
-    length (int): The short period.  Default: 13
-    drift (int): The diff period.   Default: 1
-    mamode (str): Two options: None or "sma".  Default: None
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): The short period. Default: 13
+    drift (int): The diff period. Default: 1
+    mamode (str): Two options: None or "sma". Default: None
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volume/eom.py

@@ -67,9 +67,9 @@ Args:
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
     volume (pd.Series): Series of 'volume's
-    length (int): The short period.  Default: 14
-    drift (int): The diff period.   Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): The short period. Default: 14
+    drift (int): The diff period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volume/mfi.py

@@ -76,9 +76,9 @@ Args:
     low (pd.Series): Series of 'low's
     close (pd.Series): Series of 'close's
     volume (pd.Series): Series of 'volume's
-    length (int): The sum period.  Default: 14
-    drift (int): The difference period.   Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): The sum period. Default: 14
+    drift (int): The difference period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volume/nvi.py

@@ -63,9 +63,9 @@ Calculation:
 Args:
     close (pd.Series): Series of 'close's
     volume (pd.Series): Series of 'volume's
-    length (int): The short period.  Default: 13
-    initial (int): The short period.  Default: 1000
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): The short period. Default: 13
+    initial (int): The short period. Default: 1000
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volume/obv.py

@@ -48,7 +48,7 @@ Calculation:
 Args:
     close (pd.Series): Series of 'close's
     volume (pd.Series): Series of 'volume's
-    offset (int): How many periods to offset the result.  Default: 0
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volume/pvi.py

@@ -42,7 +42,8 @@ pvi.__doc__ = \
 """Positive Volume Index (PVI)
 
 The Positive Volume Index is a cumulative indicator that uses volume change in
-an attempt to identify where smart money is active.  Used in conjunction with NVI.
+an attempt to identify where smart money is active.
+Used in conjunction with NVI.
 
 Sources:
     https://www.investopedia.com/terms/p/pvi.asp
@@ -62,9 +63,9 @@ Calculation:
 Args:
     close (pd.Series): Series of 'close's
     volume (pd.Series): Series of 'volume's
-    length (int): The short period.  Default: 13
-    initial (int): The short period.  Default: 1000
-    offset (int): How many periods to offset the result.  Default: 0
+    length (int): The short period. Default: 13
+    initial (int): The short period. Default: 1000
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volume/pvol.py

@@ -47,8 +47,8 @@ Calculation:
 Args:
     close (pd.Series): Series of 'close's
     volume (pd.Series): Series of 'volume's
-    signed (bool): Keeps the sign of the difference in 'close's.  Default: True
-    offset (int): How many periods to offset the result.  Default: 0
+    signed (bool): Keeps the sign of the difference in 'close's. Default: True
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volume/pvt.py

@@ -51,8 +51,8 @@ Calculation:
 Args:
     close (pd.Series): Series of 'close's
     volume (pd.Series): Series of 'volume's
-    drift (int): The diff period.   Default: 1
-    offset (int): How many periods to offset the result.  Default: 0
+    drift (int): The diff period. Default: 1
+    offset (int): How many periods to offset the result. Default: 0
 
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)

pandas_ta/volume/vp.py

@@ -63,7 +63,8 @@ def vp(close, volume, width=None, **kwargs):
 vp.__doc__ = \
 """Volume Profile (VP)
 
-Calculates the Volume Profile by slicing price into ranges.  Note: Value Area is not calculated.
+Calculates the Volume Profile by slicing price into ranges.
+Note: Value Area is not calculated.
 
 Sources:
     https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:volume_by_price
@@ -89,7 +90,8 @@ Args:
 Kwargs:
     fillna (value, optional): pd.DataFrame.fillna(value)
     fill_method (value, optional): Type of fill method
-    sort_close (value, optional): Whether to sort by close before splitting into ranges. Default: False
+    sort_close (value, optional): Whether to sort by close before splitting
+        into ranges. Default: False
 
 Returns:
     pd.DataFrame: New feature generated.

setup.py

@@ -18,7 +18,7 @@ setup(
         "pandas_ta.volatility",
         "pandas_ta.volume"
     ],
-    version=".".join(("0", "2", "44b")),
+    version=".".join(("0", "2", "45b")),
     description=long_description,
     long_description=long_description,
     author="Kevin Johnson",

tests/test_strategy.py

@@ -89,7 +89,6 @@ class TestStrategyMethods(TestCase):
     def test_cycles_category(self):
         self.category = "Cycles"
         self.data.ta.strategy(self.category, verbose=verbose, timed=strategy_timed)
-        print(f"\n{self.data}")
 
     # @skip
     def test_custom_a(self):