all volatility indicators fully typed

pandas_ta/volatility/aberration.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 # from numpy import sqrt as npsqrt
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from .atr import atr
 from pandas_ta.overlap import hlc3, sma
 from pandas_ta.utils import get_offset, verify_series
 
 
-def aberration(high, low, close, length=None, atr_length=None, offset=None, **kwargs):
+def aberration(high: Series, low: Series, close: Series, length: int = None, atr_length: int = None,
+               offset: int = None, **kwargs) -> DataFrame:
     """Aberration (ABER)
 
     A volatility indicator similar to Keltner Channels.

pandas_ta/volatility/accbands.py

@@ -1,10 +1,11 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.overlap import ma
 from pandas_ta.utils import get_drift, get_offset, non_zero_range, verify_series
 
 
-def accbands(high, low, close, length=None, c=None, drift=None, mamode=None, offset=None, **kwargs):
+def accbands(high: Series, low: Series, close: Series, length: int = None, c: int = None, drift: int = None,
+             mamode: str = None, offset: int = None, **kwargs) -> DataFrame:
     """Acceleration Bands (ACCBANDS)
 
     Acceleration Bands created by Price Headley plots upper and lower envelope

pandas_ta/volatility/atr.py

@@ -3,9 +3,11 @@ from .true_range import true_range
 from pandas_ta import Imports
 from pandas_ta.overlap import ma
 from pandas_ta.utils import get_drift, get_offset, verify_series
+from pandas import Series
 
 
-def atr(high, low, close, length=None, mamode=None, talib=None, drift=None, offset=None, **kwargs):
+def atr(high: Series, low: Series, close: Series, length: int = None, mamode: str = None, talib: bool = None,
+        drift: int = None, offset: int = None, **kwargs) -> Series:
     """Average True Range (ATR)
 
     Averge True Range is used to measure volatility, especially volatility caused by

pandas_ta/volatility/bbands.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta import Imports
 from pandas_ta.overlap import ma
 from pandas_ta.statistics import stdev
 from pandas_ta.utils import get_offset, non_zero_range, tal_ma, verify_series
 
 
-def bbands(close, length=None, std=None, ddof=0, mamode=None, talib=None, offset=None, **kwargs):
+def bbands(close: Series, length: int = None, std: int = None, ddof: int = 0, mamode: str = None, talib: bool = None,
+           offset: int = None, **kwargs) -> DataFrame:
     """Bollinger Bands (BBANDS)
 
     A popular volatility indicator by John Bollinger.

pandas_ta/volatility/donchian.py

@@ -1,9 +1,10 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.utils import get_offset, verify_series
 
 
-def donchian(high, low, lower_length=None, upper_length=None, offset=None, **kwargs):
+def donchian(high: Series, low: Series, lower_length: int = None, upper_length: int = None, offset: int = None,
+             **kwargs) -> DataFrame:
     """Donchian Channels (DC)
 
     Donchian Channels are used to measure volatility, similar to

pandas_ta/volatility/hwc.py

@@ -4,7 +4,8 @@ from pandas import DataFrame, Series
 from pandas_ta.utils import get_offset, verify_series
 
 
-def hwc(close, na=None, nb=None, nc=None, nd=None, scalar=None, channel_eval=None, offset=None, **kwargs):
+def hwc(close: Series, na: float = None, nb: float = None, nc: float = None, nd: float = None, scalar: float = None,
+        channel_eval: bool = None, offset: int = None, **kwargs) -> DataFrame:
     """HWC (Holt-Winter Channel)
 
     Channel indicator HWC (Holt-Winters Channel) based on HWMA - a three-parameter

pandas_ta/volatility/kc.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from .true_range import true_range
 from pandas_ta.overlap import ma
 from pandas_ta.utils import get_offset, high_low_range, verify_series
 
 
-def kc(high, low, close, length=None, scalar=None, mamode=None, offset=None, **kwargs):
+def kc(high: Series, low: Series, close: Series, length: int = None, scalar: float = None, mamode: str = None,
+       offset: int = None, **kwargs) -> DataFrame:
     """Keltner Channels (KC)
 
     A popular volatility indicator similar to Bollinger Bands and

pandas_ta/volatility/massi.py

@@ -1,9 +1,10 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.overlap import ema
 from pandas_ta.utils import get_offset, non_zero_range, verify_series
+from pandas import Series
 
 
-def massi(high, low, fast=None, slow=None, offset=None, **kwargs):
+def massi(high: Series, low: Series, fast: int = None, slow: int = None, offset: int = None, **kwargs) -> Series:
     """Mass Index (MASSI)
 
     The Mass Index is a non-directional volatility indicator that utilitizes the

pandas_ta/volatility/natr.py

@@ -2,9 +2,11 @@
 from .atr import atr
 from pandas_ta import Imports
 from pandas_ta.utils import get_drift, get_offset, verify_series
+from pandas import Series
 
 
-def natr(high, low, close, length=None, scalar=None, mamode=None, talib=None, drift=None, offset=None, **kwargs):
+def natr(high: Series, low: Series, close: Series, length: int = None, scalar: float = None, mamode: str = None,
+         talib: bool = None, drift: int = None, offset: int = None, **kwargs) -> Series:
     """Normalized Average True Range (NATR)
 
     Normalized Average True Range attempt to normalize the average true range.

pandas_ta/volatility/pdist.py

@@ -1,8 +1,10 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.utils import get_drift, get_offset, non_zero_range, verify_series
+from pandas import Series
 
 
-def pdist(open_, high, low, close, drift=None, offset=None, **kwargs):
+def pdist(open_: Series, high: Series, low: Series, close: Series, drift: int = None, offset: int = None,
+          **kwargs) -> Series:
     """Price Distance (PDIST)
 
     Measures the "distance" covered by price movements.

pandas_ta/volatility/rvi.py

@@ -3,9 +3,12 @@ from pandas_ta.overlap import ma
 from pandas_ta.statistics import stdev
 from pandas_ta.utils import get_drift, get_offset
 from pandas_ta.utils import unsigned_differences, verify_series
+from pandas import Series
 
 
-def rvi(close, high=None, low=None, length=None, scalar=None, refined=None, thirds=None, mamode=None, drift=None, offset=None, **kwargs):
+def rvi(close: Series, high: Series = None, low: Series = None, length: int = None, scalar: float = None,
+        refined: bool = None, thirds: bool = None, mamode: str = None, drift: int = None,
+        offset: int = None, **kwargs) -> Series:
     """Relative Volatility Index (RVI)
 
     The Relative Volatility Index (RVI) was created in 1993 and revised in 1995.

pandas_ta/volatility/thermo.py

@@ -1,10 +1,11 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.overlap import ma
 from pandas_ta.utils import get_offset, verify_series, get_drift
 
 
-def thermo(high, low, length=None, long=None, short=None, mamode=None, drift=None, offset=None, **kwargs):
+def thermo(high: Series, low: Series, length: int = None, long: int = None, short: int = None, mamode: str = None,
+           drift: int = None, offset: int = None, **kwargs) -> DataFrame:
     """Elders Thermometer (THERMO)
 
     Elder's Thermometer measures price volatility.

pandas_ta/volatility/true_range.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from numpy import nan as npNaN
-from pandas import concat
+from pandas import concat, Series
 from pandas_ta import Imports
 from pandas_ta.utils import get_drift, get_offset, non_zero_range, verify_series
 
 
-def true_range(high, low, close, talib=None, drift=None, offset=None, **kwargs):
+def true_range(high: Series, low: Series, close: Series, talib: bool = None, drift: int = None, offset: int = None,
+               **kwargs) -> Series:
     """True Range
 
     An method to expand a classical range (high minus low) to include

pandas_ta/volatility/ui.py

@@ -1,10 +1,11 @@
 # -*- coding: utf-8 -*-
 from numpy import sqrt as npsqrt
 from pandas_ta.overlap import sma
+from pandas import Series
 from pandas_ta.utils import get_offset, verify_series
 
 
-def ui(close, length=None, scalar=None, offset=None, **kwargs):
+def ui(close: Series, length: int = None, scalar: int = None, offset: int = None, **kwargs) -> Series:
     """Ulcer Index (UI)
 
     The Ulcer Index by Peter Martin measures the downside volatility with the use of