all volume indicators fully typed

pandas_ta/volume/ad.py

@@ -1,9 +1,11 @@
 # -*- coding: utf-8 -*-
 from pandas_ta import Imports
 from pandas_ta.utils import get_offset, non_zero_range, verify_series
+from pandas import Series
 
 
-def ad(high, low, close, volume, open_=None, talib=None, offset=None, **kwargs):
+def ad(high: Series, low: Series, close: Series, volume: Series, open_: Series = None, talib: bool = None,
+       offset: int = None, **kwargs) -> Series:
     """Accumulation/Distribution (AD)
 
     Accumulation/Distribution indicator utilizes the relative position
@@ -17,7 +19,7 @@ def ad(high, low, close, volume, open_=None, talib=None, offset=None, **kwargs):
         low (pd.Series): Series of 'low's
         close (pd.Series): Series of 'close's
         volume (pd.Series): Series of 'volume's
-        open (pd.Series): Series of 'open's
+        open_ (pd.Series): Series of 'open's
         talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
             version. Default: True
         offset (int): How many periods to offset the result. Default: 0

pandas_ta/volume/adosc.py

@@ -3,9 +3,11 @@ from .ad import ad
 from pandas_ta import Imports
 from pandas_ta.overlap import ema
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def adosc(high, low, close, volume, open_=None, fast=None, slow=None, talib=None, offset=None, **kwargs):
+def adosc(high: Series, low: Series, close: Series, volume: Series, open_: Series = None, fast: int = None,
+          slow: int = None, talib: bool = None, offset: int = None, **kwargs) -> Series:
     """Accumulation/Distribution Oscillator or Chaikin Oscillator
 
     Accumulation/Distribution Oscillator indicator utilizes
@@ -19,7 +21,7 @@ def adosc(high, low, close, volume, open_=None, fast=None, slow=None, talib=None
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
         close (pd.Series): Series of 'close's
-        open (pd.Series): Series of 'open'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

pandas_ta/volume/aobv.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from .obv import obv
 from pandas_ta.overlap import ma
 from pandas_ta.trend import long_run, short_run
 from pandas_ta.utils import get_offset, verify_series
 
 
-def aobv(close, volume, fast=None, slow=None, max_lookback=None, min_lookback=None, mamode=None, offset=None, **kwargs):
+def aobv(close: Series, volume: Series, fast: int = None, slow: int = None, max_lookback: int = None,
+         min_lookback: int = None, mamode: str = None, offset: int = None, **kwargs) -> DataFrame:
     """Archer On Balance Volume (AOBV)
 
     Archer On Balance Volume (AOBV) developed by Kevin Johnson provides

pandas_ta/volume/cmf.py

@@ -1,8 +1,10 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.utils import get_offset, non_zero_range, verify_series
+from pandas import Series
 
 
-def cmf(high, low, close, volume, open_=None, length=None, offset=None, **kwargs):
+def cmf(high: Series, low: Series, close: Series, volume: Series, open_: Series = None, length: int = None,
+        offset: int = None, **kwargs) -> Series:
     """Chaikin Money Flow (CMF)
 
     Chailin Money Flow measures the amount of money flow volume over a specific

pandas_ta/volume/efi.py

@@ -1,9 +1,11 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.overlap import ma
 from pandas_ta.utils import get_drift, get_offset, verify_series
+from pandas import Series
 
 
-def efi(close, volume, length=None, mamode=None, drift=None, offset=None, **kwargs):
+def efi(close: Series, volume: Series, length: int = None, mamode: str = None, drift: int = None, offset: int = None,
+        **kwargs) -> Series:
     """Elder's Force Index (EFI)
 
     Elder's Force Index measures the power behind a price movement using price

pandas_ta/volume/eom.py

@@ -1,9 +1,11 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.overlap import hl2, sma
 from pandas_ta.utils import get_drift, get_offset, non_zero_range, verify_series
+from pandas import Series
 
 
-def eom(high, low, close, volume, length=None, divisor=None, drift=None, offset=None, **kwargs):
+def eom(high: Series, low: Series, close: Series, volume: Series, length: int = None, divisor=None, drift: int = None,
+        offset: int = None, **kwargs) -> Series:
     """Ease of Movement (EOM)
 
     Ease of Movement is a volume based oscillator that is designed to measure the

pandas_ta/volume/kvo.py

@@ -1,10 +1,11 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.overlap import hlc3, ma
 from pandas_ta.utils import get_drift, get_offset, signed_series, verify_series
 
 
-def kvo(high, low, close, volume, fast=None, slow=None, signal=None, mamode=None, drift=None, offset=None, **kwargs):
+def kvo(high: Series, low: Series, close: Series, volume: Series, fast: int = None, slow: int = None,
+        signal=None, mamode: str = None, drift: int = None, offset: int = None, **kwargs) -> DataFrame:
     """Klinger Volume Oscillator (KVO)
 
     This indicator was developed by Stephen J. Klinger. It is designed to predict

pandas_ta/volume/mfi.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta import Imports
 from pandas_ta.overlap import hlc3
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
-def mfi(high, low, close, volume, length=None, talib=None, drift=None, offset=None, **kwargs):
+def mfi(high: Series, low: Series, close: Series, volume: Series, length: int = None, talib: bool = None,
+        drift: int = None, offset: int = None, **kwargs) -> Series:
     """Money Flow Index (MFI)
 
     Money Flow Index is an oscillator indicator that is used to measure buying and

pandas_ta/volume/nvi.py

@@ -1,9 +1,11 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.momentum import roc
 from pandas_ta.utils import get_offset, signed_series, verify_series
+from pandas import Series
 
 
-def nvi(close, volume, length=None, initial=None, offset=None, **kwargs):
+def nvi(close: Series, volume: Series, length: int = None, initial: int = None, offset: int = None,
+        **kwargs) -> Series:
     """Negative Volume Index (NVI)
 
     The Negative Volume Index is a cumulative indicator that uses volume change in

pandas_ta/volume/obv.py

@@ -1,9 +1,10 @@
 # -*- coding: utf-8 -*-
 from pandas_ta import Imports
 from pandas_ta.utils import get_offset, signed_series, verify_series
+from pandas import Series
 
 
-def obv(close, volume, talib=None, offset=None, **kwargs):
+def obv(close: Series, volume: Series, talib: bool = None, offset: int = None, **kwargs) -> Series:
     """On Balance Volume (OBV)
 
     On Balance Volume is a cumulative indicator to measure buying and selling

pandas_ta/volume/pvi.py

@@ -1,9 +1,11 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.momentum import roc
 from pandas_ta.utils import get_offset, signed_series, verify_series
+from pandas import Series
 
 
-def pvi(close, volume, length=None, initial=None, offset=None, **kwargs):
+def pvi(close: Series, volume: Series, length: int = None, initial: int = None, offset: int = None,
+        **kwargs) -> Series:
     """Positive Volume Index (PVI)
 
     The Positive Volume Index is a cumulative indicator that uses volume change in

pandas_ta/volume/pvol.py

@@ -1,8 +1,9 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.utils import get_offset, signed_series, verify_series
+from pandas import Series
 
 
-def pvol(close, volume, offset=None, **kwargs):
+def pvol(close: Series, volume: Series, offset: int = None, **kwargs) -> Series:
     """Price-Volume (PVOL)
 
     Returns a series of the product of price and volume.

pandas_ta/volume/pvr.py

@@ -4,7 +4,7 @@ from numpy import nan as npNaN
 from pandas import Series
 
 
-def pvr(close, volume):
+def pvr(close: Series, volume: Series) -> Series:
     """Price Volume Rank
 
     The Price Volume Rank was developed by Anthony J. Macek and is described in his

pandas_ta/volume/pvt.py

@@ -1,9 +1,10 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.momentum import roc
 from pandas_ta.utils import get_drift, get_offset, verify_series
+from pandas import Series
 
 
-def pvt(close, volume, drift=None, offset=None, **kwargs):
+def pvt(close: Series, volume: Series, drift: int = None, offset: int = None, **kwargs) -> Series:
     """Price-Volume Trend (PVT)
 
     The Price-Volume Trend utilizes the Rate of Change with volume to

pandas_ta/volume/vp.py

@@ -1,11 +1,11 @@
 # -*- coding: utf-8 -*-
 from numpy import array_split
 from numpy import mean
-from pandas import cut, concat, DataFrame
+from pandas import cut, concat, DataFrame, Series
 from pandas_ta.utils import signed_series, verify_series
 
 
-def vp(close, volume, width=None, **kwargs):
+def vp(close: Series, volume: Series, width: int = None, **kwargs) -> DataFrame:
     """Volume Profile (VP)
 
     Calculates the Volume Profile by slicing price into ranges.

pandas_ta/volume/wb_tsv.py

@@ -4,7 +4,8 @@ from pandas_ta.overlap import ma
 from pandas_ta.utils import get_drift, get_offset, verify_series, signed_series, zero
 
 
-def wb_tsv(close=None, volume=None, length=None, signal=None, mamode=None, drift=None, offset=None, **kwargs):
+def wb_tsv(close: Series, volume: Series, length: int = None, signal: int = None, mamode: str = None,
+           drift: int = None, offset: int = None, **kwargs) -> DataFrame:
     """Time Segmented Value (TSV)
 
     TSV is a proprietary technical indicator developed by Worden Brothers Inc.,
@@ -44,7 +45,7 @@ def wb_tsv(close=None, volume=None, length=None, signal=None, mamode=None, drift
 
     # Calculate Result
     signed_volume = volume * signed_series(close, 1)     # > 0
-    signed_volume[signed_volume < 0 ] = -signed_volume   # < 0
+    signed_volume[signed_volume < 0] = -signed_volume   # < 0
     signed_volume.apply(zero)                            # ~ 0
     cvd = signed_volume * close.diff(drift)
 
@@ -81,4 +82,4 @@ def wb_tsv(close=None, volume=None, length=None, signal=None, mamode=None, drift
     df.name = f"TSV{_props}"
     df.category = tsv.category
 
-    return df
+    return df