all overlap indicators fully typed

pandas_ta/overlap/alligator.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 # from numpy import nan as npNaN
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from .smma import smma
 from pandas_ta.utils import get_offset, verify_series
 
 
-def alligator(close, jaw=None, teeth=None, lips=None, talib=None, offset=None, **kwargs):
+def alligator(close: Series, jaw: int = None, teeth: int = None, lips: int = None, talib: bool = None,
+              offset: int = None, **kwargs) -> DataFrame:
     """Bill Williams Alligator (ALLIGATOR)
 
     The Alligator Indicator was developed by Bill Williams and combines moving

pandas_ta/overlap/alma.py

@@ -11,7 +11,8 @@ from pandas import Series
 from pandas_ta.utils import get_offset, strided_window, verify_series
 
 
-def alma(close, length=None, sigma=None, dist_offset=None, offset=None, **kwargs):
+def alma(close: Series, length: int = None, sigma: float = None, dist_offset: float = None, offset: int = None,
+         **kwargs) -> Series:
     """Arnaud Legoux Moving Average (ALMA)
 
     The ALMA moving average uses the curve of the Normal (Gauss) distribution, which

pandas_ta/overlap/dema.py

@@ -2,9 +2,10 @@
 from .ema import ema
 from pandas_ta import Imports
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def dema(close, length=None, talib=None, offset=None, **kwargs):
+def dema(close: Series, length: int = None, talib: bool = None, offset: int = None, **kwargs) -> Series:
     """Double Exponential Moving Average (DEMA)
 
     The Double Exponential Moving Average attempts to a smoother average with less

pandas_ta/overlap/ema.py

@@ -2,9 +2,10 @@
 from numpy import nan as npNaN
 from pandas_ta import Imports
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def ema(close, length=None, talib=None, offset=None, **kwargs):
+def ema(close: Series, length: int = None, talib: bool = None, offset: int = None, **kwargs) -> Series:
     """Exponential Moving Average (EMA)
 
     The Exponential Moving Average is more responsive moving average compared to the

pandas_ta/overlap/fwma.py

@@ -1,8 +1,9 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.utils import fibonacci, get_offset, verify_series, weights
+from pandas import Series
 
 
-def fwma(close, length=None, asc=None, offset=None, **kwargs):
+def fwma(close: Series, length: int = None, asc: bool = None, offset: int = None, **kwargs) -> Series:
     """Fibonacci's Weighted Moving Average (FWMA)
 
     Fibonacci's Weighted Moving Average is similar to a Weighted Moving Average

pandas_ta/overlap/hilo.py

@@ -5,7 +5,8 @@ from .ma import ma
 from pandas_ta.utils import get_offset, verify_series
 
 
-def hilo(high, low, close, high_length=None, low_length=None, mamode=None, offset=None, **kwargs):
+def hilo(high: Series, low: Series, close: Series, high_length: int = None, low_length: int = None,
+         mamode: str = None, offset: int = None, **kwargs) -> DataFrame:
     """Gann HiLo Activator(HiLo)
 
     The Gann High Low Activator Indicator was created by Robert Krausz in a 1998

pandas_ta/overlap/hl2.py

@@ -1,8 +1,9 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def hl2(high, low, offset=None, **kwargs):
+def hl2(high: Series, low: Series, offset: int = None, **kwargs) -> Series:
     """HL2
 
     HL2 is the midpoint/average of high and low.

pandas_ta/overlap/hlc3.py

@@ -1,9 +1,10 @@
 # -*- coding: utf-8 -*-
 from pandas_ta import Imports
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def hlc3(high, low, close, talib=None, offset=None, **kwargs):
+def hlc3(high: Series, low: Series, close: Series, talib: bool = None, offset: int = None, **kwargs) -> Series:
     """HLC3
 
     HLC3 is the average of high, low and close.

pandas_ta/overlap/hma.py

@@ -2,9 +2,10 @@
 from numpy import sqrt as npSqrt
 from .wma import wma
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def hma(close, length=None, offset=None, **kwargs):
+def hma(close: Series, length: int = None, offset: int = None, **kwargs) -> Series:
     """Hull Moving Average (HMA)
 
     The Hull Exponential Moving Average attempts to reduce or remove lag in moving

pandas_ta/overlap/hwma.py

@@ -3,7 +3,7 @@ from pandas import Series
 from pandas_ta.utils import get_offset, verify_series
 
 
-def hwma(close, na=None, nb=None, nc=None, offset=None, **kwargs):
+def hwma(close: Series, na: float = None, nb: float = None, nc: float = None, offset: int = None, **kwargs) -> Series:
     """HWMA (Holt-Winter Moving Average)
 
     Indicator HWMA (Holt-Winter Moving Average) is a three-parameter moving average

pandas_ta/overlap/ichimoku.py

@@ -1,10 +1,11 @@
 # -*- coding: utf-8 -*-
-from pandas import date_range, DataFrame, RangeIndex, Timedelta
+from pandas import date_range, DataFrame, RangeIndex, Timedelta, Series
 from .midprice import midprice
 from pandas_ta.utils import get_offset, verify_series
 
 
-def ichimoku(high, low, close, tenkan=None, kijun=None, senkou=None, include_chikou=True, offset=None, **kwargs):
+def ichimoku(high: Series, low: Series, close: Series, tenkan: int = None, kijun: int = None, senkou: int = None,
+             include_chikou: bool = True, offset: int = None, **kwargs) -> DataFrame:
     """Ichimoku Kinkō Hyō (ichimoku)
 
     Developed Pre WWII as a forecasting model for financial markets.

pandas_ta/overlap/jma.py

@@ -9,7 +9,7 @@ from pandas import Series
 from pandas_ta.utils import get_offset, verify_series
 
 
-def jma(close, length=None, phase=None, offset=None, **kwargs):
+def jma(close: Series, length: int = None, phase: float = None, offset: int = None, **kwargs) -> Series:
     """Jurik Moving Average Average (JMA)
 
     Mark Jurik's Moving Average (JMA) attempts to eliminate noise to see the "true"

pandas_ta/overlap/kama.py

@@ -5,7 +5,8 @@ from pandas_ta.overlap.ma import ma
 from pandas_ta.utils import get_drift, get_offset, non_zero_range, verify_series
 
 
-def kama(close, length=None, fast=None, slow=None, mamode=None, drift=None, offset=None, **kwargs):
+def kama(close: Series, length: int = None, fast: int = None, slow: int = None, mamode: str = None,
+         drift: int = None, offset: int = None, **kwargs) -> Series:
     """Kaufman's Adaptive Moving Average (KAMA)
 
     Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average

pandas_ta/overlap/linreg.py

@@ -9,7 +9,7 @@ from pandas_ta import Imports
 from pandas_ta.utils import get_offset, strided_window, verify_series
 
 
-def linreg(close, length=None, talib=None, offset=None, **kwargs):
+def linreg(close: Series, length: int = None, talib: int = None, offset: int = None, **kwargs) -> Series:
     """Linear Regression Moving Average (linreg)
 
     Linear Regression Moving Average (LINREG). This is a simplified version of a

pandas_ta/overlap/ma.py

@@ -19,7 +19,7 @@ from .vidya import vidya
 from .wma import wma
 
 
-def ma(name:str = None, source:Series = None, **kwargs) -> Series:
+def ma(name: str = None, source: Series = None, **kwargs) -> Series:
     """Simple MA Utility for easier MA selection
 
     Available MAs:
@@ -50,7 +50,7 @@ def ma(name:str = None, source:Series = None, **kwargs) -> Series:
         return _mas
     elif isinstance(name, str) and name.lower() in _mas:
         name = name.lower()
-    else: # "ema"
+    else:  # "ema"
         name = _mas[1]
 
     if   name == "dema": return dema(source, **kwargs)

pandas_ta/overlap/mcgd.py

@@ -1,8 +1,9 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def mcgd(close, length=None, offset=None, c=None, **kwargs):
+def mcgd(close: Series, length: int = None, offset: int = None, c: float = None, **kwargs) -> Series:
     """McGinley Dynamic Indicator
 
     The McGinley Dynamic looks like a moving average line, yet it is actually a

pandas_ta/overlap/midpoint.py

@@ -1,9 +1,10 @@
 # -*- coding: utf-8 -*-
 from pandas_ta import Imports
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def midpoint(close, length=None, talib=None, offset=None, **kwargs):
+def midpoint(close: Series, length: int = None, talib: bool = None, offset: int = None, **kwargs) -> Series:
     """Midpoint
 
     The Midpoint is the average of the rolling high and low of period length.

pandas_ta/overlap/midprice.py

@@ -1,9 +1,11 @@
 # -*- coding: utf-8 -*-
 from pandas_ta import Imports
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def midprice(high, low, length=None, talib=None, offset=None, **kwargs):
+def midprice(high: Series, low: Series, length: int = None, talib: bool = None, offset: int = None,
+             **kwargs) -> Series:
     """Midprice
 
     The Midprice is the average of the rolling high and low of period length.

pandas_ta/overlap/ohlc4.py

@@ -1,8 +1,9 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def ohlc4(open_, high, low, close, offset=None, **kwargs):
+def ohlc4(open_: Series, high: Series, low: Series, close: Series, offset: int = None, **kwargs) -> Series:
     """OHLC4
 
     OHLC4 is the average of open, high, low and close.

pandas_ta/overlap/pwma.py

@@ -1,8 +1,9 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.utils import get_offset, pascals_triangle, verify_series, weights
+from pandas import Series
 
 
-def pwma(close, length=None, asc=None, offset=None, **kwargs):
+def pwma(close: Series, length: int = None, asc: bool = None, offset: bool = None, **kwargs) -> Series:
     """Pascal's Weighted Moving Average (PWMA)
 
     Pascal's Weighted Moving Average is similar to a symmetric triangular window

pandas_ta/overlap/rma.py

@@ -1,8 +1,9 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def rma(close, length=None, offset=None, **kwargs):
+def rma(close: Series, length: int = None, offset: int = None, **kwargs) -> Series:
     """wildeR's Moving Average (RMA)
 
     The WildeR's Moving Average is simply an Exponential Moving Average (EMA) with

pandas_ta/overlap/sinwma.py

@@ -5,7 +5,7 @@ from pandas import Series
 from pandas_ta.utils import get_offset, verify_series, weights
 
 
-def sinwma(close, length=None, offset=None, **kwargs):
+def sinwma(close: Series, length: int = None, offset: int = None, **kwargs) -> Series:
     """Sine Weighted Moving Average (SWMA)
 
     A weighted average using sine cycles. The middle term(s) of the average have the

pandas_ta/overlap/sma.py

@@ -1,9 +1,10 @@
 # -*- coding: utf-8 -*-
 from pandas_ta import Imports
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def sma(close, length=None, talib=None, offset=None, **kwargs):
+def sma(close: Series, length: int = None, talib: bool = None, offset: int = None, **kwargs) -> Series:
     """Simple Moving Average (SMA)
 
     The Simple Moving Average is the classic moving average that is the equally

pandas_ta/overlap/smma.py

@@ -1,10 +1,12 @@
 # -*- coding: utf-8 -*-
 from numpy import nan as npNaN
 from pandas_ta.overlap.ma import ma
+from pandas import Series
 from pandas_ta.utils import get_offset, verify_series
 
 
-def smma(close, length=None, mamode=None, talib=None, offset=None, **kwargs):
+def smma(close: Series, length: int = None, mamode: str = None, talib: bool = None, offset: int = None,
+         **kwargs) -> Series:
     """SMoothed Moving Average (SMMA)
 
     The SMoothed Moving Average (SMMA) is bootstrapped by default with a Simple

pandas_ta/overlap/ssf.py

@@ -5,9 +5,10 @@ from numpy import nan as npNaN
 from numpy import pi as npPi
 from numpy import sqrt as npSqrt
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def ssf(close, length=None, poles=None, offset=None, **kwargs):
+def ssf(close: Series, length: int = None, poles: int = None, offset: int = None, **kwargs) -> Series:
     """Ehler's Super Smoother Filter (SSF) © 2013
 
     John F. Ehlers's solution to reduce lag and remove aliasing noise with his

pandas_ta/overlap/supertrend.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from numpy import nan as npNaN
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.overlap import hl2
 from pandas_ta.volatility import atr
 from pandas_ta.utils import get_offset, verify_series
 
 
-def supertrend(high, low, close, length=None, multiplier=None, offset=None, **kwargs):
+def supertrend(high: Series, low: Series, close: Series, length: int = None, multiplier: float = None,
+               offset: int = None, **kwargs) -> DataFrame:
     """Supertrend (supertrend)
 
     Supertrend is an overlap indicator. It is used to help identify trend

pandas_ta/overlap/swma.py

@@ -1,8 +1,9 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.utils import get_offset, symmetric_triangle, verify_series, weights
+from pandas import Series
 
 
-def swma(close, length=None, asc=None, offset=None, **kwargs):
+def swma(close: Series, length: int = None, asc: bool = None, offset: int = None, **kwargs) -> Series:
     """Symmetric Weighted Moving Average (SWMA)
 
     Symmetric Weighted Moving Average where weights are based on a symmetric

pandas_ta/overlap/t3.py

@@ -2,9 +2,10 @@
 from .ema import ema
 from pandas_ta import Imports
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def t3(close, length=None, a=None, talib=None, offset=None, **kwargs):
+def t3(close: Series, length: int = None, a: float = None, talib: bool = None, offset: int = None, **kwargs) -> Series:
     """Tim Tillson's T3 Moving Average (T3)
 
     Tim Tillson's T3 Moving Average is considered a smoother and more responsive

pandas_ta/overlap/tema.py

@@ -2,9 +2,10 @@
 from .ema import ema
 from pandas_ta import Imports
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def tema(close, length=None, talib=None, offset=None, **kwargs):
+def tema(close: Series, length: int = None, talib: bool = None, offset: int = None, **kwargs) -> Series:
     """Triple Exponential Moving Average (TEMA)
 
     A less laggy Exponential Moving Average.

pandas_ta/overlap/trima.py

@@ -2,9 +2,10 @@
 from .sma import sma
 from pandas_ta import Imports
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def trima(close, length=None, talib=None, offset=None, **kwargs):
+def trima(close: Series, length: int = None, talib: bool = None, offset: int = None, **kwargs) -> Series:
     """Triangular Moving Average (TRIMA)
 
     A weighted moving average where the shape of the weights are triangular and the

pandas_ta/overlap/vidya.py

@@ -4,7 +4,7 @@ from pandas import Series
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
-def vidya(close, length=None, drift=None, offset=None, **kwargs):
+def vidya(close: Series, length: int = None, drift: int = None, offset: int = None, **kwargs) -> Series:
     """Variable Index Dynamic Average (VIDYA)
 
     Variable Index Dynamic Average (VIDYA) was developed by Tushar Chande. It is

pandas_ta/overlap/vwap.py

@@ -1,8 +1,11 @@
 # -*- coding: utf-8 -*-
 from .hlc3 import hlc3
 from pandas_ta.utils import get_offset, is_datetime_ordered, verify_series
+from pandas import Series
 
-def vwap(high, low, close, volume, anchor=None, offset=None, **kwargs):
+
+def vwap(high: Series, low: Series, close: Series, volume: Series, anchor: str = None, offset: int = None,
+         **kwargs) -> Series:
     """Volume Weighted Average Price (VWAP)
 
     The Volume Weighted Average Price that measures the average typical price

pandas_ta/overlap/vwma.py

@@ -1,9 +1,10 @@
 # -*- coding: utf-8 -*-
 from .sma import sma
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def vwma(close, volume, length=None, offset=None, **kwargs):
+def vwma(close: Series, volume: Series, length: int = None, offset: int = None, **kwargs) -> Series:
     """Volume Weighted Moving Average (VWMA)
 
     Volume Weighted Moving Average.

pandas_ta/overlap/wcp.py

@@ -1,9 +1,10 @@
 # -*- coding: utf-8 -*-
 from pandas_ta import Imports
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def wcp(high, low, close, talib=None, offset=None, **kwargs):
+def wcp(high: Series, low: Series, close: Series, talib: bool = None, offset: int = None, **kwargs) -> Series:
     """Weighted Closing Price (WCP)
 
     Weighted Closing Price is the weighted price given: high, low

pandas_ta/overlap/wma.py

@@ -4,7 +4,8 @@ from pandas_ta import Imports
 from pandas_ta.utils import get_offset, verify_series
 
 
-def wma(close, length=None, asc=None, talib=None, offset=None, **kwargs):
+def wma(close: Series, length: int = None, asc: bool = None, talib: bool = None, offset: int = None,
+        **kwargs) -> Series:
     """Weighted Moving Average (WMA)
 
     The Weighted Moving Average where the weights are linearly increasing and

pandas_ta/overlap/zlma.py

@@ -4,9 +4,10 @@
 # )
 from pandas_ta.overlap import ma
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def zlma(close, length=None, mamode=None, offset=None, **kwargs):
+def zlma(close: Series, length: int = None, mamode: str = None, offset: int = None, **kwargs) -> Series:
     """Zero Lag Moving Average (ZLMA)
 
     The Zero Lag Moving Average attempts to eliminate the lag associated