all trend indicators fully typed

pandas_ta/trend/adx.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.overlap import ma
 from pandas_ta.volatility import atr
 from pandas_ta.utils import get_drift, get_offset, verify_series, zero
 
 
-def adx(high, low, close, length=None, lensig=None, scalar=None, mamode=None, drift=None, offset=None, **kwargs):
+def adx(high: Series, low: Series, close: Series, length: int = None, lensig: int = None, scalar: float = None,
+        mamode: str = None, drift: int = None, offset: int = None, **kwargs) -> DataFrame:
     """Average Directional Movement (ADX)
 
     Average Directional Movement is meant to quantify trend strength by measuring

pandas_ta/trend/amat.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from .long_run import long_run
 from .short_run import short_run
 from pandas_ta.overlap import ma
 from pandas_ta.utils import get_offset, verify_series
 
 
-def amat(close=None, fast=None, slow=None, lookback=None, mamode=None, offset=None, **kwargs):
+def amat(close: Series, fast: int = None, slow: int = None, lookback: int = None, mamode: str = None,
+         offset: int = None, **kwargs) -> DataFrame:
     """Archer Moving Averages Trends (AMAT)
 
     Archer Moving Averages Trends (AMAT) developed by Kevin Johnson provides

pandas_ta/trend/aroon.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.utils import get_offset, verify_series
 from pandas_ta.utils import recent_maximum_index, recent_minimum_index
 
 
-def aroon(high, low, length=None, scalar=None, talib=None, offset=None, **kwargs):
+def aroon(high: Series, low: Series, length: int = None, scalar: float = None, talib: bool = None, offset: int = None,
+          **kwargs) -> DataFrame:
     """Aroon & Aroon Oscillator (AROON)
 
     Aroon attempts to identify if a security is trending and how strong.

pandas_ta/trend/chop.py

@@ -3,9 +3,11 @@ from numpy import log10 as npLog10
 from numpy import log as npLn
 from pandas_ta.volatility import atr
 from pandas_ta.utils import get_drift, get_offset, verify_series
+from pandas import Series
 
 
-def chop(high, low, close, length=None, atr_length=None, ln=None, scalar=None, drift=None, offset=None, **kwargs):
+def chop(high: Series, low: Series, close: Series, length: int = None, atr_length: int = None, ln: bool = None,
+         scalar: float = None, drift: int = None, offset: int = None, **kwargs) -> Series:
     """Choppiness Index (CHOP)
 
     The Choppiness Index was created by Australian commodity trader

pandas_ta/trend/cksp.py

@@ -1,10 +1,11 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.volatility import atr
 from pandas_ta.utils import get_offset, verify_series
 
 
-def cksp(high, low, close, p=None, x=None, q=None, tvmode=None, offset=None, **kwargs):
+def cksp(high: Series, low: Series, close: Series, p: int = None, x: float = None, q: int = None, tvmode: bool = None,
+         offset: int = None, **kwargs) -> DataFrame:
     """Chande Kroll Stop (CKSP)
 
     The Tushar Chande and Stanley Kroll in their book

pandas_ta/trend/decay.py

@@ -1,10 +1,10 @@
 # -*- coding: utf-8 -*-
 from numpy import exp as npExp
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.utils import get_offset, verify_series
 
 
-def decay(close, kind=None, length=None, mode=None, offset=None, **kwargs):
+def decay(close: Series, kind=None, length: int = None, mode: str = None, offset: int = None, **kwargs) -> Series:
     """Decay
 
     Creates a decay moving forward from prior signals like crosses. The default is

pandas_ta/trend/decreasing.py

@@ -1,7 +1,10 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.utils import get_drift, get_offset, is_percent, verify_series
+from pandas import Series
 
-def decreasing(close, length=None, strict=None, asint=None, percent=None, drift=None, offset=None, **kwargs):
+
+def decreasing(close: Series, length: int = None, strict: bool = None, asint: bool = None, percent: float = None,
+               drift: int = None, offset: int = None, **kwargs) -> Series:
     """Decreasing
 
     Returns True if the series is decreasing over a period, False otherwise.

pandas_ta/trend/dpo.py

@@ -1,9 +1,10 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.overlap import sma
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def dpo(close, length=None, centered=True, offset=None, **kwargs):
+def dpo(close: Series, length: int = None, centered: bool = True, offset: int = None, **kwargs) -> Series:
     """Detrend Price Oscillator (DPO)
 
     Is an indicator designed to remove trend from price and make it easier to

pandas_ta/trend/increasing.py

@@ -1,7 +1,10 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.utils import get_drift, get_offset, is_percent, verify_series
+from pandas import Series
 
-def increasing(close, length=None, strict=None, asint=None, percent=None, drift=None, offset=None, **kwargs):
+
+def increasing(close: Series, length: int = None, strict: bool = None, asint: bool = None, percent: float = None,
+               drift: int = None, offset: int = None, **kwargs) -> Series:
     """Increasing
 
     Returns True if the series is increasing over a period, False otherwise.

pandas_ta/trend/long_run.py

@@ -2,9 +2,10 @@
 from .decreasing import decreasing
 from .increasing import increasing
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def long_run(fast, slow, length=None, offset=None, **kwargs):
+def long_run(fast: Series, slow: Series, length: int = None, offset: int = None, **kwargs) -> Series:
     """Long Run
 
     Long Run was developed by Kevin Johnson that returns a binary Series

pandas_ta/trend/psar.py

@@ -4,7 +4,8 @@ from pandas import DataFrame, Series
 from pandas_ta.utils import get_offset, verify_series, zero
 
 
-def psar(high, low, close=None, af0=None, af=None, max_af=None, offset=None, **kwargs):
+def psar(high: Series, low: Series, close: Series = None, af0: float = None, af: float = None, max_af: float = None,
+         offset: int = None, **kwargs) -> DataFrame:
     """Parabolic Stop and Reverse (psar)
 
     Parabolic Stop and Reverse (PSAR) was developed by J. Wells Wilder, that is used

pandas_ta/trend/qstick.py

@@ -1,9 +1,10 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.overlap import dema, ema, hma, rma, sma
 from pandas_ta.utils import get_offset, non_zero_range, verify_series
+from pandas import Series
 
 
-def qstick(open_, close, length=None, offset=None, **kwargs):
+def qstick(open_: Series, close: Series, length: int = None, offset: int = None, **kwargs) -> Series:
     """Q Stick
 
     The Q Stick indicator, developed by Tushar Chande, attempts to quantify and

pandas_ta/trend/short_run.py

@@ -2,9 +2,10 @@
 from .decreasing import decreasing
 from .increasing import increasing
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def short_run(fast, slow, length=None, offset=None, **kwargs):
+def short_run(fast: Series, slow: Series, length: int = None, offset: int = None, **kwargs) -> Series:
     """Short Run
 
     Short Run was developed by Kevin Johnson that returns a binary Series

pandas_ta/trend/trendflex.py

@@ -10,7 +10,8 @@ from pandas import Series
 from pandas_ta.utils import get_offset, verify_series
 
 
-def trendflex(close, length=None, smooth=None, alpha=None, offset=None, **kwargs):
+def trendflex(close: Series, length: int = None, smooth: int = None, alpha: float = None, offset: int = None,
+              **kwargs) -> Series:
     """Trendflex (trendflex)
 
     John F. Ehlers introduced two indicators within the article "Reflex: A New

pandas_ta/trend/tsignals.py

@@ -1,9 +1,10 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
-def tsignals(trend, asbool=None, trend_reset=0, trade_offset=None, drift=None, offset=None, **kwargs):
+def tsignals(trend: Series, asbool: bool = None, trend_reset=0, trade_offset=None, drift: int = None,
+             offset: int = None, **kwargs) -> DataFrame:
     """Trend Signals
 
     Given a Trend, Trend Signals returns the Trend, Trades, Entries and Exits as

pandas_ta/trend/ttm_trend.py

@@ -1,10 +1,10 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.overlap import hl2
 from pandas_ta.utils import get_offset, verify_series
 
 
-def ttm_trend(high, low, close, length=None, offset=None, **kwargs):
+def ttm_trend(high: Series, low: Series, close: Series, length: int = None, offset: int = None, **kwargs) -> DataFrame:
     """TTM Trend (TTM_TRND)
 
     This indicator is from John Carters book “Mastering the Trade” and plots the

pandas_ta/trend/vhf.py

@@ -1,9 +1,10 @@
 # -*- coding: utf-8 -*-
 from numpy import fabs as npFabs
 from pandas_ta.utils import get_drift, get_offset, non_zero_range, verify_series
+from pandas import Series
 
 
-def vhf(close, length=None, drift=None, offset=None, **kwargs):
+def vhf(close: Series, length: int = None, drift: int = None, offset: int = None, **kwargs) -> Series:
     """Vertical Horizontal Filter (VHF)
 
     VHF was created by Adam White to identify trending and ranging markets.

pandas_ta/trend/vortex.py

@@ -1,10 +1,11 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.volatility import true_range
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
-def vortex(high, low, close, length=None, drift=None, offset=None, **kwargs):
+def vortex(high: Series, low: Series, close: Series, length: int = None, drift: int = None, offset: int = None,
+           **kwargs) -> DataFrame:
     """Vortex
 
     Two oscillators that capture positive and negative trend movement.

pandas_ta/trend/xsignals.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from numpy import nan as npNaN
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from .tsignals import tsignals
 from pandas_ta.utils._signals import cross_value
 from pandas_ta.utils import get_offset, verify_series
 
 
-def xsignals(signal, xa, xb, above:bool=True, long:bool=True, asbool:bool=None, trend_reset:int=0, trade_offset:int=None, offset:int=None, **kwargs):
+def xsignals(signal: Series, xa: Series, xb: Series, above: bool = True, long: bool = True, asbool: bool = None,
+             trend_reset: int = 0, trade_offset: int = None, offset: int = None, **kwargs) -> DataFrame:
     """Cross Signals (XSIGNALS)
 
     Cross Signals returns Trend Signal (TSIGNALS) results for Signal Crossings. This