all momentum indicators fully typed.

pandas_ta/momentum/ao.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 ao(high, low, fast=None, slow=None, offset=None, **kwargs):
+def ao(high: Series, low: Series, fast: int = None, slow: int = None, offset: int = None, **kwargs) -> Series:
     """Awesome Oscillator (AO)
 
     The Awesome Oscillator is an indicator used to measure a security's momentum.

pandas_ta/momentum/apo.py

@@ -2,9 +2,11 @@
 from pandas_ta import Imports
 from pandas_ta.overlap import ma
 from pandas_ta.utils import get_offset, tal_ma, verify_series
+from pandas import Series
 
 
-def apo(close, fast=None, slow=None, mamode=None, talib=None, offset=None, **kwargs):
+def apo(close: Series, fast: int = None, slow: int = None, mamode: str = None, talib: bool = None,
+        offset: int = None, **kwargs) -> Series:
     """Absolute Price Oscillator (APO)
 
     The Absolute Price Oscillator is an indicator used to measure a security's

pandas_ta/momentum/bias.py

@@ -1,9 +1,10 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.overlap import ma
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def bias(close, length=None, mamode=None, offset=None, **kwargs):
+def bias(close: Series, length: int = None, mamode: str = None, offset: int = None, **kwargs) -> Series:
     """Bias (BIAS)
 
     Rate of change between the source and a moving average.

pandas_ta/momentum/bop.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 bop(open_, high, low, close, scalar=None, talib=None, offset=None, **kwargs):
+def bop(open_: Series, high: Series, low: Series, close: Series, scalar: float = None, talib: bool = None,
+        offset: int = None, **kwargs) -> Series:
     """Balance of Power (BOP)
 
     Balance of Power measure the market strength of buyers against sellers.

pandas_ta/momentum/brar.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, non_zero_range, verify_series
 
 
-def brar(open_, high, low, close, length=None, scalar=None, drift=None, offset=None, **kwargs):
+def brar(open_: Series, high: Series, low: Series, close: Series, length: int = None, scalar: float = None,
+         drift: int = None, offset: int = None, **kwargs) -> DataFrame:
     """BRAR (BRAR)
 
     BR and AR

pandas_ta/momentum/cci.py

@@ -3,9 +3,11 @@ from pandas_ta import Imports
 from pandas_ta.overlap import hlc3, sma
 from pandas_ta.statistics.mad import mad
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def cci(high, low, close, length=None, c=None, talib=None, offset=None, **kwargs):
+def cci(high: Series, low: Series, close: Series, length: int = None, c: float = None,
+        talib: bool = None, offset: int = None, **kwargs) -> Series:
     """Commodity Channel Index (CCI)
 
     Commodity Channel Index is a momentum oscillator used to primarily identify

pandas_ta/momentum/cfo.py

@@ -1,9 +1,11 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.overlap import linreg
 from pandas_ta.utils import get_drift, get_offset, verify_series
+from pandas import Series
 
 
-def cfo(close, length=None, scalar=None, drift=None, offset=None, **kwargs):
+def cfo(close: Series, length: int = None, scalar: float = None, drift: int = None, offset: int = None,
+        **kwargs) -> Series:
     """Chande Forcast Oscillator (CFO)
 
     The Forecast Oscillator calculates the percentage difference between the actual

pandas_ta/momentum/cg.py

@@ -1,8 +1,9 @@
 # -*- coding: utf-8 -*-
 from pandas_ta.utils import get_offset, verify_series, weights
+from pandas import Series
 
 
-def cg(close, length=None, offset=None, **kwargs):
+def cg(close: Series, length: int = None, offset: int = None, **kwargs) -> Series:
     """Center of Gravity (CG)
 
     The Center of Gravity Indicator by John Ehlers attempts to identify turning

pandas_ta/momentum/cmo.py

@@ -2,9 +2,11 @@
 from pandas_ta import Imports
 from pandas_ta.overlap import rma
 from pandas_ta.utils import get_drift, get_offset, verify_series
+from pandas import Series
 
 
-def cmo(close, length=None, scalar=None, talib=None, drift=None, offset=None, **kwargs):
+def cmo(close: Series, length: int = None, scalar: float = None, talib: bool = None, drift: int = None,
+        offset: int = None, **kwargs) -> Series:
     """Chande Momentum Oscillator (CMO)
 
     Attempts to capture the momentum of an asset with overbought at 50 and

pandas_ta/momentum/coppock.py

@@ -2,9 +2,11 @@
 from .roc import roc
 from pandas_ta.overlap import wma
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def coppock(close, length=None, fast=None, slow=None, offset=None, **kwargs):
+def coppock(close: Series, length: int = None, fast: int = None, slow: int = None, offset: int = None,
+            **kwargs) -> Series:
     """Coppock Curve (COPC)
 
     Coppock Curve (originally called the "Trendex Model") is a momentum indicator

pandas_ta/momentum/cti.py

@@ -4,7 +4,7 @@ from pandas_ta.overlap import linreg
 from pandas_ta.utils import get_offset, verify_series
 
 
-def cti(close, length=None, offset=None, **kwargs) -> Series:
+def cti(close: Series, length: int = None, offset: int = None, **kwargs) -> Series:
     """Correlation Trend Indicator (CTI)
 
     The Correlation Trend Indicator is an oscillator created by John Ehler in 2020.

pandas_ta/momentum/dm.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 ma
 from pandas_ta.utils import get_offset, verify_series, get_drift, zero
 
 
-def dm(high, low, length=None, mamode=None, talib=None, drift=None, offset=None, **kwargs):
+def dm(high: Series, low: Series, length: int = None, mamode: str = None, talib: bool = None, drift: int = None,
+       offset: int = None, **kwargs) -> DataFrame:
     """Directional Movement (DM)
 
     The Directional Movement was developed by J. Welles Wilder in 1978 attempts to

pandas_ta/momentum/er.py

@@ -1,9 +1,9 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame, concat
+from pandas import DataFrame, concat, Series
 from pandas_ta.utils import get_drift, get_offset, verify_series, signals
 
 
-def er(close, length=None, drift=None, offset=None, **kwargs):
+def er(close: Series, length: int = None, drift: int = None, offset: int = None, **kwargs) -> Series:
     """Efficiency Ratio (ER)
 
     The Efficiency Ratio was invented by Perry J. Kaufman and presented in his book "New Trading Systems and Methods". It is designed to account for market noise or volatility.

pandas_ta/momentum/eri.py

@@ -1,10 +1,10 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.overlap import ema
 from pandas_ta.utils import get_offset, verify_series
 
 
-def eri(high, low, close, length=None, offset=None, **kwargs):
+def eri(high: Series, low: Series, close: Series, length: int = None, offset: int = None, **kwargs) -> DataFrame:
     """Elder Ray Index (ERI)
 
     Elder's Bulls Ray Index contains his Bull and Bear Powers. Which are useful ways

pandas_ta/momentum/fisher.py

@@ -6,7 +6,8 @@ from pandas_ta.overlap import hl2
 from pandas_ta.utils import get_offset, high_low_range, verify_series
 
 
-def fisher(high, low, length=None, signal=None, offset=None, **kwargs):
+def fisher(high: Series, low: Series, length: int = None, signal: int = None, offset: int = None,
+           **kwargs) -> Series:
     """Fisher Transform (FISHT)
 
     Attempts to identify significant price reversals by normalizing prices over a

pandas_ta/momentum/inertia.py

@@ -2,9 +2,12 @@
 from pandas_ta.overlap import linreg
 from pandas_ta.volatility import rvi
 from pandas_ta.utils import get_drift, get_offset, verify_series
+from pandas import Series
 
 
-def inertia(close=None, high=None, low=None, length=None, rvi_length=None, scalar=None, refined=None, thirds=None, mamode=None, drift=None, offset=None, **kwargs):
+def inertia(close: Series, high: Series, low: Series, length: int = None, rvi_length: int = None, scalar: float = None,
+            refined: bool = None, thirds: bool = None, mamode: str = None, drift: int = None, offset: int = None,
+            **kwargs) -> Series:
     """Inertia (INERTIA)
 
     Inertia was developed by Donald Dorsey and was introduced his article

pandas_ta/momentum/kdj.py

@@ -1,9 +1,10 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.utils import get_offset, non_zero_range, rma_pandas, verify_series
 
 
-def kdj(high=None, low=None, close=None, length=None, signal=None, offset=None, **kwargs):
+def kdj(high: Series, low: Series, close: Series, length: int = None, signal: int = None, offset: int = None,
+        **kwargs) -> Series:
     """KDJ (KDJ)
 
     The KDJ indicator is actually a derived form of the Slow

pandas_ta/momentum/kst.py

@@ -1,10 +1,12 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from .roc import roc
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
-def kst(close, roc1=None, roc2=None, roc3=None, roc4=None, sma1=None, sma2=None, sma3=None, sma4=None, signal=None, drift=None, offset=None, **kwargs):
+def kst(close: Series, roc1: int = None, roc2: int = None, roc3: int = None, roc4: int = None, sma1: int = None,
+        sma2: int = None, sma3: int = None, sma4: int = None, signal: int = None, drift: int = None,
+        offset: int = None, **kwargs) -> DataFrame:
     """'Know Sure Thing' (KST)
 
     The 'Know Sure Thing' is a momentum based oscillator and based on ROC.

pandas_ta/momentum/macd.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
-from pandas import concat, DataFrame
+from pandas import concat, DataFrame, Series
 from pandas_ta import Imports
 from pandas_ta.overlap import ema
 from pandas_ta.utils import get_offset, verify_series, signals
 
 
-def macd(close, fast=None, slow=None, signal=None, talib=None, offset=None, **kwargs):
+def macd(close: Series, fast: int = None, slow: int = None, signal: int = None, talib: bool = None,
+         offset: int = None, **kwargs) -> DataFrame:
     """Moving Average Convergence Divergence (MACD)
 
     The MACD is a popular indicator to that is used to identify a security's trend.

pandas_ta/momentum/mom.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 mom(close, length=None, talib=None, offset=None, **kwargs):
+def mom(close: Series, length: int = None, talib: bool = None, offset: int = None, **kwargs) -> Series:
     """Momentum (MOM)
 
     Momentum is an indicator used to measure a security's speed (or strength) of

pandas_ta/momentum/pgo.py

@@ -2,9 +2,10 @@
 from pandas_ta.overlap import ema, sma
 from pandas_ta.volatility import atr
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def pgo(high, low, close, length=None, offset=None, **kwargs):
+def pgo(high: Series, low: Series, close: Series, length: int = None, offset: int = None, **kwargs) -> Series:
     """Pretty Good Oscillator (PGO)
 
     The Pretty Good Oscillator indicator was created by Mark Johnson to measure the distance of the current close from its N-day Simple Moving Average, expressed in terms of an average true range over a similar period. Johnson's approach was to

pandas_ta/momentum/ppo.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 ma
 from pandas_ta.utils import get_offset, tal_ma, verify_series
 
 
-def ppo(close, fast=None, slow=None, signal=None, scalar=None, mamode=None, talib=None, offset=None, **kwargs):
+def ppo(close: Series, fast: int = None, slow: int = None, signal: int = None, scalar: float = None,
+        mamode: str = None, talib: bool = None, offset: int = None, **kwargs) -> DataFrame:
 
     """Percentage Price Oscillator (PPO)
 

pandas_ta/momentum/psl.py

@@ -1,9 +1,11 @@
 # -*- coding: utf-8 -*-
 from numpy import sign as npSign
 from pandas_ta.utils import get_drift, get_offset, verify_series
+from pandas import Series
 
 
-def psl(close, open_=None, length=None, scalar=None, drift=None, offset=None, **kwargs):
+def psl(close: Series, open_: Series = None, length: int = None, scalar: float = None, drift: int = None,
+        offset: int = None, **kwargs) -> Series:
     """Psychological Line (PSL)
 
     The Psychological Line is an oscillator-type indicator that compares the

pandas_ta/momentum/pvo.py

@@ -1,10 +1,11 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.overlap import ema
 from pandas_ta.utils import get_offset, verify_series
 
 
-def pvo(volume, fast=None, slow=None, signal=None, scalar=None, offset=None, **kwargs):
+def pvo(volume: Series, fast: int = None, slow: int = None, signal: int = None, scalar: float = None,
+        offset: int = None, **kwargs) -> DataFrame:
     """Percentage Volume Oscillator (PVO)
 
     Percentage Volume Oscillator is a Momentum Oscillator for Volume.

pandas_ta/momentum/qqe.py

@@ -9,7 +9,8 @@ from pandas_ta.overlap import ma
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
-def qqe(close, length=None, smooth=None, factor=None, mamode=None, drift=None, offset=None, **kwargs):
+def qqe(close: Series, length: int = None, smooth: int = None, factor: float = None, mamode: str = None,
+        drift: int = None, offset: int = None, **kwargs) -> DataFrame:
     """Quantitative Qualitative Estimation (QQE)
 
     The Quantitative Qualitative Estimation (QQE) is similar to SuperTrend but uses a Smoothed RSI with an upper and lower bands. The band width is a combination of a one period True Range of the Smoothed RSI which is double smoothed using Wilder's smoothing length (2 * rsiLength - 1) and multiplied by the default factor of 4.236. A Long trend is determined when the Smoothed RSI crosses the previous upperband and a Short trend when the Smoothed RSI crosses the previous lowerband.

pandas_ta/momentum/roc.py

@@ -2,9 +2,11 @@
 from .mom import mom
 from pandas_ta import Imports
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def roc(close, length=None, scalar=None, talib=None, offset=None, **kwargs):
+def roc(close: Series, length: int = None, scalar: float = None, talib: bool = None, offset: int = None,
+        **kwargs) -> Series:
     """Rate of Change (ROC)
 
     Rate of Change is an indicator is also referred to as Momentum (yeah, confusingly).

pandas_ta/momentum/rsi.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame, concat
+from pandas import DataFrame, concat, Series
 from pandas_ta import Imports
 from pandas_ta.overlap import rma
 from pandas_ta.utils import get_drift, get_offset, verify_series, signals
 
 
-def rsi(close, length=None, scalar=None, talib=None, drift=None, offset=None, **kwargs):
+def rsi(close: Series, length: int = None, scalar: float = None, talib: bool = None, drift: int = None,
+        offset: int = None, **kwargs) -> Series:
     """Relative Strength Index (RSI)
 
     The Relative Strength Index is popular momentum oscillator used to measure the

pandas_ta/momentum/rsx.py

@@ -4,7 +4,7 @@ from pandas import concat, DataFrame, Series
 from pandas_ta.utils import get_drift, get_offset, verify_series, signals
 
 
-def rsx(close, length=None, drift=None, offset=None, **kwargs):
+def rsx(close: Series, length: int = None, drift: int = None, offset: int = None, **kwargs) -> Series:
     """Relative Strength Xtra (rsx)
 
     The Relative Strength Xtra is based on the popular RSI indicator and inspired

pandas_ta/momentum/rvgi.py

@@ -1,10 +1,11 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.overlap import swma
 from pandas_ta.utils import get_offset, non_zero_range, verify_series
 
 
-def rvgi(open_, high, low, close, length=None, swma_length=None, offset=None, **kwargs):
+def rvgi(open_: Series, high: Series, low: Series, close: Series, length: int = None, swma_length: int = None,
+         offset: int = None, **kwargs) -> Series:
     """Relative Vigor Index (RVGI)
 
     The Relative Vigor Index attempts to measure the strength of a trend relative to

pandas_ta/momentum/slope.py

@@ -2,9 +2,11 @@
 from numpy import arctan as npAtan
 from numpy import pi as npPi
 from pandas_ta.utils import get_offset, verify_series
+from pandas import Series
 
 
-def slope( close, length=None, as_angle=None, to_degrees=None, vertical=None, offset=None, **kwargs):
+def slope( close: Series, length: int = None, as_angle=None, to_degrees=None, vertical=None,
+           offset: int = None, **kwargs) -> Series:
     """Slope
 
     Returns the slope of a series of length n. Can convert the slope to angle.

pandas_ta/momentum/smi.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from .tsi import tsi
 from pandas_ta.overlap import ema
 from pandas_ta.utils import get_offset, verify_series
 
 
-def smi(close, fast=None, slow=None, signal=None, scalar=None, offset=None, **kwargs):
+def smi(close: Series, fast: int = None, slow: int = None, signal: int = None, scalar: float = None,
+        offset: int = None, **kwargs) -> DataFrame:
     """SMI Ergodic Indicator (SMI)
 
     The SMI Ergodic Indicator is the same as the True Strength Index (TSI) developed

pandas_ta/momentum/squeeze.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 from numpy import nan as npNaN
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.momentum import mom
 from pandas_ta.overlap import ema, linreg, sma
 from pandas_ta.trend import decreasing, increasing
@@ -9,7 +9,9 @@ from pandas_ta.utils import get_offset
 from pandas_ta.utils import unsigned_differences, verify_series
 
 
-def squeeze(high, low, close, bb_length=None, bb_std=None, kc_length=None, kc_scalar=None, mom_length=None, mom_smooth=None, use_tr=None, mamode=None, offset=None, **kwargs):
+def squeeze(high: Series, low: Series, close: Series, bb_length: int = None, bb_std: float = None,
+            kc_length: int = None, kc_scalar: float = None, mom_length: int = None, mom_smooth: int = None,
+            use_tr=None, mamode: str = None, offset: int = None, **kwargs) -> DataFrame:
     """Squeeze (SQZ)
 
     The default is based on John Carter's "TTM Squeeze" indicator, as discussed

pandas_ta/momentum/squeeze_pro.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 from numpy import NaN as npNaN
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.momentum import mom
 from pandas_ta.overlap import ema, sma
 from pandas_ta.trend import decreasing, increasing
@@ -9,7 +9,10 @@ from pandas_ta.utils import get_offset
 from pandas_ta.utils import unsigned_differences, verify_series
 
 
-def squeeze_pro(high, low, close, bb_length=None, bb_std=None, kc_length=None, kc_scalar_wide=None, kc_scalar_normal=None, kc_scalar_narrow=None, mom_length=None, mom_smooth=None, use_tr=None, mamode=None, offset=None, **kwargs):
+def squeeze_pro(high: Series, low: Series, close: Series, bb_length: int = None, bb_std: float = None,
+                kc_length: int = None, kc_scalar_wide: float = None, kc_scalar_normal: float = None,
+                kc_scalar_narrow: float = None, mom_length: int = None, mom_smooth: int = None, use_tr=None,
+                mamode: str = None, offset: int = None, **kwargs) -> DataFrame:
     """Squeeze PRO(SQZPRO)
 
     This indicator is an extended version of "TTM Squeeze" from John Carter.

pandas_ta/momentum/stc.py

@@ -4,7 +4,8 @@ from pandas_ta.overlap import ema
 from pandas_ta.utils import get_offset, non_zero_range, verify_series
 
 
-def stc(close, tclength=None, fast=None, slow=None, factor=None, offset=None, **kwargs):
+def stc(close: Series, tclength: int = None, fast: int = None, slow: int = None, factor: float = None,
+        offset: int = None, **kwargs) -> DataFrame:
     """Schaff Trend Cycle (STC)
 
     The Schaff Trend Cycle is an evolution of the popular MACD incorportating two

pandas_ta/momentum/stoch.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 ma
 from pandas_ta.utils import get_offset, non_zero_range, tal_ma, verify_series
 
 
-def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, talib=None, offset=None, **kwargs):
+def stoch(high: Series, low: Series, close: Series, k: int = None, d: int = None, smooth_k: int = None,
+          mamode: str = None, talib: bool = None, offset: int = None, **kwargs) -> DataFrame:
     """Stochastic (STOCH)
 
     The Stochastic Oscillator (STOCH) was developed by George Lane in the 1950's.

pandas_ta/momentum/stochf.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 ma
 from pandas_ta.utils import get_offset, non_zero_range, tal_ma, verify_series
 
 
-def stochf(high, low, close, k=None, d=None, mamode=None, talib=None, offset=None, **kwargs):
+def stochf(high: Series, low: Series, close: Series, k: int = None, d: int = None, mamode: str = None,
+           talib: bool = None, offset: int = None, **kwargs) -> DataFrame:
     """Fast Stochastic (STOCHF)
 
     The Fast Stochastic Oscillator (STOCHF) was developed by George Lane in the

pandas_ta/momentum/stochrsi.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from .rsi import rsi
 from pandas_ta.overlap import ma
 from pandas_ta.utils import get_offset, non_zero_range, verify_series
 
 
-def stochrsi(close, length=None, rsi_length=None, k=None, d=None, mamode=None, offset=None, **kwargs):
+def stochrsi(close: Series, length: int = None, rsi_length: int = None, k: int = None, d: int = None,
+             mamode: str = None, offset: int = None, **kwargs) -> DataFrame:
     """Stochastic (STOCHRSI)
 
     "Stochastic RSI and Dynamic Momentum Index" was created by Tushar Chande and Stanley Kroll and published in Stock & Commodities V.11:5 (189-199)

pandas_ta/momentum/td_seq.py

@@ -5,7 +5,7 @@ from pandas import DataFrame, Series
 from pandas_ta.utils import get_offset, verify_series
 
 
-def td_seq(close, asint=None, offset=None, **kwargs):
+def td_seq(close: Series, asint: bool = None, offset: int = None, **kwargs) -> DataFrame:
     """TD Sequential (TD_SEQ)
 
     Tom DeMark's Sequential indicator attempts to identify a price point where an

pandas_ta/momentum/trix.py

@@ -1,10 +1,11 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.overlap.ema import ema
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
-def trix(close, length=None, signal=None, scalar=None, drift=None, offset=None, **kwargs):
+def trix(close: Series, length: int = None, signal: int = None, scalar: float = None, drift: int = None,
+         offset: int = None, **kwargs) -> Series:
     """Trix (TRIX)
 
     TRIX is a momentum oscillator to identify divergences.

pandas_ta/momentum/tsi.py

@@ -1,10 +1,11 @@
 # -*- coding: utf-8 -*-
-from pandas import DataFrame
+from pandas import DataFrame, Series
 from pandas_ta.overlap import ema, ma
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
-def tsi(close, fast=None, slow=None, signal=None, scalar=None, mamode=None, drift=None, offset=None, **kwargs):
+def tsi(close: Series, fast: int = None, slow: int = None, signal: int = None, scalar: float = None,
+        mamode: str = None, drift: int = None, offset: int = None, **kwargs) -> DataFrame:
     """True Strength Index (TSI)
 
     The True Strength Index is a momentum indicator used to identify short-term

pandas_ta/momentum/uo.py

@@ -1,10 +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_drift, get_offset, verify_series
 
 
-def uo(high, low, close, fast=None, medium=None, slow=None, fast_w=None, medium_w=None, slow_w=None, talib=None, drift=None, offset=None, **kwargs):
+def uo(high: Series, low: Series, close: Series, fast: int = None, medium: int = None, slow: int = None,
+       fast_w: float = None, medium_w: float = None, slow_w: float = None, talib: bool = None, drift: int = None,
+       offset: int = None, **kwargs) -> Series:
     """Ultimate Oscillator (UO)
 
     The Ultimate Oscillator is a momentum indicator over three different

pandas_ta/momentum/willr.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 willr(high, low, close, length=None, talib=None, offset=None, **kwargs):
+def willr(high: Series, low: Series, close: Series, length: int = None, talib: bool = None, offset: int = None,
+          **kwargs) -> Series:
     """William's Percent R (WILLR)
 
     William's Percent R is a momentum oscillator similar to the RSI that