BUG #520 ENH lowerbound guardrails MAINT refactor

README.md

@@ -96,7 +96,7 @@ Performance
 -----------
 * Pandas TA is fast, with or without **TA Lib** or **Numba** installed, but one is not penalized if they are installed.
     * **TA Lib** computations are **enabled** by default. They can be disabled per indicator.
-    * The library includes a performance method, ```help(ta.performance)```, to check runtime indicator performance for a given _ohlcv_ DataFrame.
+    * The library includes a performance method, ```help(ta.speed_test)```, to check runtime indicator performance for a given _ohlcv_ DataFrame.
 * Optionable **Multiprocessing** for a Pandas TA ```Study```.
 * Check Indicator Speeds on your system with the [Indicator Speed Check Notebook](https://github.com/twopirllc/pandas-ta/tree/main/examples/Speed_Check.ipynb).
 
@@ -145,15 +145,10 @@ Pandas TA is used by Applications and Services like
 
 <br/>
 
-[Gamestonk Terminal](https://github.com/GamestonkTerminal/GamestonkTerminal)
+[Open BB](https://openbb.co/) (previously Gamestonk Terminal)
 -------------------
-> Gamestonk Terminal is an awesome stock and crypto market terminal that has been developed for fun, while I saw my GME shares tanking. But hey, I like the stock 💎🙌.
-
-<br/>
-
-[MarketMaker Lite](https://github.com/MarketMakerLite)
--------------------
-> Make the market you deserve. Market alerts, statistics and analytics, delivered through an innovative interface, made for retail investors.
+> OpenBB is a leading open source investment analysis company.
+We represent millions of investors who want to leverage state-of-the-art data science and machine learning technologies to make sense of raw unrefined data. Our mission is to make investment research effective, powerful and accessible to everyone.
 
 <br/>
 
@@ -198,7 +193,7 @@ $ pip install pandas_ta[full]
 
 Latest Version
 --------------
-Best choice! Version: *0.3.63b*
+Best choice! Version: *0.3.64b*
 * Includes all fixes and updates between **pypi** and what is covered in this README.
 ```sh
 $ pip install -U git+https://github.com/twopirllc/pandas-ta
@@ -1305,7 +1300,7 @@ Back to [Contents](#contents)
 
 # **Support**
 
-Feeling generous, like the package or want to see it become more a mature package?
+Like the package, want more indicators and features? Continued Support?
 * Donations help cover data and API costs so platform indicators (like [TradingView](https://github.com/tradingview/)) are accurate.
 * I appreciate **ALL** of those that have bought me Coffee/Beer/Wine et al. I greatly appreciate it! 😎
 

pandas_ta/candles/cdl_pattern.py

@@ -7,18 +7,20 @@ from pandas_ta.candles import cdl_doji, cdl_inside
 
 
 ALL_PATTERNS = [
-    "2crows", "3blackcrows", "3inside", "3linestrike", "3outside", "3starsinsouth",
-    "3whitesoldiers", "abandonedbaby", "advanceblock", "belthold", "breakaway",
-    "closingmarubozu", "concealbabyswall", "counterattack", "darkcloudcover", "doji",
-    "dojistar", "dragonflydoji", "engulfing", "eveningdojistar", "eveningstar",
-    "gapsidesidewhite", "gravestonedoji", "hammer", "hangingman", "harami",
-    "haramicross", "highwave", "hikkake", "hikkakemod", "homingpigeon",
-    "identical3crows", "inneck", "inside", "invertedhammer", "kicking", "kickingbylength",
-    "ladderbottom", "longleggeddoji", "longline", "marubozu", "matchinglow", "mathold",
-    "morningdojistar", "morningstar", "onneck", "piercing", "rickshawman",
-    "risefall3methods", "separatinglines", "shootingstar", "shortline", "spinningtop",
-    "stalledpattern", "sticksandwich", "takuri", "tasukigap", "thrusting", "tristar",
-    "unique3river", "upsidegap2crows", "xsidegap3methods"
+    "2crows", "3blackcrows", "3inside", "3linestrike", "3outside",
+    "3starsinsouth", "3whitesoldiers", "abandonedbaby", "advanceblock",
+    "belthold", "breakaway", "closingmarubozu", "concealbabyswall",
+    "counterattack", "darkcloudcover", "doji", "dojistar", "dragonflydoji",
+    "engulfing", "eveningdojistar", "eveningstar", "gapsidesidewhite",
+    "gravestonedoji", "hammer", "hangingman", "harami", "haramicross",
+    "highwave", "hikkake", "hikkakemod", "homingpigeon", "identical3crows",
+    "inneck", "inside", "invertedhammer", "kicking", "kickingbylength",
+    "ladderbottom", "longleggeddoji", "longline", "marubozu", "matchinglow",
+    "mathold", "morningdojistar", "morningstar", "onneck", "piercing",
+    "rickshawman", "risefall3methods", "separatinglines", "shootingstar",
+    "shortline", "spinningtop", "stalledpattern", "sticksandwich", "takuri",
+    "tasukigap", "thrusting", "tristar", "unique3river", "upsidegap2crows",
+    "xsidegap3methods"
 ]
 
 

pandas_ta/cycles/reflex.py

@@ -13,7 +13,8 @@ except ImportError:
 
 @njit
 def np_reflex(
-    x: Array, n: Int, k: Int, alpha: IntFloat, pi: IntFloat, sqrt2: IntFloat
+    x: Array, n: Int, k: Int,
+    alpha: IntFloat, pi: IntFloat, sqrt2: IntFloat
 ):
     m, ratio = x.size, 2 * sqrt2 / k
     a = exp(-pi * ratio)
@@ -88,7 +89,8 @@ def reflex(
     # Validate
     length = v_pos_default(length, 20)
     smooth = v_pos_default(smooth, 20)
-    close = v_series(close, max(length, smooth))
+    _length = max(length, smooth) + 1
+    close = v_series(close, _length)
 
     if close is None:
         return

pandas_ta/momentum/bop.py

@@ -2,7 +2,13 @@
 from pandas import Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
-from pandas_ta.utils import non_zero_range, v_offset, v_scalar, v_series, v_talib
+from pandas_ta.utils import (
+    non_zero_range,
+    v_offset,
+    v_scalar,
+    v_series,
+    v_talib
+)
 
 
 def bop(

pandas_ta/momentum/brar.py

@@ -1,8 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
-from pandas_ta.utils import non_zero_range, v_drift, v_offset
-from pandas_ta.utils import v_pos_default, v_scalar, v_series
+from pandas_ta.utils import (
+    non_zero_range,
+    v_drift,
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series
+)
 
 
 def brar(

pandas_ta/momentum/cfo.py

@@ -2,7 +2,13 @@
 from pandas import Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.overlap import linreg
-from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_scalar, v_series
+from pandas_ta.utils import (
+    v_drift,
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series
+)
 
 
 def cfo(

pandas_ta/momentum/cmo.py

@@ -3,8 +3,14 @@ from pandas import Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
 from pandas_ta.overlap import rma
-from pandas_ta.utils import v_drift, v_offset, v_pos_default
-from pandas_ta.utils import v_scalar, v_series, v_talib
+from pandas_ta.utils import (
+    v_drift,
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series,
+    v_talib
+)
 
 
 
@@ -42,7 +48,7 @@ def cmo(
     """
     # Validate
     length = v_pos_default(length, 14)
-    close = v_series(close, length)
+    close = v_series(close, length + 1)
 
     if close is None:
         return

pandas_ta/momentum/coppock.py

@@ -1,4 +1,5 @@
 # -*- coding: utf-8 -*-
+# from numpy import isnan
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.overlap import wma
@@ -39,7 +40,8 @@ def coppock(
     length = v_pos_default(length, 10)
     fast = v_pos_default(fast, 11)
     slow = v_pos_default(slow, 14)
-    close = v_series(close, max(length, fast, slow))
+    _length = length + fast + slow
+    close = v_series(close, _length)
 
     if close is None:
         return

pandas_ta/momentum/dm.py

@@ -3,8 +3,15 @@ from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.maps import Imports
-from pandas_ta.utils import v_drift, v_mamode, v_offset
-from pandas_ta.utils import v_pos_default, v_series, v_talib, zero
+from pandas_ta.utils import (
+    v_drift,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_talib,
+    zero
+)
 
 
 def dm(

pandas_ta/momentum/er.py

@@ -1,7 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, concat, Series
 from pandas_ta._typing import DictLike, Int
-from pandas_ta.utils import signals, v_drift, v_offset, v_pos_default, v_series
+from pandas_ta.utils import (
+    signals,
+    v_drift,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 
 
 def er(
@@ -34,7 +40,7 @@ def er(
     """
     # Validate
     length = v_pos_default(length, 10)
-    close = v_series(close, length)
+    close = v_series(close, length + 1)
 
     if close is None:
         return

pandas_ta/momentum/inertia.py

@@ -2,8 +2,15 @@
 from pandas import Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.overlap import linreg
-from pandas_ta.utils import v_bool, v_drift, v_mamode, v_offset
-from pandas_ta.utils import v_pos_default, v_scalar, v_series
+from pandas_ta.utils import (
+    v_bool,
+    v_drift,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series
+)
 from pandas_ta.volatility import rvi
 
 
@@ -48,7 +55,7 @@ def inertia(
     # Validate
     length = v_pos_default(length, 20)
     rvi_length = v_pos_default(rvi_length, 14)
-    _length = max(length, rvi_length)
+    _length = 2 * max(length, rvi_length) - min(length, rvi_length) // 2 - 1
     close = v_series(close, _length)
 
     if close is None:

pandas_ta/momentum/kdj.py

@@ -1,8 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int
-from pandas_ta.utils import non_zero_range, rma_pandas, v_offset
-from pandas_ta.utils import v_pos_default, v_series
+from pandas_ta.utils import (
+    non_zero_range,
+    rma_pandas,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 
 
 def kdj(
@@ -40,7 +45,7 @@ def kdj(
     # Validate
     length = v_pos_default(length, 9)
     signal = v_pos_default(signal, 3)
-    _length = max(length, signal)
+    _length = length + signal + 1
     high = v_series(high, _length)
     low = v_series(low, _length)
     close = v_series(close, _length)

pandas_ta/momentum/kst.py

@@ -53,7 +53,9 @@ def kst(
     sma4 = int(sma4) if sma4 and sma4 > 0 else 15
 
     signal = v_pos_default(signal, 9)
-    _length = max(roc1, roc2, roc3, roc4, sma1, sma2, sma3, sma4, signal)
+    _rmax = max(roc1, roc2, roc3, roc4)
+    _smax = max(sma1, sma2, sma3, sma4)
+    _length = _rmax + _smax
     close = v_series(close, _length)
 
     if close is None:

pandas_ta/momentum/macd.py

@@ -3,8 +3,13 @@ from pandas import concat, DataFrame, Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.overlap import ema
-from pandas_ta.utils import signals, v_offset, v_mamode
-from pandas_ta.utils import v_pos_default, v_series, v_talib
+from pandas_ta.utils import (
+    signals,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_talib
+)
 
 
 def macd(
@@ -47,7 +52,8 @@ def macd(
     signal = v_pos_default(signal, 9)
     if slow < fast:
         fast, slow = slow, fast
-    close = v_series(close, slow + signal)
+    _length = slow + signal - 1
+    close = v_series(close, _length)
 
     if close is None:
         return

pandas_ta/momentum/mom.py

@@ -33,7 +33,7 @@ def mom(
     """
     # Validate
     length = v_pos_default(length, 10)
-    close = v_series(close, length)
+    close = v_series(close, length + 1)
 
     if close is None:
         return

pandas_ta/momentum/pgo.py

@@ -37,9 +37,10 @@ def pgo(
     """
     # Validate
     length = v_pos_default(length, 14)
-    high = v_series(high, length)
-    low = v_series(low, length)
-    close = v_series(close, length)
+    _length = 2 * length
+    high = v_series(high, _length)
+    low = v_series(low, _length)
+    close = v_series(close, _length)
 
     if high is None or low is None or close is None:
         return

pandas_ta/momentum/ppo.py

@@ -3,8 +3,15 @@ from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.ma import ma
 from pandas_ta.maps import Imports
-from pandas_ta.utils import tal_ma, v_mamode, v_offset, v_pos_default
-from pandas_ta.utils import v_scalar, v_series, v_talib
+from pandas_ta.utils import (
+    tal_ma,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series,
+    v_talib
+)
 
 
 def ppo(
@@ -43,7 +50,8 @@ def ppo(
     signal = v_pos_default(signal, 9)
     if slow < fast:
         fast, slow = slow, fast
-    close = v_series(close, max(fast, slow, signal))
+    _length = max(fast, slow, signal)
+    close = v_series(close, _length)
 
     if close is None:
         return

pandas_ta/momentum/psl.py

@@ -2,7 +2,13 @@
 from numpy import sign
 from pandas import Series
 from pandas_ta._typing import DictLike, Int, IntFloat
-from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_scalar, v_series
+from pandas_ta.utils import (
+    v_drift,
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series
+)
 
 
 def psl(

pandas_ta/momentum/qqe.py

@@ -3,8 +3,14 @@ from numpy import isnan, maximum, minimum, nan
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.ma import ma
-from pandas_ta.utils import v_drift, v_mamode, v_offset
-from pandas_ta.utils import v_pos_default, v_scalar, v_series
+from pandas_ta.utils import (
+    v_drift,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series
+)
 from .rsi import rsi
 
 
@@ -53,7 +59,8 @@ def qqe(
     length = v_pos_default(length, 14)
     smooth = v_pos_default(smooth, 5)
     wilders_length = 2 * length - 1
-    close = v_series(close, smooth + wilders_length)
+    _length = wilders_length + smooth
+    close = v_series(close, _length)
 
     if close is None:
         return
@@ -80,7 +87,7 @@ def qqe(
         return  # Emergency Break
     dar = factor * ma("ema", smoothed_rsi_tr_ma, length=wilders_length)
     if all(isnan(dar)):
-        return                # Emergency Break
+        return  # Emergency Break
 
     # Create the Upper and Lower Bands around RSI MA.
     upperband = rsi_ma + dar

pandas_ta/momentum/roc.py

@@ -2,8 +2,13 @@
 from pandas import Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
-from pandas_ta.utils import v_offset, v_pos_default, v_scalar
-from pandas_ta.utils import v_series, v_talib
+from pandas_ta.utils import (
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series,
+    v_talib
+)
 from .mom import mom
 
 
@@ -39,7 +44,7 @@ def roc(
     """
     # Validate
     length = v_pos_default(length, 10)
-    close = v_series(close, length)
+    close = v_series(close, length + 1)
 
     if close is None:
         return

pandas_ta/momentum/rsi.py

@@ -3,8 +3,15 @@ from pandas import DataFrame, concat, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
 from pandas_ta.overlap import rma
-from pandas_ta.utils import signals, v_drift, v_offset, v_pos_default
-from pandas_ta.utils import v_scalar, v_series, v_talib
+from pandas_ta.utils import (
+    signals,
+    v_drift,
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series,
+    v_talib
+)
 
 
 def rsi(
@@ -39,7 +46,7 @@ def rsi(
     """
     # Validate
     length = v_pos_default(length, 14)
-    close = v_series(close, length)
+    close = v_series(close, length + 1)
 
     if close is None:
         return

pandas_ta/momentum/rsx.py

@@ -2,7 +2,13 @@
 from numpy import nan
 from pandas_ta._typing import DictLike, Int
 from pandas import concat, DataFrame, Series
-from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_series, signals
+from pandas_ta.utils import (
+    signals,
+    v_drift,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 
 
 def rsx(
@@ -53,7 +59,7 @@ def rsx(
     f40, f48, f50, f58, f60, f68, f70, f78 = 0, 0, 0, 0, 0, 0, 0, 0
     f80, f88, f90 = 0, 0, 0
 
-    result = [nan for _ in range(0, length - 1)] + [0]
+    result = [nan for _ in range(0, length - 1)] + [50]
     for i in range(length, m):
         if f90 == 0:
             f90 = 1.0

pandas_ta/momentum/rvgi.py

@@ -39,7 +39,7 @@ def rvgi(
     # Validate
     length = v_pos_default(length, 14)
     swma_length = v_pos_default(swma_length, 4)
-    _length = max(length, swma_length)
+    _length = length + swma_length - 1
     open_ = v_series(open_, _length)
     high = v_series(high, _length)
     low = v_series(low, _length)

pandas_ta/momentum/slope.py

@@ -30,9 +30,10 @@ def slope(
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 1
-        as_angle (value, optional): Converts slope to an angle. Default: False
-        to_degrees (value, optional): Converts slope angle to degrees.
-            Default: False
+        as_angle (value, optional): Converts slope to an angle in radians
+            per np.arctan(). Default: False
+        to_degrees (value, optional): If as_angle=True, it converts the slope
+            angle to degrees. Default: False
         offset (int): How many periods to offset the result. Default: 0
 
     Kwargs:
@@ -44,7 +45,7 @@ def slope(
     """
     # Validate
     length = v_pos_default(length, 1)
-    close = v_series(close, length)
+    close = v_series(close, length + 1)
 
     if close is None:
         return

pandas_ta/momentum/smi.py

@@ -1,4 +1,5 @@
 # -*- coding: utf-8 -*-
+from numpy import isnan
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.utils import v_offset, v_pos_default, v_scalar, v_series
@@ -47,7 +48,8 @@ def smi(
     signal = v_pos_default(signal, 5)
     if slow < fast:
         fast, slow = slow, fast
-    close = v_series(close, max(fast, slow, signal))
+    _length = slow + signal + 1
+    close = v_series(close, _length)
 
     if close is None:
         return
@@ -57,8 +59,13 @@ def smi(
 
     # Calculate
     tsi_df = tsi(close, fast=fast, slow=slow, signal=signal, scalar=scalar)
+    if tsi_df is None:
+        return  # Emergency Break
+
     smi = tsi_df.iloc[:, 0]
     signalma = tsi_df.iloc[:, 1]
+    if all(isnan(signalma)):
+        return  # Emergency Break
     osc = smi - signalma
 
     # Offset

pandas_ta/momentum/squeeze.py

@@ -4,8 +4,15 @@ from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.overlap import ema, linreg, sma
 from pandas_ta.trend import decreasing, increasing
-from pandas_ta.utils import simplify_columns, unsigned_differences, v_mamode
-from pandas_ta.utils import v_offset, v_pos_default, v_series
+from pandas_ta.utils import (
+    simplify_columns,
+    unsigned_differences,
+    v_bool,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 from pandas_ta.volatility import bbands, kc
 from .mom import mom
 
@@ -16,6 +23,7 @@ def squeeze(
     kc_length: Int = None, kc_scalar: IntFloat = None,
     mom_length: Int = None, mom_smooth: Int = None,
     use_tr: bool = None, mamode: str = None,
+    prenan: bool = None,
     offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Squeeze (SQZ)
@@ -44,6 +52,8 @@ def squeeze(
         mom_length (int): Momentum Period. Default: 12
         mom_smooth (int): Smoothing Period of Momentum. Default: 6
         mamode (str): Only "ema" or "sma". Default: "sma"
+        prenan (bool): If True, sets nan for all columns up the first
+            valid squeeze value. Default: False
         offset (int): How many periods to offset the result. Default: 0
 
     Kwargs:
@@ -67,7 +77,7 @@ def squeeze(
     kc_length = v_pos_default(kc_length, 20)
     mom_length = v_pos_default(mom_length, 12)
     mom_smooth = v_pos_default(mom_smooth, 6)
-    _length = max(bb_length, kc_length, mom_length, mom_smooth)
+    _length = max(bb_length, kc_length, mom_length, mom_smooth) + 1
     high = v_series(high, _length)
     low = v_series(low, _length)
     close = v_series(close, _length)
@@ -78,6 +88,7 @@ def squeeze(
     bb_std = v_pos_default(bb_std, 2.0)
     kc_scalar = v_pos_default(kc_scalar, 1.5)
     mamode = v_mamode(mamode, "sma")
+    prenan = v_bool(prenan, False)
     offset = v_offset(offset)
 
     use_tr = kwargs.pop("tr", True)
@@ -140,11 +151,22 @@ def squeeze(
     _props += "_LB" if lazybear else ""
     squeeze.name = f"SQZ{_props}"
 
+    if asint:
+        squeeze_on = squeeze_on.astype(int)
+        squeeze_off = squeeze_off.astype(int)
+        no_squeeze = no_squeeze.astype(int)
+
+    if prenan:
+        nanlength = max(bb_length, kc_length) - 2
+        squeeze_on[:nanlength] = nan
+        squeeze_off[:nanlength] = nan
+        no_squeeze[:nanlength] = nan
+
     data = {
         squeeze.name: squeeze,
-        f"SQZ_ON": squeeze_on.astype(int) if asint else squeeze_on,
-        f"SQZ_OFF": squeeze_off.astype(int) if asint else squeeze_off,
-        f"SQZ_NO": no_squeeze.astype(int) if asint else no_squeeze,
+        f"SQZ_ON": squeeze_on,
+        f"SQZ_OFF": squeeze_off,
+        f"SQZ_NO": no_squeeze,
     }
     df = DataFrame(data)
     df.name = squeeze.name

pandas_ta/momentum/squeeze_pro.py

@@ -4,11 +4,19 @@ from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.ma import ma
 from pandas_ta.momentum import mom
-# from pandas_ta.overlap import ema, sma
 from pandas_ta.trend import decreasing, increasing
+from pandas_ta.utils import (
+    simplify_columns,
+    unsigned_differences,
+    v_bool,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series
+)
 from pandas_ta.volatility import bbands, kc
-from pandas_ta.utils import simplify_columns, unsigned_differences, v_mamode
-from pandas_ta.utils import v_offset, v_pos_default, v_scalar, v_series
+
 
 def squeeze_pro(
     high: Series, low: Series, close: Series,
@@ -17,6 +25,7 @@ def squeeze_pro(
     kc_scalar_normal: IntFloat = None, kc_scalar_narrow: IntFloat = None,
     mom_length: Int = None, mom_smooth: Int = None,
     use_tr: bool = None, mamode: str = None,
+    prenan: bool = None,
     offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Squeeze PRO(SQZPRO)
@@ -50,6 +59,8 @@ def squeeze_pro(
         mom_length (int): Momentum Period. Default: 12
         mom_smooth (int): Smoothing Period of Momentum. Default: 6
         mamode (str): Only "ema" or "sma". Default: "sma"
+        prenan (bool): If True, sets nan for all columns up the first
+            valid squeeze value. Default: False
         offset (int): How many periods to offset the result. Default: 0
 
     Kwargs:
@@ -72,7 +83,7 @@ def squeeze_pro(
     kc_length = v_pos_default(kc_length, 20)
     mom_length = v_pos_default(mom_length, 12)
     mom_smooth = v_pos_default(mom_smooth, 6)
-    _length = max(bb_length, kc_length, mom_length, mom_smooth)
+    _length = max(bb_length, kc_length, mom_length, mom_smooth) + 1
     high = v_series(high, _length)
     low = v_series(low, _length)
     close = v_series(close, _length)
@@ -83,6 +94,7 @@ def squeeze_pro(
     kc_scalar_narrow = v_scalar(kc_scalar_narrow, 1)
     kc_scalar_normal = v_scalar(kc_scalar_normal, 1.5)
     kc_scalar_wide = v_scalar(kc_scalar_wide, 2)
+    prenan = v_bool(prenan, False)
     valid_kc_scaler = kc_scalar_wide > kc_scalar_normal \
         and kc_scalar_normal > kc_scalar_narrow
 
@@ -157,13 +169,28 @@ def squeeze_pro(
     _props += f"_{bb_length}_{bb_std}_{kc_length}_{kc_scalar_wide}_{kc_scalar_normal}_{kc_scalar_narrow}"
     squeeze.name = f"SQZPRO{_props}"
 
+    if asint:
+        squeeze_on_wide = squeeze_on_wide.astype(int)
+        squeeze_on_narrow = squeeze_on_narrow.astype(int)
+        squeeze_on_normal = squeeze_on_normal.astype(int)
+        squeeze_off_wide = squeeze_off_wide.astype(int)
+        no_squeeze = no_squeeze.astype(int)
+
+    if prenan:
+        nanlength = max(bb_length, kc_length) - 2
+        squeeze_on_wide[:nanlength] = nan
+        squeeze_on_narrow[:nanlength] = nan
+        squeeze_on_normal[:nanlength] = nan
+        squeeze_off_wide[:nanlength] = nan
+        no_squeeze[:nanlength] = nan
+
     data = {
         squeeze.name: squeeze,
-        f"SQZPRO_ON_WIDE": squeeze_on_wide.astype(int) if asint else squeeze_on_wide,
-        f"SQZPRO_ON_NORMAL": squeeze_on_normal.astype(int) if asint else squeeze_on_normal,
-        f"SQZPRO_ON_NARROW": squeeze_on_narrow.astype(int) if asint else squeeze_on_narrow,
-        f"SQZPRO_OFF": squeeze_off_wide.astype(int) if asint else squeeze_off_wide,
-        f"SQZPRO_NO": no_squeeze.astype(int) if asint else no_squeeze,
+        f"SQZPRO_ON_WIDE": squeeze_on_wide,
+        f"SQZPRO_ON_NORMAL": squeeze_on_normal,
+        f"SQZPRO_ON_NARROW": squeeze_on_narrow,
+        f"SQZPRO_OFF": squeeze_off_wide,
+        f"SQZPRO_NO": no_squeeze,
     }
     df = DataFrame(data)
     df.name = squeeze.name

pandas_ta/momentum/stc.py

@@ -1,9 +1,14 @@
 # -*- coding: utf-8 -*-
+from numpy import nan
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.overlap import ema
-from pandas_ta.utils import non_zero_range, v_offset
-from pandas_ta.utils import v_pos_default, v_series
+from pandas_ta.utils import (
+    non_zero_range,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 
 
 def stc(
@@ -106,10 +111,14 @@ def stc(
         xmacd = fastma - slowma
         pff, pf = schaff_tc(close, xmacd, tclength, factor)
 
+    pf[:_length - 1] = nan
+
     stc = Series(pff, index=close.index)
     macd = Series(xmacd, index=close.index)
     stoch = Series(pf, index=close.index)
 
+    stc.iloc[:_length - 1] = nan
+
     # Offset
     if offset != 0:
         stc = stc.shift(offset)
@@ -133,7 +142,11 @@ def stc(
     stoch.name = f"STCstoch{_props}"
     stc.category = macd.category = stoch.category = "momentum"
 
-    data = {stc.name: stc, macd.name: macd, stoch.name: stoch}
+    data = {
+        stc.name: stc,
+        macd.name: macd,
+        stoch.name: stoch
+    }
     df = DataFrame(data)
     df.name = f"STC{_props}"
     df.category = stc.category

pandas_ta/momentum/stoch.py

@@ -3,8 +3,15 @@ from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.maps import Imports
-from pandas_ta.utils import non_zero_range, tal_ma, v_mamode
-from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
+from pandas_ta.utils import (
+    non_zero_range,
+    tal_ma,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_talib
+)
 
 
 def stoch(

pandas_ta/momentum/stochf.py

@@ -3,8 +3,15 @@ from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.maps import Imports
-from pandas_ta.utils import non_zero_range, tal_ma, v_mamode
-from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
+from pandas_ta.utils import (
+    non_zero_range,
+    tal_ma,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_talib
+)
 
 
 def stochf(
@@ -44,7 +51,7 @@ def stochf(
     # Validate
     k = v_pos_default(k, 14)
     d = v_pos_default(d, 3)
-    _length = max(k, d)
+    _length = k + d - 1
     high = v_series(high, _length)
     low = v_series(low, _length)
     close = v_series(close, _length)

pandas_ta/momentum/stochrsi.py

@@ -3,8 +3,13 @@ from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.momentum import rsi
-from pandas_ta.utils import non_zero_range, v_mamode
-from pandas_ta.utils import v_offset, v_pos_default, v_series
+from pandas_ta.utils import (
+    non_zero_range,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 
 
 def stochrsi(
@@ -49,7 +54,8 @@ def stochrsi(
     rsi_length = v_pos_default(rsi_length, 14)
     k = v_pos_default(k, 3)
     d = v_pos_default(d, 3)
-    close = v_series(close, length + rsi_length + k + d)
+    _length = length + rsi_length + 2
+    close = v_series(close, _length)
 
     if close is None:
         return

pandas_ta/momentum/td_seq.py

@@ -32,7 +32,7 @@ def td_seq(
         pd.DataFrame: New feature generated.
     """
     # Validate
-    close = v_series(close)
+    close = v_series(close, 5)
 
     if close is None:
         return

pandas_ta/momentum/trix.py

@@ -1,9 +1,15 @@
 # -*- coding: utf-8 -*-
+from numpy import isnan
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.overlap.ema import ema
-from pandas_ta.utils import v_drift, v_offset, v_pos_default
-from pandas_ta.utils import v_scalar, v_series
+from pandas_ta.utils import (
+    v_drift,
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series
+)
 
 
 def trix(
@@ -35,26 +41,31 @@ def trix(
     """
     # Validate
     length = v_pos_default(length, 30)
-    _length = 3 * length - 2
+    signal = v_pos_default(signal, 9)
+    if length < signal:
+        length, signal = signal, length
+    _length = 3 * length - 1
     close = v_series(close, _length)
 
     if close is None:
         return
 
-    signal = v_pos_default(signal, 9)
     scalar = v_scalar(scalar, 100)
     drift = v_drift(drift)
     offset = v_offset(offset)
 
     # Calculate
     ema1 = ema(close=close, length=length, **kwargs)
-    # if all(isnan(ema1)): return  # Emergency Break
+    if all(isnan(ema1)):
+        return  # Emergency Break
 
     ema2 = ema(close=ema1, length=length, **kwargs)
-    # if all(isnan(ema2)): return  # Emergency Break
+    if all(isnan(ema2)):
+        return  # Emergency Break
 
     ema3 = ema(close=ema2, length=length, **kwargs)
-    # if all(isnan(ema3)): return  # Emergency Break
+    if all(isnan(ema3)):
+        return  # Emergency Break
 
     trix = scalar * ema3.pct_change(drift)
     trix_signal = trix.rolling(signal).mean()

pandas_ta/momentum/tsi.py

@@ -1,10 +1,17 @@
 # -*- coding: utf-8 -*-
+from numpy import isnan
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.ma import ma
 from pandas_ta.overlap import ema
-from pandas_ta.utils import v_drift, v_mamode, v_offset
-from pandas_ta.utils import v_pos_default, v_scalar, v_series
+from pandas_ta.utils import (
+    v_drift,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series
+)
 
 
 def tsi(
@@ -43,14 +50,18 @@ def tsi(
     # Validate
     fast = v_pos_default(fast, 13)
     slow = v_pos_default(slow, 25)
-    close = v_series(close, max(fast, slow))
+    signal = v_pos_default(signal, 13)
+    if slow < fast:
+        fast, slow = slow, fast
+    _length = slow + signal + 1
+    close = v_series(close, _length)
+
     if "length" in kwargs:
         kwargs.pop("length")
 
     if close is None:
         return
 
-    signal = v_pos_default(signal, 13)
     scalar = v_scalar(scalar, 100)
     mamode = v_mamode(mamode, "ema")
     drift = v_drift(drift)
@@ -59,13 +70,19 @@ def tsi(
     # Calculate
     diff = close.diff(drift)
     slow_ema = ema(close=diff, length=slow, **kwargs)
+    if all(isnan(slow_ema)):
+        return  # Emergency Break
     fast_slow_ema = ema(close=slow_ema, length=fast, **kwargs)
 
     abs_diff = diff.abs()
     abs_slow_ema = ema(close=abs_diff, length=slow, **kwargs)
+    if all(isnan(abs_slow_ema)):
+        return  # Emergency Break
     abs_fast_slow_ema = ema(close=abs_slow_ema, length=fast, **kwargs)
 
     tsi = scalar * fast_slow_ema / abs_fast_slow_ema
+    if all(isnan(tsi)):
+        return  # Emergency Break
     tsi_signal = ma(mamode, tsi, length=signal)
 
     # Offset

pandas_ta/momentum/uo.py

@@ -2,7 +2,13 @@
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
-from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_series, v_talib
+from pandas_ta.utils import (
+    v_drift,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_talib
+)
 
 
 def uo(
@@ -46,7 +52,7 @@ def uo(
     fast = v_pos_default(fast, 7)
     medium = v_pos_default(medium, 14)
     slow = v_pos_default(slow, 28)
-    _length = max(fast, medium, slow)
+    _length = max(fast, medium, slow) + 1
     high = v_series(high, _length)
     low = v_series(low, _length)
     close = v_series(close, _length)

pandas_ta/overlap/alma.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 from numpy import append, arange, array, exp, floor, nan, tensordot
-from numpy.version import version as npVersion
+from numpy.version import version as np_version
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas import Series
 from pandas_ta.utils import strided_window, v_offset, v_pos_default, v_series
@@ -46,6 +46,7 @@ def alma(
         return
 
     sigma = v_pos_default(sigma, 6.0)
+
     if isinstance(dist_offset, float) and 0 <= dist_offset <= 1:
         offset_ = float(dist_offset)
     else:
@@ -60,7 +61,7 @@ def alma(
     weights = exp(-0.5 * ((sigma / length) * (x - k)) ** 2)
     weights /= weights.sum()
 
-    if npVersion >= "1.20.0":
+    if np_version >= "1.20.0":
         from numpy.lib.stride_tricks import sliding_window_view
         window = sliding_window_view(np_close, length)
     else:

pandas_ta/overlap/ema.py

@@ -3,7 +3,13 @@ from numpy import nan
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
-from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series, v_talib
+from pandas_ta.utils import (
+    v_bool,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_talib
+)
 
 try:
     from numba import njit
@@ -71,7 +77,7 @@ def ema(
     adjust = kwargs.setdefault("adjust", False)
 
     # Calculate
-    if Imports["talib"] and mode_tal:
+    if Imports["talib"] and mode_tal and length > 1:
         from talib import EMA
         ema = EMA(close, length)
     else:

pandas_ta/overlap/fwma.py

@@ -1,8 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
-from pandas_ta.utils import fibonacci, v_ascending, v_offset
-from pandas_ta.utils import v_pos_default, v_series, weights
+from pandas_ta.utils import (
+    fibonacci,
+    v_ascending,
+    v_offset,
+    v_pos_default,
+    v_series,
+    weights
+)
 
 
 def fwma(

pandas_ta/overlap/hilo.py

@@ -49,7 +49,7 @@ def hilo(
     # Validate
     high_length = v_pos_default(high_length, 13)
     low_length = v_pos_default(low_length, 21)
-    _length = max(high_length, low_length)
+    _length = max(high_length, low_length) + 1
     high = v_series(high, _length)
     low = v_series(low, _length)
     close = v_series(close, _length)

pandas_ta/overlap/hma.py

@@ -32,7 +32,7 @@ def hma(
     """
     # Validate
     length = v_pos_default(length, 10)
-    close = v_series(close, length)
+    close = v_series(close, length + 2)
 
     if close is None:
         return

pandas_ta/overlap/kama.py

@@ -3,8 +3,14 @@ from numpy import nan
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
-from pandas_ta.utils import non_zero_range, v_drift, v_mamode
-from pandas_ta.utils import v_offset, v_pos_default, v_series
+from pandas_ta.utils import (
+    non_zero_range,
+    v_drift,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 
 
 def kama(

pandas_ta/overlap/linreg.py

@@ -1,12 +1,16 @@
 # -*- coding: utf-8 -*-
 from numpy import arctan, nan, pi, zeros_like
-from numpy.version import version
+from numpy.version import version as np_version
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
-from pandas_ta.utils import strided_window, v_offset, v_pos_default
-from pandas_ta.utils import v_series, v_talib
-
+from pandas_ta.utils import (
+    strided_window,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_talib
+)
 
 def linreg(
     close: Series, length: Int = None, talib: bool = None,
@@ -111,7 +115,7 @@ def linreg(
 
             return m * length + b if not tsf else m * (length - 1) + b
 
-        if version >= "1.20.0":
+        if np_version >= "1.20.0":
             from numpy.lib.stride_tricks import sliding_window_view
             linreg_ = [
                 linear_regression(_) for _ in sliding_window_view(
@@ -137,7 +141,7 @@ def linreg(
         linreg.fillna(method=kwargs["fill_method"], inplace=True)
 
     # Name and Category
-    linreg.name = f"LR"
+    linreg.name = f"LINREG"
     if slope:
         linreg.name += "m"
     if intercept:

pandas_ta/overlap/mama.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-from numpy import arctan, nan, zeros_like
+from numpy import arctan, isnan, nan, zeros_like
 from pandas import DataFrame, Series
 from pandas_ta._typing import Array, DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
@@ -14,7 +14,8 @@ except ImportError:
 
 @njit
 def np_mama(
-    x: Array, fastlimit: IntFloat, slowlimit: IntFloat, prenan: Int
+    x: Array, fastlimit: IntFloat, slowlimit: IntFloat,
+    prenan: Int
 ):
     """Ehler's Mother of Adaptive Moving Averages
     http://traders.com/documentation/feedbk_docs/2014/01/traderstips.html
@@ -155,6 +156,9 @@ def mama(
     else:
         mama, fama = np_mama(np_close, fastlimit, slowlimit, prenan)
 
+    if all(isnan(mama)) or all(isnan(fama)):
+        return  # Emergency Break
+
     # Name and Category
     _props = f"_{fastlimit}_{slowlimit}"
     df = DataFrame({

pandas_ta/overlap/pwma.py

@@ -1,8 +1,15 @@
 # -*- coding: utf-8 -*-
+# from numpy.version import version as np_version
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
-from pandas_ta.utils import pascals_triangle, v_offset
-from pandas_ta.utils import v_ascending, v_pos_default, v_series, weights
+from pandas_ta.utils import (
+    pascals_triangle,
+    v_offset,
+    v_ascending,
+    v_pos_default,
+    v_series,
+    weights
+)
 
 
 def pwma(

pandas_ta/overlap/sma.py

@@ -3,8 +3,13 @@ from numpy import convolve, ndarray, ones
 from pandas import Series
 from pandas_ta._typing import Array, DictLike, Int
 from pandas_ta.maps import Imports
-from pandas_ta.utils import np_prepend, v_offset, v_pos_default
-from pandas_ta.utils import v_series, v_talib
+from pandas_ta.utils import (
+    np_prepend,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_talib
+)
 
 
 try:

pandas_ta/overlap/smma.py

@@ -3,8 +3,13 @@ from numpy import nan
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
-from pandas_ta.utils import v_mamode, v_offset, v_pos_default
-from pandas_ta.utils import v_series, v_talib
+from pandas_ta.utils import (
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_talib
+)
 
 
 def smma(

pandas_ta/overlap/supertrend.py

@@ -40,9 +40,9 @@ def supertrend(
     """
     # Validate
     length = v_pos_default(length, 7)
-    high = v_series(high, length)
-    low = v_series(low, length)
-    close = v_series(close, length)
+    high = v_series(high, length + 1)
+    low = v_series(low, length + 1)
+    close = v_series(close, length + 1)
 
     if high is None or low is None or close is None:
         return

pandas_ta/overlap/swma.py

@@ -1,8 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
-from pandas_ta.utils import symmetric_triangle, v_offset, v_pos_default
-from pandas_ta.utils import v_series, weights
+from pandas_ta.utils import (
+    symmetric_triangle,
+    v_offset,
+    v_pos_default,
+    v_series,
+    weights
+)
 
 
 def swma(

pandas_ta/overlap/t3.py

@@ -1,4 +1,5 @@
 # -*- coding: utf-8 -*-
+from numpy import isnan
 from pandas import Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
@@ -37,7 +38,7 @@ def t3(
     """
     # Validate
     length = v_pos_default(length, 10)
-    close = v_series(close, length)
+    close = v_series(close, 5 * (length + 1))
 
     if close is None:
         return

pandas_ta/overlap/tema.py

@@ -35,7 +35,7 @@ def tema(
     """
     # Validate
     length = v_pos_default(length, 10)
-    close = v_series(close, length)
+    close = v_series(close, 3 * length)
 
     if close is None:
         return

pandas_ta/overlap/vidya.py

@@ -41,7 +41,7 @@ def vidya(
     """
     # Validate
     length = v_pos_default(length, 14)
-    close = v_series(close, length)
+    close = v_series(close, length + 1)
 
     if close is None:
         return

pandas_ta/overlap/wma.py

@@ -3,8 +3,13 @@ from numpy import arange, dot
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
-from pandas_ta.utils import v_ascending, v_offset, v_pos_default
-from pandas_ta.utils import v_series, v_talib
+from pandas_ta.utils import (
+    v_ascending,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_talib
+)
 
 
 def wma(

pandas_ta/overlap/zlma.py

@@ -1,7 +1,9 @@
 # -*- coding: utf-8 -*-
+from numpy import isnan
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series
+
 from .dema import dema
 from .ema import ema
 from .fwma import fwma
@@ -104,6 +106,8 @@ def zlma(
     kwargs.update({"length": length})
 
     zlma = _ma(mamode, **kwargs)
+    if zlma is None or all(isnan(zlma)):
+        return  # Emergency Break
 
     # Offset
     if offset != 0:

pandas_ta/performance/log_return.py

@@ -33,7 +33,7 @@ def log_return(
     """
     # Validate
     length = v_pos_default(length, 1)
-    close = v_series(close, length)
+    close = v_series(close, length + 1)
 
     if close is None:
         return

pandas_ta/performance/percent_return.py

@@ -20,7 +20,8 @@ def percent_return(
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 20
-        cumulative (bool): If True, returns the cumulative returns. Default: False
+        cumulative (bool): If True, returns the cumulative returns.
+            Default: False
         offset (int): How many periods to offset the result. Default: 0
 
     Kwargs:
@@ -32,7 +33,7 @@ def percent_return(
     """
     # Validate
     length = v_pos_default(length, 1)
-    close = v_series(close, length)
+    close = v_series(close, length + 1)
 
     if close is None:
         return

pandas_ta/statistics/entropy.py

@@ -33,7 +33,7 @@ def entropy(
     """
     # Validate
     length = v_pos_default(length, 10)
-    close = v_series(close, length)
+    close = v_series(close, 2 * length - 1)
 
     if close is None:
         return

pandas_ta/statistics/median.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
-from pandas_ta._typing import DictLike, Int, IntFloat
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import v_offset, v_pos_default, v_series
 
 

pandas_ta/statistics/tos_stdevall.py

@@ -46,7 +46,7 @@ def tos_stdevall(
         close = close.iloc[-length:]
         _props = f"{_props}_{length}"
 
-    close = v_series(close, length)
+    close = v_series(close, 2)
 
     if close is None:
         return

pandas_ta/transform/ifisher.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-from numpy import exp, logical_and, max, min
+from numpy import exp, isnan, logical_and, max, min
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.utils import v_int, v_offset, v_scalar, v_series
@@ -60,6 +60,8 @@ def ifisher(
     if not all(is_remapped):
         np_max, np_min = max(np_close), min(np_close)
         close_map = remap(close, from_min=np_min, from_max=np_max, to_min=-1, to_max=1)
+        if close_map is None or all(isnan(close_map.values)):
+            return  # Emergency Break
         np_close = close_map.values
     amped = exp(amp * np_close)
     result = (amped - 1) / (amped + 1)

pandas_ta/trend/adx.py

@@ -1,9 +1,17 @@
 # -*- coding: utf-8 -*-
+from numpy import isnan
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.ma import ma
-from pandas_ta.utils import v_drift, v_mamode, v_offset, v_pos_default
-from pandas_ta.utils import v_scalar, v_series, zero
+from pandas_ta.utils import (
+    v_drift,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series,
+    zero
+)
 from pandas_ta.volatility import atr
 
 
@@ -58,7 +66,12 @@ def adx(
     offset = v_offset(offset)
 
     # Calculate
-    atr_ = atr(high=high, low=low, close=close, length=length)
+    atr_ = atr(
+        high=high, low=low, close=close,
+        length=length, prenan=kwargs.pop("prenan", True)
+    )
+    if atr_ is None or all(isnan(atr_)):
+        return
 
     up = high - high.shift(drift)  # high.diff(drift)
     dn = low.shift(drift) - low    # low.diff(-drift).shift(drift)

pandas_ta/trend/amat.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
-from pandas_ta._typing import DictLike, Int, IntFloat
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series
 from .long_run import long_run

pandas_ta/trend/aroon.py

@@ -2,9 +2,15 @@
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
-from pandas_ta.utils import v_offset, v_pos_default
-from pandas_ta.utils import v_scalar, v_series, v_talib
-from pandas_ta.utils import recent_maximum_index, recent_minimum_index
+from pandas_ta.utils import (
+    recent_maximum_index,
+    recent_minimum_index,
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series,
+    v_talib
+)
 
 
 def aroon(
@@ -37,8 +43,8 @@ def aroon(
     """
     # Validate
     length = v_pos_default(length, 14)
-    high = v_series(high, length)
-    low = v_series(low, length)
+    high = v_series(high, length + 1)
+    low = v_series(low, length + 1)
 
     if high is None or low is None:
         return

pandas_ta/trend/chop.py

@@ -2,8 +2,14 @@
 from numpy import log, log10
 from pandas import Series
 from pandas_ta._typing import DictLike, Int, IntFloat
-from pandas_ta.utils import v_bool, v_drift, v_offset
-from pandas_ta.utils import v_pos_default, v_scalar, v_series
+from pandas_ta.utils import (
+    v_bool,
+    v_drift,
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series,
+)
 from pandas_ta.volatility import atr
 
 
@@ -45,9 +51,9 @@ def chop(
     """
     # Validate
     length = v_pos_default(length, 14)
-    high = v_series(high, length)
-    low = v_series(low, length)
-    close = v_series(close, length)
+    high = v_series(high, length + 1)
+    low = v_series(low, length + 1)
+    close = v_series(close, length + 1)
 
     if high is None or low is None or close is None:
         return

pandas_ta/trend/cksp.py

@@ -1,8 +1,14 @@
 # -*- coding: utf-8 -*-
+from numpy import isnan
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
-from pandas_ta.utils import v_mamode, v_offset, v_pos_default
-from pandas_ta.utils import v_series, v_tradingview
+from pandas_ta.utils import (
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_tradingview
+)
 from pandas_ta.volatility import atr
 
 
@@ -53,7 +59,7 @@ def cksp(
     # TODO: clean up x and q
     x = float(x) if isinstance(x, float) and x > 0 else 1 if tvmode is True else 3
     q = int(q) if isinstance(q, float) and q > 0 else 9 if tvmode is True else 20
-    _length = max(p, q, x)
+    _length = p + q
 
     high = v_series(high, _length)
     low = v_series(low, _length)
@@ -67,6 +73,8 @@ def cksp(
 
     # Calculate
     atr_ = atr(high=high, low=low, close=close, length=p, mamode=mamode)
+    if atr_ is None or all(isnan(atr_)):
+        return
 
     long_stop_ = high.rolling(p).max() - x * atr_
     long_stop = long_stop_.rolling(q).max()

pandas_ta/trend/decreasing.py

@@ -1,8 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
 from pandas_ta._typing import DictLike, Int, IntFloat
-from pandas_ta.utils import is_percent, v_bool, v_drift, v_offset
-from pandas_ta.utils import v_pos_default, v_series
+from pandas_ta.utils import (
+    is_percent,
+    v_bool,
+    v_drift,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 
 
 def decreasing(

pandas_ta/trend/dpo.py

@@ -35,7 +35,7 @@ def dpo(
     """
     # Validate
     length = v_pos_default(length, 20)
-    close = v_series(close, length)
+    close = v_series(close, length + 1)
 
     if close is None:
         return

pandas_ta/trend/increasing.py

@@ -1,8 +1,15 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
 from pandas_ta._typing import DictLike, Int, IntFloat
-from pandas_ta.utils import is_percent, v_bool, v_drift
-from pandas_ta.utils import v_offset, v_pos_default, v_series
+from pandas_ta.utils import (
+    is_percent,
+    v_bool,
+    v_drift,
+    v_offset,
+    v_pos_default,
+    v_series
+)
+
 
 def increasing(
     close: Series, length: Int = None, strict: bool = None,

pandas_ta/trend/qstick.py

@@ -2,8 +2,13 @@
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
-from pandas_ta.utils import non_zero_range, v_mamode, v_offset
-from pandas_ta.utils import v_pos_default, v_series
+from pandas_ta.utils import (
+    non_zero_range,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 
 
 def qstick(

pandas_ta/trend/trendflex.py

@@ -13,7 +13,8 @@ except ImportError:
 
 @njit
 def np_trendflex(
-    x: Array, n: Int, k: Int, alpha: IntFloat, pi: IntFloat, sqrt2: IntFloat
+    x: Array, n: Int, k: Int,
+    alpha: IntFloat, pi: IntFloat, sqrt2: IntFloat
 ):
     """Ehler's Trendflex
     http://traders.com/Documentation/FEEDbk_docs/2020/02/TradersTips.html"""
@@ -88,7 +89,7 @@ def trendflex(
     # Validate
     length = v_pos_default(length, 20)
     smooth = v_pos_default(smooth, 20)
-    close = v_series(close, max(length, smooth))
+    close = v_series(close, max(length, smooth) + 1)
 
     if close is None:
         return

pandas_ta/trend/vhf.py

@@ -2,8 +2,13 @@
 from numpy import inf, fabs, nan
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
-from pandas_ta.utils import non_zero_range, v_drift, v_offset
-from pandas_ta.utils import v_pos_default, v_series
+from pandas_ta.utils import (
+    non_zero_range,
+    v_drift,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 
 
 def vhf(

pandas_ta/utils/_math.py

@@ -8,7 +8,15 @@ from numpy import all, append, array, corrcoef, dot, exp, fabs
 from numpy import log, nan, ndarray, ones, seterr, sign, sqrt, sum, triu
 from pandas import DataFrame, Series
 
-from pandas_ta._typing import Array, DictLike, Float, Int, IntFloat, List, Optional
+from pandas_ta._typing import (
+    Array,
+    DictLike,
+    Float,
+    Int,
+    IntFloat,
+    List,
+    Optional
+)
 from pandas_ta.maps import Imports
 from pandas_ta.utils._validate import v_series
 

pandas_ta/utils/_validate.py

@@ -3,7 +3,13 @@ from functools import partial
 from pandas import DataFrame, Series
 from pandas.api.types import is_datetime64_any_dtype
 from pandas_ta._typing import (
-    Float, Int, IntFloat, List, MaybeSeriesFrame, Optional, SeriesFrame
+    Float,
+    Int,
+    IntFloat,
+    List,
+    MaybeSeriesFrame,
+    Optional,
+    SeriesFrame
 )
 
 
@@ -99,7 +105,7 @@ def v_offset(var: Int) -> Int:
 def v_pos_default(
     var: IntFloat, default: IntFloat = 0, strict: bool = True, complement: bool = False
 ) -> IntFloat:
-    return partial(v_lowerbound, bound=0)\
+    return partial(v_lowerbound, bound=0) \
         (var=var, default=default, strict=strict, complement=complement)
 
 def v_scalar(var: IntFloat, default: Optional[IntFloat] = 1) -> Float:
@@ -111,10 +117,10 @@ def v_scalar(var: IntFloat, default: Optional[IntFloat] = 1) -> Float:
 def v_series(series: Series, length: Optional[IntFloat] = 0) -> Optional[Series]:
     """Returns None if the Pandas Series does not meet the minimum length
     required for the indicator."""
-    if isinstance(series, Series) and series.empty and series.size >= length:
-        print("[X] Requires a Pandas Series or DataFrame.")
-        return None
-    return series
+    if series is not None and isinstance(series, Series):
+        if series.size >= v_pos_default(length, 0):
+            return series
+    return None
 
 def v_talib(var: bool) -> bool:
     """Returns True by default"""

pandas_ta/volatility/aberration.py

@@ -49,7 +49,7 @@ def aberration(
     # Validate
     length = v_pos_default(length, 5)
     atr_length = v_pos_default(atr_length, 15)
-    _length = max(atr_length, length)
+    _length = max(atr_length, length) + 1
     high = v_series(high, _length)
     low = v_series(low, _length)
     close = v_series(close, _length)

pandas_ta/volatility/accbands.py

@@ -2,8 +2,14 @@
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.ma import ma
-from pandas_ta.utils import non_zero_range, v_drift, v_mamode
-from pandas_ta.utils import v_offset, v_pos_default, v_series
+from pandas_ta.utils import (
+    non_zero_range,
+    v_drift,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 
 
 def accbands(

pandas_ta/volatility/atr.py

@@ -53,9 +53,10 @@ def atr(
     """
     # Validate
     length = v_pos_default(length, 14)
-    high = v_series(high, length)
-    low = v_series(low, length)
-    close = v_series(close, length)
+    _length = length + 1
+    high = v_series(high, _length)
+    low = v_series(low, _length)
+    close = v_series(close, _length)
 
     if high is None or low is None or close is None:
         return
@@ -75,9 +76,11 @@ def atr(
             high=high, low=low, close=close,
             talib=mode_tal, prenan=prenan, drift=drift
         )
-        sma_nth = tr[0:length].mean()
-        tr[:length - 1] = nan
-        tr.iloc[length - 1] = sma_nth
+        presma = kwargs.pop("presma", True)
+        if presma:
+            sma_nth = tr[0:length].mean()
+            tr[:length - 1] = nan
+            tr.iloc[length - 1] = sma_nth
         atr = ma(mamode, tr, length=length, talib=mode_tal)
 
     percent = kwargs.pop("percent", False)

pandas_ta/volatility/atrts.py

@@ -2,10 +2,16 @@
 from numpy import nan, uintc, zeros_like
 from pandas import Series
 from pandas_ta._typing import Array, DictLike, Int, IntFloat
-from pandas_ta.ma import ma
+from pandas_ta.ma import ma as _ma
 from pandas_ta.maps import Imports
-from pandas_ta.utils import v_drift, v_mamode, v_offset
-from pandas_ta.utils import v_pos_default, v_series, v_talib
+from pandas_ta.utils import (
+    v_drift,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_talib
+)
 from pandas_ta.volatility import atr
 
 
@@ -16,7 +22,7 @@ except ImportError:
 
 
 @njit
-def np_atrts(x: Array, ma_: Array, atr_: Array, length: Int, ma_length: Int):
+def np_atrts(x: Array, ma: Array, atr_: Array, length: Int, ma_length: Int):
     m = x.size
     k = max(length, ma_length)
 
@@ -24,7 +30,7 @@ def np_atrts(x: Array, ma_: Array, atr_: Array, length: Int, ma_length: Int):
     up = zeros_like(x, dtype=uintc)
     dn = zeros_like(x, dtype=uintc)
 
-    expn = x > ma_
+    expn = x > ma
     up[expn], dn[~expn] = 1, 1
     up[:k], dn[:k] = 0, 0
     result[:k] = nan
@@ -90,7 +96,7 @@ def atrts(
     # Validate
     length = v_pos_default(length, 14)
     ma_length = v_pos_default(ma_length, 20)
-    _length = max(length, ma_length)
+    _length = length + ma_length
     high = v_series(high, _length)
     low = v_series(low, _length)
     close = v_series(close, _length)
@@ -116,7 +122,7 @@ def atrts(
         )
 
     atr_ *= multiplier
-    ma_ = ma(mamode, close, length=ma_length, talib=mode_tal)
+    ma_ = _ma(mamode, close, length=ma_length, talib=mode_tal)
 
     np_close, np_ma, np_atr = close.values, ma_.values, atr_.values
     np_atrts_, _, _ = np_atrts(np_close, np_ma, np_atr, length, ma_length)

pandas_ta/volatility/bbands.py

@@ -4,8 +4,15 @@ from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.ma import ma
 from pandas_ta.maps import Imports
 from pandas_ta.statistics import stdev
-from pandas_ta.utils import non_zero_range, tal_ma, v_mamode
-from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
+from pandas_ta.utils import (
+    non_zero_range,
+    tal_ma,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_talib
+)
 
 
 def bbands(

pandas_ta/volatility/kc.py

@@ -2,8 +2,14 @@
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.ma import ma
-from pandas_ta.utils import high_low_range, v_bool, v_offset
-from pandas_ta.utils import v_mamode, v_pos_default, v_series
+from pandas_ta.utils import (
+    high_low_range,
+    v_bool,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 from .true_range import true_range
 
 
@@ -42,9 +48,10 @@ def kc(
     """
     # Validate
     length = v_pos_default(length, 20)
-    high = v_series(high, length)
-    low = v_series(low, length)
-    close = v_series(close, length)
+    _length = length + 1
+    high = v_series(high, _length)
+    low = v_series(low, _length)
+    close = v_series(close, _length)
 
     if high is None or low is None or close is None:
         return

pandas_ta/volatility/massi.py

@@ -1,4 +1,5 @@
 # -*- coding: utf-8 -*-
+from numpy import isnan
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.overlap import ema
@@ -37,7 +38,7 @@ def massi(
     slow = v_pos_default(slow, 25)
     if slow < fast:
         fast, slow = slow, fast
-    _length = max(fast, slow)
+    _length = 2 * max(fast, slow) - min(fast, slow)
     high = v_series(high, _length)
     low = v_series(low, _length)
 
@@ -51,10 +52,16 @@ def massi(
     # Calculate
     high_low_range = non_zero_range(high, low)
     hl_ema1 = ema(close=high_low_range, length=fast, **kwargs)
+    if all(isnan(hl_ema1)):
+        return  # Emergency Break
     hl_ema2 = ema(close=hl_ema1, length=fast, **kwargs)
+    if all(isnan(hl_ema2)):
+        return  # Emergency Break
 
     hl_ratio = hl_ema1 / hl_ema2
     massi = hl_ratio.rolling(slow, min_periods=slow).sum()
+    if all(isnan(massi)):
+        return  # Emergency Break
 
     # Offset
     if offset != 0:

pandas_ta/volatility/natr.py

@@ -2,8 +2,15 @@
 from pandas import Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
-from pandas_ta.utils import v_drift, v_mamode, v_offset, v_pos_default
-from pandas_ta.utils import v_scalar, v_series, v_talib
+from pandas_ta.utils import (
+    v_drift,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_scalar,
+    v_series,
+    v_talib
+)
 from pandas_ta.volatility import atr
 
 
@@ -40,9 +47,10 @@ def natr(
     """
     # Validate
     length = v_pos_default(length, 14)
-    high = v_series(high, length)
-    low = v_series(low, length)
-    close = v_series(close, length)
+    _length = length + 1
+    high = v_series(high, _length)
+    low = v_series(low, _length)
+    close = v_series(close, _length)
 
     if high is None or low is None or close is None:
         return

pandas_ta/volatility/pdist.py

@@ -1,4 +1,5 @@
 # -*- coding: utf-8 -*-
+from numpy import isnan
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import non_zero_range, v_drift, v_offset, v_series
@@ -32,18 +33,24 @@ def pdist(
         pd.Series: New feature generated.
     """
     # Validate
+    drift = v_drift(drift)
     open_ = v_series(open_)
     high = v_series(high)
     low = v_series(low)
     close = v_series(close)
-    drift = v_drift(drift)
     offset = v_offset(offset)
 
     # Calculate
     pdist = 2 * non_zero_range(high, low)
+    if all(isnan(pdist)):
+        return  # Emergency Break
+
     pdist += non_zero_range(open_, close.shift(drift)).abs()
     pdist -= non_zero_range(close, open_).abs()
 
+    if all(isnan(pdist)):
+        return  # Emergency Break
+
     # Offset
     if offset != 0:
         pdist = pdist.shift(offset)

pandas_ta/volatility/rvi.py

@@ -1,10 +1,18 @@
 # -*- coding: utf-8 -*-
+from numpy import isnan
 from pandas import Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.ma import ma
 from pandas_ta.statistics import stdev
-from pandas_ta.utils import unsigned_differences, v_bool, v_drift
-from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series
+from pandas_ta.utils import (
+    unsigned_differences,
+    v_bool,
+    v_drift,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 
 
 def _rvi(source, length, scalar, mode, drift):
@@ -62,7 +70,7 @@ def rvi(
     """
     # Validate
     length = v_pos_default(length, 14)
-    close = v_series(close, length)
+    close = v_series(close, length + 2)
 
     if close is None:
         return
@@ -94,6 +102,9 @@ def rvi(
     else:
         rvi = _rvi(close, length, scalar, mamode, drift)
 
+    if all(isnan(rvi)):
+        return  # Emergency Break
+
     # Offset
     if offset != 0:
         rvi = rvi.shift(offset)

pandas_ta/volatility/thermo.py

@@ -2,8 +2,14 @@
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
-from pandas_ta.utils import v_bool, v_drift, v_mamode
-from pandas_ta.utils import v_offset, v_pos_default, v_series
+from pandas_ta.utils import (
+    v_bool,
+    v_drift,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 
 
 def thermo(
@@ -40,8 +46,8 @@ def thermo(
     """
     # Validate
     length = v_pos_default(length, 20)
-    high = v_series(high, length)
-    low = v_series(low, length)
+    high = v_series(high, length + 1)
+    low = v_series(low, length + 1)
 
     if high is None or low is None:
         return

pandas_ta/volatility/true_range.py

@@ -1,10 +1,16 @@
 # -*- coding: utf-8 -*-
-from numpy import nan
+from numpy import isnan, nan
 from pandas import concat, Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
-from pandas_ta.utils import non_zero_range, v_bool, v_drift
-from pandas_ta.utils import v_offset, v_series, v_talib
+from pandas_ta.utils import (
+    non_zero_range,
+    v_bool,
+    v_drift,
+    v_offset,
+    v_series,
+    v_talib
+)
 
 
 def true_range(
@@ -39,12 +45,13 @@ def true_range(
         pd.Series: New feature
     """
     # Validate
+    drift = v_drift(drift)
     high = v_series(high)
     low = v_series(low)
     close = v_series(close)
+
     mode_tal = v_talib(talib)
     prenan = v_bool(prenan, False)
-    drift = v_drift(drift)
     offset = v_offset(offset)
 
     # Calculate
@@ -60,6 +67,9 @@ def true_range(
         if prenan:
             true_range.iloc[:drift] = nan
 
+    if all(isnan(true_range)):
+        return  # Emergency Break
+
     # Offset
     if offset != 0:
         true_range = true_range.shift(offset)

pandas_ta/volatility/ui.py

@@ -40,7 +40,7 @@ def ui(
     # Validate
     length = v_pos_default(length, 14)
     scalar = v_pos_default(scalar, 100)
-    close = v_series(close, length)
+    close = v_series(close, 2 * length - 1)
 
     if close is None:
         return
@@ -56,9 +56,10 @@ def ui(
     everget = kwargs.pop("everget", False)
     if everget:
         # Everget uses SMA instead of SUM for calculation
-        ui = (sma(d2, length) / length).apply(sqrt)
+        _ui = sma(d2, length)
     else:
-        ui = (d2.rolling(length).sum() / length).apply(sqrt)
+        _ui = d2.rolling(length).sum()
+    ui = sqrt(_ui / length)
 
     # Offset
     if offset != 0:

pandas_ta/volume/aobv.py

@@ -50,7 +50,7 @@ def aobv(
 
     if slow < fast:
         fast, slow = slow, fast
-    _length = max(fast, slow, max_lookback, min_lookback)
+    _length = max(max_lookback, min_lookback) + slow
 
     close = v_series(close, _length)
     volume = v_series(volume, _length)

pandas_ta/volume/efi.py

@@ -2,8 +2,13 @@
 from pandas import Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
-from pandas_ta.utils import v_drift, v_mamode, v_offset
-from pandas_ta.utils import v_pos_default, v_series
+from pandas_ta.utils import (
+    v_drift,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 
 
 def efi(

pandas_ta/volume/eom.py

@@ -2,8 +2,13 @@
 from pandas import Series
 from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.overlap import hl2, sma
-from pandas_ta.utils import non_zero_range, v_drift
-from pandas_ta.utils import v_pos_default, v_offset, v_series
+from pandas_ta.utils import (
+    non_zero_range,
+    v_drift,
+    v_pos_default,
+    v_offset,
+    v_series
+)
 
 
 def eom(
@@ -41,10 +46,11 @@ def eom(
     """
     # Validate
     length = v_pos_default(length, 14)
-    high = v_series(high, length)
-    low = v_series(low, length)
-    close = v_series(close, length)
-    volume = v_series(volume, length)
+    _length = length + 1
+    high = v_series(high, _length)
+    low = v_series(low, _length)
+    close = v_series(close, _length)
+    volume = v_series(volume, _length)
 
     if high is None or low is None or close is None or volume is None:
         return

pandas_ta/volume/kvo.py

@@ -4,8 +4,14 @@ from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.overlap import hlc3
-from pandas_ta.utils import signed_series, v_drift, v_mamode, v_offset
-from pandas_ta.utils import  v_pos_default, v_series
+from pandas_ta.utils import (
+    signed_series,
+    v_drift,
+    v_mamode,
+    v_offset,
+    v_pos_default,
+    v_series
+)
 
 
 def kvo(
@@ -44,7 +50,8 @@ def kvo(
     # Validate
     fast = v_pos_default(fast, 34)
     slow = v_pos_default(slow, 55)
-    _length = max(fast, slow - 1)
+    signal = v_pos_default(signal, 13)
+    _length = max(fast, slow) + signal
     high = v_series(high, _length)
     low = v_series(low, _length)
     close = v_series(close, _length)
@@ -53,7 +60,6 @@ def kvo(
     if high is None or low is None or close is None or volume is None:
         return
 
-    signal = v_pos_default(signal, 13)
     mamode = v_mamode(mamode, "ema")
     drift = v_drift(drift)
     offset = v_offset(offset)

pandas_ta/volume/mfi.py

@@ -3,7 +3,13 @@ from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.overlap import hlc3
-from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_series, v_talib
+from pandas_ta.utils import (
+    v_drift,
+    v_offset,
+    v_pos_default,
+    v_series,
+    v_talib
+)
 
 
 def mfi(
@@ -39,10 +45,11 @@ def mfi(
     """
     # Validate
     length = v_pos_default(length, 14)
-    high = v_series(high, length)
-    low = v_series(low, length)
-    close = v_series(close, length)
-    volume = v_series(volume, length)
+    _length = length + 1
+    high = v_series(high, _length)
+    low = v_series(low, _length)
+    close = v_series(close, _length)
+    volume = v_series(volume, _length)
 
     if high is None or low is None or close is None or volume is None:
         return
@@ -59,9 +66,12 @@ def mfi(
         typical_price = hlc3(high=high, low=low, close=close, talib=mode_tal)
         raw_money_flow = typical_price * volume
 
-        tdf = DataFrame(
-            {"diff": 0, "rmf": raw_money_flow, "+mf": 0, "-mf": 0}
-        )
+        tdf = DataFrame({
+            "diff": 0,
+            "rmf": raw_money_flow,
+            "+mf": 0,
+            "-mf": 0
+        })
 
         tdf.loc[(typical_price.diff(drift) > 0), "diff"] = 1
         tdf.loc[tdf["diff"] == 1, "+mf"] = raw_money_flow

pandas_ta/volume/pvt.py

@@ -32,8 +32,9 @@ def pvt(
     """
     # Validate
     drift = v_drift(drift)
-    close = v_series(close, drift)
-    volume = v_series(volume, drift)
+    _drift = drift + 1
+    close = v_series(close, _drift)
+    volume = v_series(volume, _drift)
 
     if close is None or volume is None:
         return

pandas_ta/volume/wb_tsv.py

@@ -2,8 +2,15 @@
 from pandas import DataFrame, Series
 from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
-from pandas_ta.utils import signed_series, v_drift, v_mamode
-from pandas_ta.utils import v_pos_default, v_offset, v_series, zero
+from pandas_ta.utils import (
+    signed_series,
+    v_drift,
+    v_mamode,
+    v_pos_default,
+    v_offset,
+    v_series,
+    zero
+)
 
 
 def wb_tsv(
@@ -45,7 +52,8 @@ def wb_tsv(
     # Validate
     length = v_pos_default(length, 18)
     signal = v_pos_default(signal, 10)
-    close = v_series(close, max(length, signal))
+    _length = max(length, signal) - 2
+    close = v_series(close, _length)
 
     if close is None:
         return

setup.py

@@ -20,7 +20,7 @@ setup(
         "pandas_ta.volatility",
         "pandas_ta.volume"
     ],
-    version=".".join(("0", "3", "63b")),
+    version=".".join(("0", "3", "64b")),
     description=long_description,
     long_description=long_description,
     author="Kevin Johnson",

tests/config.py

@@ -88,7 +88,7 @@ _tdpy = pandas_ta.RATE["TRADING_DAYS_PER_YEAR"]
 sample_data = load(
         n = [
             -2 * _tdpy, -_tdpy,
-            -90, 0, 90,
+            -89, 0, 89,
             _tdpy, 2 * _tdpy
         ][0],
         verbose=VERBOSE

tests/test_ext_indicator_overlap_ext.py

@@ -86,7 +86,7 @@ class TestOverlapExtension(TestCase):
     def test_linreg_ext(self):
         self.data.ta.linreg(append=True)
         self.assertIsInstance(self.data, DataFrame)
-        self.assertEqual(self.data.columns[-1], "LR_14")
+        self.assertEqual(self.data.columns[-1], "LINREG_14")
 
     def test_mama_ext(self):
         self.data.ta.mama(append=True)