diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml
index 0c004d8b..bb487093 100644
--- a/.github/FUNDING.yml
+++ b/.github/FUNDING.yml
@@ -1,2 +1,2 @@
-github: [bukosabino]
+github: [ bukosabino ]
 tidelift: pypi/ta
diff --git a/README.md b/README.md
index 80dac080..acd2edac 100644
--- a/README.md
+++ b/README.md
@@ -51,6 +51,7 @@ The library has implemented 42 indicators:
 * Ichimoku Kinkō Hyō (Ichimoku)
 * Parabolic Stop And Reverse (Parabolic SAR)
 * Schaff Trend Cycle (STC)
+* SuperTrend (ST)
 
 #### Momentum
 
diff --git a/docs/conf.py b/docs/conf.py
index cbb8ecee..3e76c0ff 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -19,7 +19,6 @@
 import sys
 
 sys.path.append("..")  # Adds higher directory to python modules path.
-from ta import *
 
 # -- Project information -----------------------------------------------------
 
@@ -32,7 +31,6 @@
 # The full version, including alpha/beta/rc tags
 release = "0.1.4"
 
-
 # -- General configuration ---------------------------------------------------
 
 # If your documentation needs a minimal Sphinx version, state it here.
@@ -78,7 +76,6 @@
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = "sphinx"
 
-
 # -- Options for HTML output -------------------------------------------------
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
@@ -113,7 +110,6 @@
 # Output file base name for HTML help builder.
 htmlhelp_basename = "TechnicalAnalysisLibraryinPythondoc"
 
-
 # -- Options for LaTeX output ------------------------------------------------
 
 latex_elements = {
@@ -144,7 +140,6 @@
     ),
 ]
 
-
 # -- Options for manual page output ------------------------------------------
 
 # One entry per manual page. List of tuples
@@ -159,7 +154,6 @@
     )
 ]
 
-
 # -- Options for Texinfo output ----------------------------------------------
 
 # Grouping the document tree into Texinfo files. List of tuples
@@ -177,5 +171,4 @@
     ),
 ]
 
-
 # -- Extension configuration -------------------------------------------------
diff --git a/docs/index.rst b/docs/index.rst
index 89ecc1a9..c789e07f 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -1,7 +1,7 @@
 .. Technical Analysis Library in Python documentation master file, created by
-   sphinx-quickstart on Tue Apr 10 15:47:09 2018.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
+sphinx-quickstart on Tue Apr 10 15:47:09 2018.
+You can adapt this file completely to your liking, but it should at least
+contain the root `toctree` directive.
 
 Welcome to Technical Analysis Library in Python's documentation!
 ================================================================
diff --git a/examples_to_use/all_features_example.py b/examples_to_use/all_features_example.py
index dafd15ed..87afe430 100644
--- a/examples_to_use/all_features_example.py
+++ b/examples_to_use/all_features_example.py
@@ -2,6 +2,7 @@
 this library.
 """
 import pandas as pd
+
 import ta
 
 # Load data
diff --git a/examples_to_use/bollinger_band_features_example.py b/examples_to_use/bollinger_band_features_example.py
index 4a4c4a5c..c6bedfed 100644
--- a/examples_to_use/bollinger_band_features_example.py
+++ b/examples_to_use/bollinger_band_features_example.py
@@ -1,6 +1,7 @@
 """This is a example adding bollinger band features.
 """
 import pandas as pd
+
 import ta
 
 # Load data
diff --git a/examples_to_use/roc.py b/examples_to_use/roc.py
index c6cd5c63..d4ebc79d 100644
--- a/examples_to_use/roc.py
+++ b/examples_to_use/roc.py
@@ -1,6 +1,7 @@
 """This is a example adding volume features.
 """
 import pandas as pd
+
 import ta
 
 # Load data
diff --git a/examples_to_use/volume_features_example.py b/examples_to_use/volume_features_example.py
index e9f0ea04..fd344619 100644
--- a/examples_to_use/volume_features_example.py
+++ b/examples_to_use/volume_features_example.py
@@ -1,6 +1,7 @@
 """This is a example adding volume features.
 """
 import pandas as pd
+
 import ta
 
 # Load data
diff --git a/ta/__init__.py b/ta/__init__.py
index ac190cc1..a2f5b628 100644
--- a/ta/__init__.py
+++ b/ta/__init__.py
@@ -5,14 +5,6 @@
 .. moduleauthor:: Dario Lopez Padial (Bukosabino)
 
 """
-from ta.wrapper import (
-    add_all_ta_features,
-    add_momentum_ta,
-    add_others_ta,
-    add_trend_ta,
-    add_volatility_ta,
-    add_volume_ta,
-)
 
 __all__ = [
     "add_all_ta_features",
diff --git a/ta/momentum.py b/ta/momentum.py
index b86d974b..9c1bd753 100644
--- a/ta/momentum.py
+++ b/ta/momentum.py
@@ -8,7 +8,7 @@
 import numpy as np
 import pandas as pd
 
-from ta.utils import IndicatorMixin, _ema
+from ta.ta.utils import IndicatorMixin, _ema
 
 
 class RSIIndicator(IndicatorMixin):
@@ -75,11 +75,11 @@ class TSIIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        close: pd.Series,
-        window_slow: int = 25,
-        window_fast: int = 13,
-        fillna: bool = False,
+            self,
+            close: pd.Series,
+            window_slow: int = 25,
+            window_fast: int = 13,
+            fillna: bool = False,
     ):
         self._close = close
         self._window_slow = window_slow
@@ -95,16 +95,16 @@ def _run(self):
             diff_close.ewm(
                 span=self._window_slow, min_periods=min_periods_r, adjust=False
             )
-            .mean()
-            .ewm(span=self._window_fast, min_periods=min_periods_s, adjust=False)
-            .mean()
+                .mean()
+                .ewm(span=self._window_fast, min_periods=min_periods_s, adjust=False)
+                .mean()
         )
         smoothed_abs = (
             abs(diff_close)
-            .ewm(span=self._window_slow, min_periods=min_periods_r, adjust=False)
-            .mean()
-            .ewm(span=self._window_fast, min_periods=min_periods_s, adjust=False)
-            .mean()
+                .ewm(span=self._window_slow, min_periods=min_periods_r, adjust=False)
+                .mean()
+                .ewm(span=self._window_fast, min_periods=min_periods_s, adjust=False)
+                .mean()
         )
         self._tsi = smoothed / smoothed_abs
         self._tsi *= 100
@@ -149,17 +149,17 @@ class UltimateOscillator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
-        window1: int = 7,
-        window2: int = 14,
-        window3: int = 28,
-        weight1: float = 4.0,
-        weight2: float = 2.0,
-        weight3: float = 1.0,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            close: pd.Series,
+            window1: int = 7,
+            window2: int = 14,
+            window3: int = 28,
+            weight1: float = 4.0,
+            weight2: float = 2.0,
+            weight3: float = 1.0,
+            fillna: bool = False,
     ):
         self._high = high
         self._low = low
@@ -183,25 +183,25 @@ def _run(self):
         min_periods_m = 0 if self._fillna else self._window2
         min_periods_len = 0 if self._fillna else self._window3
         avg_s = (
-            buying_pressure.rolling(self._window1, min_periods=min_periods_s).sum()
-            / true_range.rolling(self._window1, min_periods=min_periods_s).sum()
+                buying_pressure.rolling(self._window1, min_periods=min_periods_s).sum()
+                / true_range.rolling(self._window1, min_periods=min_periods_s).sum()
         )
         avg_m = (
-            buying_pressure.rolling(self._window2, min_periods=min_periods_m).sum()
-            / true_range.rolling(self._window2, min_periods=min_periods_m).sum()
+                buying_pressure.rolling(self._window2, min_periods=min_periods_m).sum()
+                / true_range.rolling(self._window2, min_periods=min_periods_m).sum()
         )
         avg_l = (
-            buying_pressure.rolling(self._window3, min_periods=min_periods_len).sum()
-            / true_range.rolling(self._window3, min_periods=min_periods_len).sum()
+                buying_pressure.rolling(self._window3, min_periods=min_periods_len).sum()
+                / true_range.rolling(self._window3, min_periods=min_periods_len).sum()
         )
         self._uo = (
-            100.0
-            * (
-                (self._weight1 * avg_s)
-                + (self._weight2 * avg_m)
-                + (self._weight3 * avg_l)
-            )
-            / (self._weight1 + self._weight2 + self._weight3)
+                100.0
+                * (
+                        (self._weight1 * avg_s)
+                        + (self._weight2 * avg_m)
+                        + (self._weight3 * avg_l)
+                )
+                / (self._weight1 + self._weight2 + self._weight3)
         )
 
     def ultimate_oscillator(self) -> pd.Series:
@@ -234,13 +234,13 @@ class StochasticOscillator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
-        window: int = 14,
-        smooth_window: int = 3,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            close: pd.Series,
+            window: int = 14,
+            smooth_window: int = 3,
+            fillna: bool = False,
     ):
         self._close = close
         self._high = high
@@ -300,12 +300,12 @@ class KAMAIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        close: pd.Series,
-        window: int = 10,
-        pow1: int = 2,
-        pow2: int = 30,
-        fillna: bool = False,
+            self,
+            close: pd.Series,
+            window: int = 10,
+            pow1: int = 2,
+            pow2: int = 30,
+            fillna: bool = False,
     ):
         self._close = close
         self._window = window
@@ -324,11 +324,11 @@ def _run(self):
         efficiency_ratio = er_num / er_den
 
         smoothing_constant = (
-            (
-                efficiency_ratio * (2.0 / (self._pow1 + 1) - 2.0 / (self._pow2 + 1.0))
-                + 2 / (self._pow2 + 1.0)
-            )
-            ** 2.0
+                (
+                        efficiency_ratio * (2.0 / (self._pow1 + 1) - 2.0 / (self._pow2 + 1.0))
+                        + 2 / (self._pow2 + 1.0)
+                )
+                ** 2.0
         ).values
 
         self._kama = np.zeros(smoothing_constant.size)
@@ -343,7 +343,7 @@ def _run(self):
                 first_value = False
             else:
                 self._kama[i] = self._kama[i - 1] + smoothing_constant[i] * (
-                    close_values[i] - self._kama[i - 1]
+                        close_values[i] - self._kama[i - 1]
                 )
 
     def kama(self) -> pd.Series:
@@ -389,9 +389,9 @@ def __init__(self, close: pd.Series, window: int = 12, fillna: bool = False):
 
     def _run(self):
         self._roc = (
-            (self._close - self._close.shift(self._window))
-            / self._close.shift(self._window)
-        ) * 100
+                            (self._close - self._close.shift(self._window))
+                            / self._close.shift(self._window)
+                    ) * 100
 
     def roc(self) -> pd.Series:
         """Rate of Change (ROC)
@@ -438,12 +438,12 @@ class AwesomeOscillatorIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        window1: int = 5,
-        window2: int = 34,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            window1: int = 5,
+            window2: int = 34,
+            fillna: bool = False,
     ):
         self._high = high
         self._low = low
@@ -457,8 +457,8 @@ def _run(self):
         min_periods_s = 0 if self._fillna else self._window1
         min_periods_len = 0 if self._fillna else self._window2
         self._ao = (
-            median_price.rolling(self._window1, min_periods=min_periods_s).mean()
-            - median_price.rolling(self._window2, min_periods=min_periods_len).mean()
+                median_price.rolling(self._window1, min_periods=min_periods_s).mean()
+                - median_price.rolling(self._window2, min_periods=min_periods_len).mean()
         )
 
     def awesome_oscillator(self) -> pd.Series:
@@ -510,12 +510,12 @@ class WilliamsRIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
-        lbp: int = 14,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            close: pd.Series,
+            lbp: int = 14,
+            fillna: bool = False,
     ):
         self._high = high
         self._low = low
@@ -564,12 +564,12 @@ class StochRSIIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        close: pd.Series,
-        window: int = 14,
-        smooth1: int = 3,
-        smooth2: int = 3,
-        fillna: bool = False,
+            self,
+            close: pd.Series,
+            window: int = 14,
+            smooth1: int = 3,
+            smooth2: int = 3,
+            fillna: bool = False,
     ):
         self._close = close
         self._window = window
@@ -584,7 +584,7 @@ def _run(self):
         ).rsi()
         lowest_low_rsi = self._rsi.rolling(self._window).min()
         self._stochrsi = (self._rsi - lowest_low_rsi) / (
-            self._rsi.rolling(self._window).max() - lowest_low_rsi
+                self._rsi.rolling(self._window).max() - lowest_low_rsi
         )
         self._stochrsi_k = self._stochrsi.rolling(self._smooth1).mean()
 
@@ -633,12 +633,12 @@ class PercentagePriceOscillator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        close: pd.Series,
-        window_slow: int = 26,
-        window_fast: int = 12,
-        window_sign: int = 9,
-        fillna: bool = False,
+            self,
+            close: pd.Series,
+            window_slow: int = 26,
+            window_fast: int = 12,
+            window_sign: int = 9,
+            fillna: bool = False,
     ):
         self._close = close
         self._window_slow = window_slow
@@ -707,12 +707,12 @@ class PercentageVolumeOscillator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        volume: pd.Series,
-        window_slow: int = 26,
-        window_fast: int = 12,
-        window_sign: int = 9,
-        fillna: bool = False,
+            self,
+            volume: pd.Series,
+            window_slow: int = 26,
+            window_fast: int = 12,
+            window_sign: int = 9,
+            fillna: bool = False,
     ):
         self._volume = volume
         self._window_slow = window_slow
@@ -807,16 +807,16 @@ def tsi(close, window_slow=25, window_fast=13, fillna=False) -> pd.Series:
 
 
 def ultimate_oscillator(
-    high,
-    low,
-    close,
-    window1=7,
-    window2=14,
-    window3=28,
-    weight1=4.0,
-    weight2=2.0,
-    weight3=1.0,
-    fillna=False,
+        high,
+        low,
+        close,
+        window1=7,
+        window2=14,
+        window3=28,
+        weight1=4.0,
+        weight2=2.0,
+        weight3=1.0,
+        fillna=False,
 ) -> pd.Series:
     """Ultimate Oscillator
 
@@ -896,7 +896,7 @@ def stoch(high, low, close, window=14, smooth_window=3, fillna=False) -> pd.Seri
 
 
 def stoch_signal(
-    high, low, close, window=14, smooth_window=3, fillna=False
+        high, low, close, window=14, smooth_window=3, fillna=False
 ) -> pd.Series:
     """Stochastic Oscillator Signal
 
@@ -1070,11 +1070,11 @@ def roc(close: pd.Series, window: int = 12, fillna: bool = False) -> pd.Series:
 
 
 def stochrsi(
-    close: pd.Series,
-    window: int = 14,
-    smooth1: int = 3,
-    smooth2: int = 3,
-    fillna: bool = False,
+        close: pd.Series,
+        window: int = 14,
+        smooth1: int = 3,
+        smooth2: int = 3,
+        fillna: bool = False,
 ) -> pd.Series:
     """Stochastic RSI
 
@@ -1100,11 +1100,11 @@ def stochrsi(
 
 
 def stochrsi_k(
-    close: pd.Series,
-    window: int = 14,
-    smooth1: int = 3,
-    smooth2: int = 3,
-    fillna: bool = False,
+        close: pd.Series,
+        window: int = 14,
+        smooth1: int = 3,
+        smooth2: int = 3,
+        fillna: bool = False,
 ) -> pd.Series:
     """Stochastic RSI %k
 
@@ -1130,11 +1130,11 @@ def stochrsi_k(
 
 
 def stochrsi_d(
-    close: pd.Series,
-    window: int = 14,
-    smooth1: int = 3,
-    smooth2: int = 3,
-    fillna: bool = False,
+        close: pd.Series,
+        window: int = 14,
+        smooth1: int = 3,
+        smooth2: int = 3,
+        fillna: bool = False,
 ) -> pd.Series:
     """Stochastic RSI %d
 
@@ -1160,11 +1160,11 @@ def stochrsi_d(
 
 
 def ppo(
-    close: pd.Series,
-    window_slow: int = 26,
-    window_fast: int = 12,
-    window_sign: int = 9,
-    fillna: bool = False,
+        close: pd.Series,
+        window_slow: int = 26,
+        window_fast: int = 12,
+        window_sign: int = 9,
+        fillna: bool = False,
 ) -> pd.Series:
     """
     The Percentage Price Oscillator (PPO) is a momentum oscillator that measures
@@ -1191,7 +1191,7 @@ def ppo(
 
 
 def ppo_signal(
-    close: pd.Series, window_slow=26, window_fast=12, window_sign=9, fillna=False
+        close: pd.Series, window_slow=26, window_fast=12, window_sign=9, fillna=False
 ) -> pd.Series:
     """
     The Percentage Price Oscillator (PPO) is a momentum oscillator that measures
@@ -1218,11 +1218,11 @@ def ppo_signal(
 
 
 def ppo_hist(
-    close: pd.Series,
-    window_slow: int = 26,
-    window_fast: int = 12,
-    window_sign: int = 9,
-    fillna: bool = False,
+        close: pd.Series,
+        window_slow: int = 26,
+        window_fast: int = 12,
+        window_sign: int = 9,
+        fillna: bool = False,
 ) -> pd.Series:
     """
     The Percentage Price Oscillator (PPO) is a momentum oscillator that measures
@@ -1249,11 +1249,11 @@ def ppo_hist(
 
 
 def pvo(
-    volume: pd.Series,
-    window_slow: int = 26,
-    window_fast: int = 12,
-    window_sign: int = 9,
-    fillna: bool = False,
+        volume: pd.Series,
+        window_slow: int = 26,
+        window_fast: int = 12,
+        window_sign: int = 9,
+        fillna: bool = False,
 ) -> pd.Series:
     """
     The Percentage Volume Oscillator (PVO) is a momentum oscillator for volume.
@@ -1283,11 +1283,11 @@ def pvo(
 
 
 def pvo_signal(
-    volume: pd.Series,
-    window_slow: int = 26,
-    window_fast: int = 12,
-    window_sign: int = 9,
-    fillna: bool = False,
+        volume: pd.Series,
+        window_slow: int = 26,
+        window_fast: int = 12,
+        window_sign: int = 9,
+        fillna: bool = False,
 ) -> pd.Series:
     """
     The Percentage Volume Oscillator (PVO) is a momentum oscillator for volume.
@@ -1317,11 +1317,11 @@ def pvo_signal(
 
 
 def pvo_hist(
-    volume: pd.Series,
-    window_slow: int = 26,
-    window_fast: int = 12,
-    window_sign: int = 9,
-    fillna: bool = False,
+        volume: pd.Series,
+        window_slow: int = 26,
+        window_fast: int = 12,
+        window_sign: int = 9,
+        fillna: bool = False,
 ) -> pd.Series:
     """
     The Percentage Volume Oscillator (PVO) is a momentum oscillator for volume.
diff --git a/ta/others.py b/ta/others.py
index 0cb43a57..a8e37d8c 100644
--- a/ta/others.py
+++ b/ta/others.py
@@ -26,8 +26,8 @@ def __init__(self, close: pd.Series, fillna: bool = False):
 
     def _run(self):
         self._dr = (
-            self._close / self._close.shift(1, fill_value=self._close.mean())
-        ) - 1
+                           self._close / self._close.shift(1, fill_value=self._close.mean())
+                   ) - 1
         self._dr *= 100
 
     def daily_return(self) -> pd.Series:
diff --git a/ta/trend.py b/ta/trend.py
index edba1440..778ea4ac 100644
--- a/ta/trend.py
+++ b/ta/trend.py
@@ -8,7 +8,8 @@
 import numpy as np
 import pandas as pd
 
-from ta.utils import IndicatorMixin, _ema, _get_min_max, _sma
+from ta.utils import IndicatorMixin, _ema, _sma, _get_min_max
+from ta.volatility import AverageTrueRange
 
 
 class AroonIndicator(IndicatorMixin):
@@ -90,12 +91,12 @@ class MACD(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        close: pd.Series,
-        window_slow: int = 26,
-        window_fast: int = 12,
-        window_sign: int = 9,
-        fillna: bool = False,
+            self,
+            close: pd.Series,
+            window_slow: int = 26,
+            window_fast: int = 12,
+            window_sign: int = 9,
+            fillna: bool = False,
     ):
         self._close = close
         self._window_slow = window_slow
@@ -295,12 +296,12 @@ class MassIndex(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        window_fast: int = 9,
-        window_slow: int = 25,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            window_fast: int = 9,
+            window_slow: int = 25,
+            fillna: bool = False,
     ):
         self._high = high
         self._low = low
@@ -345,14 +346,14 @@ class IchimokuIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        window1: int = 9,
-        window2: int = 26,
-        window3: int = 52,
-        visual: bool = False,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            window1: int = 9,
+            window2: int = 26,
+            window3: int = 52,
+            visual: bool = False,
+            fillna: bool = False,
     ):
         self._high = high
         self._low = low
@@ -367,12 +368,12 @@ def _run(self):
         min_periods_n1 = 0 if self._fillna else self._window1
         min_periods_n2 = 0 if self._fillna else self._window2
         self._conv = 0.5 * (
-            self._high.rolling(self._window1, min_periods=min_periods_n1).max()
-            + self._low.rolling(self._window1, min_periods=min_periods_n1).min()
+                self._high.rolling(self._window1, min_periods=min_periods_n1).max()
+                + self._low.rolling(self._window1, min_periods=min_periods_n1).min()
         )
         self._base = 0.5 * (
-            self._high.rolling(self._window2, min_periods=min_periods_n2).max()
-            + self._low.rolling(self._window2, min_periods=min_periods_n2).min()
+                self._high.rolling(self._window2, min_periods=min_periods_n2).max()
+                + self._low.rolling(self._window2, min_periods=min_periods_n2).min()
         )
 
     def ichimoku_conversion_line(self) -> pd.Series:
@@ -417,8 +418,8 @@ def ichimoku_b(self) -> pd.Series:
             pandas.Series: New feature generated.
         """
         spanb = 0.5 * (
-            self._high.rolling(self._window3, min_periods=0).max()
-            + self._low.rolling(self._window3, min_periods=0).min()
+                self._high.rolling(self._window3, min_periods=0).max()
+                + self._low.rolling(self._window3, min_periods=0).min()
         )
         spanb = (
             spanb.shift(self._window2, fill_value=spanb.mean())
@@ -454,18 +455,18 @@ class KSTIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        close: pd.Series,
-        roc1: int = 10,
-        roc2: int = 15,
-        roc3: int = 20,
-        roc4: int = 30,
-        window1: int = 10,
-        window2: int = 10,
-        window3: int = 10,
-        window4: int = 15,
-        nsig: int = 9,
-        fillna: bool = False,
+            self,
+            close: pd.Series,
+            roc1: int = 10,
+            roc2: int = 15,
+            roc3: int = 20,
+            roc4: int = 30,
+            window1: int = 10,
+            window2: int = 10,
+            window3: int = 10,
+            window4: int = 15,
+            nsig: int = 9,
+            fillna: bool = False,
     ):
         self._close = close
         self._r1 = roc1
@@ -487,47 +488,47 @@ def _run(self):
         min_periods_n4 = 0 if self._fillna else self._window4
         rocma1 = (
             (
-                (
-                    self._close
-                    - self._close.shift(self._r1, fill_value=self._close.mean())
-                )
-                / self._close.shift(self._r1, fill_value=self._close.mean())
+                    (
+                            self._close
+                            - self._close.shift(self._r1, fill_value=self._close.mean())
+                    )
+                    / self._close.shift(self._r1, fill_value=self._close.mean())
             )
-            .rolling(self._window1, min_periods=min_periods_n1)
-            .mean()
+                .rolling(self._window1, min_periods=min_periods_n1)
+                .mean()
         )
         rocma2 = (
             (
-                (
-                    self._close
-                    - self._close.shift(self._r2, fill_value=self._close.mean())
-                )
-                / self._close.shift(self._r2, fill_value=self._close.mean())
+                    (
+                            self._close
+                            - self._close.shift(self._r2, fill_value=self._close.mean())
+                    )
+                    / self._close.shift(self._r2, fill_value=self._close.mean())
             )
-            .rolling(self._window2, min_periods=min_periods_n2)
-            .mean()
+                .rolling(self._window2, min_periods=min_periods_n2)
+                .mean()
         )
         rocma3 = (
             (
-                (
-                    self._close
-                    - self._close.shift(self._r3, fill_value=self._close.mean())
-                )
-                / self._close.shift(self._r3, fill_value=self._close.mean())
+                    (
+                            self._close
+                            - self._close.shift(self._r3, fill_value=self._close.mean())
+                    )
+                    / self._close.shift(self._r3, fill_value=self._close.mean())
             )
-            .rolling(self._window3, min_periods=min_periods_n3)
-            .mean()
+                .rolling(self._window3, min_periods=min_periods_n3)
+                .mean()
         )
         rocma4 = (
             (
-                (
-                    self._close
-                    - self._close.shift(self._r4, fill_value=self._close.mean())
-                )
-                / self._close.shift(self._r4, fill_value=self._close.mean())
+                    (
+                            self._close
+                            - self._close.shift(self._r4, fill_value=self._close.mean())
+                    )
+                    / self._close.shift(self._r4, fill_value=self._close.mean())
             )
-            .rolling(self._window4, min_periods=min_periods_n4)
-            .mean()
+                .rolling(self._window4, min_periods=min_periods_n4)
+                .mean()
         )
         self._kst = 100 * (rocma1 + 2 * rocma2 + 3 * rocma3 + 4 * rocma4)
         self._kst_sig = self._kst.rolling(self._nsig, min_periods=0).mean()
@@ -588,10 +589,10 @@ def __init__(self, close: pd.Series, window: int = 20, fillna: bool = False):
     def _run(self):
         min_periods = 0 if self._fillna else self._window
         self._dpo = (
-            self._close.shift(
-                int((0.5 * self._window) + 1), fill_value=self._close.mean()
-            )
-            - self._close.rolling(self._window, min_periods=min_periods).mean()
+                self._close.shift(
+                    int((0.5 * self._window) + 1), fill_value=self._close.mean()
+                )
+                - self._close.rolling(self._window, min_periods=min_periods).mean()
         )
 
     def dpo(self) -> pd.Series:
@@ -625,13 +626,13 @@ class CCIIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
-        window: int = 20,
-        constant: float = 0.015,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            close: pd.Series,
+            window: int = 20,
+            constant: float = 0.015,
+            fillna: bool = False,
     ):
         self._high = high
         self._low = low
@@ -648,14 +649,14 @@ def _mad(x):
         min_periods = 0 if self._fillna else self._window
         typical_price = (self._high + self._low + self._close) / 3.0
         self._cci = (
-            typical_price
-            - typical_price.rolling(self._window, min_periods=min_periods).mean()
-        ) / (
-            self._constant
-            * typical_price.rolling(self._window, min_periods=min_periods).apply(
-                _mad, True
-            )
-        )
+                            typical_price
+                            - typical_price.rolling(self._window, min_periods=min_periods).mean()
+                    ) / (
+                            self._constant
+                            * typical_price.rolling(self._window, min_periods=min_periods).apply(
+                        _mad, True
+                    )
+                    )
 
     def cci(self) -> pd.Series:
         """Commodity Channel Index (CCI)
@@ -693,12 +694,12 @@ class ADXIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
-        window: int = 14,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            close: pd.Series,
+            window: int = 14,
+            fillna: bool = False,
     ):
         self._high = high
         self._low = low
@@ -718,14 +719,14 @@ def _run(self):
 
         self._trs_initial = np.zeros(self._window - 1)
         self._trs = np.zeros(len(self._close) - (self._window - 1))
-        self._trs[0] = diff_directional_movement.dropna()[0 : self._window].sum()
+        self._trs[0] = diff_directional_movement.dropna()[0: self._window].sum()
         diff_directional_movement = diff_directional_movement.reset_index(drop=True)
 
         for i in range(1, len(self._trs) - 1):
             self._trs[i] = (
-                self._trs[i - 1]
-                - (self._trs[i - 1] / float(self._window))
-                + diff_directional_movement[self._window + i]
+                    self._trs[i - 1]
+                    - (self._trs[i - 1] / float(self._window))
+                    + diff_directional_movement[self._window + i]
             )
 
         diff_up = self._high - self._high.shift(1)
@@ -734,27 +735,27 @@ def _run(self):
         neg = abs(((diff_down > diff_up) & (diff_down > 0)) * diff_down)
 
         self._dip = np.zeros(len(self._close) - (self._window - 1))
-        self._dip[0] = pos.dropna()[0 : self._window].sum()
+        self._dip[0] = pos.dropna()[0: self._window].sum()
 
         pos = pos.reset_index(drop=True)
 
         for i in range(1, len(self._dip) - 1):
             self._dip[i] = (
-                self._dip[i - 1]
-                - (self._dip[i - 1] / float(self._window))
-                + pos[self._window + i]
+                    self._dip[i - 1]
+                    - (self._dip[i - 1] / float(self._window))
+                    + pos[self._window + i]
             )
 
         self._din = np.zeros(len(self._close) - (self._window - 1))
-        self._din[0] = neg.dropna()[0 : self._window].sum()
+        self._din[0] = neg.dropna()[0: self._window].sum()
 
         neg = neg.reset_index(drop=True)
 
         for i in range(1, len(self._din) - 1):
             self._din[i] = (
-                self._din[i - 1]
-                - (self._din[i - 1] / float(self._window))
-                + neg[self._window + i]
+                    self._din[i - 1]
+                    - (self._din[i - 1] / float(self._window))
+                    + neg[self._window + i]
             )
 
     def adx(self) -> pd.Series:
@@ -776,12 +777,12 @@ def adx(self) -> pd.Series:
         directional_index = 100 * np.abs((dip - din) / (dip + din))
 
         adx_series = np.zeros(len(self._trs))
-        adx_series[self._window] = directional_index[0 : self._window].mean()
+        adx_series[self._window] = directional_index[0: self._window].mean()
 
         for i in range(self._window + 1, len(adx_series)):
             adx_series[i] = (
-                (adx_series[i - 1] * (self._window - 1)) + directional_index[i - 1]
-            ) / float(self._window)
+                                    (adx_series[i - 1] * (self._window - 1)) + directional_index[i - 1]
+                            ) / float(self._window)
 
         adx_series = np.concatenate((self._trs_initial, adx_series), axis=0)
         adx_series = pd.Series(data=adx_series, index=self._close.index)
@@ -838,12 +839,12 @@ class VortexIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
-        window: int = 14,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            close: pd.Series,
+            window: int = 14,
+            fillna: bool = False,
     ):
         self._high = high
         self._low = low
@@ -912,13 +913,13 @@ class PSARIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
-        step: float = 0.02,
-        max_step: float = 0.20,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            close: pd.Series,
+            step: float = 0.02,
+            max_step: float = 0.20,
+            fillna: bool = False,
     ):
         self._high = high
         self._low = low
@@ -946,7 +947,7 @@ def _run(self):  # noqa
 
             if up_trend:
                 self._psar.iloc[i] = self._psar.iloc[i - 1] + (
-                    acceleration_factor * (up_trend_high - self._psar.iloc[i - 1])
+                        acceleration_factor * (up_trend_high - self._psar.iloc[i - 1])
                 )
 
                 if min_low < self._psar.iloc[i]:
@@ -969,7 +970,7 @@ def _run(self):  # noqa
                         self._psar.iloc[i] = low1
             else:
                 self._psar.iloc[i] = self._psar.iloc[i - 1] - (
-                    acceleration_factor * (self._psar.iloc[i - 1] - down_trend_low)
+                        acceleration_factor * (self._psar.iloc[i - 1] - down_trend_low)
                 )
 
                 if max_high > self._psar.iloc[i]:
@@ -1073,14 +1074,14 @@ class STCIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        close: pd.Series,
-        window_slow: int = 50,
-        window_fast: int = 23,
-        cycle: int = 10,
-        smooth1: int = 3,
-        smooth2: int = 3,
-        fillna: bool = False,
+            self,
+            close: pd.Series,
+            window_slow: int = 50,
+            window_fast: int = 23,
+            cycle: int = 10,
+            smooth1: int = 3,
+            smooth2: int = 3,
+            fillna: bool = False,
     ):
         self._close = close
         self._window_slow = window_slow
@@ -1092,7 +1093,6 @@ def __init__(
         self._run()
 
     def _run(self):
-
         _emafast = _ema(self._close, self._window_fast, self._fillna)
         _emaslow = _ema(self._close, self._window_slow, self._fillna)
         _macd = _emafast - _emaslow
@@ -1462,16 +1462,16 @@ def dpo(close, window=20, fillna=False):
 
 
 def kst(
-    close,
-    roc1=10,
-    roc2=15,
-    roc3=20,
-    roc4=30,
-    window1=10,
-    window2=10,
-    window3=10,
-    window4=15,
-    fillna=False,
+        close,
+        roc1=10,
+        roc2=15,
+        roc3=20,
+        roc4=30,
+        window1=10,
+        window2=10,
+        window3=10,
+        window4=15,
+        fillna=False,
 ):
     """KST Oscillator (KST)
 
@@ -1513,7 +1513,7 @@ def kst(
 
 
 def stc(
-    close, window_slow=50, window_fast=23, cycle=10, smooth1=3, smooth2=3, fillna=False
+        close, window_slow=50, window_fast=23, cycle=10, smooth1=3, smooth2=3, fillna=False
 ):
     """Schaff Trend Cycle (STC)
 
@@ -1550,17 +1550,17 @@ def stc(
 
 
 def kst_sig(
-    close,
-    roc1=10,
-    roc2=15,
-    roc3=20,
-    roc4=30,
-    window1=10,
-    window2=10,
-    window3=10,
-    window4=15,
-    nsig=9,
-    fillna=False,
+        close,
+        roc1=10,
+        roc2=15,
+        roc3=20,
+        roc4=30,
+        window1=10,
+        window2=10,
+        window3=10,
+        window4=15,
+        nsig=9,
+        fillna=False,
 ):
     """KST Oscillator (KST Signal)
 
@@ -1603,7 +1603,7 @@ def kst_sig(
 
 
 def ichimoku_conversion_line(
-    high, low, window1=9, window2=26, visual=False, fillna=False
+        high, low, window1=9, window2=26, visual=False, fillna=False
 ) -> pd.Series:
     """Tenkan-sen (Conversion Line)
 
@@ -1634,7 +1634,7 @@ def ichimoku_conversion_line(
 
 
 def ichimoku_base_line(
-    high, low, window1=9, window2=26, visual=False, fillna=False
+        high, low, window1=9, window2=26, visual=False, fillna=False
 ) -> pd.Series:
     """Kijun-sen (Base Line)
 
@@ -1857,3 +1857,143 @@ def psar_down_indicator(high, low, close, step=0.02, max_step=0.20, fillna=False
         high=high, low=low, close=close, step=step, max_step=max_step, fillna=fillna
     )
     return indicator.psar_down_indicator()
+
+
+class SuperTrendIndicator(object):
+    """
+
+    Supertrend (ST)
+    """
+    # code from pandas-ta
+    close: pd.Series
+    high: pd.Series
+    low: pd.Series
+
+    def __init__(self,
+                 close: pd.Series,
+                 high: pd.Series,
+                 low: pd.Series,
+                 multiplier: float = 3.0,
+                 length: int = 10):
+        self.close = close
+        self.high = high
+        self.low = low
+        self.multiplier: float = float(multiplier) if multiplier and multiplier > 0 else 3.0
+        self.length = int(length) if length and length > 0 else 7
+        self._all = self._get_all_ST()
+
+    def get_supertrend(self) -> pd.Series:
+        return self._all['ST']
+
+    def get_supertrend_upper(self) -> pd.Series:
+        return self._all['ST_upper']
+
+    def get_supertrend_lower(self) -> pd.Series:
+        return self._all['ST_lower']
+
+    def get_supertrend_strategy_returns(self) -> pd.Series:
+        """
+
+        :return: pd.Series with 1 or -1 (buy, sell)
+        """
+        return self._all['ST_strategy']
+
+    def get_all_ST(self) -> pd.DataFrame:
+        return self._all
+
+    def _get_all_ST(self) -> pd.DataFrame:
+        """
+
+        ST Indicator, trading predictions, ST high/low
+        """
+        m = self.close.size
+        dir_, trend = [1] * m, [0] * m
+        long, short = [np.NaN] * m, [np.NaN] * m
+        ATR = AverageTrueRange(self.high, self.low, self.close, self.length)
+
+        hl2_ = (self.high + self.low) / 2
+        matr = ATR.average_true_range() * self.multiplier
+        upperband = hl2_ + matr
+        lowerband = hl2_ - matr
+
+        for i in range(1, m):
+            if self.close.iloc[i] > upperband.iloc[i - 1]:
+                dir_[i] = 1
+            elif self.close.iloc[i] < lowerband.iloc[i - 1]:
+                dir_[i] = -1
+            else:
+                dir_[i] = dir_[i - 1]
+                if dir_[i] > 0 and lowerband.iloc[i] < lowerband.iloc[i - 1]:
+                    lowerband.iloc[i] = lowerband.iloc[i - 1]
+                if dir_[i] < 0 and upperband.iloc[i] > upperband.iloc[i - 1]:
+                    upperband.iloc[i] = upperband.iloc[i - 1]
+
+            if dir_[i] > 0:
+                trend[i] = long[i] = lowerband.iloc[i]
+            else:
+                trend[i] = short[i] = upperband.iloc[i]
+
+        # Prepare DataFrame to return
+        df = pd.DataFrame(
+            {
+                f"ST": trend,
+                f"ST_strategy": dir_,
+                f"ST_lower": long,
+                f"ST_upper": short,
+            },
+            index=self.close.index
+        )
+
+        return df
+
+
+def supertrend_indicator(close: pd.Series,
+                         high: pd.Series,
+                         low: pd.Series,
+                         multiplier: float = 3.0,
+                         length: int = 10) -> pd.Series:
+    ST = SuperTrendIndicator(close=close,
+                             high=high,
+                             low=low,
+                             multiplier=multiplier,
+                             length=length)
+    return ST.get_supertrend()
+
+
+def supertrend_strategy(close: pd.Series,
+                        high: pd.Series,
+                        low: pd.Series,
+                        multiplier: float = 3.0,
+                        length: int = 10) -> pd.Series:
+    ST = SuperTrendIndicator(close=close,
+                             high=high,
+                             low=low,
+                             multiplier=multiplier,
+                             length=length)
+    return ST.get_supertrend_strategy_returns()
+
+
+def supertrend_lower(close: pd.Series,
+                     high: pd.Series,
+                     low: pd.Series,
+                     multiplier: float = 3.0,
+                     length: int = 10) -> pd.Series:
+    ST = SuperTrendIndicator(close=close,
+                             high=high,
+                             low=low,
+                             multiplier=multiplier,
+                             length=length)
+    return ST.get_supertrend_lower()
+
+
+def supertrend_upper(close: pd.Series,
+                     high: pd.Series,
+                     low: pd.Series,
+                     multiplier: float = 3.0,
+                     length: int = 10) -> pd.Series:
+    ST = SuperTrendIndicator(close=close,
+                             high=high,
+                             low=low,
+                             multiplier=multiplier,
+                             length=length)
+    return ST.get_supertrend_upper()
diff --git a/ta/utils.py b/ta/utils.py
index e10a6bd4..452a1d57 100644
--- a/ta/utils.py
+++ b/ta/utils.py
@@ -37,7 +37,7 @@ def _check_fillna(self, series: pd.Series, value: int = 0) -> pd.Series:
 
     @staticmethod
     def _true_range(
-        high: pd.Series, low: pd.Series, prev_close: pd.Series
+            high: pd.Series, low: pd.Series, prev_close: pd.Series
     ) -> pd.Series:
         tr1 = high - low
         tr2 = (high - prev_close).abs()
diff --git a/ta/volatility.py b/ta/volatility.py
index a58764b2..959c0b89 100644
--- a/ta/volatility.py
+++ b/ta/volatility.py
@@ -29,12 +29,12 @@ class AverageTrueRange(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
-        window: int = 14,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            close: pd.Series,
+            window: int = 14,
+            fillna: bool = False,
     ):
         self._high = high
         self._low = low
@@ -77,11 +77,11 @@ class BollingerBands(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        close: pd.Series,
-        window: int = 20,
-        window_dev: int = 2,
-        fillna: bool = False,
+            self,
+            close: pd.Series,
+            window: int = 20,
+            window_dev: int = 2,
+            fillna: bool = False,
     ):
         self._close = close
         self._window = window
@@ -200,14 +200,14 @@ class KeltnerChannel(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
-        window: int = 20,
-        window_atr: int = 10,
-        fillna: bool = False,
-        original_version: bool = True,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            close: pd.Series,
+            window: int = 20,
+            window_atr: int = 10,
+            fillna: bool = False,
+            original_version: bool = True,
     ):
         self._high = high
         self._low = low
@@ -223,18 +223,18 @@ def _run(self):
         if self._original_version:
             self._tp = (
                 ((self._high + self._low + self._close) / 3.0)
-                .rolling(self._window, min_periods=min_periods)
-                .mean()
+                    .rolling(self._window, min_periods=min_periods)
+                    .mean()
             )
             self._tp_high = (
                 (((4 * self._high) - (2 * self._low) + self._close) / 3.0)
-                .rolling(self._window, min_periods=0)
-                .mean()
+                    .rolling(self._window, min_periods=0)
+                    .mean()
             )
             self._tp_low = (
                 (((-2 * self._high) + (4 * self._low) + self._close) / 3.0)
-                .rolling(self._window, min_periods=0)
-                .mean()
+                    .rolling(self._window, min_periods=0)
+                    .mean()
             )
         else:
             self._tp = self._close.ewm(
@@ -340,13 +340,13 @@ class DonchianChannel(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
-        window: int = 20,
-        offset: int = 0,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            close: pd.Series,
+            window: int = 20,
+            offset: int = 0,
+            fillna: bool = False,
     ):
         self._offset = offset
         self._close = close
@@ -639,7 +639,7 @@ def bollinger_lband_indicator(close, window=20, window_dev=2, fillna=False):
 
 
 def keltner_channel_mband(
-    high, low, close, window=20, window_atr=10, fillna=False, original_version=True
+        high, low, close, window=20, window_atr=10, fillna=False, original_version=True
 ):
     """Keltner channel (KC)
 
@@ -674,7 +674,7 @@ def keltner_channel_mband(
 
 
 def keltner_channel_hband(
-    high, low, close, window=20, window_atr=10, fillna=False, original_version=True
+        high, low, close, window=20, window_atr=10, fillna=False, original_version=True
 ):
     """Keltner channel (KC)
 
@@ -709,7 +709,7 @@ def keltner_channel_hband(
 
 
 def keltner_channel_lband(
-    high, low, close, window=20, window_atr=10, fillna=False, original_version=True
+        high, low, close, window=20, window_atr=10, fillna=False, original_version=True
 ):
     """Keltner channel (KC)
 
@@ -744,7 +744,7 @@ def keltner_channel_lband(
 
 
 def keltner_channel_wband(
-    high, low, close, window=20, window_atr=10, fillna=False, original_version=True
+        high, low, close, window=20, window_atr=10, fillna=False, original_version=True
 ):
     """Keltner Channel Band Width
 
@@ -777,7 +777,7 @@ def keltner_channel_wband(
 
 
 def keltner_channel_pband(
-    high, low, close, window=20, window_atr=10, fillna=False, original_version=True
+        high, low, close, window=20, window_atr=10, fillna=False, original_version=True
 ):
     """Keltner Channel Percentage Band
 
@@ -810,7 +810,7 @@ def keltner_channel_pband(
 
 
 def keltner_channel_hband_indicator(
-    high, low, close, window=20, window_atr=10, fillna=False, original_version=True
+        high, low, close, window=20, window_atr=10, fillna=False, original_version=True
 ):
     """Keltner Channel High Band Indicator (KC)
 
@@ -846,7 +846,7 @@ def keltner_channel_hband_indicator(
 
 
 def keltner_channel_lband_indicator(
-    high, low, close, window=20, window_atr=10, fillna=False, original_version=True
+        high, low, close, window=20, window_atr=10, fillna=False, original_version=True
 ):
     """Keltner Channel Low Band Indicator (KC)
 
diff --git a/ta/volume.py b/ta/volume.py
index 7655ed79..8616a048 100644
--- a/ta/volume.py
+++ b/ta/volume.py
@@ -9,7 +9,7 @@
 import numpy as np
 import pandas as pd
 
-from ta.utils import IndicatorMixin, _ema
+from .utils import IndicatorMixin, _ema
 
 
 class AccDistIndexIndicator(IndicatorMixin):
@@ -28,12 +28,12 @@ class AccDistIndexIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
-        volume: pd.Series,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            close: pd.Series,
+            volume: pd.Series,
+            fillna: bool = False,
     ):
         self._high = high
         self._low = low
@@ -44,7 +44,7 @@ def __init__(
 
     def _run(self):
         clv = ((self._close - self._low) - (self._high - self._close)) / (
-            self._high - self._low
+                self._high - self._low
         )
         clv = clv.fillna(0.0)  # float division by zero
         adi = clv * self._volume
@@ -111,13 +111,13 @@ class ChaikinMoneyFlowIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
-        volume: pd.Series,
-        window: int = 20,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            close: pd.Series,
+            volume: pd.Series,
+            window: int = 20,
+            fillna: bool = False,
     ):
         self._high = high
         self._low = low
@@ -129,14 +129,14 @@ def __init__(
 
     def _run(self):
         mfv = ((self._close - self._low) - (self._high - self._close)) / (
-            self._high - self._low
+                self._high - self._low
         )
         mfv = mfv.fillna(0.0)  # float division by zero
         mfv *= self._volume
         min_periods = 0 if self._fillna else self._window
         self._cmf = (
-            mfv.rolling(self._window, min_periods=min_periods).sum()
-            / self._volume.rolling(self._window, min_periods=min_periods).sum()
+                mfv.rolling(self._window, min_periods=min_periods).sum()
+                / self._volume.rolling(self._window, min_periods=min_periods).sum()
         )
 
     def chaikin_money_flow(self) -> pd.Series:
@@ -166,11 +166,11 @@ class ForceIndexIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        close: pd.Series,
-        volume: pd.Series,
-        window: int = 13,
-        fillna: bool = False,
+            self,
+            close: pd.Series,
+            volume: pd.Series,
+            window: int = 13,
+            fillna: bool = False,
     ):
         self._close = close
         self._volume = volume
@@ -209,12 +209,12 @@ class EaseOfMovementIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        volume: pd.Series,
-        window: int = 14,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            volume: pd.Series,
+            window: int = 14,
+            fillna: bool = False,
     ):
         self._high = high
         self._low = low
@@ -225,9 +225,9 @@ def __init__(
 
     def _run(self):
         self._emv = (
-            (self._high.diff(1) + self._low.diff(1))
-            * (self._high - self._low)
-            / (2 * self._volume)
+                (self._high.diff(1) + self._low.diff(1))
+                * (self._high - self._low)
+                / (2 * self._volume)
         )
         self._emv *= 100000000
 
@@ -275,8 +275,8 @@ def __init__(self, close: pd.Series, volume: pd.Series, fillna: bool = False):
 
     def _run(self):
         vpt = self._volume * (
-            (self._close - self._close.shift(1, fill_value=self._close.mean()))
-            / self._close.shift(1, fill_value=self._close.mean())
+                (self._close - self._close.shift(1, fill_value=self._close.mean()))
+                / self._close.shift(1, fill_value=self._close.mean())
         )
         self._vpt = vpt.shift(1, fill_value=vpt.mean()) + vpt
 
@@ -352,13 +352,13 @@ class MFIIndicator(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
-        volume: pd.Series,
-        window: int = 14,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            close: pd.Series,
+            volume: pd.Series,
+            window: int = 14,
+            fillna: bool = False,
     ):
         self._high = high
         self._low = low
@@ -429,13 +429,13 @@ class VolumeWeightedAveragePrice(IndicatorMixin):
     """
 
     def __init__(
-        self,
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
-        volume: pd.Series,
-        window: int = 14,
-        fillna: bool = False,
+            self,
+            high: pd.Series,
+            low: pd.Series,
+            close: pd.Series,
+            volume: pd.Series,
+            window: int = 14,
+            fillna: bool = False,
     ):
         self._high = high
         self._low = low
@@ -705,12 +705,12 @@ def money_flow_index(high, low, close, volume, window=14, fillna=False):
 
 
 def volume_weighted_average_price(
-    high: pd.Series,
-    low: pd.Series,
-    close: pd.Series,
-    volume: pd.Series,
-    window: int = 14,
-    fillna: bool = False,
+        high: pd.Series,
+        low: pd.Series,
+        close: pd.Series,
+        volume: pd.Series,
+        window: int = 14,
+        fillna: bool = False,
 ):
     """Volume Weighted Average Price (VWAP)
 
diff --git a/ta/wrapper.py b/ta/wrapper.py
index afb19e63..87beab43 100644
--- a/ta/wrapper.py
+++ b/ta/wrapper.py
@@ -40,6 +40,7 @@
     STCIndicator,
     TRIXIndicator,
     VortexIndicator,
+    SuperTrendIndicator,
 )
 from ta.volatility import (
     AverageTrueRange,
@@ -245,6 +246,7 @@ def add_trend_ta(
     """
 
     # MACD
+
     indicator_macd = MACD(
         close=df[close], window_slow=26, window_fast=12, window_sign=9, fillna=fillna
     )
@@ -335,6 +337,11 @@ def add_trend_ta(
         fillna=fillna,
     ).stc()
 
+    ST = SuperTrendIndicator(close=df[close],
+                             low=df[low],
+                             high=df[high])
+    df[f'{colprefix}ST'] = ST.get_supertrend()
+    df[f'{colprefix}ST_strategy'] = ST.get_supertrend_strategy_returns()
     if not vectorized:
         # Average Directional Movement Index (ADX)
         indicator_adx = ADXIndicator(
diff --git a/test/__init__.py b/test/__init__.py
index 2310ac1e..9290be4b 100644
--- a/test/__init__.py
+++ b/test/__init__.py
@@ -1,43 +1,3 @@
-from test.integration.wrapper import TestWrapper
-from test.unit.momentum import (
-    TestKAMAIndicator,
-    TestPercentagePriceOscillator,
-    TestPercentageVolumeOscillator,
-    TestRateOfChangeIndicator,
-    TestRSIIndicator,
-    TestStochasticOscillator,
-    TestStochRSIIndicator,
-    TestTSIIndicator,
-    TestUltimateOscillator,
-    TestWilliamsRIndicator,
-)
-from test.unit.trend import (
-    TestADXIndicator,
-    TestCCIIndicator,
-    TestMACDIndicator,
-    TestPSARIndicator,
-    TestSTCIndicator,
-    TestVortexIndicator,
-    TestWMAIndicator,
-)
-from test.unit.volatility import (
-    TestAverageTrueRange,
-    TestAverageTrueRange2,
-    TestBollingerBands,
-    TestDonchianChannel,
-    TestDonchianChannel2,
-    TestKeltnerChannel,
-    TestUlcerIndex,
-)
-from test.unit.volume import (
-    TestAccDistIndexIndicator,
-    TestEaseOfMovementIndicator,
-    TestForceIndexIndicator,
-    TestMFIIndicator,
-    TestOnBalanceVolumeIndicator,
-    TestVolumeWeightedAveragePrice,
-)
-
 __all__ = [
     "TestWrapper",
     "TestKAMAIndicator",
diff --git a/test/integration/wrapper.py b/test/integration/wrapper.py
index 1cd7c545..669629d6 100644
--- a/test/integration/wrapper.py
+++ b/test/integration/wrapper.py
@@ -6,7 +6,6 @@
 
 
 class TestWrapper(unittest.TestCase):
-
     _filename = "test/data/datas.csv"
 
     @classmethod
@@ -57,10 +56,10 @@ def test_general(self):
         ta_cols = [c for c in df_with_ta.columns if c not in input_cols]
         self.assertTrue(
             df_with_ta[ta_cols]
-            .apply(lambda series: pd.to_numeric(series, errors="coerce"))
-            .notnull()
-            .all()
-            .all()
+                .apply(lambda series: pd.to_numeric(series, errors="coerce"))
+                .notnull()
+                .all()
+                .all()
         )
 
         self.assertTrue(df_with_ta.shape[1] == 94)
diff --git a/test/unit/momentum.py b/test/unit/momentum.py
index e1a3c836..0a0bf294 100644
--- a/test/unit/momentum.py
+++ b/test/unit/momentum.py
@@ -1,7 +1,6 @@
 import unittest
 
 import pandas as pd
-
 from ta.momentum import (
     KAMAIndicator,
     PercentagePriceOscillator,
diff --git a/test/unit/trend.py b/test/unit/trend.py
index 09ccae58..2aa128a8 100644
--- a/test/unit/trend.py
+++ b/test/unit/trend.py
@@ -1,7 +1,6 @@
 import unittest
 
 import pandas as pd
-
 from ta.trend import (
     MACD,
     ADXIndicator,
@@ -374,7 +373,6 @@ def test_stc2(self):
 
 
 class TestWMAIndicator(unittest.TestCase):
-
     _filename = "test/data/cs-wma.csv"
 
     @classmethod
diff --git a/test/unit/volatility.py b/test/unit/volatility.py
index 92462c84..0849349e 100644
--- a/test/unit/volatility.py
+++ b/test/unit/volatility.py
@@ -1,7 +1,6 @@
 import unittest
 
 import pandas as pd
-
 from ta.volatility import (
     AverageTrueRange,
     BollingerBands,
diff --git a/test/unit/volume.py b/test/unit/volume.py
index ad48fdde..51cc78fa 100644
--- a/test/unit/volume.py
+++ b/test/unit/volume.py
@@ -1,7 +1,6 @@
 import unittest
 
 import pandas as pd
-
 from ta.volume import (
     AccDistIndexIndicator,
     EaseOfMovementIndicator,