add vwap stdev bands indicator

ta/momentum.py

@@ -7,7 +7,6 @@
 """
 import numpy as np
 import pandas as pd
-
 from ta.utils import IndicatorMixin, _ema
 
 

ta/others.py

@@ -7,7 +7,6 @@
 """
 import numpy as np
 import pandas as pd
-
 from ta.utils import IndicatorMixin
 
 

ta/trend.py

@@ -7,7 +7,6 @@
 """
 import numpy as np
 import pandas as pd
-
 from ta.utils import IndicatorMixin, _ema, _get_min_max, _sma
 
 

ta/volatility.py

@@ -7,7 +7,6 @@
 """
 import numpy as np
 import pandas as pd
-
 from ta.utils import IndicatorMixin
 
 

ta/volume.py

@@ -6,9 +6,10 @@
 
 """
 
+from typing import List
+
 import numpy as np
 import pandas as pd
-
 from ta.utils import IndicatorMixin, _ema
 
 
@@ -423,6 +424,7 @@ class VolumeWeightedAveragePrice(IndicatorMixin):
         volume(pandas.Series): dataset 'Volume' column.
         window(int): n period.
         fillna(bool): if True, fill nan values.
+        custom_typical_price(pandas.Series): custom typical price.
 
     Returns:
         pandas.Series: New feature generated.
@@ -436,21 +438,27 @@ def __init__(
         volume: pd.Series,
         window: int = 14,
         fillna: bool = False,
+        custom_typical_price: pd.Series = None,
     ):
         self._high = high
         self._low = low
         self._close = close
         self._volume = volume
         self._window = window
         self._fillna = fillna
+        self._custom_typical_price = custom_typical_price
         self._run()
 
     def _run(self):
         # 1 typical price
-        typical_price = (self._high + self._low + self._close) / 3.0
+        self._typical_price = (
+            self._custom_typical_price
+            if self._custom_typical_price is not None
+            else (self._high + self._low + self._close) / 3.0
+        )
 
         # 2 typical price * volume
-        typical_price_volume = typical_price * self._volume
+        typical_price_volume = self._typical_price * self._volume
 
         # 3 total price * volume
         min_periods = 0 if self._fillna else self._window
@@ -459,9 +467,11 @@ def _run(self):
         ).sum()
 
         # 4 total volume
-        total_volume = self._volume.rolling(self._window, min_periods=min_periods).sum()
+        self._total_volume = self._volume.rolling(
+            self._window, min_periods=min_periods
+        ).sum()
 
-        self.vwap = total_pv / total_volume
+        self.vwap = total_pv / self._total_volume
 
     def volume_weighted_average_price(self) -> pd.Series:
         """Volume Weighted Average Price (VWAP)
@@ -472,6 +482,37 @@ def volume_weighted_average_price(self) -> pd.Series:
         vwap = self._check_fillna(self.vwap)
         return pd.Series(vwap, name=f"vwap_{self._window}")
 
+    def stdev_bands(
+        self,
+        bands: list,
+    ) -> List[pd.Series]:
+        """VWAP Standard Deviation Bands
+
+        Args:
+            bands(Sequence): list of N's where N denotes deviations count.
+
+        Returns:
+            Sequence(pandas.Series): list of new features.
+        """
+        if not bands:
+            bands = [2, -2]
+
+        volume_sqr_vwap = self._volume * self._typical_price.pow(2)
+        min_periods = 0 if self._fillna else self._window
+        volume_sqr_vwap_sum = volume_sqr_vwap.rolling(
+            self._window, min_periods=min_periods
+        ).sum()
+        sqr_price = volume_sqr_vwap_sum / self._total_volume - self.vwap.pow(2)
+        sqr_price = sqr_price.clip(0.0)
+        deviation = sqr_price.pow(1 / 2.0)
+
+        band_series = [self._check_fillna(self.vwap + n * deviation) for n in bands]
+        result = [
+            band.rename(f"vwap_{self._window}_{bands[i]}stdev")
+            for i, band in enumerate(band_series)
+        ]
+        return result
+
 
 def acc_dist_index(high, low, close, volume, fillna=False):
     """Accumulation/Distribution Index (ADI)
@@ -711,6 +752,7 @@ def volume_weighted_average_price(
     volume: pd.Series,
     window: int = 14,
     fillna: bool = False,
+    custom_typical_price: pd.Series = None,
 ):
     """Volume Weighted Average Price (VWAP)
 
@@ -729,12 +771,19 @@ def volume_weighted_average_price(
         volume(pandas.Series): dataset 'Volume' column.
         window(int): n period.
         fillna(bool): if True, fill nan values.
+        custom_typical_price(pandas.Series): custom typical price.
 
     Returns:
         pandas.Series: New feature generated.
     """
 
     indicator = VolumeWeightedAveragePrice(
-        high=high, low=low, close=close, volume=volume, window=window, fillna=fillna
+        high=high,
+        low=low,
+        close=close,
+        volume=volume,
+        window=window,
+        fillna=fillna,
+        custom_typical_price=custom_typical_price,
     )
     return indicator.volume_weighted_average_price()

ta/wrapper.py

@@ -6,7 +6,6 @@
 """
 
 import pandas as pd
-
 from ta.momentum import (
     AwesomeOscillatorIndicator,
     KAMAIndicator,

test/data/cs-vwap.csv

@@ -1,31 +1,31 @@
-,Date,High,Low,Close,Typical Price,Volume,vwap
-0,3-Dec-10,24.8283,24.3205,24.7486,24.63247,18730.144,24.632466666666662
-1,6-Dec-10,24.7586,24.5993,24.7088,24.6889,12271.74,24.654805154030853
-2,7-Dec-10,25.1568,24.7785,25.0373,24.99087,24691.414,24.80379676048873
-3,8-Dec-10,25.5848,24.9477,25.545,25.359170000000002,18357.606,24.941475861906202
-4,9-Dec-10,25.6844,24.8083,25.0672,25.186629999999997,22964.08,24.999506235713717
-5,10-Dec-10,25.336,25.0572,25.107,25.166729999999998,15918.948,25.023078243028557
-6,13-Dec-10,25.2862,24.8482,24.888,25.00747,16067.044,25.021133824528068
-7,14-Dec-10,25.1269,24.7496,24.9975,24.958000000000002,16568.487,25.013948032058988
-8,15-Dec-10,25.2762,24.9278,25.0473,25.08377,16018.729,25.02086936568546
-9,16-Dec-10,25.3857,25.0274,25.336,25.2497,9773.569,25.033920655429075
-10,17-Dec-10,25.5351,25.0473,25.0572,25.2132,22572.712000000003,25.054787605744547
-11,20-Dec-10,25.6048,25.0622,25.4455,25.370829999999998,12986.669,25.074623090960745
-12,21-Dec-10,25.7441,25.5351,25.5649,25.6147,10906.659,25.10166479387297
-13,22-Dec-10,25.7242,25.4554,25.555,25.5782,5799.259,25.11402265022072
-14,23-Dec-10,25.6744,25.2862,25.4057,25.45543,7395.274,25.168402599215423
-15,27-Dec-10,25.4455,25.1667,25.3658,25.326,5818.161999999999,25.201444302952908
-16,28-Dec-10,25.3161,24.9178,25.0373,25.0904,7164.726,25.224830276069333
-17,29-Dec-10,25.2563,24.9079,24.9178,25.02733,5672.914000000001,25.204409310250934
-18,30-Dec-10,25.0373,24.8283,24.878,24.91453,5624.741999999999,25.196687502882995
-19,31-Dec-10,25.0074,24.7088,24.9676,24.8946,5023.469,25.189626838746538
-20,3-Jan-11,25.3061,25.0274,25.0473,25.126929999999998,7457.090999999999,25.20734712046974
-21,4-Jan-11,25.116999999999997,24.3404,24.45,24.6358,11798.008999999998,25.187857717587416
-22,5-Jan-11,24.6889,24.2708,24.5694,24.5097,12366.132,25.13631726721325
-23,6-Jan-11,24.5495,23.8925,24.0219,24.154629999999997,13294.865,25.030555113789646
-24,7-Jan-11,24.2708,23.778000000000002,23.8825,23.9771,9256.87,24.91547606305806
-25,10-Jan-11,24.2708,23.7232,24.2011,24.06503,9690.604,24.794771487236023
-26,11-Jan-11,24.5993,24.2011,24.2807,24.36037,8870.318000000001,24.68372645384523
-27,12-Jan-11,24.4798,24.2409,24.3305,24.3504,7168.965,24.618745747839238
-28,13-Jan-11,24.5595,23.4345,24.44,24.14467,11356.18,24.52276889908593
-29,14-Jan-11,25.16,24.27,25.0,24.81,13379.374,24.51628803159466
+,Date,High,Low,Close,Typical Price,Volume,vwap,vwap_2stdev,vwap_-2stdev
+0,2010-12-03,24.8283,24.3205,24.7486,24.63247,18730.144,24.632466666666666,24.632466666666662,24.632466666666662
+1,2010-12-06,24.7586,24.5993,24.7088,24.6889,12271.74,24.654805154030853,24.71000034533528,24.599609962726426
+2,2010-12-07,25.1568,24.7785,25.0373,24.99087,24691.414,24.80379676048873,25.140223920731994,24.467369600245465
+3,2010-12-08,25.5848,24.9477,25.545,25.35917,18357.606,24.9414758619062,25.502861378042404,24.38009034577
+4,2010-12-09,25.6844,24.8083,25.0672,25.18663,22964.08,24.99950623571372,25.532414278378035,24.4665981930494
+5,2010-12-10,25.336,25.0572,25.107,25.16673,15918.948,25.02307824302856,25.53052786548367,24.515628620573445
+6,2010-12-13,25.2862,24.8482,24.888,25.00747,16067.044,25.021133824528068,25.496043511132505,24.54622413792363
+7,2010-12-14,25.1269,24.7496,24.9975,24.958,16568.487,25.013948032058988,25.462809721807695,24.56508634231028
+8,2010-12-15,25.2762,24.9278,25.0473,25.08377,16018.729,25.020869365685463,25.448940784559365,24.592797946811555
+9,2010-12-16,25.3857,25.0274,25.336,25.2497,9773.569,25.03392065542907,25.462941168391286,24.604900142466864
+10,2010-12-17,25.5351,25.0473,25.0572,25.2132,22572.712000000003,25.054787605744554,25.47414152565047,24.635433685838624
+11,2010-12-20,25.6048,25.0622,25.4455,25.37083,12986.669,25.074623090960745,25.508584520598532,24.64066166132296
+12,2010-12-21,25.7441,25.5351,25.5649,25.6147,10906.659,25.101664793872967,25.585799669777597,24.617529917968344
+13,2010-12-22,25.7242,25.4554,25.555,25.5782,5799.259,25.11402265022072,25.615274327006222,24.612770973435214
+14,2010-12-23,25.6744,25.2862,25.4057,25.45543,7395.274,25.168402599215426,25.60111772887163,24.735687469559217
+15,2010-12-27,25.4455,25.1667,25.3658,25.326,5818.161999999998,25.20144430295291,25.571205925454088,24.831682680451728
+16,2010-12-28,25.3161,24.9178,25.0373,25.0904,7164.726,25.224830276069326,25.57962732059043,24.870033231548238
+17,2010-12-29,25.2563,24.9079,24.9178,25.02733,5672.914000000002,25.20440931025093,25.56607077707847,24.8427478434234
+18,2010-12-30,25.0373,24.8283,24.878,24.91453,5624.741999999998,25.196687502882995,25.592476558669752,24.800898447096237
+19,2010-12-31,25.0074,24.7088,24.9676,24.8946,5023.469,25.189626838746534,25.613988291424807,24.76526538606827
+20,2011-01-03,25.3061,25.0274,25.0473,25.12693,7457.0909999999985,25.207347120469738,25.62624723114063,24.78844700979885
+21,2011-01-04,25.117,24.3404,24.45,24.6358,11798.008999999998,25.187857717587413,25.702145480396673,24.67356995477816
+22,2011-01-05,24.6889,24.2708,24.5694,24.5097,12366.132,25.13631726721325,25.79241066525781,24.480223869168693
+23,2011-01-06,24.5495,23.8925,24.0219,24.15463,13294.865,25.03055511378965,25.89856317362721,24.162547053952082
+24,2011-01-07,24.2708,23.778,23.8825,23.9771,9256.87,24.91547606305806,25.964058276814093,23.866893849302024
+25,2011-01-10,24.2708,23.7232,24.2011,24.06503,9690.604,24.794771487236023,25.89902091884207,23.690522055629977
+26,2011-01-11,24.5993,24.2011,24.2807,24.36037,8870.318000000001,24.683726453845228,25.681309464048176,23.686143443642287
+27,2011-01-12,24.4798,24.2409,24.3305,24.3504,7168.965,24.618745747839235,25.532390203812742,23.705101291865734
+28,2011-01-13,24.5595,23.4345,24.44,24.14467,11356.18,24.522768899085932,25.34944515779455,23.69609264037731
+29,2011-01-14,25.16,24.27,25.0,24.81,13379.374,24.51628803159466,25.26472975048104,23.767846312708276

test/integration/wrapper.py

@@ -1,7 +1,6 @@
 import unittest
 
 import pandas as pd
-
 import ta
 
 

test/unit/momentum.py

@@ -1,7 +1,6 @@
 import unittest
 
 import pandas as pd
-
 from ta.momentum import (
     KAMAIndicator,
     PercentagePriceOscillator,

test/unit/trend.py

@@ -1,7 +1,6 @@
 import unittest
 
 import pandas as pd
-
 from ta.trend import (
     MACD,
     ADXIndicator,

test/unit/volatility.py

@@ -1,7 +1,6 @@
 import unittest
 
 import pandas as pd
-
 from ta.volatility import (
     AverageTrueRange,
     BollingerBands,

test/unit/volume.py

@@ -1,7 +1,6 @@
 import unittest
 
 import pandas as pd
-
 from ta.volume import (
     AccDistIndexIndicator,
     EaseOfMovementIndicator,
@@ -253,6 +252,15 @@ def test_vwap2(self):
             self._df[target].tail(), result.tail(), check_names=False
         )
 
+    def test_vwap_stdev_bands(self):
+        bands = self._indicator.stdev_bands([2, -2])
+        pd.testing.assert_series_equal(
+            self._df["vwap_2stdev"].tail(), bands[0].tail(), check_names=False
+        )
+        pd.testing.assert_series_equal(
+            self._df["vwap_-2stdev"].tail(), bands[1].tail(), check_names=False
+        )
+
 
 if __name__ == "__main__":
     unittest.main()