Created VWAP with standard deviation bands function

Used the equation from here:
https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=108&Name=Volume_Weighted_Average_Price_-_VWAP_-_with_Standard_Deviation_Lines

pandas_ta/core.py

@@ -1309,6 +1309,18 @@ class AnalysisIndicators(object):
         result = vwap(high=high, low=low, close=close, volume=volume, anchor=anchor, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
+    def vwap_with_bands(self, anchor=None, offset=None, **kwargs):
+        high = self._get_column(kwargs.pop("high", "high"))
+        low = self._get_column(kwargs.pop("low", "low"))
+        close = self._get_column(kwargs.pop("close", "close"))
+        volume = self._get_column(kwargs.pop("volume", "volume"))
+
+        if not self.datetime_ordered:
+            volume.index = self._df.index
+
+        result = vwap_with_bands(high=high, low=low, close=close, volume=volume, anchor=anchor, offset=offset, **kwargs)
+        return self._post_process(result, **kwargs)
+
     def vwma(self, volume=None, length=None, offset=None, **kwargs):
         close = self._get_column(kwargs.pop("close", "close"))
         volume = self._get_column(kwargs.pop("volume", "volume"))

pandas_ta/overlap/__init__.py

@@ -31,6 +31,7 @@ from .tema import tema
 from .trima import trima
 from .vidya import vidya
 from .vwap import vwap
+from .vwap_with_bands import vwap_with_bands
 from .vwma import vwma
 from .wcp import wcp
 from .wma import wma

pandas_ta/overlap/vwap_with_bands.py

@@ -0,0 +1,94 @@
+# -*- coding: utf-8 -*-
+import math
+import pandas
+from pandas import Series, DataFrame
+from pandas_ta.overlap import hlc3
+from pandas_ta.utils import get_offset, is_datetime_ordered, verify_series
+
+
+def vwap_with_bands(
+    high: Series, low: Series, close: Series, volume: Series, bands: list = [-1,1],
+    anchor: str = None,
+    offset: int = None, **kwargs
+) -> DataFrame:
+    """Volume Weighted Average Price (VWAP)
+
+    The Volume Weighted Average Price that measures the average typical price
+    by volume.  It is typically used with intraday charts to identify general
+    direction.
+
+    Sources:
+        https://www.tradingview.com/wiki/Volume_Weighted_Average_Price_(VWAP)
+        https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/volume-weighted-average-price-vwap/
+        https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vwap_intraday
+        https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=108&Name=Volume_Weighted_Average_Price_-_VWAP_-_with_Standard_Deviation_Lines
+
+    Args:
+        high (pd.Series): Series of 'high's
+        low (pd.Series): Series of 'low's
+        close (pd.Series): Series of 'close's
+        volume (pd.Series): Series of 'volume's
+        bands (list): List of volume weighted standard deviation bands to be processed
+        anchor (str): How to anchor VWAP. Depending on the index values,
+            it will implement various Timeseries Offset Aliases
+            as listed here:
+            https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases
+            Default: "D".
+        offset (int): How many periods to offset the result. Default: 0
+
+    Kwargs:
+        fillna (value, optional): pd.DataFrame.fillna(value)
+        fill_method (value, optional): Type of fill method
+
+    Returns:
+        pd.DataFrame: New feature generated.
+    """
+    # Validate
+    high = verify_series(high)
+    low = verify_series(low)
+    close = verify_series(close)
+    volume = verify_series(volume)
+    anchor = anchor.upper() if anchor and isinstance(
+        anchor, str) and len(anchor) >= 1 else "D"
+    offset = get_offset(offset)
+
+    typical_price = hlc3(high=high, low=low, close=close)
+    if not is_datetime_ordered(volume):
+        _s = "[!] VWAP volume series is not datetime ordered."
+        print(f"{_s} Results may not be as expected.")
+    if not is_datetime_ordered(typical_price):
+        _s = "[!] VWAP price series is not datetime ordered."
+        print(f"{_s} Results may not be as expected.")
+
+    # Calculate vwap
+    wp = typical_price * volume
+    vwap = wp.groupby(wp.index.to_period(anchor)).cumsum()
+    vwap /= volume.groupby(volume.index.to_period(anchor)).cumsum()
+
+    # Calculate vwap stdev bands
+    var = volume * (typical_price - vwap) ** 2
+    var_sum = var.groupby(var.index.to_period(anchor)).cumsum()
+    volume_sum = volume.groupby(volume.index.to_period(anchor)).cumsum()
+    std_volume_weighted = (var_sum/volume_sum) ** 0.5
+
+    # Build Dataframe
+    df = pandas.DataFrame()
+    df[f"VWAP_{anchor}"] = vwap
+    for i in bands:
+        df[f"{i}_VWAP_band"] = vwap + i*std_volume_weighted
+
+    # Offset
+    if offset != 0:
+        vwap = vwap.shift(offset)
+
+    # Fill
+    if "fillna" in kwargs:
+        vwap.fillna(kwargs["fillna"], inplace=True)
+    if "fill_method" in kwargs:
+        vwap.fillna(method=kwargs["fill_method"], inplace=True)
+
+    # Name and Category
+    vwap.name = f"VWAP_{anchor}_bands"
+    vwap.category = "overlap"
+
+    return df