text: Title: How to compute the band of VWAP?
  Body: **Which version are you running? The latest version is on Github. Pip is for major releases.**
  ```python
  import pandas_ta as ta
  print(ta.version)
  0.3.14b0
  ```
  
  **Do you have _TA Lib_ also installed in your environment?**
  ```sh
  $ pip list
  ...
  TA-Lib                  0.4.21
  ...
  ```
  
  **Did you upgrade? Did the upgrade resolve the issue?**
  ```sh
  $ pip install -U git+https://github.com/twopirllc/pandas-ta
  ```
  
  **Describe the bug**
  
  The help only shows VWAP. But there is no mentioning the bands. On the 2nd link, it mentions `sigma = sqrt(sum_{i=1}^N(x_i-x_bar)^2/N)`. But this is not weighted by the volume. Since the x_bar is averaged using volume, should the computation of sigma also weighted?
  
  ```python
  vwap(high, low, close, volume, anchor=None, offset=None, **kwargs)
      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
  
      Calculation:
          tp = typical_price = hlc3(high, low, close)
          tpv = tp * volume
          VWAP = tpv.cumsum() / volume.cumsum()
  
      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
          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.Series: New feature generated.
  ```
  
  **To Reproduce**
  NA
  
  **Expected behavior**
  See above.
  
  **Screenshots**
  NA
  
  **Additional context**
  NA