text: Title: CMO (Chande Momentum Oscillator) calculation
  Body: Original CMO explanation from Chande's book:
  ![206595953-ab02f2ce-514e-4941-a711-76298c174bf4](https://user-images.githubusercontent.com/31756078/206600175-0b3bf9e7-d48d-42d2-a40e-472b5a27666d.png)
  ![206595986-af72ad1e-f8a1-4786-bf6c-b17d3f82aa73](https://user-images.githubusercontent.com/31756078/206600204-aa245a98-9af9-4e63-87c0-0cfc295df9eb.png)
  
  **To Reproduce**
  ```python
  df = ta.DataFrame({'close': [101.0313, 101.125, 101.9687, 102.7813, 103, 102.9687, 103.0625, 102.9375, 102.7188, 102.75, 102.9063, 102.9687 ]})
  df['cmo'] = ta.cmo(close = ff['close'], length=10)
  df
  ```
  
  **Expected behavior**
  This should generate THREE cmo values - for the last three rows of data, equivalent to what is in the book.
  Instead, we get:
  ```sh
  close   | cmo
  101.031 | NaN
  101.125 | NaN
  101.969 | NaN
  102.781 | NaN
  103.000 | NaN
  102.969 | NaN
  103.062 | NaN
  102.938 | NaN
  102.719 | NaN
  102.750 | NaN
  102.906 | 60.091
  102.969 | 61.928
  ```
  Remark: TA-LIB also mis-calculates CMO and returns different results from the book (and different results from Pandas-TA)...