text: Title: ha_open has Nan Values when open is smoothed with ema
  Body: **Which version are you running? The lastest version is on Github. Pip is for major releases.**
  0.3.14b0
  
  **Do you have _TA Lib_ also installed in your environment?**
  yes
  
  **Did you upgrade? Did the upgrade resolve the issue?**
  nope, pandas_ta is newly installed on my new laptop
  
  **Describe the bug**
  i am trying to create smoothed heikin ashi. however ha_open has Nan values. i am not sure if this is a bug or if this is a expected result
  
  **To Reproduce**
  ```python
  import ccxt
  import os,sys
  import pandas_ta as pta
  import pandas as pd
  from datetime import datetime
  import time
  import pprint
  import math
  
  exchange = ccxt.binance({
      'enableRateLimit': True,
      'options': {
          'defaultType': 'future',  # ←-------------- quotes and 'future'
      },
  })
  
  pair='BTC/USDT'
  ohlc = exchange.fetch_ohlcv(pair, timeframe='5m')
  df = pd.DataFrame(ohlc, columns = ['time', 'open', 'high', 'low', 'close', 'volume'])
  df['time'] = pd.to_datetime(df['time'], unit='ms')
  df['sopen']=pta.ema(df['open'], length=10)
  df['shigh']=pta.ema(df['high'], length=10)
  df['slow']=pta.ema(df['low'], length=10)
  df['sclose']=pta.ema(df['close'], length=10)
  df[['ha_open','ha_high','ha_low','ha_close']] = pta.ha(df['open'],df['high'],df['low'],df['close'])
  df[['sha_open','sha_high','sha_low','sha_close']] = pta.ha(df['sopen'],df['shigh'],df['slow'],df['sclose'])
  pd.set_option('display.max_rows', None)
  pd.set_option('display.max_columns', None)
  pd.set_option('display.width', None)
  pd.set_option('display.max_colwidth', None)
  print(df)
  ```
  
  **Expected behavior**
  sha_open is heiken ashi_open with smoothed open values using ema. it is returning Nan values but this doesnt happen in smoothed High,smoothed low and smoothed close. only on smoothed heikin ashi open (sha_open)
  
  **Screenshots**
  ![image](https://user-images.githubusercontent.com/80815775/159968983-f098451c-af81-46f6-a922-107103122431.png)
  
  **Additional context**
  NA
  
  Thanks for using Pandas TA!
  