[![enter image description here](https://i.stack.imgur.com/vIEKx.png)](https://i.stack.imgur.com/vIEKx.png)
I was defining a function Heiken Ashi which is one of the popular chart type in Technical Analysis.
I was writing a function on it using Pandas but finding little difficulty.
This is how Heiken Ashi [HA] looks like-

```
                 Heikin-Ashi Candle Calculations
           HA_Close = (Open + High + Low + Close) / 4
           HA_Open = (previous HA_Open + previous HA_Close) / 2
           HA_Low = minimum of Low, HA_Open, and HA_Close
           HA_High = maximum of High, HA_Open, and HA_Close

               Heikin-Ashi Calculations on First Run
            HA_Close = (Open + High + Low + Close) / 4
                   HA_Open = (Open + Close) / 2
                           HA_Low = Low
                           HA_High = High

```

There is a lot of stuff available on various websites using for loop and pure python but i think Pandas can also do job well.
This is my progress-

```
   def HA(df):

       df['HA_Close']=(df['Open']+ df['High']+ df['Low']+ df['Close'])/4

       ha_o=df['Open']+df['Close']  #Creating a Variable
       #(for 1st row)

       HA_O=df['HA_Open'].shift(1)+df['HA_Close'].shift(1) #Another variable
       #(for subsequent rows)

       df['HA_Open']=[ha_o/2 if df['HA_Open']='nan' else HA_O/2]     
       #(error Part Where am i going wrong?)

       df['HA_High']=df[['HA_Open','HA_Close','High']].max(axis=1)

       df['HA_Low']=df[['HA_Open','HA_Close','Low']].min(axis=1)

       return df

```

Can Anyone Help me with this please?`
It doesnt work....
I tried on this-

```
  import pandas_datareader.data as web 
  import HA
  import pandas as pd
  start='2016-1-1'
  end='2016-10-30'
  DAX=web.DataReader('^GDAXI','yahoo',start,end)

```

This is the New Code i wrote

```
    def HA(df):
            df['HA_Close']=(df['Open']+ df['High']+ df['Low']+df['Close'])/4
...:        ha_o=df['Open']+df['Close']
...:        df['HA_Open']=0.0
...:        HA_O=df['HA_Open'].shift(1)+df['HA_Close'].shift(1)
...:        df['HA_Open']= np.where( df['HA_Open']==np.nan, ha_o/2, HA_O/2 )
...:        df['HA_High']=df[['HA_Open','HA_Close','High']].max(axis=1)
...:        df['HA_Low']=df[['HA_Open','HA_Close','Low']].min(axis=1)
...:        return df

```

But still the HA\_Open result was not satisfactory