Heiken Ashi Using pandas python

Question

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

Answer 1

Here is the fastest, accurate and efficient implementation as per my tests:

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

    idx = df.index.name
    df.reset_index(inplace=True)

    for i in range(0, len(df)):
        if i == 0:
            df.set_value(i, 'HA_Open', ((df.get_value(i, 'Open') + df.get_value(i, 'Close')) / 2))
        else:
            df.set_value(i, 'HA_Open', ((df.get_value(i - 1, 'HA_Open') + df.get_value(i - 1, 'HA_Close')) / 2))

    if idx:
        df.set_index(idx, inplace=True)

    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

Here is my test algorithm (essentially I used the algorithm provided in this post to benchmark the speed results):

import quandl
import time

df = quandl.get("NSE/NIFTY_50", start_date='1997-01-01')

def test_HA():
    print('HA Test')
    start = time.time()
    HA(df)
    end = time.time()
    print('Time taken by set and get value functions for HA {}'.format(end-start))

    start = time.time()
    df['HA_Close_t']=(df['Open']+ df['High']+ df['Low']+df['Close'])/4

    from collections import namedtuple
    nt = namedtuple('nt', ['Open','Close'])
    previous_row = nt(df.ix[0,'Open'],df.ix[0,'Close'])
    i = 0
    for row in df.itertuples():
        ha_open = (previous_row.Open + previous_row.Close) / 2
        df.ix[i,'HA_Open_t'] = ha_open
        previous_row = nt(ha_open, row.Close)
        i += 1

    df['HA_High_t']=df[['HA_Open_t','HA_Close_t','High']].max(axis=1)
    df['HA_Low_t']=df[['HA_Open_t','HA_Close_t','Low']].min(axis=1)
    end = time.time()
    print('Time taken by ix (iloc, loc) functions for HA {}'.format(end-start))

Here is the output I got on my i7 processor (please note the results may vary depending on your processor speed but I assume that the results will be similar):

HA Test
Time taken by set and get value functions for HA 0.05005788803100586
Time taken by ix (iloc, loc) functions for HA 0.9360761642456055

My experience with Pandas shows that functions like ix, loc, iloc are slower in comparison to set_value and get_value functions. Moreover computing value for a column on itself using shift function gives erroneous results.