Heiken Ashi 使用 pandas python

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

各种网站上有很多使用 for 循环和纯 python 的东西，但我认为 Pandas 也可以做得很好。 这就是我的进步——

   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

有人可以帮我吗？ 这不起作用...... 我尝试过这个-

  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)

这是我写的新代码

    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

但HA_Open结果仍然不理想

根据我的测试，这是最快、准确和高效的实现：

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

这是我的测试算法（基本上我使用了本文中提供的算法来对速度结果进行基准测试）：

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))

这是我在 i7 处理器上得到的输出（请注意，结果可能会根据您的处理器速度而有所不同，但我假设结果会类似）：

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

我对 Pandas 的经验表明，其功能类似于ix, loc, iloc相比之下较慢set_value and get_value功能。此外，使用以下方法计算列本身的值shift函数给出错误的结果。