使用python的Heiken Ashi

我定义了一个函数Heiken Ashi，它是技术分析中流行的图表类型之一。 我用Pandas在上面写了一个函数，但没有发现什么困难。 这就是阿希平肯的样子-

                 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

在各种各样的网站上都可以使用for-loop和纯python，但我认为Pandas也可以做得很好。 这是我的进步-

   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

有人能帮我吗` 它不起作用。。。。 我试过这个-

  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

---

但是HA_Open的结果仍然不令人满意

我对Python或Pandas的知识不太了解，但是经过一些研究，我认为这是一个很好的解决方案

请随时添加任何评论。我非常感激

我使用了namedtuples和（如果在数据帧中循环的话，似乎是最快的）

我希望有帮助

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

    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'] = ha_open
        previous_row = nt(ha_open, row.Close)
        i += 1

    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

---

根据我的测试，以下是最快、准确和高效的实施：

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测试速度结果）：

以下是我在i7处理器上获得的输出（请注意，结果可能会因处理器速度而异，但我假设结果会类似）：

---

我使用Pandas的经验表明，像

ix

、

loc

、

iloc

这样的函数与

set\u value

和

get\u value

函数相比速度较慢。此外，使用

shift

函数计算列本身的值会产生错误的结果。

使用numpy会更快

def heikenashi(df):
    df['HA_Close'] = (df['Open'] + df['High'] + df['Low'] + df['Close']) / 4
    df['HA_Open'] = (df['Open'].shift(1) + df['Open'].shift(1)) / 2
    df.iloc[0, df.columns.get_loc("HA_Open")] = (df.iloc[0]['Open'] + df.iloc[0]['Close'])/2
    df['HA_High'] = df[['High', 'Low', 'HA_Open', 'HA_Close']].max(axis=1)
    df['HA_Low'] = df[['High', 'Low', 'HA_Open', 'HA_Close']].min(axis=1)
    df = df.drop(['Open', 'High', 'Low', 'Close'], axis=1)  # remove old columns
    df = df.rename(columns={"HA_Open": "Open", "HA_High": "High", "HA_Low": "Low", "HA_Close": "Close", "Volume": "Volume"})
    df = df[['Open', 'High', 'Low', 'Close', 'Volume']]  # reorder columns
    return df

 def HEIKIN(O, H, L, C, oldO, oldC):
     HA_Close = (O + H + L + C)/4
     HA_Open = (oldO + oldC)/2
     elements = numpy.array([H, L, HA_Open, HA_Close])
     HA_High = elements.max(0)
     HA_Low = elements.min(0)
     out = numpy.array([HA_Close, HA_Open, HA_High, HA_Low])  
     return out

不幸的是，set_value（）和get_value（）已被弃用。在arkochhar's附近建造 答案是，我能够通过使用下面的列表理解方法和我自己的OHLC数据（7000行数据）获得75%的速度提升。它也比at和iat快

def HA( dataframe ):

    df = dataframe.copy()

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

    df.reset_index(inplace=True)

    ha_open = [ (df.Open[0] + df.Close[0]) / 2 ]
    [ ha_open.append((ha_open[i] + df.HA_Close.values[i]) / 2) \
    for i in range(0, len(df)-1) ]
    df['HA_Open'] = ha_open

    df.set_index('index', 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

---

我对代码进行了调整，使其能够与Python 3.7一起工作

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

    #idx = df_HA.index.name
    #df_HA.reset_index(inplace=True)

    for i in range(0, len(df)):
        if i == 0:
            df_HA['Open'][i]= ( (df['Open'][i] + df['Close'][i] )/ 2)
        else:
            df_HA['Open'][i] = ( (df['Open'][i-1] + df['Close'][i-1] )/ 2)


    #if idx:
        #df_HA.set_index(idx, inplace=True)

    df_HA['High']=df[['Open','Close','High']].max(axis=1)
    df_HA['Low']=df[['Open','Close','Low']].min(axis=1)
    return df_HA

---

完美工作的HekinAshi功能。 我不是这段代码的原始作者。我在Github（）上找到了这个

---

它有用吗？如果没有，问题是什么？请同时提供一个示例数据框。它不起作用。。。。我试过这个-导入pandas\u datareader.data作为web导入HA导入pandas作为pd start='2016-1-1'end='2016-10-30'DAX=web.datareader（'^GDAXI'，'yahoo'，start，end）为您尝试这个行，它会给您一个错误：

df['HA_Open']=np.where（df['HA_Open']=np.nan，HA_/2，HA_/2）

，但我认为您也没有定义

df['HA_Open']

？如果您还没有开始操作，也可以将numpy导入为np。运气不好。我初始化了df['HA_Open']=0.0，就在您建议的行之前，但仍然得到error-KeyError:“HA_Open”我已经在帖子中添加了解释。要了解更多信息，您可以参考我的技术指标项目，该代码并不完全正确，因为HA_Open应该是（HA_Open（-1）+HA_Close（-1））/2，这需要按照arkochhar的回答迭代数据帧。

def HA( dataframe ):

    df = dataframe.copy()

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

    df.reset_index(inplace=True)

    ha_open = [ (df.Open[0] + df.Close[0]) / 2 ]
    [ ha_open.append((ha_open[i] + df.HA_Close.values[i]) / 2) \
    for i in range(0, len(df)-1) ]
    df['HA_Open'] = ha_open

    df.set_index('index', 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

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

    #idx = df_HA.index.name
    #df_HA.reset_index(inplace=True)

    for i in range(0, len(df)):
        if i == 0:
            df_HA['Open'][i]= ( (df['Open'][i] + df['Close'][i] )/ 2)
        else:
            df_HA['Open'][i] = ( (df['Open'][i-1] + df['Close'][i-1] )/ 2)


    #if idx:
        #df_HA.set_index(idx, inplace=True)

    df_HA['High']=df[['Open','Close','High']].max(axis=1)
    df_HA['Low']=df[['Open','Close','Low']].min(axis=1)
    return df_HA

def heikin_ashi(df):
        heikin_ashi_df = pd.DataFrame(index=df.index.values, columns=['open', 'high', 'low', 'close'])
    
    heikin_ashi_df['close'] = (df['open'] + df['high'] + df['low'] + df['close']) / 4
    
    for i in range(len(df)):
        if i == 0:
            heikin_ashi_df.iat[0, 0] = df['open'].iloc[0]
        else:
            heikin_ashi_df.iat[i, 0] = (heikin_ashi_df.iat[i-1, 0] + heikin_ashi_df.iat[i-1, 3]) / 2
        
    heikin_ashi_df['high'] = heikin_ashi_df.loc[:, ['open', 'close']].join(df['high']).max(axis=1)
    
    heikin_ashi_df['low'] = heikin_ashi_df.loc[:, ['open', 'close']].join(df['low']).min(axis=1)
    
    return heikin_ashi_df