Heiken Ashi使用pandas python
Heiken Ashi使用pandas python
提问于 2016-11-15 23:19:24
我正在定义一个函数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，但没发现有什么困难。这是Heiken Ashi HA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在不同的网站上有很多使用for loop和纯python的东西，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有谁能帮我弄一下这个吗？`它不能用了.我试过了-
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。
回答 8
Stack Overflow用户
发布于 2017-10-02 16:35:19
根据我的测试，下面是最快、最准确、最高效的实现：
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测试速度结果)：
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))这是我在我的i7处理器上得到的输出(请注意，结果可能会因处理器速度的不同而不同，但我假设结果会是相似的)：
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我使用Pandas的经验表明，与set_value和get_value函数相比，像ix、loc、iloc这样的函数要慢一些。此外，使用shift函数计算列本身的值会得到错误的结果。
Stack Overflow用户
发布于 2017-02-09 21:03:35
我对Python或Pandas不是很了解，但经过一些研究，我认为这是一个很好的解决方案。
请随时添加任何评论。我非常感谢。
我使用了命名元组和itertuples (如果循环通过DataFrame，似乎是最快的)。
我希望它能有所帮助！
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 dfStack Overflow用户
发布于 2018-02-03 12:48:09
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页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/40613480
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