Python/Pandas计算I嵌oku图表组件

Question

我有潘达斯DataFrame对象的日期，开放，关闭，低和高的每日库存数据。我想要计算井谷图表的组成部分。我可以使用以下代码获取数据：

high_prices = data['High']
close_prices = data['Close']
low_prices = data['Low']
dates = data['Date']  # contains datetime objects

我需要计算以下系列(I嵌oku称它为Tenkan-Sen行)：

(9周期高+9周期低)/ 2

• 9-周期最高=最后9天的最高值，
• 9周期低点=最后9天的最低低值，因此两者都应从第9天开始。

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我在R语言这里中找到了一个解决方案，但很难将其转换为Python代码。

I嵌oku图包含了更多的组件，但是当我知道如何计算Pandas中的Tenkan-Sen行时，我将能够计算所有这些组件(我将共享代码)。

Answer 1

我不是财务专家或绘图专家，但以下展示了财务数据示例以及如何使用rolling_max和rolling_min

In [60]:

import pandas.io.data as web
import datetime
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2013, 1, 27)
data=web.DataReader("F", 'yahoo', start, end)
high_prices = data['High']
close_prices = data['Close']
low_prices = data['Low']
dates = data.index
nine_period_high = df['High'].rolling(window=9).max()
nine_period_low = df['Low'].rolling(window=9).min()
ichimoku = (nine_period_high + nine_period_low) /2
ichimoku
Out[60]:
Date
2010-01-04       NaN
2010-01-05       NaN
2010-01-06       NaN
2010-01-07       NaN
2010-01-08       NaN
2010-01-11       NaN
2010-01-12       NaN
2010-01-13       NaN
2010-01-14    11.095
2010-01-15    11.270
2010-01-19    11.635
2010-01-20    11.730
2010-01-21    11.575
2010-01-22    11.275
2010-01-25    11.220
...
2013-01-04    12.585
2013-01-07    12.685
2013-01-08    13.005
2013-01-09    13.030
2013-01-10    13.230
2013-01-11    13.415
2013-01-14    13.540
2013-01-15    13.675
2013-01-16    13.750
2013-01-17    13.750
2013-01-18    13.750
2013-01-22    13.845
2013-01-23    13.990
2013-01-24    14.045
2013-01-25    13.970
Length: 771

调用data[['High', 'Low', 'Close', 'ichimoku']].plot()的结果如下所示：

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更新

在@PedroLobito指出了不完整/不正确的公式之后，我接受了@chilliq的回答，并将其修改为0.16.1及以上版本：

import pandas as pd
from pandas_datareader import data, wb
import datetime
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2013, 1, 27)
d=data.DataReader("F", 'yahoo', start, end)
high_prices = d['High']
close_prices = d['Close']
low_prices = d['Low']
dates = d.index
nine_period_high =  df['High'].rolling(window=9).max()
nine_period_low = df['Low'].rolling(window=9).min()
d['tenkan_sen'] = (nine_period_high + nine_period_low) /2

# Kijun-sen (Base Line): (26-period high + 26-period low)/2))
period26_high = high_prices.rolling(window=26).max()
period26_low = low_prices.rolling(window=26).min()
d['kijun_sen'] = (period26_high + period26_low) / 2

# Senkou Span A (Leading Span A): (Conversion Line + Base Line)/2))
d['senkou_span_a'] = ((d['tenkan_sen'] + d['kijun_sen']) / 2).shift(26)

# Senkou Span B (Leading Span B): (52-period high + 52-period low)/2))
period52_high = high_prices.rolling(window=52).max()
period52_low = low_prices.rolling(window=52).min()
d['senkou_span_b'] = ((period52_high + period52_low) / 2).shift(26)

# The most current closing price plotted 22 time periods behind (optional)
d['chikou_span'] = close_prices.shift(-22) # 22 according to investopedia
d.plot()

结果在下面的情节，不清楚，因为已经说过，我不是一个金融专家：

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Answer 2

感谢前面的回答，下面是代码：

# Tenkan-sen (Conversion Line): (9-period high + 9-period low)/2))
period9_high = pd.rolling_max(high_prices, window=9)
period9_low = pd.rolling_min(low_prices, window=9)
tenkan_sen = (period9_high + period9_low) / 2

# Kijun-sen (Base Line): (26-period high + 26-period low)/2))
period26_high = pd.rolling_max(high_prices, window=26)
period26_low = pd.rolling_min(low_prices, window=26)
kijun_sen = (period26_high + period26_low) / 2

# Senkou Span A (Leading Span A): (Conversion Line + Base Line)/2))
senkou_span_a = ((tenkan_sen + kijun_sen) / 2).shift(26)

# Senkou Span B (Leading Span B): (52-period high + 52-period low)/2))
period52_high = pd.rolling_max(high_prices, window=52)
period52_low = pd.rolling_min(low_prices, window=52)
senkou_span_b = ((period52_high + period52_low) / 2).shift(26)

# The most current closing price plotted 22 time periods behind (optional)
chikou_span = close_prices.shift(-22) # 22 according to investopedia

Answer 3

EdChum的回答在计算I嵌oku云的分量方面非常接近。

这些方法是正确的，但没有考虑到两个leading_spans的未来日期。当我们把领先的时间移到26个时，熊猫只会移动到最后一个日期或最后一个指数，而额外的(或未来的)26个值就被忽略了。

下面是一个适合未来日期或未来云形成的实现

from datetime import timedelta

high_9 = df['High'].rolling(window= 9).max()
low_9 = df['Low'].rolling(window= 9).min()
df['tenkan_sen'] = (high_9 + low_9) /2

high_26 = df['High'].rolling(window= 26).max()
low_26 = df['Low'].rolling(window= 26).min()
df['kijun_sen'] = (high_26 + low_26) /2

# this is to extend the 'df' in future for 26 days
# the 'df' here is numerical indexed df
last_index = df.iloc[-1:].index[0]
last_date = df['Date'].iloc[-1].date()
for i in range(26):
    df.loc[last_index+1 +i, 'Date'] = last_date + timedelta(days=i)

df['senkou_span_a'] = ((df['tenkan_sen'] + df['kijun_sen']) / 2).shift(26)

high_52 = df['High'].rolling(window= 52).max()
low_52 = df['Low'].rolling(window= 52).min()
df['senkou_span_b'] = ((high_52 + low_52) /2).shift(26)

# most charting softwares dont plot this line
df['chikou_span'] = df['Close'].shift(-22) #sometimes -26 

tmp = df[['Close','senkou_span_a','senkou_span_b','kijun_sen','tenkan_sen']].tail(300)
a1 = tmp.plot(figsize=(15,10))
a1.fill_between(tmp.index, tmp.senkou_span_a, tmp.senkou_span_b)

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