问题
我有 Pandas DataFrame 对象，其中包含日期、开盘价、收盘价、每日股价的高低数据。我想计算一个房间图的组成。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（家人称之为 Tenkan-Sen 线）：
(9 周期高点 + 9 周期低点)/2
我在 R 中找到了一个解决方案，但我很难将其转换为 Python/Pandas 代码。
Ichimoku 图表包含更多组件，但是当我知道如何在 pandas 中计算 Tenkan Sen 线时，我将能够计算所有这些元素（我将分享代码）。
回答
我不是金融或绘图方面的专家，但这里有一个金融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 = pd.rolling_max(data['High'], window= 9 )
- nine_period_low = pd.rolling_min(data['Low'], window= 9 )
- 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() 将产生以下情节：
更新
在@PedroLobito 指出不完整/不正确的公式后，我接受了@chilliq 的回答，并对熊猫版本 0.16.1 及更高版本进行了修改：
- 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 = pd.rolling_max(d['High'], window= 9 )
- nine_period_low = pd.rolling_min(d['Low'], window= 9 )
- d['tenkan_sen'] = (nine_period_high + nine_period_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)
- 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 = pd.rolling_max(high_prices, window=52)
- period52_low = pd.rolling_min(low_prices, window=52)
- 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()
这是结果，由于我不是金融专家，所以不清楚：
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