如何从OHLC数据计算枢轴值 [英] How to calculate pivot value from OHLC data

问题描述

我有一个带有open，high，low，close和key列的熊猫数据集.现在，我想按键对数据集进行分组，并使用公式-(高+低+闭合)/3计算枢轴.到目前为止，我已经能够做到.但是要求是将计算出的数据移到我无法编码的下一组.

I've a pandas dataset with open, high, low, close and key column. Now I want to group the dataset by key and calculate pivot with the formula - (high + low + close) / 3. Upto this I'm able to do. But the requirement is to shift the calculated data to next group which I'm unable to code.

我能够按关键列对数据集进行分组，并能够计算数据透视表数据.

I'm able to group the dataset by key column and able to calculate pivot data.

import pandas as pd
data = pd.DataFrame([[110, 115, 105, 111, 1],[11, 16, 6, 12, 1],[12, 17, 7, 13, 1],[12, 16, 6, 11, 2],[9, 13, 4, 13, 2],[13, 18, 9, 12, 3],[14, 16, 10, 13, 3]], columns=["open","high","low","close","key"])
data['p'] = (data.high.groupby(data.key).transform('max') + data.low.groupby(data.key).transform('min') + data.close.groupby(data.key).transform('last')) / 3
print(data)

当前我的输出低于输出.

Currently I'm getting below output.

   open  high  low  close  key      p
0   110   115  105    111    1  44.666667
1    11    16    6     12    1  44.666667
2    12    17    7     13    1  44.666667
3    12    16    6     11    2  11.000000
4     9    13    4     13    2  11.000000
5    13    18    9     12    3  13.333333
6    14    16   10     13    3  13.333333

但是将值转移到下一组后，预期输出应如下所述.

But after shifting value to next group the expected output should be as mentioned below.

   open  high  low  close  key      p
0   110   115  105    111    1     NaN
1    11    16    6     12    1     NaN
2    12    17    7     13    1     NaN
3    12    16    6     11    2  44.666667
4     9    13    4     13    2  44.666667
5    13    18    9     12    3  11.000000
6    14    16   10     13    3  11.000000

推荐答案

改为使用

Instead 3 dimes groupby use GroupBy.agg with dictionary, then sum values per rows and divide 3. Last use Series.map with Series.shifted values for new column:

s = data.groupby('key').agg({'low':'min','high':'max','close':'last'}).sum(axis=1) / 3

data['s'] = data['key'].map(s.shift())
print(data)
   open  high  low  close  key          s
0   110   115  105    111    1        NaN
1    11    16    6     12    1        NaN
2    12    17    7     13    1        NaN
3    12    16    6     11    2  44.666667
4     9    13    4     13    2  44.666667
5    13    18    9     12    3  11.000000
6    14    16   10     13    3  11.000000

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