如何計算每日柱線的會話變化

Question

我有一個看起來像這樣的 DF：

日期	體積	打開	關	高的	低的	上一個收盤價
2022-05-02	1756159.0	118.38	119.57	120.34	116.49
2022-05-03	3217838.0	119.72	122.4	123.98	119.09	119.57
2022-05-04	2460350.0	121.69	126.3	126.69	121.44	122.4
2022-05-05	2123645.0	124.62	122.15	125.21	120.8	126.3
2022-05-06	1629034.0	120.88	121.08	121.88	118.0	122.15
2022-05-09	1861704.0	119.13	113.11	119.13	112.64	121.08
2022-05-10	2141753.0	115.44	116.64	117.94	113.14	113.11
2022-05-11	1607013.0	115.7	113.99	118.0	113.84	116.64
2022-05-12	1338023.0	113.61	116.13	116.25	112.78	113.99
2022-05-13	1328411.0	117.38	119.38	120.715	117.27	116.13

我正在嘗試創建一個列，以找出前一個收盤價和開盤價之間的區別：

def get_change(current, previous):
    print(current, 'current')
    print(previous, 'previous')
    if current == previous:
        return 0
    try:
        return (abs(current - previous) / previous) * 100.0
    except ZeroDivisionError:
        return float('inf')

final_df['change'] = get_change(df['o'], final_df['previous close'])

像這樣（其中 2 是先前關閉和打開之間的差異）：

日期	體積	打開	關	高的	低的	上一個收盤價	改變
2022-05-02	1756159.0	118.38	119.57	120.34	116.49		2
2022-05-03	3217838.0	119.72	122.4	123.98	119.09	119.57	2
2022-05-04	2460350.0	121.69	126.3	126.69	121.44	122.4	2
2022-05-05	2123645.0	124.62	122.15	125.21	120.8	126.3	2
2022-05-06	1629034.0	120.88	121.08	121.88	118.0	122.15	2
2022-05-09	1861704.0	119.13	113.11	119.13	112.64	121.08	2
2022-05-10	2141753.0	115.44	116.64	117.94	113.14	113.11	2
2022-05-11	1607013.0	115.7	113.99	118.0	113.84	116.64	2
2022-05-12	1338023.0	113.61	116.13	116.25	112.78	113.99	2
2022-05-13	1328411.0	117.38	119.38	120.715	117.27	116.13	2

如何獲得零錢？

Answer 1

IUUC，你可以

df['change'] = df['open'].sub(df['previous close']).abs().div(df['previous close']).mul(100.0)

print(df)

         date     volume    open   close     high     low  previous close    change
0  2022-05-02  1756159.0  118.38  119.57  120.340  116.49             NaN       NaN
1  2022-05-03  3217838.0  119.72  122.40  123.980  119.09          119.57  0.125450
2  2022-05-04  2460350.0  121.69  126.30  126.690  121.44          122.40  0.580065
3  2022-05-05  2123645.0  124.62  122.15  125.210  120.80          126.30  1.330166
4  2022-05-06  1629034.0  120.88  121.08  121.880  118.00          122.15  1.039705
5  2022-05-09  1861704.0  119.13  113.11  119.130  112.64          121.08  1.610505
6  2022-05-10  2141753.0  115.44  116.64  117.940  113.14          113.11  2.059942
7  2022-05-11  1607013.0  115.70  113.99  118.000  113.84          116.64  0.805898
8  2022-05-12  1338023.0  113.61  116.13  116.250  112.78          113.99  0.333363
9  2022-05-13  1328411.0  117.38  119.38  120.715  117.27          116.13  1.076380