从最后 700 秒开始每 5 分钟从 DF 中提取数据，并对其进行详细说明以创建新的 DF

Question

我在从数据集中提取数据时遇到了麻烦，该数据集的索引是日期时间到毫秒的粒度，每 5 分钟（例如）前 400 秒的数据（例如），并详细说明这些数据以创建新的 DF 每行与上一个和下一个以及新的列/功能有 5 分钟的距离。

我拥有的数据是这样的刻度数据

Timestamp,Bid price,Ask price,Bid volume,Ask volume
2022-09-19 00:00:00:710,3876.152,3876.652,0.00044000000343658,0.000119999996968545
2022-09-19 00:00:00:761,3875.942,3876.652,0.00270000007003546,0.00270000007003546
2022-09-19 00:00:01:033,3875.949,3876.364,0.00044000000343658,0.000119999996968545
2022-09-19 00:00:01:084,3875.636,3876.367,0.00270000007003546,0.00270000007003546
2022-09-19 00:00:01:190,3875.851,3876.351,0.00270000007003546,3.99999998990097E-06
2022-09-19 00:00:01:241,3875.845,3876.501,0.00270000007003546,0.00270000007003546
2022-09-19 00:00:01:865,3875.936,3876.563,0.00270000007003546,3.99999998990097E-06
2022-09-19 00:00:01:925,3875.942,3876.661,0.00270000007003546,0.00270000007003546
2022-09-19 00:00:02:971,3876.034,3876.655,0.00044000000343658,0.000119999996968545
2022-09-19 00:00:03:022,3876.054,3876.761,0.00270000007003546,0.00270000007003546
2022-09-19 00:00:03:072,3876.142,3876.864,0.00270000007003546,0.00270000007003546
2022-09-19 00:00:06:035,3876.454,3876.848,0.00270000007003546,3.99999998990097E-06
2022-09-19 00:00:06:085,3876.451,3877.146,0.00270000007003546,0.00270000007003546
2022-09-19 00:00:07:302,3876.699,3877.103,0.00270000007003546,3.99999998990097E-06
2022-09-19 00:00:07:353,3876.651,3877.349,0.00270000007003546,0.00270000007003546
2022-09-19 00:00:08:147,3876.954,3877.303,0.00270000007003546,3.99999998990097E-06
2022-09-19 00:00:08:198,3876.942,3877.649,0.00270000007003546,0.00270000007003546
2022-09-19 00:00:09:018,3876.937,3877.558,0.00044000000343658,0.000119999996968545
2022-09-19 00:00:09:068,3876.842,3877.552,0.00270000007003546,0.00270000007003546
2022-09-19 00:00:09:602,3876.945,3877.569,0.00270000007003546,3.99999998990097E-06
2022-09-19 00:00:09:653,3876.933,3877.664,0.00270000007003546,0.00270000007003546
2022-09-19 00:00:09:763,3876.642,3877.352,0.00270000007003546,0.00270000007003546

我尝试使用 function df.resample('5min').agg(...) 调整我在其他帖子中找到的一些示例，但这似乎在同一个 df 上运行并且对于创建新的 DF 没有用，使用自定义函数获得的新列。 这是我的尝试：

import pandas as pd
import numpy as np
from scipy.signal import lombscargle
import matplotlib.pyplot as plt
df = pd.read_csv('SP500.csv')
df["Timestamp"] = pd.to_datetime(df["Timestamp"], format="%Y-%m-%d %H:%M:%S:%f")
df.set_index("Timestamp", inplace=True)
candles = df.resample('5min').agg({
                                        "MEAN":  "mean",
                                        "HIGH":  "max",
                                        "LOW":   "min",
                                    })

这只是尝试查看该方法是否有效，需要将内置函数 mean、high、low 替换为自定义函数。

预期的 DF output 将类似于以下内容

• 列时间戳：2022-09-19 00:00:00:000, 2022-09-19 00:05:00:000, 022-09-19 00:10:00:000, ....
• 列平均值：在 2022 年 9 月 19 日 00:00:00:000 的前 400 秒、2022 年 9 月 19 日的前 400 秒 00:05:00 中计算的买入（或卖出）值的平均值： 000，……
• 高列：在 2022-09-19 00:00:00:000 的前 400 秒内，在 2022-09-19 00:05:00:000 的前 400 秒内的买入（或卖出）值的最大值, 2022-09-19 00:10:00:000, ....
• 低列：在 2022-09-19 00:00:00:000 的前 400 秒内，在 2022-09-19 00:05:00:000 的前 400 秒内的最低买入（或卖出）值, 2022-09-19 00:10:00:000, ....

无法提供真实的计算，因为我还没有工具。

对相关文档或示例的任何建议或参考表示赞赏。