我在 Pandas 中使用 OHLC 对 1 分钟时间序列数据进行重新采样,15 分钟将完美运行,例如在以下数据帧上:
ohlc_dict = {'Open':'first', 'High':'max', 'Low':'min', 'Close': 'last'}
df.resample('15Min').apply(ohlc_dict).dropna(how='any').loc['2011-02-01']
Date Time Open High Low Close
------------------------------------------------------------------
2011-02-01 09:30:00 3081.940 3086.860 3077.832 3081.214
2011-02-01 09:45:00 3082.422 3083.730 3071.922 3073.801
2011-02-01 10:00:00 3073.303 3078.345 3069.130 3078.345
2011-02-01 10:15:00 3078.563 3078.563 3071.522 3072.279
2011-02-01 10:30:00 3071.873 3071.873 3063.497 3067.364
2011-02-01 10:45:00 3066.735 3070.523 3063.402 3069.974
2011-02-01 11:00:00 3069.561 3069.981 3066.286 3069.981
2011-02-01 11:15:00 3070.602 3074.088 3070.373 3073.919
2011-02-01 13:00:00 3074.778 3074.823 3069.925 3069.925
2011-02-01 13:15:00 3070.096 3070.903 3063.457 3063.457
2011-02-01 13:30:00 3063.929 3067.358 3063.929 3067.358
2011-02-01 13:45:00 3067.570 3072.455 3067.570 3072.247
2011-02-01 14:00:00 3072.927 3081.357 3072.767 3080.175
2011-02-01 14:15:00 3078.843 3079.435 3076.733 3076.782
2011-02-01 14:30:00 3076.721 3081.980 3076.721 3081.912
2011-02-01 14:45:00 3082.822 3083.381 3076.722 3077.283
但是,当我重新采样 1 分钟到 1 小时时,问题就出来了。我使用默认设置,并发现时间从上午 9 点开始,但标记在上午 9:30 开放。
df.resample('1H').apply(ohlc_dict).dropna(how='any').loc['2011-02-01']
1HourOHLC Wrong in Morning
然后我尝试更改
base
设置,但在下午的 session 中失败。市场应该在下午 13 点开盘并在下午 15 点结束,所以应该有 13 点、14 点、15 点,总共 3 个酒吧。df.resample('60MIN',base=30).apply(ohlc_dict).dropna(how='any').loc['2011-02-01']
1HourOHLC Wrong in afternoon
总之,问题是我希望它适合市场并且有 6 个条形
(9:30,10:30,11:30,1:00,2:00,3:00)
,但是 resample
在 pandas
只给我 5 块 (9:30,10:30,11:30,1:30,2:30)
我在网上找了很久。但是没有用。请帮助或尝试提供一些想法如何实现这一目标。
谢谢。
最佳答案
我遇到了同样的问题,无法在线找到帮助。所以我写了这个脚本来将 1 分钟的 OHLC 数据转换为 1 小时。
这假设市场时间为上午 9:15 至下午 3:30。如果市场时间不同,只需编辑 start_time 和 end_time 以满足您的需要。
如果交易在市场时间暂停,我没有进行任何额外的检查。
希望代码对某人有帮助。 :)
示例 csv 格式
Date,O,H,L,C,V
2020-03-12 09:15:00,3860,3867.8,3763.35,3830,58630
2020-03-12 09:16:00,3840.05,3859.4,3809.65,3834.6,67155
2020-03-12 09:17:00,3832.55,3855.4,3823.75,3852,51891
2020-03-12 09:18:00,3851.65,3860.95,3846.35,3859,42205
2020-03-12 09:19:00,3859.45,3860,3848.1,3851.55,33194
代码 from pandas import read_csv, to_datetime, DataFrame
from datetime import time
file_path = 'BAJFINANCE-EQ.csv'
def add(data, b):
# utility function
# appends the value in dictionary 'b'
# to corresponding key in dictionary 'data'
for (key, value) in b.items():
data[key].append(value)
df = read_csv(file_path,
parse_dates=True,
infer_datetime_format=True,
na_filter=False)
df['Date'] = to_datetime(df['Date'], format='%Y-%m-%d %H:%M:%S')
# stores hourly data to convert to dataframe
data = {
'Date': [],
'O': [],
'H': [],
'L': [],
'C': [],
'V': []
}
start_time = [time(9, 15), time(10, 15), time(11, 15), time(
12, 15), time(13, 15), time(14, 15), time(15, 15)]
end_time = [time(10, 14), time(11, 14), time(12, 14), time(
13, 14), time(14, 14), time(15, 14), time(15, 29)]
# Market timings 9:15am to 3:30pm (6 hours 15 mins)
# We create 6 hourly bars and one 15 min bar
# as usually depicted in candlestick charts
i = 0
no_bars = df.shape[0]
while i < no_bars:
if df.loc[i]['Date'].time() in end_time:
end_idx = i + 1
hour_df = df[start_idx:end_idx]
add(data, {
'Date': df.loc[start_idx]['Date'],
'O': hour_df['O'].iloc[0],
'H': hour_df['H'].max(),
'L': hour_df['L'].min(),
'C': hour_df['C'].iloc[-1],
'V': hour_df['V'].sum()
})
if df.loc[i]['Date'].time() in start_time:
start_idx = i
# optional optimisation for large datasets
# skip ahead to loop faster
i += 55
i += 1
df = DataFrame(data=data).set_index(keys=['Date'])
# df.to_csv('out.csv')
print(df)
关于Pandas 对 OHLC 数据从 1 分钟到 1 小时重新采样,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/45428326/