OHLC数据上的 pandas OHLC聚合

问题描述

我了解使用一列数据对熊猫中的时间序列数据进行OHLC重采样将非常有效，例如在以下数据帧上进行操作：

I understand that OHLC re-sampling of time series data in Pandas, using one column of data, will work perfectly, for example on the following dataframe:

>>df
ctime       openbid
1443654000  1.11700
1443654060  1.11700
...

df['ctime']  = pd.to_datetime(df['ctime'], unit='s')
df           = df.set_index('ctime')
df.resample('1H',  how='ohlc', axis=0, fill_method='bfill')


>>>
                     open     high     low       close
ctime                                                   
2015-09-30 23:00:00  1.11700  1.11700  1.11687   1.11697
2015-09-30 24:00:00  1.11700  1.11712  1.11697   1.11697
...

但是如果数据已经是OHLC格式怎么办？根据我的收集，API的OHLC方法为每列计算OHLC切片，因此，如果我的数据采用以下格式：

But what do I do if the data is already in an OHLC format? From what I can gather the OHLC method of the API calculates an OHLC slice for every column, hence if my data is in the format:

             ctime  openbid  highbid   lowbid  closebid
0       1443654000  1.11700  1.11700  1.11687   1.11697
1       1443654060  1.11700  1.11712  1.11697   1.11697
2       1443654120  1.11701  1.11708  1.11699   1.11708

当我尝试重新采样时，每个列都会得到OHLC，就像这样：

When I try to re-sample I get an OHLC for each of the columns, like so:

                     openbid                             highbid           \
                        open     high      low    close     open     high   
ctime                                                                       
2015-09-30 23:00:00  1.11700  1.11700  1.11700  1.11700  1.11700  1.11712   
2015-09-30 23:01:00  1.11701  1.11701  1.11701  1.11701  1.11708  1.11708 
...
                                        lowbid                             \
                         low    close     open     high      low    close   
ctime                                                                       
2015-09-30 23:00:00  1.11700  1.11712  1.11687  1.11697  1.11687  1.11697   
2015-09-30 23:01:00  1.11708  1.11708  1.11699  1.11699  1.11699  1.11699  
...

                    closebid                             
                        open     high      low    close  
ctime                                                    
2015-09-30 23:00:00  1.11697  1.11697  1.11697  1.11697  
2015-09-30 23:01:00  1.11708  1.11708  1.11708  1.11708  

是否有一种快速的解决方法，有人愿意分享，而我不必深陷熊猫手册？

Is there a quick(ish) workaround for this that someone is willing to share please, without me having to get knee-deep in pandas manual?

谢谢。

ps，有这个答案--但那是4年前的事了，所以我希望

ps, there is this answer - Converting OHLC stock data into a different timeframe with python and pandas - but it was 4 years ago, so I am hoping there has been some progress.

推荐答案

这类似于您链接的答案，但是它更干净，更快捷，因为

This is similar to the answer you linked, but it a little cleaner, and faster, because it uses the optimized aggregations, rather than lambdas.

请注意， resample（...）。agg（...）语法要求熊猫版本 0.18.0 。

Note that the resample(...).agg(...) syntax requires pandas version 0.18.0.

In [101]: df.resample('1H').agg({'openbid': 'first', 
                                 'highbid': 'max', 
                                 'lowbid': 'min', 
                                 'closebid': 'last'})
Out[101]: 
                      lowbid  highbid  closebid  openbid
ctime                                                   
2015-09-30 23:00:00  1.11687  1.11712   1.11708    1.117

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