Pandas OHLC aggregation on OHLC data

user_3439187 0
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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
...

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

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

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?

Thanks.

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.

pandas python-2.7 resampling dataframe python
5 Answers

Given a dataframe with price and amount columns

def agg_ohlcv(x):
    arr = x['price'].values
    names = {
        'low': min(arr) if len(arr) > 0 else np.nan,
        'high': max(arr) if len(arr) > 0 else np.nan,
        'open': arr[0] if len(arr) > 0 else np.nan,
        'close': arr[-1] if len(arr) > 0 else np.nan,
        'volume': sum(x['amount'].values) if len(x['amount'].values) > 0 else 0,
    }
    return pd.Series(names)

df = df.resample('1min').apply(agg_ohlcv)
df = df.ffill()
user_4184000

Converstion from OHLC to OHLC for me worked like this:

df.resample('1H').agg({
    'openbid':'first',
    'highbid':'max',
    'lowbid':'min',
    'closebid':'last'
})
user_49993620

This one seems to work,

def ohlcVolume(x):
    if len(x):
        ohlc={ "open":x["open"][0],"high":max(x["high"]),"low":min(x["low"]),"close":x["close"][-1],"volume":sum(x["volume"])}
        return pd.Series(ohlc)

daily=df.resample('1D').apply(ohlcVolume)
user_75886680
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