Pandas OHLC aggregation on OHLC data

Question

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:

>         


        

Answer 1

Converstion from OHLC to OHLC for me worked like this:

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

Answer 2

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()

Answer 3

You need to use an OrderedDict to keep row order in the newer versions of pandas, like so:

import pandas as pd
from collections import OrderedDict

df['ctime'] = pd.to_datetime(df['ctime'], unit='s')
df = df.set_index('ctime')
df = df.resample('5Min').agg(
    OrderedDict([
        ('open', 'first'),
        ('high', 'max'),
        ('low', 'min'),
        ('close', 'last'),
        ('volume', 'sum'),
    ])
)

Answer 4

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

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

Answer 5

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)