Converting OHLC stock data into a different timeframe with python and pandas

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Converting OHLC stock data into a different timeframe with python and pandas

Could someone please point me in the right direction with respect to OHLC data timeframe conversion with Pandas? What I’m trying to do is build a Dataframe with data for higher timeframes, given data with lower timeframe.

For example, given I have the following one-minute (M1) data:

                       Open    High     Low   Close  Volume
Date                                                       
1999-01-04 10:22:00  1.1801  1.1819  1.1801  1.1817       4
1999-01-04 10:23:00  1.1817  1.1818  1.1804  1.1814      18
1999-01-04 10:24:00  1.1817  1.1817  1.1802  1.1806      12
1999-01-04 10:25:00  1.1807  1.1815  1.1795  1.1808      26
1999-01-04 10:26:00  1.1803  1.1806  1.1790  1.1806       4
1999-01-04 10:27:00  1.1801  1.1801  1.1779  1.1786      23
1999-01-04 10:28:00  1.1795  1.1801  1.1776  1.1788      28
1999-01-04 10:29:00  1.1793  1.1795  1.1782  1.1789      10
1999-01-04 10:31:00  1.1780  1.1792  1.1776  1.1792      12
1999-01-04 10:32:00  1.1788  1.1792  1.1788  1.1791       4

which has Open, High, Low, Close (OHLC) and volume values for every minute I would like to build a set of 5-minute readings (M5) which would look like so:

                       Open    High     Low   Close  Volume
Date                                                       
1999-01-04 10:25:00  1.1807  1.1815  1.1776  1.1789      91
1999-01-04 10:30:00  1.1780  1.1792  1.1776  1.1791      16

So the workflow is that:

  • Open is the Open of the first row in the timewindow
  • High is the highest High in the timewindow
  • Low is the lowest Low
  • Close is the last Close
  • Volume is simply a sum of Volumes

There are few issues though:

  • the data has gaps ( note there is no 10:30:00 row)
  • the 5-minute intervals have to start at round time, e.g. M5 starts at 10:25:00 not 10:22:00
  • first, incomplete set can be omitted like in this example, or included (so we could have 10:20:00 5-minute entry)

The Pandas documentation on up-down sampling gives an example, but they use mean value as the value of up-sampled row, which won’t work here. I have tried using groupby and agg but to no avail. For one getting highest High and lowest Low might be not so hard, but I have no idea how to get first Open and last Close.

What I tried is something along the lines of:

grouped = slice.groupby( dr5minute.asof ).agg( 
    { 'Low': lambda x : x.min()[ 'Low' ], 'High': lambda x : x.max()[ 'High' ] } 
)

but it results in following error, which I don’t understand:

In [27]: grouped = slice.groupby( dr5minute.asof ).agg( { 'Low' : lambda x : x.min()[ 'Low' ], 'High' : lambda x : x.max()[ 'High' ] } )
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
/work/python/fxcruncher/<ipython-input-27-df50f9522a2f> in <module>()
----> 1 grouped = slice.groupby( dr5minute.asof ).agg( { 'Low' : lambda x : x.min()[ 'Low' ], 'High' : lambda x : x.max()[ 'High' ] } )

/usr/lib/python2.7/site-packages/pandas/core/groupby.pyc in agg(self, func, *args, **kwargs)
    242         See docstring for aggregate
    243         """
--> 244         return self.aggregate(func, *args, **kwargs)
    245 
    246     def _iterate_slices(self):

/usr/lib/python2.7/site-packages/pandas/core/groupby.pyc in aggregate(self, arg, *args, **kwargs)
   1153                     colg = SeriesGroupBy(obj[col], column=col,
   1154                                          grouper=self.grouper)
-> 1155                     result[col] = colg.aggregate(func)
   1156 
   1157             result = DataFrame(result)

/usr/lib/python2.7/site-packages/pandas/core/groupby.pyc in aggregate(self, func_or_funcs, *args, **kwargs)
    906                 return self._python_agg_general(func_or_funcs, *args, **kwargs)
    907             except Exception:
--> 908                 result = self._aggregate_named(func_or_funcs, *args, **kwargs)
    909 
    910             index = Index(sorted(result), name=self.grouper.names[0])

/usr/lib/python2.7/site-packages/pandas/core/groupby.pyc in _aggregate_named(self, func, *args, **kwargs)
    976             grp = self.get_group(name)
    977             grp.name = name
--> 978             output = func(grp, *args, **kwargs)
    979             if isinstance(output, np.ndarray):
    980                 raise Exception('Must produce aggregated value')

/work/python/fxcruncher/<ipython-input-27-df50f9522a2f> in <lambda>(x)
----> 1 grouped = slice.groupby( dr5minute.asof ).agg( { 'Low' : lambda x : x.min()[ 'Low' ], 'High' : lambda x : x.max()[ 'High' ] } )

IndexError: invalid index to scalar variable.

So any help on doing that would be greatly appreciated. If the path I chose is not going to work, please suggest other relatively efficient approach (I have millions of rows). Some resources on using Pandas for financial processing would also be nice.

Asked By: kgr

||

Answer #1:

Your approach is sound, but fails because each function in the dict-of-functions applied to agg()
receives a Series object reflecting the column matched by the key value. Therefore, it’s not necessary to
filter on column label again. With this, and assuming groupby preserves order,
you can slice the Series to extract the first/last element of the Open/Close
columns (note: groupby documentation does not claim to preserve order of original data
series, but seems to in practice.)

In [50]: df.groupby(dr5minute.asof).agg({'Low': lambda s: s.min(), 
                                         'High': lambda s: s.max(),
                                         'Open': lambda s: s[0],
                                         'Close': lambda s: s[-1],
                                         'Volume': lambda s: s.sum()})
Out[50]: 
                      Close    High     Low    Open  Volume
key_0                                                      
1999-01-04 10:20:00  1.1806  1.1819  1.1801  1.1801      34
1999-01-04 10:25:00  1.1789  1.1815  1.1776  1.1807      91
1999-01-04 10:30:00  1.1791  1.1792  1.1776  1.1780      16

For reference, here is a table to summarize the expected
input and output types of an aggregation function based on the groupby object type and how the aggregation function(s) is/are passed to agg().

                  agg() method     agg func    agg func          agg()
                  input type       accepts     returns           result
GroupBy Object
SeriesGroupBy     function         Series      value             Series
                  dict-of-funcs    Series      value             DataFrame, columns match dict keys
                  list-of-funcs    Series      value             DataFrame, columns match func names
DataFrameGroupBy  function         DataFrame   Series/dict/ary   DataFrame, columns match original DataFrame
                  dict-of-funcs    Series      value             DataFrame, columns match dict keys, where dict keys must be columns in original DataFrame
                  list-of-funcs    Series      value             DataFrame, MultiIndex columns (original cols x func names)

From the above table, if aggregation requires access to more than one
column, the only option is to pass a single function to a
DataFrameGroupBy object. Therefore, an alternate way to accomplish the original task is to define
a function like the following:

def ohlcsum(df):
    df = df.sort()
    return {
       'Open': df['Open'][0],
       'High': df['High'].max(),
       'Low': df['Low'].min(),
       'Close': df['Close'][-1],
       'Volume': df['Volume'].sum()
      }

and apply agg() with it:

In [30]: df.groupby(dr5minute.asof).agg(ohlcsum)
Out[30]: 
                       Open    High     Low   Close  Volume
key_0                                                      
1999-01-04 10:20:00  1.1801  1.1819  1.1801  1.1806      34
1999-01-04 10:25:00  1.1807  1.1815  1.1776  1.1789      91
1999-01-04 10:30:00  1.1780  1.1792  1.1776  1.1791      16

Though pandas may offer some cleaner built-in magic in the future, hopefully this explains how to work with today’s agg() capabilities.

Answered By: Garrett

Answer #2:

With a more recent version of Pandas, there is a resample method very fast and useful to accomplish the same task:

ohlc_dict = {                                                                                                             
'Open':'first',                                                                                                    
'High':'max',                                                                                                       
'Low':'min',                                                                                                        
'Close': 'last',                                                                                                    
'Volume': 'sum'
}

df.resample('5T', how=ohlc_dict, closed='left', label='left')
Answered By: Andrea

Answer #3:

Within my main() function I’m receiving streaming bid/ask data. I then do the following:

df = pd.DataFrame([])

for msg_type, msg in response.parts():
    if msg_type == "pricing.Price":
        sd = StreamingData(datetime.now(),instrument_string(msg),
                           mid_string(msg),account_api,account_id,
                           's','5min',balance)
        df = df.append(sd.df())
        sd.resample(df)

I created a class StreamingData() which takes the provided input (also created some functions to break up the bid/ask data into individual components (bid, ask, mid, instrument, etc.).

The beauty of this is all you have to do is change the ‘s’ and ‘5min’ to whatever timeframes you want. Set it to ‘m’ and ‘D’ to get daily prices by the minute.

This is what my StreamingData() looks like:

class StreamingData(object):
def __init__(self, time, instrument, mid, api, _id, xsec, xmin, balance):
    self.time = time
    self.instrument = instrument
    self.mid = mid
    self.api = api
    self._id = _id
    self.xsec = xsec
    self.xmin = xmin
    self.balance = balance
    self.data = self.resample(self.df())

def df(self):
    df1 = pd.DataFrame({'Time':[self.time]})
    df2 = pd.DataFrame({'Mid':[float(self.mid)]})
    df3 = pd.concat([df1,df2],axis=1,join='inner')
    df = df3.set_index(['Time'])
    df.index = pd.to_datetime(df.index,unit='s')
    return df

def resample(self, df):
    xx = df.to_period(freq=self.xsec)
    openCol = xx.resample(self.xmin).first()
    highCol = xx.resample(self.xmin).max()
    lowCol = xx.resample(self.xmin).min()
    closeCol = xx.resample(self.xmin).last()
    self.data = pd.concat([openCol,highCol,lowCol,closeCol],
                           axis=1,join='inner')
    self.data['Open'] = openCol.round(5)
    self.data['High'] = highCol.round(5)
    self.data['Low'] = lowCol.round(5)
    self.data['Close'] = closeCol.round(5)
    return self.data

So it takes in the data from StreamingData(), creates a time indexed dataframe within df(), appends it, then sends through to resample(). The prices I calculate are based off of: mid = (bid+ask)/2

Answered By: Poopy McFartnoise

Answer #4:

bitfinex_klines_pd = bitfinex_klines_pd.resample('4h').agg({
    'open': lambda s: s[0],
    'high': lambda df: df.max(),
    'low': lambda df: df.min(),
    'close': lambda df: df[-1],
    'volume': lambda df: df.sum()
})
Answered By: Yundong Cai

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