Converting OHLC stock data into a different timeframe with python and pandas
Solution 1:
With a more recent version of Pandas, there is a resample method. It is very fast and is useful to accomplish the same task:
ohlc_dict = {
'Open': 'first',
'High': 'max',
'Low': 'min',
'Close': 'last',
'Volume': 'sum',
}
df.resample('5T', closed='left', label='left').apply(ohlc_dict)
Solution 2:
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.
Solution 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
Solution 4:
df = df.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()
})