In [1]:

    

import pandas as pd
from beakerx import *

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)


    

In [2]:

    

prices=pd.read_csv("data/price.csv", index_col=0, header=0, parse_dates=True)


    

In [3]:

    

SimpleTimePlot(prices, prices.keys())


    

In [4]:

    

from pyutil.timeseries.merge import *


    

In [5]:

    

ts = prices["A"]


    

In [6]:

    

ts.tail(5)


    

    
Out[6]:





2015-04-16    1198.56
2015-04-17    1204.27
2015-04-20    1195.88
2015-04-21    1202.34
2015-04-22    1200.59
Name: A, dtype: float64

In [7]:

    

to_date(ts).tail(5)


    

    
Out[7]:





2015-04-16    1198.56
2015-04-17    1204.27
2015-04-20    1195.88
2015-04-21    1202.34
2015-04-22    1200.59
Name: A, dtype: float64

In [8]:

    

to_datetime(ts).tail(5)


    

    
Out[8]:





2015-04-16    1198.56
2015-04-17    1204.27
2015-04-20    1195.88
2015-04-21    1202.34
2015-04-22    1200.59
Name: A, dtype: float64

In [9]:

    

first_index(ts)


    

    
Out[9]:





Timestamp('2013-01-01 00:00:00')

In [10]:

    

first_index(to_date(ts))


    

    
Out[10]:





datetime.date(2013, 1, 1)

In [11]:

    

last_index(to_date(ts))


    

    
Out[11]:





datetime.date(2015, 4, 22)

In [12]:

    

SimpleTimePlot(np.log(prices["A"]).diff().dropna().to_frame("returns"), ["returns"])


    

In [13]:

    

from pyutil.timeseries.signal import *


    

In [14]:

    

SimpleTimePlot(prices["A"].to_frame("price"), ["price"])