

In [1]:

    
import pandas as pd
from beakerx import *

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



In [2]:

    
# import prices from csv file using pandas
prices=pd.read_csv("data/price.csv", index_col=0, header=0, parse_dates=True)



In [3]:

    
# plot using beakerx
SimpleTimePlot(prices, prices.keys())

Drawdown



In [4]:

    
from pyutil.performance.drawdown import drawdown



In [5]:

    
SimpleTimePlot(prices.pct_change().apply(drawdown), prices.keys())

Month/Year Table



In [6]:

    
from pyutil.performance.month import monthlytable



In [7]:

    
TableDisplay(100*monthlytable(prices["A"].pct_change()))

Value at Risk



In [8]:

    
from pyutil.performance.var import cvar, var



In [9]:

    
prices.pct_change().apply(var, alpha=0.95)









    Out[9]:





A    0.019077
B    0.015518
C    0.023442
D    0.012359
E    0.012404
F    0.015653
G    0.010803
dtype: float64



In [10]:

    
prices.pct_change().apply(var, alpha=0.99)









    Out[10]:





A    0.029495
B    0.023364
C    0.034154
D    0.020042
E    0.019999
F    0.029403
G    0.017731
dtype: float64



In [11]:

    
prices.pct_change().apply(cvar, alpha=0.95)









    Out[11]:





A    0.027620
B    0.020367
C    0.029857
D    0.018043
E    0.016325
F    0.023326
G    0.015331
dtype: float64

Periods



In [12]:

    
from pyutil.performance.periods import period_returns



In [13]:

    
period_returns(prices["A"].pct_change(), today=prices["A"].dropna().index[-1])









    Out[13]:





Two weeks       -0.007120
Month-to-Date    0.014286
Year-to-Date     0.013276
One Month        0.015186
Three Months    -0.071541
One Year        -0.069166
Three Years     -0.282707
Five Years      -0.282707
Ten Years       -0.282707
dtype: float64



In [14]:

    
TableDisplay(prices.pct_change().apply(lambda x: period_returns(x, today=x.dropna().index[-1])))



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