

In [1]:

    
import subprocess
import os
import warnings
warnings.filterwarnings('ignore')
os.chdir(subprocess.getoutput("git rev-parse --show-toplevel"))
%matplotlib inline



In [2]:

    
from src import get_data, preprocess, utility, linear_model
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

S&P500 daily stock value get and watch

what's S&P500?: http://www.investopedia.com/terms/s/sp500.asp



In [4]:

    
panel = get_data.symbols2daily_values(kinds='sandp500')
panel









    Out[4]:





<class 'pandas.core.panel.Panel'>
Dimensions: 6 (items) x 1780 (major_axis) x 504 (minor_axis)
Items axis: Open to Adj Close
Major_axis axis: 2010-01-04 00:00:00 to 2017-01-27 00:00:00
Minor_axis axis: A to ZTS

dim-1: key(item axis)



In [6]:

    
panel.items









    Out[6]:





Index(['Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close'], dtype='object')

dim-2: date index(monday-friday during 2010~2016)



In [7]:

    
panel.major_axis









    Out[7]:





DatetimeIndex(['2010-01-04', '2010-01-05', '2010-01-06', '2010-01-07',
               '2010-01-08', '2010-01-11', '2010-01-12', '2010-01-13',
               '2010-01-14', '2010-01-15',
               ...
               '2017-01-13', '2017-01-17', '2017-01-18', '2017-01-19',
               '2017-01-20', '2017-01-23', '2017-01-24', '2017-01-25',
               '2017-01-26', '2017-01-27'],
              dtype='datetime64[ns]', name='Date', length=1780, freq=None)

dim-3: enterprise lists (S&P500 symbols)



In [8]:

    
panel.minor_axis









    Out[8]:





Index(['A', 'AA', 'AAL', 'AAP', 'AAPL', 'ABBV', 'ABC', 'ABT', 'ACN', 'ADBE',
       ...
       'XLNX', 'XOM', 'XRAY', 'XRX', 'XYL', 'YHOO', 'YUM', 'ZBH', 'ZION',
       'ZTS'],
      dtype='object', length=504)

preprocessing

notation
- $t$: time index
- $i$: enterprise symbols index
- $x_{t,i}$: $i$-th enterprise adj close (conitinuation) value at time $t$
to note change rate,
- drop enterprise includes missing value
- change rate: $c_{t,i} \leftarrow \log x_{t,i} - \log x_{t-1,i}$

all $c_{t,i}$ stats



In [6]:

    
adj_value = preprocess.filter_key_nan(panel, key='Adj Close')
pd.DataFrame(utility.np1(adj_value), columns=['all change rate stats']).describe()









    Out[6]:






  
    
      
      all change rate stats
    
  
  
    
      count
      832572.000000
    
    
      mean
      0.000514
    
    
      std
      0.017712
    
    
      min
      -0.497378
    
    
      25%
      -0.007713
    
    
      50%
      0.000639
    
    
      75%
      0.009030
    
    
      max
      0.481849

mean, variance distribution

	all change rate stats
count	832572.000000
mean	0.000514
std	0.017712
min	-0.497378
25%	-0.007713
50%	0.000639
75%	0.009030
max	0.481849