Access historical data



In [1]:

    
%matplotlib inline
%pylab inline --no-import-all
pylab.rcParams['figure.figsize'] = (18, 10)









    



Populating the interactive namespace from numpy and matplotlib



In [2]:

    
from ntfdl import Dl



In [3]:

    
stl = Dl('STL', exchange='OSE', download=False)



In [4]:

    
history = stl.get_history()



In [5]:

    
history.tail()



In [6]:

    
fig, ax = plt.subplots()
ax.tick_params(labeltop=False, labelright=True)
history.close.plot()
plt.grid()

# Annotate last quote
xmin, xmax = ax.get_xlim()
plt.annotate(history.iloc[-1].close, xy=(1.005, history.iloc[-1].close), xytext=(0, 0), \
                 xycoords=('axes fraction', 'data'), textcoords='offset points', backgroundcolor='k', color='w')









    Out[6]:





<matplotlib.text.Annotation at 0x7f3871d6a278>

Including moving averages

Moving averages are calculated using pandas rolling.mean() and adds a column for each window size given in the list where each columns is prefixed 'ma' followed by window size.



In [7]:

    
history_ma = stl.get_history(mas=[10,20,50,100,200])



In [8]:

    
history_ma.tail(5)



In [9]:

    
fig, ax = plt.subplots()
ax.tick_params(labeltop=False, labelright=True)
history_ma[['close','ma10','ma20','ma50','ma100','ma200']].plot(ax=ax)
plt.grid()

Busy chart, let's instead slice the pandas with the [from:to] syntax looking at some days of horror between 2008 and 2009.



In [10]:

    
fig, ax = plt.subplots()
ax.tick_params(labeltop=False, labelright=True)
history_ma['2008-01-01':'2010-01-01'][['close','ma10','ma20','ma50','ma100','ma200']].plot(ax=ax)
plt.grid()



In [11]:

    
history.turnover.plot()









    Out[11]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f3871f07f98>

	date	open	high	low	close	volume	turnover
date
2017-11-14	2017-11-14	167.6	168.8	166.1	166.4	3080709	514900048
2017-11-15	2017-11-15	164.2	165.0	163.1	164.3	3294372	541059056
2017-11-16	2017-11-16	165.2	166.1	162.9	164.0	3265504	536706254
2017-11-17	2017-11-17	163.9	164.5	162.3	164.0	2598704	425665367
2017-11-20	2017-11-20	163.8	165.0	163.3	164.2	1968301	323219544

	date	open	high	low	close	volume	turnover	ma10	ma20	ma50	ma100	ma200
date
2017-11-14	2017-11-14	167.6	168.8	166.1	166.4	3080709	514900048	168.06	165.910	160.180	152.012	149.7775
2017-11-15	2017-11-15	164.2	165.0	163.1	164.3	3294372	541059056	167.84	166.060	160.486	152.245	149.8265
2017-11-16	2017-11-16	165.2	166.1	162.9	164.0	3265504	536706254	167.69	166.230	160.786	152.487	149.8795
2017-11-17	2017-11-17	163.9	164.5	162.3	164.0	2598704	425665367	167.55	166.275	161.116	152.743	149.9275
2017-11-20	2017-11-20	163.8	165.0	163.3	164.2	1968301	323219544	167.24	166.315	161.432	152.990	149.9835