Notebook

get_pricing for specific minutes example

In [13]:
# run the get_pricing method to pull in actual data
# the result is a Pandas dataframe
prices = get_pricing('MTSL', 
                     start_date="2018-07-19", 
                     end_date="2018-07-19", 
                     fields=['close_price', 'price'], 
                     frequency='minute')
In [14]:
# display the resulting dataframe in the default Pandas format
# Notice that pandas only displays the first and last rows when the series or dataframe is very large
# The entire data exists in memory but it's just not displayed
prices
Out[14]:
close_price price
2018-07-19 13:31:00+00:00 2.959 2.959
2018-07-19 13:32:00+00:00 3.125 3.125
2018-07-19 13:33:00+00:00 3.353 3.353
2018-07-19 13:34:00+00:00 3.491 3.491
2018-07-19 13:35:00+00:00 3.370 3.370
2018-07-19 13:36:00+00:00 3.310 3.310
2018-07-19 13:37:00+00:00 3.430 3.430
2018-07-19 13:38:00+00:00 3.390 3.390
2018-07-19 13:39:00+00:00 3.350 3.350
2018-07-19 13:40:00+00:00 3.580 3.580
2018-07-19 13:41:00+00:00 3.669 3.669
2018-07-19 13:42:00+00:00 3.470 3.470
2018-07-19 13:43:00+00:00 3.600 3.600
2018-07-19 13:44:00+00:00 3.520 3.520
2018-07-19 13:45:00+00:00 3.520 3.520
2018-07-19 13:46:00+00:00 3.280 3.280
2018-07-19 13:47:00+00:00 3.200 3.200
2018-07-19 13:48:00+00:00 3.249 3.249
2018-07-19 13:49:00+00:00 3.289 3.289
2018-07-19 13:50:00+00:00 3.191 3.191
2018-07-19 13:51:00+00:00 3.110 3.110
2018-07-19 13:52:00+00:00 3.130 3.130
2018-07-19 13:53:00+00:00 3.040 3.040
2018-07-19 13:54:00+00:00 3.080 3.080
2018-07-19 13:55:00+00:00 3.070 3.070
2018-07-19 13:56:00+00:00 3.170 3.170
2018-07-19 13:57:00+00:00 3.180 3.180
2018-07-19 13:58:00+00:00 3.176 3.176
2018-07-19 13:59:00+00:00 3.150 3.150
2018-07-19 14:00:00+00:00 3.225 3.225
... ... ...
2018-07-19 19:31:00+00:00 2.774 2.774
2018-07-19 19:32:00+00:00 2.835 2.835
2018-07-19 19:33:00+00:00 2.830 2.830
2018-07-19 19:34:00+00:00 2.846 2.846
2018-07-19 19:35:00+00:00 2.860 2.860
2018-07-19 19:36:00+00:00 2.854 2.854
2018-07-19 19:37:00+00:00 2.860 2.860
2018-07-19 19:38:00+00:00 2.801 2.801
2018-07-19 19:39:00+00:00 2.818 2.818
2018-07-19 19:40:00+00:00 2.810 2.810
2018-07-19 19:41:00+00:00 2.800 2.800
2018-07-19 19:42:00+00:00 2.760 2.760
2018-07-19 19:43:00+00:00 2.832 2.832
2018-07-19 19:44:00+00:00 2.821 2.821
2018-07-19 19:45:00+00:00 2.810 2.810
2018-07-19 19:46:00+00:00 2.819 2.819
2018-07-19 19:47:00+00:00 NaN 2.819
2018-07-19 19:48:00+00:00 2.813 2.813
2018-07-19 19:49:00+00:00 2.816 2.816
2018-07-19 19:50:00+00:00 2.825 2.825
2018-07-19 19:51:00+00:00 2.822 2.822
2018-07-19 19:52:00+00:00 2.840 2.840
2018-07-19 19:53:00+00:00 2.851 2.851
2018-07-19 19:54:00+00:00 2.870 2.870
2018-07-19 19:55:00+00:00 2.920 2.920
2018-07-19 19:56:00+00:00 2.950 2.950
2018-07-19 19:57:00+00:00 2.860 2.860
2018-07-19 19:58:00+00:00 2.850 2.850
2018-07-19 19:59:00+00:00 2.920 2.920
2018-07-19 20:00:00+00:00 2.950 2.950

390 rows × 2 columns

In [15]:
# A convenient way to show the entire dataframe in an interactive scrollable format
# is to use qgrid. Something like this.
import qgrid  
qgrid.show_grid(prices, show_toolbar=True)

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