Non-relational databases are very widespread. Because the nature of the data is increasingly based on time series, this type of database has developed rapidly during the last decade. The best non-relational database for time series is called KDB. This database is designed to achieve performance with time series. There are many other competitors, including InfluxDB, MongoDB, Cassandra, TimescaleDB, OpenTSDB, and Graphite.

All of these databases have their pros and cons:

As shown in the table, it is difficult to choose an alternative to KDB. We will code an example of Python code using the KDB library, pyq. We will create an example similar to the one we created for PostGresSQL:

from pyq import q
 from datetime import date

# This is the part to be run on kdb
 #googdata:([]dt:();high:();low:();open:();close:();volume:(),adj_close:())
 
 q.insert('googdata', (date(2014,01,2), 555.263550, 550.549194, 554.125916, 552.963501, 3666400.0, 552.963501))
 q.insert('googdata', (date(2014,01,3), 554.856201, 548.894958, 553.897461, 548.929749, 3355000.0, 548.929749))
 
 q.googdata.show()
                   High         Low        Open       Close     Volume   Adj Close
 Date
 2014-01-02  555.263550  550.549194  554.125916  552.963501  3666400.0  552.963501
 2014-01-03  554.856201  548.894958  553.897461  548.929749  3355000.0  548.929749
 
 # This is the part to be run on kdb
 # f:{[s]select from googdata where date=d}
 
 x=q.f('2014-01-02')
 print(x.show())
 
 2014-01-02  555.263550  550.549194  554.125916  552.963501  3666400.0  552.963501

This code ends this section on data storage. This part is critical in the design of your backtester since the running time of your backtesting will enable you to save time so as to be able to run many more backtests to validate your trading strategy. Following this section on different ways of storing financial data, we will introduce how a backtester works.