veyepar: Zipline in the Cloud: Optimizing Financial Trading Algorithms

Author(s):	Thomas Wiecki
Location	A1
Date	mar Wed 20	Days Raw Files
Start	13:05	First Raw Start	13:04
Duration	00:50:00	Offset	0:00:01
End	13:55	Last Raw End	14:29
Chapters	00:00 0:39:17
Total cuts_time	46 min.

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Simulation has become an indispensable research tool across different
scientific disciplines ranging from neuroscience to econometrics and
quantitative finance. These computational simulations often involve parameters
which have to be optimized on data. This parameter optimization gets
increasingly challenging the more complex and longer simulations take to run.
Cloud services like Amazon Web Services (AWS) provide a compelling tool in
scaling this optimization problem by offering computing resources that allow
everyone to spawn their own personal cluster within minutes.

With a focus on algorithmic trading models, in this talk I will show how
large-scale simulations can be optimized in parallel in the cloud.
Specifically, I will (i) provide a tutorial on how trading strategies of
varying sophistication can be developed using Zipline -- our open-source
financial backtesting system written in Python; (ii) how StarCluster provides
an easy interface to launch an Amazon EC2 cluster; (iii) how IPython Parallel
can then be used to test large parameter ranges in parallel; and (iv) a brief
demo of how Quantopian.com can greatly simplify parts of this process by
offering a completely web-based solution free-of-charge. While a case study in
quantitative finance, the general approach has direct application to other
research domains.

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