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Name: Zipline in the Cloud: Optimizing Financial Trading Algorithms
--client continuum --show pydata_sv_2013 --room a1 2247 --force
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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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assets release.pdf Zipline_in_the_Cloud_Optimizing_Financial_Trading_Algorithms.json
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Description:	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. markdown
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