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crunchdata
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Joined: May 14th, 2018, 1:20 pm

Dask - Parallel Trading agents Horizontal Scale

May 14th, 2018, 1:25 pm

Hello, 

Been reviewing designs for an intraday trading platform - prefer Python based for the algos.  Need to run a few hundred agents in parallel just during the day - fed by KAFKA topics.  The agents basically calculate the data frame and perform some methods to evaluate a signal, fire a signal if the algo hits.   We have been reviewing some designs for this - like to get some advice from the folks here?   Needs to scale horizontally - cluster.  

Latency few hundred milliseconds is fine. 

Dask:  to run agents concurrently in python, will this work, not sure the use case matches dask? 
Akka:  using java actor framework bundled with python to execute?
Erlang/Elixir:  use erlang actor framework bundled with python? 
Spark:  use spark to run many spark jobs 

Not sure which way to go, like to keep all python for ease of maintainability - any advice appreciated..   The trick is link with python - the algo libraries.. 
 
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Cuchulainn
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Joined: July 16th, 2004, 7:38 am
Location: Amsterdam
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Re: Dask - Parallel Trading agents Horizontal Scale

May 15th, 2018, 7:18 am

My 2 cents

I have ported an OO MC pricer using system deccomposition to two actor .NET actor systems Asynchronous Agent Library (C++) and  then to TP:L Dataflow Library whose goal seems to be low latency. The functionality looks good but I have no idea how it would scale. These libraries are for multi-core machines..


http://blogs.microsoft.co.il/bnaya/2011 ... ow-part-1/

Needs to scale horizontally - cluster.  
You mean more dynamic consumers and producers and algorithms?

The original OO plan is in the pdf. The actor model is more suitable here.
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outrun
Posts: 4573
Joined: April 29th, 2016, 1:40 pm

Re: Dask - Parallel Trading agents Horizontal Scale

May 15th, 2018, 8:23 am

If you want to stick with python then you can manage and deploy your algos with flask -a popular framework to build python webservices http://flask.pocoo.org/-, then run them as services via any of the popular webservers or standalone http://flask.pocoo.org/docs/0.12/deploying/

Linking thinks between processes or across machines up can easily be done in any language with (web)sockets and some data serialization protocol.
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