Walter's DConf 2014 Talks - Topics in Finance
Paulo Pinto via Digitalmars-d
digitalmars-d at puremagic.com
Mon Dec 22 13:11:31 PST 2014
On Monday, 22 December 2014 at 19:25:51 UTC, aldanor wrote:
> On Monday, 22 December 2014 at 17:28:39 UTC, Daniel Davidson
> wrote:
> I don't see D attempting to tackle that at this point.
>> If the bulk of the work for the "data sciences" piece is the
>> maths, which I believe it is, then the attraction of D as a
>> "data sciences" platform is muted. If the bulk of the work is
>> preprocessing data to get to an all numbers world, then in
>> that space D might shine.
> That is one of my points exactly -- the "bulk of the work", as
> you put it, is quite often the data processing/preprocessing
> pipeline (all the way from raw data parsing, aggregation,
> validation and storage to data retrieval, feature extraction,
> and then serialization, various persistency models, etc). One
> thing is fitting some model on a pandas dataframe on your lap
> in an ipython notebook, another thing is running the whole
> pipeline on massive datasets in production on a daily basis,
> which often involves very low-level technical stuff, whether
> you like it or not. Coming up with cool algorithms and doing
> fancy maths is fun and all, but it doesn't take nearly as much
> effort as integrating that same thing into an existing
> production system (or developing one from scratch). (and again,
> production != execution in this context)
>
> On Monday, 22 December 2014 at 17:28:39 UTC, Daniel Davidson
> wrote:
>> What is a backtesting system in the context of Winton Capital?
>> Is it primarily a mathematical backtesting system? If so it
>> still may be better suited to platforms focusing on maths.
> Disclaimer: I don't work for Winton :) Backtesting in trading
> is usually a very CPU-intensive (and sometimes RAM-intensive)
> task that can be potentially re-run millions of times to
> fine-tune some parameters or explore some sensitivities.
> Another common task is reconciling with how the actual trading
> system works which is a very low-level task as well.
From what I have learned in Skills Matter presentations, for that
type of use cases, D has to fight against Scala/F# code running
in Hadoop/Spark/Azure clusters, backed up by big data databases.
--
Paulo
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