Walter's DConf 2014 Talks - Topics in Finance

[Paulo Pinto via Digitalmars-d]

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