Walter's DConf 2014 Talks - Topics in Finance

[aldanor via Digitalmars-d]

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.