Alan Green wrote:
> On 12/1/06, James Bennett <[EMAIL PROTECTED]> wrote:
>
> > Now, does anybody want to talk about Django?
>
> Yes!
>
> Is anyone writing financial or money-handling applications in Django?
> Any particular issues or "gotchas" the world should know about?

Sort of. I work at a hedge fund and am writing all of our internal
applications in Django/Python. I think the statement that if you are
dealing with money at all that you *must* use decimal is bit of an
over-generalization. All our applications are working with financial
data, but none of them require decimal accuracy. Here are a few
examples:

- risk analysis: we take our positions, lookup some related data from
internal and external systems, and run them through various financial
models which output sensitivities to various market indices. There is
so much estimation, guesswork and uncertainty in risk analysis that
arguing for decimal precision is silly. We use float for speed and
because its more accurate than the analysis itself.

- reconciliation: comparing trades, positions and P&L from various
systems and vendors and reporting discrepencies. We compare quantities
exactly (which are typically whole numbers) and money values within
some tolerance. Again, floating point is more than accurate enough for
the job.

- reporting: aggregating *lots* of data and producing "drill down"
reports for analysis of our performance. The keep the reports clean and
easy to read, most people want the numbers rounded to the nearest
thousand or even to the nearest million. No problem with floating point
here.

The big advantages for using floating point are speed and compatibility
(no need to manually convert to/from float to pass to other routines
and systems). I can understand supporting float AND Decimal, or just
float, but supporting ONLY Decimal is limiting.

-Dave


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