>> and some good experience manipulating continuous functions and their derivatives.
> Nope
1. "good experience manipulating differentiable functions and their derivatives" sounds weird in prose.
2. Some continuous functions are differentiable. Those ones have derivatives you can manipulate. In fact knowing when a function is not differentiable is a pretty useful skill.
> The interview was in their computer group, but I indicated that I'd like to get into quantitative trading -- they acted like they had never heard of any such thing.
Sounds like you interviewed for position X and talked about wanting position Y, and were rightly rejected as "not a good fit; likely to leave at first opportunity".
> only a very tiny fraction of the people on Wall Street had good backgrounds in measure theory and stochastic processes based on measure theory
Probably true. Think of this as "calculus for engineers vs. analysis", and imagine how well a civil engineering interview would go if you talked about different types of integrals instead of talking about how to use the basic stuff to build good bridges. Fact is most people in industry are looking for "calculus for engineers" levels of formal understanding. Enough to be useful and make money while avoiding expensive mistakes.
> and were looking to hire people like themselves.
Also probably true. This is a good assumption not just on Wall St but everywhere.
> Think of this as "calculus for engineers vs. analysis", and imagine how well a civil engineering interview would go if you talked about different types of integrals instead of talking about how to use the basic stuff to build good bridges.
That analogy shouldn't apply to
quantitative trading on Wall Street:
That challenge needs more than
just engineering math approaches
if only to read the literature.
E.g., apparently broadly the first cut
way to evaluate exotic options is
to use the Brownian motion solution to
the Dirichlet problem, that is,
the subject of Markov processes and
potential theory. The subject is
awash in measure theory, e.g.,
stopping times, the strong Markov
property, regular conditional
probabilities, of course
conditioning and the Radon-Nikodym theorem.
This isn't advanced calculus
for engineers. E.g., the work of
Marco Avellaneda at NYU Courant,
Steve Shreve at CMU, no doubt the
work of E. Cinlar at Princeton.
> Sounds like you interviewed for position X and talked about wanting position Y, and were rightly rejected as "not a good fit; likely to leave at first opportunity".
My point is not that I was rejected but
just that they had no hint that anyone
at Morgan Stanley was doing applied math
for automatic trading.
> Nope
1. "good experience manipulating differentiable functions and their derivatives" sounds weird in prose.
2. Some continuous functions are differentiable. Those ones have derivatives you can manipulate. In fact knowing when a function is not differentiable is a pretty useful skill.
> The interview was in their computer group, but I indicated that I'd like to get into quantitative trading -- they acted like they had never heard of any such thing.
Sounds like you interviewed for position X and talked about wanting position Y, and were rightly rejected as "not a good fit; likely to leave at first opportunity".
> only a very tiny fraction of the people on Wall Street had good backgrounds in measure theory and stochastic processes based on measure theory
Probably true. Think of this as "calculus for engineers vs. analysis", and imagine how well a civil engineering interview would go if you talked about different types of integrals instead of talking about how to use the basic stuff to build good bridges. Fact is most people in industry are looking for "calculus for engineers" levels of formal understanding. Enough to be useful and make money while avoiding expensive mistakes.
> and were looking to hire people like themselves.
Also probably true. This is a good assumption not just on Wall St but everywhere.