Predicting the Market at Rosenblatt Securities
We’re running a business and we want to
bring value to our clients, we want to bring
value to our organization.
So, the impact is real and we measure it;
it's been significant.
Scott Burrill, Partner & Managing Director,
Rosenblatt Securities
We've done things with Tableau that would
have -- with a team of five people, that would
have taken 50 a significant amount of time
to do.
Filter data the way you want
The objective of a tool like Tableau is to
make it simple -- not simplistic but simple.
We wanted to deploy this real simply for our
traders and clients to determine when we might
suggest they sell a security, and when they
could buy it.
So we can filter this by economic sector,
security, date, and trader type.
And you can really very quickly get an idea
of when the high probability entry or exit
points are for security.
We’re able to perform derived analytics
on hundreds, thousands of different fields,
and then bring that in, visualize it, get
insights, act on it, tell stories over it,
in a very quick way.
Get insights from big data
Tableau has opened up a whole new field for
us in being able to act on insights where
normally we may have had to just do sampling,
we can now look at whole populations of data.
What I have here is basically about seven
months of iterative development where we're
consuming every tick and quote on basically
9,000 stocks.
We have 800 securities that we're calculating
predictive analytics on to determine from
a real time standpoint when to enter or exit
positions.
We are currently accurate about 80, 85 percent
of the time.
So, this has incredible implications for us
and for our clients.
The insights from, for example, how could
I have implemented a trade better can yield
100-fold thousand time improvement in your
cost.
And in our economic environment those type
of value -- insights are incredibly valuable.
We're talking about the difference between
staying in business and not.
