I don't think it's suggesting that quantum effects play a major role in the movement of the stock market, just that quantum models can be applied to finance with different parameters That said, I can't find a clear rationale for choosing this particular model.
I think we both agree that it makes no sense to believe that the financial market is an actual quantum oscillator, and it's somewhat clear in the paper. But I just suffering because in a few days we will see a horrible press coverage with the title "Science says financial market is totally quantum".
I try to avoid reading this kind of papers, but I give it a chance. I only skimmed it, so I'm not sure my takeaway is totally accurate. If you can read it and make any corrections I'd be happy.
They model the financial market with 3 methods.
* geometric Brownian motion (GBM) that has 2 parameters
* Heston that has 1 parameter
* "Quantum" that in their case has 6 parameters
The quantum method is actually the decomposition of using Hermite polynomials multiplied by a Gaussian. This is the base that has the solutions of the quantum oscillator, but they pick the coefficients without any justification. They probably can use whatever smooth localize base of L2(R) they can find. I guess they can use some smooth localized wavelets and get almost the same result (and they can choose one with compact support).
It's possible to use these bases wavelets/HermiteXGaussian/whatever to decompose any signal, like sound or an image, but there is no relation with a background process. In particular that HermiteXGaussian is good doesn't prove that there is a quantum component of the market, only that orthonormal decomposition is a good mathematical tool.
The main differences between the models in their work is that the first one has 2 parameters, the second one has 1 parameter and the "quantum" model has 6 parameters. (Why 6? Why not 5 or 7?) It's easy to get a better fit using 6 parameters instead of 2 if your selection of the model is not horrible, and you can tweak the model a little, and you can pick the time frame, and you publish only the model that has the best fit. This is totally standard, nothing shady, but it may give the impression that the selection of the model has some meaning.
I didnt think the Scala Coursera was very good for general developers - a bit too academic and focusing on functional / recursive patterns which aren't a good way to get a feel for the language in everyday web usage (vs Php, Java, etc).
I'd recommend the book 'Scala for the Impatient', tutorials and projects from Twitter, and Maven or SBT template projects.
And spoken language, and writing, and masonry, and metalcasting, and cultivation, and domestication, scientific theory, logical thought, etc. A lot of things mattered a lot to get us where we are, and fundamentally changed the world when they happened (or at least changed the founders world in the short term as it spread globally).
Huge. Cats to eat the mice that would eat the
grain. Dogs to help in the hunting.
Goats for milk and meat. Sheep for
wool, milk, and meat. Horses for
power and meat. Cows for milk and meat.
Biggies.
Another biggie was open ocean sailing.
Why? Because there were no toll gates
on the open ocean! Across land
had to pay up to the local castle
each few miles. So, if got some
silk in the eastern Black Sea and
want to sell it in England,
go across Europe? Heck no: Just
get a ship and go by water.
Same for spices from India for
Europe, etc.
I would guess the wheel was part of the agricultural revolution? Maybe that's wrong.
edit: yes, it seems I was wrong, the wheel was discovered much after the agricultural revolution. http://en.wikipedia.org/wiki/Wheel#History Given that though, I wonder if its impact was smaller than the other great revolutions.
It's hard for someone who was not the author to dive in to the middle of the code and understand it, debug it or upgrade it, due to the lack of explicit types.
Additionally there static analysis was limited to lint and refactoring tools didn't exist, or at least nobody seemed to use them (I never used PyCharm but I heard it can do some cool stuff).
Also for whatever reason these codebases often weren't very well structured and there was a common tendency to define configuration files that were themselves Python, resulting in the codebase spilling out into things that were theoretically just static data, complicating unit testing.
PyCharm is pretty neat, but coming form IntelliJ it's baby stuff. The tooling around Java can simply not be matched by a dynamic interpreted langauge. Probably ever.
And the more expressive your type system is, the more that becomes true. It will be interesting to see what is done for Idris tooling if it gets some popularity.
There's also lamdu[0] being made for Haskell which is pretty interesting.
I find it really helpful to write little notes and diagrams to help visualise the problem and as a basic mnemonic technique. There's not much space in your short term memory so it's better to offload some of the work if you can. I've started reading Your Brain at Work. It explains how your brain operates in laymans terms and offers some workarounds for common problems.
Here's a how-to video https://www.youtube.com/watch?v=1IGytOTiZJg