bladecatcher

The FPGAs run entire algorithmic strategies. It's not just the order management that they handle.

>> As a developer I wouldn't buy a car that would drive off the cliff.

What does being a developer have anything to do with it?

You could start with a book like "Practical Data Science with R". This review http://www.amazon.com/review/R3IOXEI69G6044 gives a very good summary of it's contents and the intended audience

This is a very useful guide. Although I'd imagine that you'd have to have atleast some experience with ML before you truly appreciate what's being explained in the paper.

I'm just speculating here. Maybe the seeds of other trees are not as tasty for rodents? So other trees might not be facing the same problems as the bamboos, and could have a better chance of survival

I'm not sure if I understand your question correctly. kx provides shared libraries to make R and q talk to each other. The specific approach I followed used a shared library that brings R into the kdb+ memory space, meaning all the R statistical routines and graphing capabilities can be invoked directly from kdb+. Using this method means data is not passed between remote processes.

I do not have experience with Julia. I'm interested in languages built for time series data specifically. APL derivatives shine in this regard, in the sense that they provide the bare metal language composed of a few very powerful verbs and adverbs. For example, I could whip up a simple linear regression package in about 10 lines of tight code. These languages also lend themselves to parallelism in a very straightforward manner by means of the "parallel apply" verb. In addition , the brevity that languages like q offer is simply unparalleled.

While this is a good concept in theory, I'd be skeptical about building on top of such a system. The primary reason is the slowness of R. I built heavy duty data mining systems using a stack of kdb/q and R. In my experience, R, when used for simple clustering algorithms like k-means and k-medoids slowed down my system by nearly 70 times. This is despite running parallelized versions of these algorithms (by means of the SPRINT R package) using mpiexec.

IMO, there is a very big gap in this space. There is an urgent need for high performant data inference languages. MATLAB is decent, but is still clunky for my taste. Plus, I prefer the simplicity of a file mapped column oriented database like the one offered by kdb. As KDB is too expensive for me right now, I'm considering building on top of the excellent J language/JDB database stack for my big data needs.

Another one that was posted on HN recently was https://www.lawgeex.com/

D language is also worth taking a look at. It's a very smart language designed for systems programming.

I am a quant in a bank and I work in similar massive open spaces. Being in such noisy places has actually helped me find my focus zone! No matter how noisy it gets, I seem to be able to find a calm place within my head

I don't have numbers handy. The real power of kdb+/q comes from the column oriented architecture and the extremely powerful vector functional language q. The language q is inspired by APL. I highly recommend you to check out this article to get a sense of the APL family of languages https://scottlocklin.wordpress.com/2013/07/28/ruins-of-forgo...

If you want a database for blazing fast data-storage and retrieval, there are many options available. You start seeing the real benefits of kdb+/q when you use q to simplify very complex operations that aren't easily done in SQL. Also, the high level operators that q makes your code extremely terse. I've written complex backtesting systems that perform data mining on massive datasets - all in one page of very tight q code!

Many banks, hedge funds use kdb+/q for time series databases. This (very expensive) software is literally unheard of outside of these niche domains. I've been using it for close to 5 years for high frequency data, and honestly nothing out there comes close to this awesomeness of kdb+

It's interesting how you consider placing a limit order as a criterion for being eligible to comment on this topic.

I agree. Psychedelics give access to altered states of mind. Very often, these altered states are mistaken to be transcendental states (like what yogics and other saintly beings experience). It's best to avoid these shortcuts to the mind, unless one is feeling adventurous and is willing to face the consequences of the unknown that lies beneath

>>One exchange's shadiness will always affect every other exchange.

I don't think that is correct. A shady exchange will be avoided by market participants if it significantly deviates from a fair price.

The reverse engineering is near impossible. My guess is that their execution algorithms will be as complex as their strategies themselves, so finding out their trades and figuring out the underlying strategies (which constantly evolve with the market) is really hard. Moreover, if one had so much quant ability, they'd be better off making some money for themselves and beating RenTec at their own game.

I would like to see Dälek included in the study. I'd be surprised if they didn't show up on the far right on the scale.

There's a con scene in a movie, Nine Queens[0], in which a grandkid sends a friend to con his aunt. The movie is quite awesome btw..

[0]http://www.imdb.com/title/tt0247586/