Quote from Jerry030:
Not really.
What makes you think that the folks that NSA wants to track aren't converting their instructions from Arabic to say Icelandic or Navaho and then translating it back on the other end? They aren't so stupid that they can't learn little known languages at least for the 50 words it would take for an attack.
If the NSA has little more than 50 words (perhaps a few hundred bytes) of data, even if transmitted in the clear, there's absolutely no way they can take meaning from it... except for meta-data associated with the transmission (the fact a code has never been used before is meaningful information).
At the same time, Navajo or Icelandic or Arabic or any other human language would still be a "known" language, and there are very well known patterns for each that makes decoding it much easier than randomly generated data.
By contrast a trading system is a very constrained universe with few primitives: you know the market from the price feed/market traded as few could trade EUR/USD from a price feed of Corn futures. You know that the action is either no action (hold current position or stay out of the market if not in), or buy, sell (to enter or exit a current position), or adjust stops. What other primitive functions can you suggest?
The *point* here is not just to witness the output, but be able to replicate it. The "simpler" the output signal, the "simpler" the input, the
more difficult the replication task.
Let's use a standard pattern-recognition example. You want to write an application that recognizes the pattern of "fish". I'll put a 3-year old next to your application. The 3-year old will happily (and with very little training) look at items coming down an assembly line, and tell you whether it's a 1 (fish) or 0 (no fish).
If your application can replicate the 3-year old's fish pattern recognition capabilities just by looking at raw pixel data without domain knowledge of the specific feature set that the 3-year old is using (size, color, shape), you're already doing an incredibly impressive job.
Now, let's talk about what a serious quant fund may be doing. It's not a 3 year old dealing with a linear problem that can be broken down into convenient primary components; it's a 65 year old Middle Eastern Studies PhD distinguishing between "good" versus "bad" poetry written by some Armenian scholar 2000 years in the grave. If you can replicate that recognition process and start reviewing old literature just by monitoring input (a sequence of pixels)/output ("good" versus "bad"), then you deserve the Turing award.
As far as what I'm doing, I'm trading my own prop account with plans for a statistical arbitrage quant fund in Q1 of next year.