Why Genetic Algorithms and Neural Networks will always be a lower priority of forecasting the markets vs quant or even technical analysis

(Last Updated On: February 24, 2013)



Why Genetic  Algorithms and Neural Networks will always be a lower priority of forecasting the markets vs quant or even technical analysis

I just got this message from someone on Linked In:



Bryan I need to know more details about this GPU CUDA 3rd party high level C++ library for math awesomeness with genetic algorith,, please give me more details , I think i have for you a Job implementations .

First off, I appreciate people asking me about jobs to implement this and that. I just turned away about 3 requests like this in the past week. Sorry but we just focus on proven concepts that are part of toolboxes within Matlab.

My opinion on  genetic algorithms and neural network is pretty simple. They take less priority than pure math or statistical methods. I just feel GA and NN are just concepts in trying to train something that is dynamically changing within markets. One of the reasons I like Matlab is it has a highly secretive weapon that I will never release to the public. Even R does not that have this capability. I will tell you that this feature in Matlab, NN is one way to measure how data flows.

Certain optimisation problems (they are called variant problems) cannot be solved by means of genetic algorithms. This occurs due to poorly known fitness functions which generate bad chromosome blocks in spite of the fact that only good chromosome blocks cross-over.
There is no absolute assurance that a genetic algorithm will find a global optimum. It happens very often when the populations have a lot of subjects.
Like other artificial intelligence techniques, the genetic algorithm cannot assure constant optimisation response times. Even more, the difference between the shortest and the longest optimisation response time is much larger than with conventional gradient methods. This unfortunate genetic algorithm property limits the genetic algorithms’ use in real time applications.
Genetic algorithm applications in controls which are performed in real time are limited because of random solutions and convergence, in other words this means that the entire population is improving, but this could not be said for an individual within this population. Therefore, it is unreasonable to use genetic algorithms for on-line controls in real systems without testing them first on a simulation model.


As for CUDA or GPU, I have already proven to myself that FPGA is still lower latency than CUDA or GPU. It can only be used for backtesing or real time analytics with a delay that would make the use of it kind of useless in a live trading environment. Just look for FPGA on this site for some videos that I have posted about it.






NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!
Don't miss out!

You will received instantly the download links.

Invalid email address
Give it a try. You can unsubscribe at any time.


Check NEW site on stock forex and ETF analysis and automation

Scroll to Top