Machine learning in trading: theory, models, practice and algo-trading - page 1068
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
By the way, I am trying to use 1000 features and the training is on for 1 hour now...
you can set only 1 agentCRLAgents *ag1=new CRLAgents("RlExp1iter",1,100,50,regularize,learn);
and in library set #define _models 1
so it will be fast
sure you can use different values for each predictor, its just a simple example, every close value = 1 divergent predictor value
So the number 100 or 1000 or 500 etc has to be the same in both the codes copyclose and in declaration.right?
So the number 100 or 1000 or 500 etc has to be the same in both the codes copyclose and in declaration.right?
yes
yes
Ok, but in your current sample code and implementation I am not sure what exactly is happening during training and what is the difference between agents and models:))
I hope you will explain this in your article when you will publish it. I mean what an agent is doing and what a model is doing using kernels...
Ok, but in your current sample code and implementation I am not sure what exactly is happening during training and what is the difference between agents and models:))
I hope you will explain this in your article when you will publish it. I mean what an agent is doing and what a model is doing using kernels...
every RL agent can have unique predictors, then we average result of all agents
number of models - number of iterations of oeature transformations with cos. Forget about it now, because we make gdmh
every RL agent can have unique predictors, then we average result of all agents
number of models - number of iterations of oeature transformations with cos. Forrot about it now, becouse we make gdmh
Yes, right...You can try to use GDMH and let me know if you progress or get stuck in implementation, because anyway finally after seeing LIVE results we can make some conclusions about the algo.But looking into the algo of GDMH, it seems very promising...
By the way, try to use natural log in case of optimization and training formulas. In my experience, using Mathpow() of exponents seem to converge a solution rather quickly.
Yes, right..You can try to use GDMH and let me know if you progress or get stuck in implementation, because anyway finally after seeing LIVE results we can make some conclusions about the algo.But looking into the algo of GDMH, it seems very promising...
By the way, try to use natural log in case of optimization and training formulas. In my experience, using Mathpow() of exponents seems to convert a solution rather quickly.
Also can use trigonometric polynomials. This is will something like "recursive feature eilimination", not actually gdmh... something medium )
because gdmh its linear quadratic algorithm, but we use RDFcan also use trigonometric polynomials. This is will something like "recursive feature eilimination", not actually gdmh... something medium )
I don't know about that...I have to read to understand :))...In fact, I didn't know anything about GDMH and you just told me yesterday and I just learnt and wrote the code...I think I am learning fast:)))))
what I am referring to is when you are approximating a random function to get a solution, then by using natural log or exponent generally converges quickly...why? because that is the definition and purpose of natural log or ln or exponential() or e
Here is an example code what I am referring to:
double x=MathRandomUniform(0,1,unierr);
double likelyhood = 1/(1+exp(MathPow(x,3)));
I understand GDMH somewhat...but RDF is still not 100% clear. I was just trying to implement monte carlo instead of RDF, but if we can do it by RDF, then I don't see the use of Monte carlo. What do you think which is better, monte carlo or RDF?
But I will summarize here what I am expecting from this algo:
1.It will take the indicators or close prices and break it into m small pieces and create polynomials or approximate functions during training
2.When we will run it in trading, then for every candle it will check the past training data and find which polynomial piece is matching our current price and predict what's next going to happen and it should iterate
I don't know about that...I have to read to understand :))
what I am referring to is when you are approximating a random function to get a solution, then by using natural log or exponent generally converges quickly...why? because that is the definition and purpose of natural log or ln or exponenent() or e
Here is an example code what I am referring to:
double x=MathRandomUniform(0,1,unierr);
double likelyhood = 1/(1+exp(MathPow(x,3)));
I understand GDMH somewhat...but RDF is still not 100% clear. I was just trying to implement monte carlo instead of RDF, but if we can do it by RDF, then I don't see the use of Monte carlo. What do you think which is better, monte carlo or RDF?
But I will summarize here what I am expecting from this algo:
1.It will take the indicators or close prices and break it into m small pieces and create polynomials or approximate functions during training
2.When we will run it in trading, then for every candle it will check the past training data and find which polynomial piece is matching our current price and predict what's next going to happen and it should iterate
RDF approximate the agent's polisy directly, on other hand q-learning with monte carlo or TD and Markov chains do it with too many iterations, so it can take much longer
1,2 yes, absolutely right
RDF approximate the agent's polysy directly, on the other hand q-learning with monte carlo or TD do it with too many iterations, so it can take much longer
1,2 yes, absolutely right
So you mean RDF is better and faster than Monte carlo which is definitely required for instant trading decisions upon candle close....So we are on the right path towards creating the forex version
of "ALPHA ZERO" ...let's see:)))))))))