Machine learning in trading: theory, models, practice and algo-trading - page 1067

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Basurmans have perked up the forum. )
 
Slow down, please, I'm writing it down. ) Maybe it's time to switch to Russian? I can't understand anything.
 
Grigoriy Chaunin:
Please write slower, I'm writing it down. ) Maybe it's time to switch to Russian? Because I am an illiterate, and nothing is clear to me.
I agree about volumes.
 
Maxim Dmitrievsky:

yes, you can delete it

By the way, this version of the EA takes quite sometime during training and so I guess it is not required for retraining everyday...but still I will decide after I see the drawdown and consecutive losses...

Also, do you have any idea how to feed random values into RDF for training? I mean I don't want to feed anything from past data to RDF. I don't know whether it is possible in this version of the RDF or not and so before I proceed I just thought to check with you

Here is my algo which I am trying to implement:

1.No optimization

2.No past data training

3.No indicators or No price feed and nothing

4.Just using random candle simulations

5.I mean in step 4, one agent will create random candle lengths based on a maximum value set for the candle length and candle directions BUY or SELL and the second agent will

take those values of candle length and the direction as input to RDF at each step and place BUY and SELL orders and will try to maximise the reward. In this case, the reward will be the summation or subtraction of candle lengths from starting point.

6.So we can just train this model for millions of such candles with different variations in which RDF will learn to maximize the profits directly and we can just keep the training ON...

If it is feasible, the only hurdle is just the computing power as how many simulations and checks we can do...

By the way, I have the sample code for random candle generation and candle direction (BUY or SELL), but don't have visual mode, but only values. So I am looking for as how to feed those values to RDF for training and then, it should use that model while trading

 

I see the magician is already aiming for the bourgeois. Then here's my English.

Vi DURAKI

How does it sound? Sound? :-)))))))

 
mytarmailS:

levels....

levels work....

I'll finish a little more, and then we can hang a neuronc as a filter on top of it )

neuronics?

It seems to me that the local forum (this thread concerns) is hoping for machine learning as that will save them from having to think

If you already have a visually good result, can't you make up an algorithm on the principle of "if that"?

 
Mihail Marchukajtes:

I see the magician is already aiming for the bourgeois. Then here's my English.

Vi DURAKI

How does it sound? Sound? :-)))))))

no way, it won't work, it has to be in international: U LOUSER!!!

))))

 
FxTrader562:

By the way, this version of the EA takes quite sometime during training and so I guess it is not required for retraining everyday...but still I will decide after I see the drawdown and consecutive losses...

Also, do you have any idea how to feed random values into RDF for training? I mean I don't want to feed anything from past data to RDF. I don't know whether it is possible in this version of the RDF or not and so before I proceed I just thought to check with you

Here is my algo which I am trying to implement:

1.No optimization

2.No past data training

3.No indicators or No price feed and nothing

4.Just using random candle simulations

5.I mean in step 4, one agent will create random candle lengths based on a maximum value set for the candle length and candle directions BUY or SELL and the second agent will

take those values of candle length and the direction as input to RDF at each step and place BUY and SELL orders and will try to maximise the reward. In this case, the reward will be the summation or subtraction of candle lengths from starting point.

6.So we can just train this model for millions of such candles with different variations in which RDF will learn to maximize the profits directly and we can just keep the training ON...

If it is feasible, the only hurdle is just the computing power as how many simulations and checks we can do...

By the way, I have the sample code for random candle generation and candle direction (BUY or SELL), but don't have visual mode, but only values. So I am looking for as how to feed those values to RDF for training and then, it should use that model while trading

You can just feed random price values instead of close prices in learning stage

void calcSignal()
  {
   sig1=0;
   
   if(learn==false)   
   for(int i=0;i<ArraySize(ag1.agent);i++)
     {   
      CopyClose(_Symbol,0,0,ArraySize(ag1.agent[i].inpVector),ag1.agent[i].inpVector);
      normalizeArrays(ag1.agent[i].inpVector);
     }
     
   else
   for(int i=0;i<ArraySize(ag1.agent);i++)
     {
      for(int l=0;i<ArraySize(ag1.agent[l].inpVector);l++)
        {
         double pr[];
         CopyClose(_Symbol,0,rand(),1,pr);
         ag1.agent[i].inpVector[l] = pr[0];
        }   
   
      normalizeArrays(ag1.agent[i].inpVector);
     }
   sig1=ag1.getTradeSignal();
  }
something like this
 
Maxim Dmitrievsky:

You can just buy a random price

Ok, but I am confused about some of the codes that you used before.

CRLAgents * ag1 = new CRLAgents ("RlExp1iter", 5,100,50, regularize, learn);

and this one:

CopyClose (_Symbol, 0,0,100, ag1.agent [i] .inpVector);

So if we have to use 100 candles in copyclose. right? I mean both candles and features are the same. right?

Or we can use different values for features and candles?

By the way, I am trying to use 1000 features and the training is on for 1 hour now.
 
FxTrader562:

Ok, but I am confused some of the codes before I implement something of my own.The below code:

CRLAgents *ag1=new CRLAgents("RlExp1iter",5,100,50,regularize,learn);

and this one:

CopyClose(_Symbol,0,0,100,ag1.agent[i].inpVector);

So if we have to use 100 features in the above code means we have to use 100 candles in copyclose. right?

Or we can use different values for features and candles?

sure you can use different values for each predictor, its just a simple example, every close value = 1 divergent predictor value

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