The Strategy Tester does allow you to optimise based on Maths Calculations instead of actual trading optimisations.
I've never used it myself, I don't really know what you are trying to achieve, but maybe that can be used for training your Neural Network?
Math calculations — in this mode the tester does not download history data and information on symbols, as well as does not generate ticks. Only functions OnInit(), OnTester() and OnDeinit() are called. Thus a tester can be used for various mathematical calculations where the selection of parameters is required.
Maybe you can have a single input, which will serve as the index 1...15000 into the samples.
I don't really know much about Neural Networks, so I am only speculating.
Maybe you can have a single input, which will serve as the index 1...15000 into the samples.
I don't really know much about Neural Networks, so I am only speculating.
Thanks
In a simplified approach the samples can be "In the EA" , the network is a matrix of doubles essentially.
So what the user needs in that optimization is the adjustment of the weights resulting from that one sample's lets say "analysis".
So a network of 10 layers with 10 nodes on each layer would have 900 weights . Its these 900 weight adjustments (900 doubles) that each agent would send back ideally.
Then i collect the 900 values from each agent and adjust the network . So there is no need for profit or stats etc or even the genetic algorithm.
I may be butchering terms here like "send back" because i dont know how it worksLet's say i am training a neural net .
I have 15000 samples.
These samples are not going to change until the next "harvesting" of data let's say.
Can the cloud network work in a way where 15000 agents can process one sample each in the neural network AND send back adjustment data .
Can it be done ?
(note there are no inputs , the features in the samples are the inputs and they are not ranges but unique fixed values)
Yes it can be done. With Frames.
And i will receive files back ?
This method needs no genetic algorithm has no reward value needs no stats its just a lot of samples and their "analysis" resulting in the adjustments needed.
To simplify : If i manage to do it in my tester does it mean it can happen on the cloud ?
Thank
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Let's say i am training a neural net .
I have 15000 samples.
These samples are not going to change until the next "harvesting" of data let's say.
Can the cloud network work in a way where 15000 agents can process one sample each in the neural network AND send back adjustment data .
Can it be done ?
(note there are no inputs , the features in the samples are the inputs and they are not ranges but unique fixed values)