Wait, you mean you are training your SNN model in MT4? And then you are reading/writing to the disk during every calculation? Then no wonder your model behaves so slow... You could try to do some profiling to see where your model is slow, I believe it is most likely during the disk read/write process. MT4 is also extremely slow to run backtests compared to other methods. I train my models in Matlab and just use the trading platform for trade execution.
No, for any machine learning algos we don't use MT4. Yes, we tried in MT4, but left. We use only MT5 for now and we are aware about MT4 backtesting issues.
That's why we keep telling machine learning and NN separately.For us, NN means no learning at all and everything is simple and coded in MQL4 with no models or anything like that. All the input optimised weights are hard coded inside the MQL4 code with indicator values and it's internal algorithm etc and there are no trained models in it, but it has just optimised weights for neurons and we keep changing the weights time to time. Probably, you are confusing the weights of the neurons as models.
But when we say machine learning, we actually mean algorithm based separate training models in MT5 using SNN and, then either we create a bridge using libraries to trade in MT4 or we simply copy trades as well from MT5 to MT4 if we want to use MT4.
We are not sure what exactly you mean by 'disk read/write process' and how you can then search the trading conditions inside the trained model if you will not read. May be you are referring to loading the model to virtual memory or something like that permanently during the live trading.
By the way, we are not concerned about slowness during training. We are referring to slowness while trading.
We are perfectly fine if it takes a couple of days for training also and in fact, we already tried this as well. It doesn't matter if it takes a month to complete the training if it is a one time training and then, if the trained model can execute all trades perfectly, then it will trade like a holy grail. It is very much normal and in fact, we can directly write the 'Monte Carlo tree search' algo to train itself without any past trading history at all and without using any MT5 backtester and purely based on self training to itself. It is very simple, but the model size will grow exponentially large may be in terms of GB.
So it is mostly the computational power, because if more data will be trained or more filters will be added, then file size will be more and ultimately it will not execute the trades.Otherwise, it is very simple task for us if any file size models can be execute the trades in real live markets since the algos are already available.
Irrespective of wherever you keep the trained model, you need to feed the input variables to the model and search inside the model for the output signal and it is always a whole bunch of nested for or while loops of 4 to 5 layers in it with if else statements while searching inside the models. Each model is like a trained brain structure where the search operation need to happen before making a trading decision.
Note that we are not talking about feeding 4 to 5 input variables or indicator values to generate a output signal like a 2 to 3 layer NN. The input variables to the NN usually vary in order of few thousands which are calculated from formulas and each variable going through the search inside 50 different models. It is basically similar to trying to imitate the human brain based on which SNN functions considering a time factor.