For the Fractal.mq5 file, I receive the following error while debugging:
2022.01.13 08:30:54.502 Fractal_1 (BTCUSD,M1) CSeries::CheckLoadHistory: requested too much data (100801)
Any suggestions on how to edit the code to not request greater than the max number of bars of data (10,000)?
For the Fractal.mq5 file, I receive the following error while debugging:
2022.01.13 08:30:54.502 Fractal_1 (BTCUSD,M1) CSeries::CheckLoadHistory: requested too much data (100801)
Any suggestions on how to edit the code to not request greater than the max number of bars of data (10,000)?
You are using M1 timeframe. And 10 000 min is only 7 days. It too small to train NN.
You are using M1 timeframe. And 10 000 min is only 7 days. It too small to train NN.
Thanks for the reply, Dmitriy!! Should have started with 1H like you do at the end of the article.
Really appreciate your Neural Network Made Easy series! I hope to master these concepts in MQL myself (although it is easier to pass data to R using MTR lol).
These are high potential algorithms and you have done a masterful job with the library and series, thank you!!
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New article Neural networks made easy (Part 2): Network training and testing has been published:
In this second article, we will continue to study neural networks and will consider an example of using our created CNet class in Expert Advisors. We will work with two neural network models, which show similar results both in terms of training time and prediction accuracy.
The first epoch is strongly dependent on the weights of the neural network that were randomly selected at the initial stage.
After 35 epochs of training, the difference in statistics increased slightly - the regression neural network model performed better:
Testing results show that both neural network organization variants generate similar results in terms of training time and prediction accuracy. At the same time, the obtained results show that the neural network needs additional time and resources for training. If you wish to analyze the neural network learning dynamics, please check out the screenshots of each learning epoch in the attachment.
Author: Dmitriy Gizlyk