- www.mql5.com
there is a problem with onyx lib any idea ?
while installing onyx got an error
ERROR: Could not find a version that satisfies the requirement onyx (from versions: 0.0.5, 0.0.17, 0.0.19, 0.0.20, 0.0.21, 0.1, 0.1.1, 0.1.3, 0.1.4, 0.1.5, 0.2, 0.2.1, 0.3, 0.3.2, 0.3.3, 0.3.4, 0.3.5, 0.3.6, 0.3.7, 0.3.8, 0.3.9, 0.3.10, 0.3.11, 0.3.12, 0.4, 0.4.1, 0.4.2, 0.4.4, 0.4.5, 0.4.6, 0.4.7, 0.5, 0.6.1, 0.6.2, 0.6.3, 0.6.4, 0.7.3, 0.7.4, 0.7.5, 0.7.6, 0.7.7, 0.7.8, 0.7.10, 0.7.11, 0.7.12, 0.7.13, 0.8.5, 0.8.7, 0.8.10, 0.8.11)
ERROR: No matching distribution found for onyx
then when running t2fonnx
import onyx
there is a problem with onyx lib any idea ?
while installing onyx got an error
ERROR: Could not find a version that satisfies the requirement onyx (from versions: 0.0.5, 0.0.17, 0.0.19, 0.0.20, 0.0.21, 0.1, 0.1.1, 0.1.3, 0.1.4, 0.1.5, 0.2, 0.2.1, 0.3, 0.3.2, 0.3.3, 0.3.4, 0.3.5, 0.3.6, 0.3.7, 0.3.8, 0.3.9, 0.3.10, 0.3.11, 0.3.12, 0.4, 0.4.1, 0.4.2, 0.4.4, 0.4.5, 0.4.6, 0.4.7, 0.5, 0.6.1, 0.6.2, 0.6.3, 0.6.4, 0.7.3, 0.7.4, 0.7.5, 0.7.6, 0.7.7, 0.7.8, 0.7.10, 0.7.11, 0.7.12, 0.7.13, 0.8.5, 0.8.7, 0.8.10, 0.8.11)
ERROR: No matching distribution found for onyx
then when running t2fonnx
import onyx
Hi, thanks for the article. A great walkthrough on how to build an ML model and incorporate it into an EA!
I have tried to reproduce your results, but am having some issues. I was hoping you might be able to help me understand why.
I followed the article carefully, but ended up with radically different results in the strategy tester. I understand there are some random characteristics to these algorithms, although I'm still surprised by the difference. I was also careful to utilise the same time periods so that at least my training and test data was the same for model building purposes, and my MT5 backtest was over the same period. I got very different outcomes.
I've tried to identify possible causes, and I think the most interesting difference starts during model building. My loss functions suggest that you achieved a far better generalization when looking at performance over the test/validation data. I've included them at the end of this message.
Can you suggest possible causes of this? Is the model just so fragile that this isn't unexpected?
My most recent effort to reproduce involved simply copy-pasting your final Python code, inserting some Matplotlib calls to produce the loss graphs, but I had basically the same results. Can you suggest how I might better reproduce your results?
Thanks
Hi, thanks for the article. A great walkthrough on how to build an ML model and incorporate it into an EA!
I have tried to reproduce your results, but am having some issues. I was hoping you might be able to help me understand why.
I followed the article carefully, but ended up with radically different results in the strategy tester. I understand there are some random characteristics to these algorithms, although I'm still surprised by the difference. I was also careful to utilise the same time periods so that at least my training and test data was the same for model building purposes, and my MT5 backtest was over the same period. I got very different outcomes.
I've tried to identify possible causes, and I think the most interesting difference starts during model building. My loss functions suggest that you achieved a far better generalization when looking at performance over the test/validation data. I've included them at the end of this message.
Can you suggest possible causes of this? Is the model just so fragile that this isn't unexpected?
My most recent effort to reproduce involved simply copy-pasting your final Python code, inserting some Matplotlib calls to produce the loss graphs, but I had basically the same results. Can you suggest how I might better reproduce your results?
Thanks
Facing the same issue here too.
Can someone help please?
Continue my investigation of the issue I am facing (likely others too); and updates of my findings.
First of all, thank you very much MetaQuotes (the author) for sharing this detailed article. I learn a great deal in my ML trading quest.
Running the original onnx files from the article on my MetaQuates-Demo account, I manage to reproduce the same results. However, retraining the onnx model with the attached ONNX.eurusd.H1.120.Training.py:
data start date = 2022-09-03 00:00:00 data end date = 2023-01-01 00:00:00
the model (onnx attached: ) scores:
RMSE : 0.005212606864326095 MSE : 2.7171270322019527e-05 R2 score : -3.478924709873314
and the 1Jan2023-26Mar2023 backtest results attached: "backtest results.png"
I retrain the attached ONNX.eurusd.H1.120.Training.py with the following:
data start date = 2022-11-28 12:28:00 data end date = 2023-03-28 12:28:00
the model (onnx attached:) scores:
RMSE : 0.0014680559413400179 MSE : 2.155188246903726e-06 R2 score : 0.9699715149559284
and the 1Jan2023-26Mar2023 bactest results attached: "bacttest result2.png"
So, from the above exercises, I guess the model used to produce the final result from the article would likely not trained with the following dates?
data start date = 2022-09-03 00:00:00 data end date = 2023-01-01 00:00:00Would appreciate someone can comment on these.
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
You agree to website policy and terms of use
New article How to use ONNX models in MQL5 has been published:
ONNX (Open Neural Network Exchange) is an open format built to represent machine learning models. In this article, we will consider how to create a CNN-LSTM model to forecast financial timeseries. We will also show how to use the created ONNX model in an MQL5 Expert Advisor.
There are two ways to create a model: You can use OnnxCreate to create a model from an onnx file or OnnxCreateFromBuffer to create it from a data array.
If an ONNX model is used as a resources in an EA, you will need to recompile the EA every time you change the model.
Not all models have fully defined sizes input and/or output tensor. This is normally the first dimension responsible for the package size. Before running a model, you must explicitly specify the sizes using the OnnxSetInputShape and OnnxSetOutputShape functions. The model's input data should be prepared in the same way as it was done when training the model.
Author: MetaQuotes