I adapted this strategy for optimization according to my example.
oprimizator_per_asset__1___2_ (2) (2).ipynb
It just shows how to perform optimization with multiple parameters for each asset.
Pay attention to #TODO. It is necessary to set the right ranges for better optimization.
Unfortunately, this process is slow. It is better to reduce the number of parameters and assets...
I also see zeros in the output of your strategies. Seems, your strategy does not trade during the out of sample.
You can inspect the output in the logs (button "download weights")
Hello.
Both of these issues have been resolved. Briefly, the cause was in the trading days number.
Regards.
Hello.
I guess there is a warning on the strategy "The strategy uses outdated libraries. Clone it to update the libraries."
Just clone this strategy, it will create a new environment with the latest library.
Regards.
Hello. Sorry, there was a typo. Try this:
conda install 'quantiacs-source::qnt>=0.0.250' -n qntdev
(double ':' )
Regards.
Hello.
There are 2 options for how to test this model:
The first option is to split the data into 2 pieces: "training" and "testing".
The training data will contain all the data except some last years (1-3). The test data will contain the remaining piece of data. You train the ML model using the "training" piece and evaluate the performance using the "testing" piece (you can use the backtester for this). It will give you a rough estimate of how your model will perform in the contest. Before submitting, you can train your model using all available data.
The second approach is more tricky. You can slice the data into multiple training and testing pieces.
For example, if you want to evaluate a model for futures, you need the output which contains 16 years of data. Obviously, the rare model will work for so long time without retraining. So you can decide, that your model can work properly for 4 years. In that case, you need to train and evaluate your model 4 times (min). Suppose, your model needs 10 years for training, then you have to perform these passes:
1 pass:
training piece: 1995-04-18 - 2005-04-18
testing piece: 2005-04-19 - 2009-04-19
2 pass:
training piece: 1999-04-18 - 2009-04-18
testing piece: 2009-04-19 - 2013-04-19
3 pass:
training piece: 2003-04-18 - 2013-04-18
testing piece: 2013-04-19 - 2017-04-19
4 pass:
training piece: 2007-04-18 - 2017-04-18
testing piece: 2017-04-19 - 2021-04-19
When you finish these passes, you will get 4 outputs. You can join these outputs and estimate the performance of your model.
Before sending your model, you can train your model using 10 last years of data.
I guess you need the second approach. I am working on the ML example right now. I will add the necessary code for this option.
I notify you when I finish and publish this example.
Regards.
Hello.
This strategy correlates with the examples.
The cofactor(correlation factor) must be lower than 0.9 or the Shape Ratio of your strategy must be higher (for the last 3 years).
Try to use the other features: volume, ROC(rate of change), or other technical indicators.
Regards.