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There is a possibility that your monitor confuses colors, sometimes it is difficult to distinguish because of the wrong calibration
What did you build? If it's not a secretThere's no such thing.
Remember how I told you that the pound is in the wrong color on the arbitrage chart
What did you build - it's a secret.
There is no such an option.
Remember how I told you that the pound on the arbitrage chart is the wrong color
what i have built - it's a secret
All my colors matched.
Not about the market, but very useful and about the general approach to effectively build a model (at any stage something can go wrong, and we do not even realize it):
Teacherless learning (clustering) and RL(reinforcement learning). In an attempt to reduce the parameters to be optimized. Has anyone thought about how clustering can be applied? The second one is more complicated, you need specialized packages
example: https://robotwealth.com/unsupervised-candlestick-classification-for-fun-and-profit-part-1/
https://robotwealth.com/unsupervised-candlestick-classification-for-fun-and-profit-part-2/
Teacherless learning (clustering) and RL (reinforcement learning). In an attempt to reduce the parameters to be optimized. Has anyone thought about how clustering can be applied? The second one is more complicated, you need specialized packages
example: https://robotwealth.com/unsupervised-candlestick-classification-for-fun-and-profit-part-1/
https://robotwealth.com/unsupervised-candlestick-classification-for-fun-and-profit-part-2/
I began to think about reinforcement learning. I think this is what is needed for the exchanges.
I, too, am mastering Python at the same time. R pisses me off. There was an ancient article by o_o about Kohonen's layer, he wrote something there on plusses and so on, without examples and development
https://www.mql5.com/ru/articles/1562
I, too, am mastering Python at the same time. R pisses me off. There was an old article by o_o about Kohonen's layer, he wrote something on plusses and so on, without any examples or development.
https://www.mql5.com/ru/articles/1562
In general I can add you as a contributor to my repository, I will explain scheme how it works for me.
I wrote on qt and opennn, to be honest nothing has been developed longer than mlp (opennn).
In general I can add you as a counterpart to my repository, I will explain scheme how it works for me.
I'm afraid I need to learn a little more first, I'm not that much of a programmer :) maybe later in the next year
Teacherless learning (clustering) and RL (reinforcement learning). In an attempt to reduce the parameters to be optimized. Has anyone thought about how clustering can be applied? The second one is more complicated, you need specialized packages
example: https://robotwealth.com/unsupervised-candlestick-classification-for-fun-and-profit-part-1/
https://robotwealth.com/unsupervised-candlestick-classification-for-fun-and-profit-part-2/
I also keep it in mind. But I can't find the time to do it.
Clustering is also interesting method. I think it should be used before training of model, because in this way you can sift out parameters that are not correlated at all.
I also keep it in mind. I just can't get my hands on it.
especially if we use multidimensional clustering, we can try to feed vectors with attributes and vectors with lag, say, increments... so that we could divide them into groups - which attribute properties would correspond to which increments in the future
and then use this set for training of NS, for example... i.e. like datamining
yes, exactly before training... or as a separate thing for TS