Machine learning in trading: theory, models, practice and algo-trading - page 3729
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What's there to try, symbolic regression?
"The key idea is simple: we train a conventional machine learning model and then derive a mathematical formula from it. We use polynomial regression with Ridge regularisation to predict price and logistic regression for direction. SymPy then turns the coefficients into equations."
Well symbolic regression. Next, I would be wary of reading articles like this (dangerous for the brain)
the most useful thing is just to know that there is symbolic regression :)))
It seems that Gemini 3 was trained on mql5.com - probably for the first time the AI produced code that was immediately compiled and launched without errors.
input bool getprofit = true? :)
https://xbox-dns.ru/ for more pleasant usage
input bool getprofit = true? :)
if(getprofit) {MessageBox("BABLO?", "BABLO?"); Alert("BABLO!");}
https://xbox-dns.ru/ for more pleasant usage
input bool getprofit = true? :)
if(getprofit) {MessageBox("BABLO?", "BABLO?"); Alert("BABLO!");}
Asked to write a script with the most complete verification of admission to trading. The result seemed to be based on examples from Short's book.
if(load_profit) {MessageBox("BABLO?", "BABLO?"); Alert("BABLO!");}
Thanks, I'll try to bolt it on my linux.It seems simple.
but no, it doesn't work ;)
The trick of the markup is that it does not look ahead, i.e. without looking ahead. For example, signals using any oscillator (overbought/oversold). Then generalisation.
Therefore, the errors of the models are small, because they approximate the indicator in essence. But it is not possible to find such a magic oscillator for all pairs.
Attempt at a more detailed description of what was done:
https://www.mql5.com/en/articles/20343