Machine learning in trading: theory, models, practice and algo-trading - page 1048
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No matter how much you twist a carrot, it won't turn into a turnip.
It will.
It was only necessary to patiently reach quasi-stationarity, and this is starting from the Erlang flow of order 300(!!!).
I was ruined by the thirst for the earliest possible obtaining of the coveted Grail, and the desire to show everyone here how to work properly.
As a result - fatigue, apathy and a broken trough...
No matter how much you twist a carrot, it will not turn into a turnip.
Natalia... Well, how come :).
It will work.
It was only necessary to patiently reach quasi-stationarity, and this is starting from the Erlang flow of order 300(!!!).
I was ruined by the thirst for the earliest possible obtaining of the coveted Grail and the desire to show everyone here how to work.
As a result - fatigue, apathy, and a broken trough...
>quasi-stationarity
Creepy word, they could not find anything simpler?
Why do they simplify everything for understanding abroad, and we use clever words to add color .... Why would it be so....
>quasi-stationarity
Creepy word, couldn't you find anything simpler?
Why do they simplify everything for understanding abroad, but we use clever words to add color .... Why would it be so....
It's interesting that all the clever words were invented abroad)).
From my youth I remember trying to read psychology literature, with very, very abstruse words. And all for what, to impress the opposite sex :).
I mean, using words whose meaning few people understand, what you write about does not become more meaningful than it really is.
Hardly written material is not a mark of quality. (my opinion)
From my youth I remember trying to read psychology literature, with very, very abstruse words. And all for what, to impress the opposite sex :).
I mean, using words whose meaning few people understand, what you write about does not become more meaningful than it really is.
Difficultly presented material is not a mark of quality. (my opinion).
Psychology is related to medicine, where there are a lot of incomprehensible words. And this is normal, because they make sense.
Here, on the contrary, there is no meaning.
So you have to choose the original medium, which is MKUL. Dock Yelman used, because he had a converter from R to MKUL, so I tried it, but otherwise yes. To build TC in R and then fuck with connect to the MKUL is a lost cause IMHO.
And what is the problem with R/mql4(5)? Everything is fine-tuned and works like clockwork.
I didn't think I would suggest such a thing, but still...
I created a system (indicator) based on a neural network that builds some levels under the contract, it works pretty well.
Philosophy of the indicator search for some real overbought/oversold or sentiment
It gives about 1-2 signals per week, if the signal is detected correctly it works with a probability close to 100%.
The problem is that I don't know mql and indicator is written in R (with use of many libraries), I have no powers to learn mql.
If there is a developer here who is ready to integrate the code into mql and visualize it in mt4 I'm ready to discuss and collaborate with him in the future
If you have a code I will see what I can do. I will do it for personal use or with free access for all?
Wanting to breathe life into this thread and fill your pockets on a neural network trading signal, I give:
Algorithm of input data preparation for the Grail
1. In the Erlang flow of order 300 and higher for tick quotes (analog of OPEN/CLOSE M5) there is a stable Laplace distribution on the increments.
2. The sum of moduli of such increments will give a xy-squared distribution.
In the limit - normal distribution.
3. Thus, the sum of the moduli for a given flow, in a sliding window, say, 1440 such values = week (determined from Chebyshev inequality), will form a nearly normal distribution with a known quantile function and expectation.
4. Surely unthinkable cash nets can be extracted from such a process.
So why don't I use this algorithm to calculate foreshortenings, outliers, etc. nonsense?
Yes, because it's a VERY long process of waiting for a single trade. The window is a week! Nah, I don't have the patience for that.
And the neuronet just has to bring the Grail in a hurry on such inputs.
Good luck, everyone!
Wanting to breathe life into this thread and fill your pockets on a neural network trading signal, I give:
Algorithm of input data preparation for the Grail
1. In the Erlang flow of order 300 and higher for tick quotes (analog of OPEN/CLOSE M5) there is a stable Laplace distribution on the increments.
2. The sum of moduli of such increments will give a xy-squared distribution.
In the limit - normal distribution.
3. Thus, the sum of the moduli for a given flow, in a sliding window, say, 1440 such values = week (determined from Chebyshev inequality), will form a nearly normal distribution with a known quantile function and expectation.
4. Surely unthinkable cash nets can be extracted from such a process.
So why don't I use this algorithm to calculate foreshortenings, outliers, etc. nonsense?
Yes, because it's a VERY long process of waiting for a single trade. The window is a week! Nah, I don't have the patience for that.
And the neuronet just has to bring the Grail in a hurry on such inputs.
Good luck to all!
If what you're describing gives the result of correct entry 75% to 25% once a week, then I assure you, you won't have to trade on your hard-earned money, because there is a 100% investor.
In addition, what prevents you from trading 7 or more instruments, each with one entry with the above returns, you should be rich in a year.