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Entry/exit rules*capital management=+/- deposit
I'm telling you, there's a TC with a source. It is possible to log every TC fart. It's not a black box in terms of the algorithm. It's simple:
For example, you take a standard EA from Meta and obtain a good profit on some interval. What is the pattern used by the TS? You can, of course, call this pattern as "the pattern of getting a profit of this TS". But that's buttery. We need to really figure out what is it about prices that TC used. Apparently no one is facing such a problem, since there is no understanding of it at all.
I'm telling you, there's a TC with a source. It is possible to log every TC fart. It's not a black box in terms of the algorithm. It's simple:
For instance, you take a standard Expert Advisor from Meta and gain a good profit on some interval. What pattern has the TS used? Of course, we can call this pattern "a pattern of getting profit of this TS". But that's buttery. We need to really figure out what is it about prices that TC used. Apparently no one is facing such a problem, since there is no understanding of it at all.
I don't quite understand why you can't see the parameters you've written yourself? For example, if price exceeds the sliding price by 10 points, then make such and such a step. This will be the pattern.
I don't quite understand why you can't see the parameters you wrote? For example, if the price is 10 pips above the sliding price, take that step. That would be the pattern.
Okay, let me try again. Here you are doing machine learning on some data. Suppose you do not have much time and there is no time for a thorough statistical study of the input data. You build some model based on your experience, train it and get a decent result on OOS. What you have in the end:
Many developers will say that pattern == this model. I am against this formulation. It is the model itself that sometimes falls into a given pattern. But that is why this hitting occurs - no answer.
Now to our price sheep. Again, we have a flat TS that is positive on flat patterns and negative on trends. But suddenly, such a pair appears on which it is showing profit even on trends, and on some of the fluxes it is losing. But in the end it is much better than on other pairs: heaven and earth.
You try to understand what is so great about this pair. That is, what is the pattern in it, which is absent in other pairs. I could call it a pattern like "my TS is earning cool". But not in kindergarten. I want to understand what peculiarities of the pair make money. I hope it's clear now. I can't think of any other explanation.
OK, let me try again. You are doing machine learning on some data. Let's say you don't have much time, and you don't have time to do a thorough statistical study of the input data. You build some model based on your experience, train it and get a decent result on OOS. What you have in the end:
Many developers will say that the pattern == this model. I am against such a formulation. It is the model itself that sometimes falls within the given pattern. But there is no answer as to why it hits the pattern.
Now to our price rams. Again, we have a flat TS that is positive on flat patterns and negative on trends. But suddenly, such a pair appears on which it is showing profit even on trends, and on some of the fluxes it is losing. But in the end it is much better than on other pairs: heaven and earth.
You try to understand what is so great about this pair. That is, what is the pattern in it, which is absent in other pairs. I could call it a pattern like "my TS is earning cool". But not in kindergarten. I want to understand what peculiarities of the pair make money. I hope it's clear now. I can't think of any other explanation.
I haven't done anything like this before, but I'll tell you what I'd start with.
You can write a set of criteria that can be formalized quite easily. For simplicity, for example, size of a flat in points and candlesticks, size of a trend in points and candlesticks and ratio of the trend size to the size of the flat.
Then we run the Expert Advisor on the symbols of interest for a certain period. And we look at the average by the criteria provided. In other words, we try to evaluate the presented criteria. If we see the correlation of profits and a certain criterion - we are on the right side. So it goes like this...
Now to our price rams. Again, we have a flat trader, that shows profit on different pairs during flat periods and minus on trends. But suddenly, such a pair appears on which it is showing profit even on trends, and on some of the fluxes it is losing. But in the end it is much better than on other pairs: heaven and earth.
That's your statement confirms my words. We need to define the trend/float criteria. And look at the ratio with the profit.
You can also try to formalize the parameter "transition" between a flat and a trend.
OK, let me try again. You are doing machine learning on some data. Let's say you don't have much time, and you don't have time to do a thorough statistical study of the input data. You build some model based on your experience, train it and get a decent result on OOS. What you have in the end:
Many developers will say that pattern == this model. I am against this formulation. It is the model itself that sometimes falls into a given pattern. But that is why this hitting occurs - no answer.
Now to our price rams. Again, we have a flat TS that is positive on flat patterns and negative on trends. But suddenly, such a pair appears on which it is showing profit even on trends, and on some of the fluxes it is losing. But in the end it is much better than on other pairs: heaven and earth.
You try to understand what is so great about this pair. That is, what is the pattern in it, which is absent in other pairs. I could call it a pattern like "my TS is earning cool". But not in kindergarten. I want to understand what peculiarities of the pair make money. I hope it's clear now. There is no other explanation I can come up with.
I haven't done this sort of thing, but I'll tell you where I'd start.
You can write a set of criteria that can be formalized quite easily. For the sake of simplicity, for example, size of the flat in points and candlesticks, size of the trend in points and candlesticks and ratio of the trend size to the size of the flat.
Then we run the Expert Advisor on the symbols of interest for a certain period. And we look at the average by the criteria provided. In other words, we try to evaluate the presented criteria. If we see the correlation of profit and a certain criterion - we are on the right side. Something like this...
This is your statement that confirms what I am saying. We need to define the trend/flat criteria. And look at the correlation with profit.
You can also try to formalize the "switch" parameter between a flat and a trend.
However this pair turned out to be such a fairy tale.
This koshey was discussed in a separate thread. This thread is about something else. And the example with peculiar pairs was +flet/trend was invented on purpose in an attempt to reach an understanding.
The question of the branch is how to reengineer any of your TS
This koshey was discussed in a separate thread. This thread is about something else. And the example with special pairs was +flet/trend was invented on purpose in an attempt to reach an understanding.
The question of the branch is how to reengineer any of your TS
a trend as well as a flat is detected with a delay, there should always be a lifeline
If the author of the EA can not understand the principle on which his EA works - then it probably is not his EA
It would be easier to just lay it out and you would get the information - show where it will lose and under what conditions.
For example, let me insert an input parameter here and optimise it. Bam - and the picture is completely different. But we cannot understand what this parameter exploits.