There is a widely accepted doctrine about the balance between science and pseudoscience, called Popper's pseudoscience.
The common saying is that all scientific laws can never be proven, and the common saying is that scientific laws can never be proven to you by a method of stating facts, especially by proving them to those who have bull horns. For example, we all now know that the Earth revolves around the Sun, and I want to show you what to do, the best way to do it is to have a huge telescope and send you a video from a distance. But you can say that the Earth now revolves around the Sun, you can't prove that the Tang Dynasty Earth also revolves around the Sun, and that's a much harder result.
Here is the problem, how to define science. Science can be defined as follows: science is empiricism, that is, it has historically been able to prove and disprove, and to be able to make some false prophecies, which prophecies are precisely to be falsifiable, that is, the prophecies made by this scientific theory are likely to be refuted by experiment, and only if the two conditions are met, can we be crowned as science. On the other hand, if you propose a theory and the prophecies made are never possible to be refuted by experiment, then this can be called pseudoscience.
For example, you say that a person can grow up to 5 meters, and we have counted all the people in the world and have not found one, but we still can't overturn your conclusion because we can't prove that a person in the Tang Dynasty grew up to 5 meters, and since we can't overturn your conclusion, why not admit that you are a scientist, because you can't prove that a person is 5 meters tall, and you can't make a prediction about when there will be a person of 5 meters. Therefore, when a theory can only prove a person of 5 meters without proving a false prediction, and we can't admit that he is a scientist for the time being.
The above science is 100% correct science, and can be tested countless times, and once tested for falsehood, it is not science, such as finding a black swan, it can be determined that all swans are white nonscience. Then the question arises, if I make a proposition that 95% of swans are white, is it not science?
He's embarrassed that everything in our lives is based on probabilities, for example, that I've come to the conclusion that there's a 90% probability of a typhoon in August every year based on the statistics of the last few hundred years, believe it or not. For example, there's a 50% probability of rain tomorrow, believe it or not.
Of course, you can choose theoretically unreliable statistical probabilities, but in fact, whether you believe it or not, you are profoundly influenced. For example, you have fought for 10 years, you want to go to the front line, and then once you have 10 percent mortality in the front line for 10 years, you think you're lucky, you want to enlist, but if a statistic, 60% mortality, you feel scared, you definitely don't go, you say you don't believe this statistical probability, and your mother definitely stops you.
Based on the statistical probabilities, it is obviously easy to prove that there is an inverse case, but it is difficult to disprove this probability. Statistical probabilities are not scientific, this is controversial, not that I said it, I do not argue here, I can only talk about how it can be believed.
This is a test case, and the more test cases you have in the past, the more credible you are, and the more test cases you have in the future, the more credible you are. You've done 11,000 tests in the past, and the results are 1,000 times more credible than the tests. You've done 10,000 tests in the past, and you're right, and you're 1,000 times more credible than the tests.
So here comes the key, how to trust a high-yielding quantification strategy?
In the scientific method, first of all, a strategy that has been historically proven to be highly profitable is, of course, better the longer it is tested and the more times it is tested. Then make a prediction that will remain highly profitable for the next few years (e.g. 3 years).
For example, the small-scale trading strategy of GoJin has been proven to run at 300 and I predict that it will also run in the next 10 years, although it will take a long time to prove, and in 10 years if the prediction is correct, it can be considered credible.
Some people will say that the creation of strategies is all from 2007 to now, waiting too long to test, waiting too long. I mentioned a good way, the strategy testing time is set to 2007 to the end of 11 years, make an optimal strategy, and then look at 2007 to 16 years, it is equivalent to testing 5 years, after the prediction of another 5 years, to see if it works. (in my eyes those tests only once a year new stock strategies are unreliable)
As for the number of tests, for example, all the 2007 years to date strategies, the reset cycle is 2 days less than half the number of tests of the reset cycle is 1 day, I also often find that for very complex weighted strategies, the reset time is changed by 1 day, the strategy ageing can be reduced by 100%.
When you have two or more competing theories coming to the same conclusion, the simplest or provable one is better. There is also a more common strong form of this statement: if you have two or more principles that can all explain observed facts, then you should use the simplest or provable one until more evidence is found. For the simplest explanation of a phenomenon, the more complex explanation is often more correct.
For example, the Emperor's new clothes; the Prime Minister and his neighbour with their nostrils running out of their nostrils have two different explanations for the strange phenomenon of the Emperor walking down the street with his butt shining; the Prime Minister's explanation: one, suppose the Emperor is wearing one of the most beautiful clothes in the world; two, suppose the smartest person can see; three, suppose I'm stupid; so I see the Emperor with his butt shining; the Little Hair explanation: one, suppose the Emperor is not wearing any clothes at all; so I see the Emperor with his hair shining; according to the principle of Occam's razor, the explanation of the little hair is the most likely to be closer to the truth! because his assumption is the most likely.
The simpler the strategy, the more effective it is. The same strategy with the same payoff, less selection.
The answer is yes. Especially the relatively small number of checks.
For example, new stocks, large stocks, will have a particularly good time, performing particularly well this year, but it is very likely that it will fail next year.
For example, the 28 trends, which is based on 28 styles, will definitely differentiate and have a long enough trend, but why not 28 in the future?
For example, when choosing a straight line, we think that the MA ((2.20) is very good for the Shenzhen 300, very good, but very bad for the S&P 500, but in fact the MA ((2.20) of the Shenzhen 300 has not been effective before, then it is either a failure.
For example, the medium-sized board pb ((3,6.5) is very good, but it has only been tested three times in the last seven years, how do you know that it will not be as low as the Zhengfeng 300 in the long term?
And the last example is small transactions, which is such a good strategy, you wouldn't have thought it would fail at the 300 level.
The part about the Occam's Razor is not enough, and it's more convincing to think about what you can write later.
The above text, referring to the shape of the yoga time, the yoga philosophers have done something about yoga.