Forget random quotes - page 60
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First of all, not at random. Secondly, I mean the timing, fuck the stops.
Here it is more interesting to conduct this test by comparing the price of the instrument itself with the positions of the operators. The message is: is the price level the cause of the current positions of the operators, or vice versa, is the level of the operators' positions the cause of the low price. It is important. It is necessary to make sure that the tail does not wag the dog, as it happens with many technical indicators.
Time for the signal to "sour"? :)
So what's your problem with Beard's wording then? It's just that N will have to be looked for. The less in the market the better.
Sort of a crutch for SL. That's what you don't like.
And as far as time is concerned, we don't put TP horizontally, but obliquely: life gets better :)
With stops dick, and without stops no dick at all.)
Eureka! I found an indicator which predicts the market by 97%!!!! 97% of profitable trades is a serious bid for the most accurate mathematical model of the market. I'll call it (19).
(19) is a bit finicky and if it's misunderstood, it can drain the depo.
A kind of crutch for SL. That's what I don't like about it.
And as far as time is concerned, we don't put TP horizontally, but obliquely: life gets better :)
With a caveat. There is one BUT for the causality test - it only considers linear relationships; hence, if it shows a negative result, it does not mean that there is no causal relationship: it is quite possible that it is strong enough, but non-linear. All in all, roughly the same mess as with correlation, as has been correctly pointed out.
I would like to add that in statistics any abstract truths are questionable. You always have to look at the conditions of application, in statistics more than anywhere else the devil hides in the details.
If about Granger, we need to clarify the word "linearity", which comes of two kinds: linearity in the parameters we are estimating, and linearity in the variables, non-linearity of which is usually not taken into account, as it is removed by substitution.
I'm not sure, but it seems to me that causality cannot be considered without the model itself being used for these variables. If our model is linear in parameters and its parameters are estimated by MNC, then the Granger test can be trusted, if the model is non-linear in parameters, then most likely (I am not sure) the Granger test cannot be trusted.
Unfortunately C-4 has shown interest in the test, but hasn't given the raw data yet, and as far as I remember his model, you could try Granger under its conditions and get a result you can trust.
Unfortunately C-4 has shown interest in the test, but hasn't given the raw data so far, and as far as I remember his model, you could try Granger in its conditions and get a result you can trust.
Well it's quotes. Actually, I have attached the wheat and soybean quotes in the archive. I'm also attaching gold and euro here (weekly timeframe). That should be enough for now.
p.s. Alex, why is there a discussion here about some paterns on history and stop sizes? everything from pages 57 to 60 is an offtop for this thread. Please stick to the context.
p.s.s I suggest a specialised branch for such topics.
Chickens, eggs, Granger causal test or who was first?
the Granger test... so what conclusion can be drawn about this test? ;)))