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One more thing to follow:
A total Gauss, as they call it))
And against the trend too).
And it could be against the trend and a loss, with a high probability.
One more thing:
A total Gaussian, as they call it))
Is that the sum of the moduli of the increments? Doesn't seem to be...
And you may turn out against the trend and lose, with a high probability.
People are losing massively both on the trend and against the trend). And the trend itself counts depending on how you play. What is trending for me may be flat for you. And vice versa.
Is it the sum of the incremental modules? It doesn't seem to be...
Is that the sum of the incremental modules? It doesn't look like it...
Give me the formula, or rather what's on what.
So far I have read all of your messages as follows:
1. count the Close modules of two adjacent bars.
2. Let's strain the obtained series with Erlang's flow
...????
A script to unload this into a text file:
You should always enter against the trend to catch a new trend.
I don't care how you go in. As long as it moves. We may catch it).
Give me the formula, or rather what it's for.
So far, all I have read in your posts is as follows:
1. count Close modules of two adjacent bars.
2. let's strain the obtained series with Erlang's flow
...????
Ahem... We take a sliding window of 1440 values of CLOSE M5 and at each new bar count the sum of increment moduli. There should, just has to be a Gaussian distribution for such sliding sums. And with periodic ACF (and not only), as Kolmogorov bequeathed, this process is revealed by a neuronet.
Ahem... Take a sliding window of 1440 values CLOSE M5 and at each new bar count the sum of incremental moduli. There must, just has to be a Gaussian distribution for such sliding sums. And with periodic ACF (and not only), as Kolmogorov bequeathed, this process is revealed by a neuronet.
So explain how to count?