From theory to practice - page 299
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Well, I would have to disagree about where.
I've never been interested in that question. It's her, the prices, her own business. Where it goes, that's fine. What we have is what we work with.
I have never been interested in this question. It's her, the prices, her own business. Where it goes, that's fine. What we have is what we work with.
This applies to the minute charts, on longer timeframes you can predict with some degree of probability where it will go.
I have never been interested in this question. It's her, the prices, her own business. Where it goes, that's fine. What we have is what we work with.
found some figure - 0.0018, it all came up without calculation.
What's the point?
I've been racking my brain for two days, I don't know where or how to apply it...
If you've encountered something like this, please give me a hint.I recall that in November 2017 Alexander talked about some invariant, which often turned out to be 0.0018. I think he was referring to the t2 parameters of the Student distribution, the scale parameter, and I think the drift. The number stuck out to me for some reason.
Thank you!
I will check the viability of this figure.
Waiting to see if there will be a return to some sort of mean, so far the graph is almost unmoving
I recall in November 2017 Alexander was talking about some kind of invariant, which often turned out to be 0.0018. I think he was referring to the t2 parameters of the Student distribution, the scale parameter, and I think the drift. I remember the number for some reason.
0.18
Yes, I still use this invariant.
It is the average value of the asymmetry coefficient of the non-parametric skew of the price probability distribution.
Once again - if we take a certain volume of tick sampling (for example = 10.000) and calculate the variance and skewness for this volume at the arrival of each new tick, they are always different - from zero to infinity. But if, at each step, you calculate the average of these values, you will see that they are practically constants.
I've been watching this for six months now. Never before this average value, for example for a month, has been >0.2 or <0.16 for any of 32 currency pairs.
The conclusion is that the average price probability distribution is stable. We try to destroy this structure by our actions but we fail. The price series restores its structure by trends. This is what I call the "memory" effect of the process.
0.18
Yes, I still use this invariant.
It is the average value of the asymmetry coefficient of the non-parametric skew of the price probability distribution.
Once again - if we take a certain volume of tick sampling (for example = 10.000) and calculate the variance and skewness for this volume at the arrival of each new tick, they are always different - from zero to infinity. But if, at each step, you calculate the average of these values, you will see that they are practically constants.
I've been watching this for six months now. Never before this average value, for example for a month, has been >0.2 or <0.16 for any of 32 currency pairs.
The conclusion is that the average price probability distribution is stable. We are trying to destroy this structure by our actions but we can't. The price series restores its structure by trends. This is what I call the "memory" effect of the process.
When you take the exponent of maximum values, it decreases faster than the series of increments. If you change the coefficient, it turns out to be 1.6, but this is a crude value.
0.18
Yes, I still use this invariant.
It is the average value of the asymmetry coefficient of the non-parametric skew of the price probability distribution.
Once again - if we take a certain volume of tick sampling (for example = 10.000) and calculate the variance and skewness for this volume at the arrival of each new tick, they are always different - from zero to infinity. But if, at each step, you calculate the average of these values, you will see that they are practically constants.
I've been watching this for six months now. Never before this average value, for example for a month, has been >0.2 or <0.16 for any of 32 currency pairs.
The conclusion is that the average price probability distribution is stable. We try to destroy this structure by our actions but we fail. The price series restores its structure by trends. This is what I call the "memory" effect of the process.
Well, I was just dividing by a point in order to compare pairs in some way... and got 0.0018
Yes, indeed, it's an average.
however, there's not much of an effect from this idea yet either
1. Once again - if we take a certain volume of tick sample (for example = 10.000) and calculate variance and asymmetry for this volume at the arrival of each new tick, they are always different - from zero to infinity. But if you calculate the average of these values at each step, you will see that they are practically constants.
2. Conclusion - the probability distribution of prices is stable on average. We are trying to destroy this structure by our actions but we can't do it. The price series restores its structure by trends. This is what I call the "memory" effect of the process.
1. it is called the law of large numbers or the average temperature in a hospital.)
2. The regulator just lets the price wobble as long as it moves within the necessary limits, and otherwise it corrects it in the desired direction by the trend. The regulator "remembers" what the price should be))
Looking for mysticism and some mysterious random price formation process is of course naive, but it is quite possible that one can come across some clever mathematical formulas that will somehow analyze and predict this without taking into account the trend, which is initially non-accidental...