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So do it and post the result here. Everyone will be interested.
I'd rather run TC in GA tester than collect figures and then figure out what it would mean.
In my opinion, there is a point in such calculations. Any opportunity to profit is some deviation of price from SB, but not every deviation from SB is an opportunity to profit. Having said that, useless deviations get in the way of looking for useful ones. For example, if you look in natural time, it seems that price reversals bunch up in certain time slots. But if we pass to a time in which volatility is uniform, the price reversals appear to be distributed much more uniformly there.
It is necessary to calculate theoretical distributions for SB and check how big deviations from them are for real prices. Then we need to look for some indicators depending on the value of which this deviation may change. Honestly, it's hard to do all this for the forum)
a flat would be SB - price is looking for big buyers/sellers or trying to get them interested in an offer
trend - competing buyers/sellers, in my opinion this cannot be SB
how to separate the trend from the flat - I wrote long ago - there was a trend, then there will be a flat, when is not clear, but it is clear that there will be a transition from one state to the second, i.e. the task is to trade between states using 2 TS (or not to trade if there is 1 TS)
And the research in this thread is there - but the goals? And the methodology "limps on both legs" - we know the time of the market(trading sessions), but why do we add to the statistics an intersessionary flat? - well, it's easier that way
excluded, it's quicker for me to run the TS in the GA tester than to collect the figures and then work out what that would mean
A flat would be SB - price is looking for big buyers/sellers or trying to get them interested in an offer
trend - competing buyers/sellers, in my opinion this cannot be SB
how to separate the trend from the flat - I wrote long ago - there was a trend, then there will be a flat, when is not clear, but it is clear that there will be a transition from one state to the second, i.e. the task is to trade between states using 2 TS (or not to trade if there is 1 TS)
And the research in this thread is there - but the goals? And the methodology "limps on both legs" - we know the time of the market(trading sessions), but why do we add to the statistics an intersessionary flat? - Well, it's easier that way.
There is a deterministic function of time (trend in econometric terms) and random fluctuations around it (random process with zero expected payoff).
Flat - zero trend plus a stationary process
SB - zero trend plus non-stationary process
Trend (in the trader's sense) - a monotonically growing (falling) trend plus some process, depending on the type of this process there are variants of TS and DS series
The question has already been asked - So what?
Do you have any idea how to use it? Or is it just a statistic, and what, how and why is up to you?
The question has already been asked - So what?
Do you have any idea how to use it?
No, but you can make your own suggestions on how to use it. It would be interesting to hear your expert opinion.
would you look for example - how many times a day does the price cross the opening price of the day? or how many times does the high/low on the TF get updated?
A warrant officer asks a soldier to sweep the square and says: Soldier get a crowbar and sweep up.
The soldier replies: Comrade Warrant Officer let me take a broom and sweep up. Will it be good and of good quality?
Warrant Officer: Soldier, I don't want it to be nice and good. I want you to fuck off)
Knowing the amplitude and periods of volatility you can make dynamic SL/TP depending on the periodfor example...
No, but you can make suggestions on how to use it. It will be interesting to hear your expert opinion.
Honestly - the answer is as good as it gets... If my question offended you, I sincerely apologise.
And another question - did you put me in the "experts" in order to make fun of me? Did I give you a reason to consider me an "expert"?
I understand that you are not interested in my "expert" opinion, but I had a much better opinion of you...
Study #3 - the goal is to determine the deviation of the ZigZag lead time from the average value depending on the time of day.
Input data: graph period M1 ( server time zone in winter UTC+2, in summer UTC+3 ), ZigZag plotting segments with threshold = minimum step = 100 points 5-sign. Sample for average calculation n = 100 last ZigZag segments.
The formula and approach are the same - we calculate the average value of absolute time point differences in minutes between adjacent extrema (how many minutes the motion has lasted) and compute the relative value to the current motion interval.
Zt = (Zt_recurrent - Zt_average)/ Zt_average
Pass in sliding window, collect data and group by time of day.
Let's look at some results for EURUSD:
We can see that there is some structure and it repeats on 3 charts.
From this we can conclude that the intervals between the adjacent zigzag extremums are larger in the morning and in the evening, and smaller in the period between 10:00 and 18:00.
Study #3 - the goal is to determine the deviation of the ZigZag segment duration from the average value depending on the time of day.
Input data: graph period M1 ( server time zone in winter UTC+2, in summer UTC+3 ), ZigZag plotting segments with threshold = minimum step = 100 points 5-sign. Sample for average calculation n = 100 last ZigZag segments.
The formula and approach are the same - we calculate the average value of absolute time point differences in minutes between adjacent extrema (how many minutes the motion has lasted) and compute the relative value to the current motion interval.
Zt = (Zt_recurrent - Zt_average)/ Zt_average
Pass in sliding window, collect data and group by time of day.
Let's look at some results for EURUSD:
We can see that there is some structure and it repeats on 3 charts.
From this we can conclude that there are longer intervals between adjacent zigzag extremums in the morning and in the evening, and smaller intervals between 10:00 and 18:00.
It turns out that it is more correct to trade along the trend in the morning and evening, and short-term pullbacks in the daytime...
Volatility is lower in the morning and evening based on previous studies, and higher in the afternoon. It turns out that ZZ time intervals are smaller during the day, but the spread in price is larger.
Volatility is lower in the morning and evening based on previous studies, and higher in the afternoon. It turns out that ZZ time intervals are smaller during the day, but the spread in price is larger.