Fractal Nesting Based on Multiple Timeframes

Theses plots show 24 hours of timeframe alignment. 'Timeframe alignment' is when multiple timeframes (say 1 min, 2 min, and 5 min) close a the same time.

Using 1 min, 2 min, and 5 min as a simplified example:
At the close of minutes 2,4,6,8, and 10, there are two timeframes aligning (the 1 and 2 minute ones)
At minute 5, there are two aligning (1min and 5 min)
At minute 10, all 3 align.

The number of aligning closes is on the vertical axis. Time on the horizontal

This first plot is of what I consider "common" timeframes. I'm certainly interested in opinions of what people think are the most important/common TFs.

TF Alignment Common TFs.JPG



This second plot has timeframes that are factors of 1440 minutes. Are these reasonable TFs to examine? IDK, but I thought them to be reasonable. This plot highlights the slow pace/volatility of lunch time more than the common TFs plot above. Fewer people making decisions all at the same time, making it more likely to range? maybe

TF Alignment all TFs factors of 1440.JPG


How do you use this information or why am I interested? I believe the prices with the most eyes on them could be some of the most important.
 
I must say I am surprised at the level of complexity for what is mundane and un-important.
It's not like you have insight as to what or how data is being used. And it's easily argued, you don't have the when either.

Some vernacular from the advertising world... Reach. Frequency. Gross impressions.

The number of "eyeballs" is Reach. Plain and simple. The nodes in current form do not account for Frequency... A 60 min bar at 6am, has potentially already been analyzed 5 previous times since midnight. When is action taken?? Gross impressions would be the actions taken by participants and are completely unknown to retail. Institutional has some in-house insight. Exchanges have more, but still largely incomplete. Ai to the rescue.

Now, what happens to reach AND frequency when planned or random events occur? Things like economic reports, press releases, political doings, etc.
In the US, many planned economic reports occur at 8:30am EST. Does this skew the reach?
Where is the node for the skew, if any? And what about trading session Open and Close times? Do <_investors_ or _intermediate term swing traders_> care about a 60 minute squiggle?

Volume and price are readily seen... As long as market open, close, lunchtime, and days/times of press releases are stable, market open and market close are daily bookends when using volume and price. And you don't recognize open/close as unique nodes?
As you know, price in relation to volume is it's own body of work. ;)

Not saying there is nothing to take away, I've seen relative time studies (same time/diff days) that are tradable, but takeaways on this are mostly temporary before the data expresses long term aspects such as seasonal tendencies and cycles for example.
How many Tuesdays at say 2:30pm does it take before it's actually a cycle, ST or LT??


More vernacular, non-advertising... Circadian Rhythms. Planetary Hours. Strange Attractors.

It's no secret I use a pinch of astrology, albeit not critical to my trading.
It started with Circadian rhythms. Bottomline... good luck in applying circadian to the market. Time zones destroy the idea! That led me to planetary hours. Hint... Maybe find an anchor for your nodes. A 24 hour clock is obvious, conventional, and ineffective imo.
Does DST clock change affect your nodes? By design it affects human behavior. But I don't think it makes agriculture grow differently, although it allows *more* to be planted. And then there are the strange attractors. Frankly, beyond my current methods... I still have trouble drawing straight lines! But there is something going on with my ongoing studies with Lorenz.

fwiw, here's a quick and easy to read, but not necessarily up to date site titled Chaos and Fractals... https://www.stsci.edu/~lbradley/seminar/index.html


Carry On!
 
Sprout said:
It just calculates the probabilities of the next case of a price case as one builds bar strings.
What has emerged from the tool is that as one goes to higher timeframes the market roughly is up 1/3, down 1/3 and ranges 1/3. It's only as one drills down
View attachment 342867

It's in exploratory mode, where the string of cases: XB -> OB -> OB has as the next price case be an XB with 36% probability. The strings can be extended to create a 'map' of monthly and weekly profiles based on historical data.
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Aloha Sprout,
I’ve been working with this and wondering how much data you’ve used to calculate probabilities?

For ten cases with 7 bar strings = 10^7. that’s a lot of paths!

Back to fractals, have you looked at the same instrument with different bar intervals?

For example, does a XB -> OB -> OB have the same XB 36% probability on a 5 minute chart as a 1 minute? Maybe you have “found the limit” of the fastest fractal? Meaning, a 5 second chart could have so many internals it makes it hard to distinguish translation from internal, and sways the statistics away from 1/3 range, up, and down.

Thank you for sharing this nugget.
 
HawaiianIceberg said:
Aloha Sprout,
I’ve been working with this and wondering how much data you’ve used to calculate probabilities?

For ten cases with 7 bar strings = 10^7. that’s a lot of paths!

It is a lot of paths. The TV sub tier I'm on allows for 20k bars, so the script pulls up to that amount depending on timeframe.


Back to fractals, have you looked at the same instrument with different bar intervals?

The script can pull all timeframes but I haven't done much with data analysis yet, just building the toolset to perform the data modeling.

For example, does a XB -> OB -> OB have the same XB 36% probability on a 5 minute chart as a 1 minute? Maybe you have “found the limit” of the fastest fractal? Meaning, a 5 second chart could have so many internals it makes it hard to distinguish translation from internal, and sways the statistics away from 1/3 range, up, and down.

The faster timeframes have a tendency to have greater probability for direction. As one goes up in timeframes, the probabilities for direction diminish.


Thank you for sharing this nugget.

YVW
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Answers in quoted text
 
HawaiianIceberg said:
Thank you for the response Strout! Great info
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If I remember correctly, you had asked about prime numbers. A book that I’m currently reading is Robert Grant’s “Philomath” which you might enjoy. While not specifically trading related, it has some great chapters on number theory and has great visuals.
 
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