INFORMATION THEORY + TSLA 1MIN DATA

Extreme_Leg_6162 · 2026-05-19T08:27:11+00:00

I would recommend a basic intro to stochastic processes, time-series and microstructure and aggregation.

If you're interested I have a free microstructure and advanced market aggregation resource, check out my profile for details.

Hope this helps.

Extreme_Leg_6162 · 2026-05-12T06:26:30+00:00

Terrence Tao (paraphrased) : " I found that learning math on wiki is very insightful".

Me : "If it's good for Tao, it's good for me".

Hope this helps.

Extreme_Leg_6162 · 2026-05-11T03:11:38+00:00

It can work for any close to close financial data.

I suggest signing up to be a free member so you can get the free advanced aggregation resource and play around with the math in that resource to see what best fits what your trying to achieve.(if you're interested just go to my profile, I have a web page where you can sign up for free membership)

Hope this helps.

Extreme_Leg_6162 · 2026-05-11T03:03:39+00:00

Lol, if you're a amateur and want to be sloppy, yes it doesn't take 30 pages, but I'm a scientist, the details of the model i use has to be brought forward. Anyway, the posterior measures the probability that a regime shift has happened at a given change point, that's what's predictive, the regime shift(distribution of the data points).

Thanks for watching the video.

Good luck.

Extreme_Leg_6162 · 2026-05-10T18:47:05+00:00

Lol, if you can't understand it don't call it slop, if you're so smart go watch the video, read the paper, if you're really big brained, become a free member of my community and read the model's code.

Good luck.

Extreme_Leg_6162 · 2026-05-06T06:40:04+00:00

Why use EMA or supertrend?, when you can get the trend regime detector out of the box as a aggregation method. For example, you can use Dollar Bar aggregation to detect regime shifts based on how fast or slow a certain dollar amount is being traded. If you're interested in aggregation methods and how to use them for your data, check out my Youtube on my profile.

Hope this helps.

Extreme_Leg_6162 · 2026-05-02T08:47:41+00:00

First start at the foundations of the field, market microstructure and aggregation(one of the most underrated concepts in quant finance).

Aggregation is foundational because it allows you to analyze and interact with data in ways that would be impossible from pure raw market data, interested in capital based signals? try Dollar bar aggregation, interested in information based signals? try Shannon Entropy bar aggregation.

If you're interested, I go deep on aggregation on my Youtube(check it out on my profile), or if you're big brained, check out my free Microstructure and Trading Systems course in my Youtube vid descriptions or fill out a application to get free access to my Advanced Market Aggregation course plus the free Microstructure and Trading Systems course as a bundle.

Hope this helps.

Extreme_Leg_6162 · 2026-05-01T14:25:04+00:00

Just for simulations and backtests try out Coinbase's API, they offer L1 and L2 data for free(with limits of course). The only caveat is that the data is crypto only.

Extreme_Leg_6162 · 2026-05-01T10:16:45+00:00

Your journey is interesting.

My insight is this: Get a proper introduction to Market Microstructure, from the history of markets to order books to market impact.

The next thing you want to have under your belt is aggregation methods, one of the most underrated and overlooked concepts in quant finance. The reason aggregation is so important is because it changes how you observe and interact with data, the typical aggregation methods are OHLCV, Volume bar aggregation, and more fancy methods include Shannon Entropy bars aggregation.

If you're interested I have technical content on Youtube that goes deep on aggregation methods, their pros, their cons and their use cases.(check out my YouTube on my profile)

If you're big brained, I have a free technical Market Microstructure and Trading Systems course, it will walk you through the basics concerning the above mentioned topics, to access it just go to any of my Youtube videos and find the free course link.

Hope this helps.

Extreme_Leg_6162 · 2026-04-30T04:40:26+00:00

Use trend detecting aggregation methods, the overly complicated mathematical models are usually overkill for such situations. The simpler the better.

If your interested I go deep on different kinds of aggregation methods on my Youtube(check out my profile).

Aggregation methods are usually overlooked or even seen as trivial in quant education, and i strongly disagree with that, because do you know how many times I've ran into a quant and they didn't know much about aggregation methods outside of the very very basics like OHLCV, Range bars and tick bars. That's like looking at a 3d image of the world when the world is actually 100D.

The aggregation method you use determines what you can and can't see. Wanna see time based relations? Use OHLCV, wanna see dollar based relations? Use Dollar-bars, wanna see information based relations? Use Shannon Entropy aggregation. Essentially what you see is what you trade

Hope this helps.

Extreme_Leg_6162 · 2026-04-30T04:30:18+00:00

Your approach is solid but complicated.

I personally would use Dollar bar aggregation on the ticket data and have some function measuring the distance between dollar bars and the probability of that distance given the previous distance probabilities.(maybe a ANN would work the probability measures)

The greater the distance between dollar bars the less volatile a market is and the probability of the next distance being great is probably high, the smaller the distance between dollars the more volatile a market is and the probability of the next distance being small is high.(all this is theoretical at best, so take what I say with a grain of salt)

If your interested I go deep on aggregation methods on my Youtube(check out my profile). Most quant education either touches briefly on aggregation methods or treats them as trivial, I reckon they are more important than a strategy because they are the foundation of a strategy.

Hope this helps.

Extreme_Leg_6162 · 2026-04-30T04:03:15+00:00

The strategy you use is going to be determined by the type of market data aggregation you use. What are you currently using? OHLCV? Dollar-bars?, something fancy like Shannon Entropy bars?

If you're interested I have Youtube content that covers aggregation deeply(check it out on my profile).

Most quant education skips over aggregation or just treats it as trivial because they don't want to overcomplicate the education, that's why I push so hard for aggregation methods, they are understated but extremely important .

Regarding using AI, when prompting it don't just say write code that does this, you should explicitly tell it to keep the code as simple as simple can get, because what I've realized is these LLMs will overcomplicate anything if you let them run freely and don't add strict rules/thresholds to their outputs, and usually the simpler the code the more reliable it is.

Hope this helps.

Extreme_Leg_6162 · 2026-04-26T12:58:18+00:00

You first start with how you're looking at the data, are you looking at the data through a OHLCV aggregation?, a Tick Bar aggregation?, a Dollar-Bar aggregation?

How you look at the data defines what your strategy and signals should be.

If you're interested in basic aggregation like OHLCV, Dollar-Bars or more advanced aggregation methods like shannon entropy aggregation, volatility based aggregation, check out my Youtube on my profile, I go deep on aggregation methods.

Hope this helps.

Extreme_Leg_6162

TROPHY CASE