Separating randomness

[931]

Recommend materials on separating randomness.

Could be books,videos etc.. not even directly related to markets or algos.
For instance DSP lectures, lectures about noise filtering algos, some aspect of math in general or whatever gave you useful clues.
Whatever you found to be useful or holding clues about separating useful info form noise aka "finding order in chaos".

I found https://www.3blue1brown.com YT channel extremely useful for math.
Custom coding&effort put into making visualizations in order to make complex math simpler to understand.
For instance FFT gets broken down using simple visualizations. Before i found it hard to understand on wikipedia or by some lectures.

 

[murray t turtle]

Recommend materials on separating randomness.

Could be books,videos etc.. not even directly related to markets or algos.
For instance DSP lectures, lectures about noise filtering algos, some aspect of math in general or whatever gave you useful clues.
Whatever you found to be useful or holding clues about separating useful info form noise aka "finding order in chaos".

I found https://www.3blue1brown.com YT channel extremely useful for math.
Custom coding&effort put into making visualizations in order to make complex math simpler to understand.
For instance FFT gets broken down using simple visualizations. Before i found it hard to understand on wikipedia or by some lectures.

%%
200day moving average;
+50 week ma= Greatt Visual.....................................................................

 

[.sigma]

I follow that guy on tweet ah, he’s pretty entertaining and smart.

as far as separating randomness, how would you accomplish this via technical analysis?

 

[931]

I follow that guy on tweet ah, he’s pretty entertaining and smart.

as far as separating randomness, how would you accomplish this via technical analysis?

Developing and using algos/models to find useful info in chaotic data classifies as TA right?

IMO it also means separating randomness/noise to some extent.

Separating randomness might be just as hard as finding useful info...

 

[.sigma]

Developing and using algos/models to find useful info in chaotic data classifies as TA right?

IMO finding info also means separating randomness/noise to some extent.

Separating randomness might be just as hard as finding useful info...

ohhhhhhkay gotcha I know what ya mean now.

extracting the alpha from the beta

the signal within the noise

the calm within the chaos

it’s out there man, probably hiding in plain sight.

I think a lot of it comes down to the interpretation of data, having the right measurements, looking at the right metrics, reading it rapidly, reactionary immediacy, and order execution.

for example, I am a big fan of visual finance. When I can see a visual representation (like a chart) it helps a lot. But I’m also a quant guy, in the sense that I like quantified data from the chart.

for example, let’s say I’m looking at a 6 month daily chart of TSLA. Visually it’s making higher highs and lows, booming up. Thats good to know. But I could also count how many days were up compared to down in that 6 months. I could also measure the daily range and divide it by the total iterates and get a feel for the average daily move etc etc

 

[931]

hiding in plain sight

Yes.

If we would have all beta noise, then alpha could be viewable without hiding?

 

[Real Money]

Probability Theory really is very good. But a lot of it is too technical and too impractical for trading.

Knowing how taking products and ratios of random variables will affect the distributions, or how linear combinations affect the variance based on independence and correlation can give you great insight into risk management. But all of the various techniques for parameter estimation get so complicated that only quants and statisticians can understand it.

It's the ideas that are important, not the technical mastery.

An example would be the classic 60/40 split. Owning the sum of inversely correlated assets reduces risk, while still achieving the benefit of returns. Different weightings can add return, but will affect the portfolio risk.

That idea is being applied to every imaginable combination of assets, on all timeframes, and with every kind of trading objective.

It makes for very interesting markets.

 

[931]

Probability Theory

Is there improbability theory also to approach from other side?

But all of the various techniques for parameter estimation get so complicated that only quants and statisticians can understand it.

It makes sense.
Some of those people can work for years on single problem without reaching breakthrough.

But failures are also very valuable info.

Because while getting new ideas all your failures will be consulting you.

Noise might be valuable if it's separatable.

 

[Grantx]

But all of the various techniques for parameter estimation get so complicated that only quants and statisticians can understand it.

It's the ideas that are important, not the technical mastery.

Defeatist attitude. It is easy to understand if you put the effort in.

Also, a certain level of technical proficiency is very important. Become self sufficient with your application of stats don't dismiss it because it looks hard.

 

[Real Money]

Defeatist attitude. It is easy to understand if you put the effort in.

If you think estimation theory is easy then you don't understand what I meant.

Also, a certain level of technical proficiency is very important.

A certain level, yes. But knowing markets and trading is much more important than mastering Ph.D level statistics.