Machine learning in trading: theory, models, practice and algo-trading - page 2994

[Valeriy Yastremskiy]

mytarmailS #:

Oh, come on, it's not like methaquats are interested. They have other targets.

You can blog about the ideas there, if the quotas don't like it.

[Valeriy Yastremskiy]

Aleksey Nikolayev #:

For me, the point is always to look for deviations of price from the SB. Econometrics differs from financial stochastics by modelling time - discrete in the former and continuous in the latter, which leads to rather different mathematics, but the essence is the same.

I'm reading books slowly. I don't see such an approach among the people.)))))) The idea is clear, but in the presence of uncertainty it is impossible to predict the lifetime of a state. More all estimation of fair and current price methods are voiced. And this approach is understandable at least.

[Rashid Umarov]

mytarmailS #:
A question for the moderators, administrators...

Can I write an article using Jap. R or is it taboo?

R is no longer supported. There's no future

Update Why teach R in 2023?

The author is a dreamer

My next piece of advice will not be for university applicants, students and graduates, but for those who are deciding now to change their career path. I recommend you to start your journey specifically with data analysis in R. And become an analyst in your industry. Maybe right in your current company. This way you will have two huge advantages: knowledge of modern and powerful tools for data analysis and broad industry experience (what abroad is called domain specific knowledge and is considered a huge advantage for analysts). And then gain experience and new knowledge, and your career will actively develop.

[mytarmailS]

Valeriy Yastremskiy #:

I'm R teaching)))) and python))))))

it's better to learn one thing

[Rorschach]

Aleksey Nikolayev #:

For me, the point is always to look for deviations of price from SB. Econometrics differs from financial stochastics by modelling time - discrete in the former and continuous in the latter, which leads to rather different mathematics, but the essence is the same.

Here is a fairly standard example of such a search within econometrics article1 and article2. The approach is exactly related to the search for stationarity (in the asset price or spread) - that is, stationarity is assumed to be possible only sometimes and is defined as a deviation from a more typical SB, rather than being a constant property as in the study of signals in DSP.

For stochasticity, it is difficult to give a simple but meaningful example. My paper on gaps may serve as a hint in this direction, because the distribution studied there is easier to be considered at continuous time. And if we assume the dependence of this distribution on some features, we can develop the idea in the direction of MO.

imho, the cornerstone is confidence intervals. For 10 bars you can easily get a deviation from SB, but the error will be huge. For 10000 the error is small, but during this time everything has had time to change 100 times and on average you get the SB.

[Rorschach]

Aleksey Vyazmikin #:
Has anyone tried making time series convolutions?

convolutions with what?

[Aleksey Vyazmikin]

Rorschach #:

rolls of what?

Neighbouring indicators.

[mytarmailS]

Rashid Umarov #:

The author is a fantasist.

curious about what?

[Rashid Umarov]

mytarmailS #:

wondering what?

I just quoted

[mytarmailS]

Rashid Umarov #:

I quoted

I couldn't pick out anything fantastic or fantasy from your quote, nor from the article itself.