Discussion of article "Probability theory and mathematical statistics with examples (part I): Fundamentals and elementary theory"
The main purpose of this article is to use the commentary to provide a meaningful discussion of the application of these sciences.
Two more parts on random variables and random processes are planned.
Let's keep the intrigue alive.)
It seems to me that forecast and fact should not only correlate but also partially (on average) even coincide) or at least tend to do so with the growing number of forecasts).
Probably, it should be some price of forecast error.
good article, I liked the style of presentation - all nerdy mathematical terms are presented in simple and understandable language, and even with reproducible examples under MQL
it's a pity that the first article was introductory ((((
we will wait for the next article
Thank you!
It seems to me that forecast and fact should not only correlate but also partially (on average) even coincide) or at least tend to do so with the growing number of forecasts).
I guess it must be some price of forecast error.
Read with great pleasure, thanks to the author! Looking forward to the continuation about random processes: Wiener's, Ornstein-Uhlenbeck, etc....
Thanks! I'm afraid that in this series I won't be able to get to processes with continuous time - I'll have to limit myself to discrete time ( econometrics approach). The main reason is that after this series I plan to write an article about cointegration, Dickey-Fuller test, etc.
About processes with continuous time, in my opinion, it is necessary to write in the framework of stochastic calculus, leading to the Black-Scholes model. And for this purpose a separate article is clearly needed.
good article, I liked the style of presentation - all nerdy mathematical terms are presented in simple and understandable language, and even with reproducible examples under MQL
it's a pity that the first article turned out to be introductory (((
we will wait for the next article
Thank you!
Thank you! I, on the contrary, find the article insufficiently introductory) I would like an introductory article about how to get a probabilistic model from a game model) by Alexey Savvateev, of course - as we discussed).
I'd like an introductory-introductory article to it about how to get a probabilistic model from a game model
I didn't want to, but I will...
I don't know what the whole series of your articles will be about, but if the articles will once again predict the price, no matter how - using statistics, theory..., dancing with tambourines... tambourine dancing...
then alas, it has been discussed 100500 times and on this resource and on others, the result of these studies will be either the price is random in nature or there are patterns (on history), here hold "on a platter"!
Perhaps with your training and good presentation it will be interesting.
but for practical purposes, it is necessary to be able to evaluate a trading strategy in the future, not to forecast a price series.
If your series of articles on evaluation of trading strategies from the position of probability theory, imho, it will be a masterpiece.
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New article Probability theory and mathematical statistics with examples (part I): Fundamentals and elementary theory has been published:
Trading is always about making decisions in the face of uncertainty. This means that the results of the decisions are not quite obvious at the time these decisions are made. This entails the importance of theoretical approaches to the construction of mathematical models allowing us to describe such cases in meaningful manner.
I would like to highlight two such approaches: probability theory and game theory. Sometimes, they are combined together as the theory of "games against nature" in topics related to probabilistic methods. This clearly showcases the existence of two different types of uncertainty. The first one (probabilistic) is usually associated with natural phenomena. The second one (purely game-related) is associated with the activities of other subjects (individuals or communities). The game uncertainty is much more difficult to deal with theoretically. Sometimes, these uncertainties are even called "bad" and "good". The progress in understanding of initially game-related uncertainty is often associated with reducing it to a probabilistic form.
In case of financial markets, the uncertainty of the game nature is obviously more important, since the activity of people is the key factor here. The transition to probabilistic models here is usually based on considerations of a large number of players, each of whom individually has little effect on price changes. In part, this is similar to the approach used in statistical physics, which led to the emergence of a scientific approach called econophysics.
In fact, the topic of such a transition is very interesting, non-trivial, and deserves more detailed consideration. Hopefully, the related articles will appear on our forum someday. In this article, we will look at the very foundations of probability theory and mathematical statistics.
Author: Aleksey Nikolayev