# From theory to practice

**Alexander_K:**

Good evening, dear traders!

I decided to leave the forum for a while, and immediately got bored:)))) And just reading, alas, is not interesting. Everyone is busy with small problems within their personal models and their personal view of the market. They advise me to read about some miserable GARCHs...

So I'll continue my literary and technical exercises little by little, who is interested - read.

Once again I strongly recommend reading the topics:

https://www.mql5.com/ru/forum/219894

https://www.mql5.com/ru/forum/218475

https://www.mql5.com/ru/forum/220237

Hypotheses have been put forward, as follows:

1. The distribution of price increments is a Student's t2-distribution.

2. The pricing process is non-markovian and NO EXACTIONS can destroy this non-markovianity.

3. As a first approximation, the pricing model is a Wiener process with drift described in terms of the Fokker-Planck equation.

The most important thing is to understand that **this equation describes the probability density** function of the pricing process, not the price itself!!!!

Our task is more complicated - we add an integral component to this equation.

So we will numerically solve this equation given the following terms for our case:

drift - moving weighted average WMA

diffusion - weighted dispersion

integral component - the averaged variance of the archive data at a given sample size.

We will continue tomorrow.

Regards,

Alexander_K

Once again, on behalf of the community I strongly recommend to use links instead of text

Personally, I would not lift a finger to follow a link of a person who cannot even do such a simple thing

**Alexey Volchanskiy:**

once again, on behalf of the community, I strongly recommend that links be linked rather than text

Personally, I will not lift a finger to follow a link of a person incapable even of such a simple action.

OK, Alexey - corrected.

**Alexander_K:**

OK, Alexey - corrected.

sorry for the harshness )) it's just a bit annoying with the same thing - links and unformatted sources

**Alexander_K:**

Thus, we will numerically solve this equation given the following terms for our case:

drift - moving weighted average WMA

Tomorrow - we will continue.

Regards,

Alexander_K

WMA will have mad group and phase delays, such that the drift will be exactly the opposite. As a result, all the distributions, etc. will not be about the market, but about these very delays.

It's with sampling that it's nice and pleasant to work).

Regards.

ZZY I've managed to get into my computer at night).

**Alexander_K:**

Good evening, dear traders!

I decided to leave the forum for a while, and immediately got bored:)))) And just reading, alas, is not interesting. Everyone is busy with small problems within their personal models and their personal view of the market. They advise me to read about some miserable GARCHs...

So I'll continue my literary and technical exercises little by little, who is interested - read.

Once again I strongly recommend reading the topics:

probability density function of the pricing process, not the price itself!!!!

Our task is more complicated - we add an integral component to this equation.

So we will numerically solve this equation given the following terms for our case:

drift - moving weighted average WMA

diffusion - weighted dispersion

integral component - the averaged variance of the archive data at a given sample size.

We will continue tomorrow.

Regards,

Alexander_K

What is the reason for choosing WMA (weighted moving average)?

Let's continue.

So, it is very important for us to understand the physical and mathematical meaning of EVERY variable in the Fokker-Planck equation, which we have also complicated with an additional integral term.

1.** The probability density ** W(x,t) is the probability that at some point t the price MAY have a certain value. Moreover, for any volume of sample the values of price lie in the ranges defined by Chebyshev's inequality.

2. **Price x** is the value of Ask or Bid at a certain moment t. Moreover, it **is exactly the tick quotes**, i.e. if there were no deals, the price is not read over time and does not participate in calculations. Please note that ANY attempt to filter tick data does NOT destroy the non-mark sequence. so you can simply work with clean quotes and not complicate the task. It's complicated enough as it is :))

3. **Time t. ** No, I would even say ** TIME ** t. This is the most important parameter! I've read so many topics here - how to properly arrange receipt of tick data, whether each tick is important, etc., etc. If the formula contained just W(x) - then yes, I'd have to receive every tick, and if W(x(t)) - then it would read the price with a certain frequency, regardless of whether it was a real tick or not. But, we have exactly

**W(x,t),**which means that

**both of these approaches to receiving data are wrong**. The correct algorithm for reading quotes is as follows - we select a constant t = 1 second and read tick quotes with this frequency .This will be correct.

The left part of the equation is sorted out.

The question - where is the practice, where are all these lots, profits and money at the end of the day? The answer - it will be, it will definitely be. Be patient.

**Alexander_K:**

Let's continue.

The question is - where is the practice, where are all those lots, profits and money in the end??? The answer is - it will, it definitely will. Be patient.Is it just me, or is the author going to create his own market theory... and his own pricing...?

It seems to me that PRACTICE is not about creating your own market, but about following an EXISTING market...

By and large, creating a DIFFERENT market model will do nothing in terms of PRACTICE with the existing market.

It is possible to try to fit the NEW market and the current one, but a complete coincidence will never be achieved, so this task is purely theoretical and has nothing to do with practice ...

**Alexander_K:**

Let's continue.

So, it is very important for us to understand the physical and mathematical meaning of EVERY variable in the Fokker-Planck equation, which we have also complicated with an additional integral term.

1.** The probability density ** W(x,t) is the probability that at some point t the price MAY have some value. Moreover, for any volume of sample the values of price lie in the ranges defined by Chebyshev's inequality.

2. **Price x** is the value of Ask or Bid at a certain moment t. Moreover, it **is exactly the tick quotes**, i.e. if there were no deals, the price is not read over time and does not participate in calculations. Please note that ANY attempt to filter tick data does NOT destroy the non-mark sequence. so you can simply work with clean quotes and not complicate the task. It's complicated enough as it is :))

3. **Time t. ** No, I would even say ** TIME ** t. This is the most important parameter! I've read so many topics here - how to properly arrange receipt of tick data, whether each tick is important, etc., etc. If the formula contained just W(x) - then yes, I'd have to receive every tick, and if W(x(t)) - then it would read the price with a certain frequency, regardless of whether it was a real tick or not. But, we have exactly

**W(x,t),**which means that

**both of these approaches to receiving data are wrong**. The correct algorithm for reading quotes is as follows - we select a constant t = 1 second and read tick quotes with this frequency .This will be correct.

The left part of the equation is sorted out.

The question - where is the practice, where are all these lots, profits and money at the end of the day? The answer - it will be, it will definitely be. Be patient.

What is the difference between:

...считывать значения цены с определенной частотой в независимости от того был ли это реальный тик или нет...

from

`...выбираем константу t = 1 сек. и с этой частотой считываем тиковые котировки...`

?

**Serqey Nikitin:**

Is it just me, or is the author going to DESIGN his own market theory... and his own pricing...?

It seems to me that PRACTICE is not about creating YOUR market, but about following an EXISTING market...

By and large, creating a DIFFERENT market model will do nothing in terms of PRACTICE with the existing market.

It's possible to try to fit NEW market and current one, but a complete coincidence will never be achieved, so this task is purely theoretical and has nothing to do with practice...

I am simply solving the Fokker-Planck equation numerically, carefully and consistently. At the moment I have already created a model in VisSim system, programmed VisSim+MT4 combination, all this on ECN demo account shows very good results. After the New Year I will move to a real account, which I have consciously opened by investing a good amount.

During this time I want to share my theoretical reasoning with traders-professionals - what if I am mistaken somewhere and it is too early to open a real account?

I'm not trying to teach anyone, on the contrary - I expect a well-rounded, substantiated criticism. And if someone just reads with interest - also good.

**Yury Kirillov:**

How is it different:

from

?

Every t=1 sec. you do not read the current price value, but a specific tick value. I.e. in 1 min=60 sec. you will always have <=60 tick data for calculations, and it will be floating depending on trading intensity.

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Good evening, dear traders!

I decided to leave the forum for a while, and immediately got bored:)))) And just to read, alas - not interesting. Everyone is busy with small problems within their personal models and their personal view of the market. They advise me to read about some miserable GARCHs...

So I'll continue my literary and technical exercises little by little, who is interested - read.

Once again I strongly recommend reading the topics:

probability density function of the pricing process, not the price itself!!!!

Our task is more complicated - we add an integral component to this equation.

So we will numerically solve this equation given the following terms for our case:

drift - moving weighted average WMA

diffusion - weighted dispersion

integral component - the averaged variance of the archive data at a given sample size.

We will continue tomorrow.

Regards,

Alexander_K