Discussion of article "A scientific approach to the development of trading algorithms"

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New article A scientific approach to the development of trading algorithms has been published:

The article considers the methodology for developing trading algorithms, in which a consistent scientific approach is used to analyze possible price patterns and to build trading algorithms based on these patterns. Development ideals are demonstrated using examples.

Testing was performed in the period from 01.01.2018 to 28.07.2020, on the M1 timeframe, using the real tick mode. Parameters were not optimized, because I want to show that there can be no need to optimize a thoroughly prepared algorithm for each individual currency pair. We will change block size, minimum block size and lot, in an effort to have profit significantly exceeding commission size.



Figure 7.

For EURUSD, as expected, spread and delta took all the profit that we should have received from the asset trend. As a result, the expected payoff us -$1.67 per trade. The lot was changed dynamically, depending on the block size, with the average lot being 0.078. Let us try to understand where the loss comes from. The robot logs information about the spread. The average spread during position opening and closing is 0.00008. We paid swaps of $159.76, and opened 614 positions. So, the average swap per position was 159.76/614=$0.2602.

If the average spread is 0.00008 and the average lot is 0.078, 1 EURUSD pip with a lot of 0.078 is equal to $0.078, and so spread costs 0.078*8=$0.624. In total, the commission is equal to $0.624+$0.2602=$1.104. If we were losing a commission on each deal, the expected payoff would be -$1.104, but it is $1.67, which is $0.566 more. The minimum block size is set to 0.002 in settings, so it makes $15.6 for an average lot of 0.078. Let us roughly estimate the negative balance change if the balance chart were a random walk and the block size was always minimal. It is calculated as 15,6*(614^0.5)=386.55$. Now, add the average commission per trade multiplied by the number of trades. 1.104*614+386.55=$1064.406.

The value is equal to $1064.406, which means the average drawdown of a balance chart if the probability of position opening in the right direction is 50% and a commission is paid for each open position. In reality, we received a loss of $1027.45, which is close to this value. We can conclude that we had a loss, because the expected payoff of our algorithm is zero for EURUSD. 

Let us see the results on the more trending AAPL stocks. The result is shown in Figure 8 below.



Figure 8.

Author: Maxim Romanov

 
I appreciate your articles for their crystalline introductions - masterpieces of understanding the essence of the issue are rare, but you are consistently observed.

I remember I found a simple pattern in the futures market, providing ~50% probability of earning. Almost every session, at the opening, the price would sharply move to buy or sell and it was only necessary to choose the direction. It was impossible to predict it, but it was possible to guess it, and the very fact of a jump at the opening of the session was predicted confidently by the level of pre-market volumes. These conclusions were confirmed statistically. In case of a mistake in choosing the direction, it was necessary to simply exit until the next day and not trade any more. The use of this strategy was successful, but after some time the sharp movements at the opening of the session stopped.

Indeed, patterns exist in the conditions of a session and a particular asset, although the probability of getting into them is never a constant, and the period of use is temporary. That is, patterns have a "shelf life" in the market.


 
Реter Konow:
I appreciate your articles for the crystal introductions - masterpieces of insight are rare, but yours are observed consistently.

I remember I found a simple pattern in the futures market, providing ~50% probability of earning. Almost every session, at the opening, the price would sharply move to buy or sell and it was only necessary to choose the direction. It was impossible to predict it, but it was possible to guess it, and the fact of a jump at the opening of the session was predicted confidently by the level of pre-market volumes. These conclusions were confirmed statistically. In case of a mistake in choosing the direction, it was necessary to simply exit until the next day and not trade any more. The use of this strategy was successful, but after some time sharp movements at the opening of the session stopped.

Indeed, patterns exist in the conditions of a session and a particular asset, although the probability of getting into them is never a constant, and the period of use is temporary. That is, regularities have a "shelf life" in the market.


So I didn't write for nothing, if you like it.

Yes, patterns have a shelf life, one of the options is to create an algorithm that searches for them in real time.... I had a big project, but it was too complicated, so I shelved it.

 
This is the second article I've read that wasn't "diagonally" read. Very interesting. I saw some valuable ideas for myself, which I will definitely use in my own developments.
 
Maxim Romanov:

So you're writing for a reason if you like it.

Yes, patterns have a shelf life, one of the options is to create an algorithm that will search for them in real time.... I had a big project, but it was too complicated, so I shelved it.

I wonder what that project was?

 

in the text

Торговый алгоритм будет следовать напрямую из формулы для определения матожидания прибыли:

m=(P(tp)*tp)-(P(sl)*sl)

there's a vague doubt in my mind that it's not a expectation matrix.

 
Maxim Kuznetsov:

in the text

there's a vague doubt in my mind that it's not a expectation matrix.

why? Please comment, maybe I've made a mistake somewhere

 
Aliaksandr Hryshyn:

I wonder, and what's the project?

I wanted to create an algorithm that learns in real time. In essence, an artificial life, only for financial markets, where the food for individuals will be profit, which they will be able to extract by analysing intra-market and inter-market regularities. Everything should work in real time, without learning from history. That is, you can learn by history, but in fact all the work is in real time on the basis of the deals made. do not put into the algorithm any knowledge about the market, instead give it maximum opportunity and freedom in its development. This is very brief.

 
Maxim Romanov:

why? Please comment on this, maybe I'm wrong somewhere

I'm a little tipsy right now, and I can't do a full search.

but it's something like "average profitability".

Just offhand, checking the formulas - the probability expectation includes probability with -1 degree and it is measured in T (the same values that are on the X axis). The dimensionality does not match, and the probabilities are wrong.

 
Maxim Romanov:

I wanted to create an algorithm that learns in real time. In essence, an artificial life, only for financial markets, where the food for individuals will be profit, which they will be able to extract by analysing intra-market and inter-market regularities. Everything should work in real time, without learning from history. That is, you can learn by history, but in fact all the work is in real time on the basis of the deals made. do not put into the algorithm any knowledge about the market, instead give it maximum opportunity and freedom in its development. That's very brief.

It is ingenious, but VERY expensive, because while the algorithm is learning, it will lose a lot of money ...

Of course, you can train on demo, but who said that when we enter the market for real, it does not change? ;)
 

Maksim, you have a lot of respect for your courage to explore the complex nature of markets!

I noticed for myself that the author is a mathematician and cooperates with a programmer to implement the proposed algorithms. I guess it's easier to solve complex problems that way...

The topics touched upon are very interesting, so keep it up!

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