# Discussion of article "Self-adapting algorithm (Part IV): Additional functionality and tests"

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232803

New article Self-adapting algorithm (Part IV): Additional functionality and tests has been published:

I continue filling the algorithm with the minimum necessary functionality and testing the results. The profitability is quite low but the articles demonstrate the model of the fully automated profitable trading on completely different instruments traded on fundamentally different markets.

In the previous article, I demonstrated how the algorithm generates a signal for opening a position and analyzes several scales simultaneously for defining the maximum trend scale. The basic operation algorithm was described. The price series chart does not consist of one scale. The trend can be present on several scales at the same time, while there can be flat on other scales. This feature should be used to make a profit.

Here, a trend section is a segment, on which the trend continuation probability exceeds 50%, while a flat segment is one, on which the trend reversal probability exceeds 50%. In other words, if the previous block was growing, then in the trend section, the new block will also grow with a probability higher than 50%. On a flat chart, a growing block is most probably followed by a falling block. I described the proposed definition in detail in the article "What is a trend and is the market structure based on trend or flat?"

Figure 1. Trend and flat on different scales

Figure 1 shows a clearly visible bearish trend on 32 blocks of 0.00061. The trend is almost absent on 32 blocks with the scale of 0.00131. In most cases, there are simultaneously the scales featuring both trend and flat.

Author: Maxim Romanov

396

Excellent article! Gives much inspiration. Ty for your hard work!

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Marcel Fitzner:
2955

Congratulations!!
Very good article. Thanks for share your knowledge.
It began with a simple idea and was getting more complex.

35

A wonderful research on your self adapting Ai. I find it fascinating. Could it be even better as part of a deep learning machine ??

Perhaps look into the time when price start to deliver as well. they always occur 2 hours before London starts and 3 Hours before NY session.

Institutional price at 00/50/80/20 is the key price level they tend to work from.

and with regards to start cycle and end cycle of the price delivery, It occurs from MOnday to Wednesday, THursday if lucky but tend to reverse for the remainder of the week and Friday.

News is purge session to take out both buyers and sellers .

Just have a look into that see if it can detect that within your block in changes of a trend. use from Daily to m1 to analyse it.

Then you see how Price and why it moves from High to low or low to high. IT depends on the liquidity in the market, the more liquidity, the more it tends to go to it.

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warithu:

A wonderful research on your self adapting Ai. I find it fascinating. Could it be even better as part of a deep learning machine ??

Perhaps look into the time when price start to deliver as well. they always occur 2 hours before London starts and 3 Hours before NY session.

Institutional price at 00/50/80/20 is the key price level they tend to work from.

and with regards to start cycle and end cycle of the price delivery, It occurs from MOnday to Wednesday, THursday if lucky but tend to reverse for the remainder of the week and Friday.

News is purge session to take out both buyers and sellers .

Just have a look into that see if it can detect that within your block in changes of a trend. use from Daily to m1 to analyse it.

Then you see how Price and why it moves from High to low or low to high. IT depends on the liquidity in the market, the more liquidity, the more it tends to go to it.

I was thinking about machine learning and most likely it is possible to tan better results. But I haven't implemented it myself yet. I am not attached to trading sessions and news because I am doing a universal algorithm. The main goal is to find it to work completely automatically in both the stock and foreign exchange markets. I do this in order to better understand the fundamental nature of pricing. The better my theoretical model, the better and more versatile the algorithm becomes.