If can we separate the stagnant market from other markets, can we be so much more accurate?
This project was written to research it. It is just the tiny part of the begining.
And this is a very necessary but very small side function in the main function. Lets start :
Hi users, I had this idea in my mind for a long time but I had a hard time finding the parameters that would make the market stagnant. This idea is my first original command system. Although it is very difficult to make sense of the stagnant market, I think that this command system can achieve realistic proportions. With 's money flow index, I opened the track to determine the level. On the other hand, the prices were also using a money flow index, and it forced me to make the limitations between the levels in a logical way. But the good thing is that since the bollinger bandwidth uses a larger period, we are able to print normal values at extreme buy and sell values.
In terms of price, we can define excessive purchase and sale values as the period is smaller. I have repeatedly looked at the limit values that determine the bull, bear, and bollinger bandwidth (mfi), and I think this is the right one. Then I have included these values in the probability set.
The bull and bear market did not form the intersection of the cluster, and because there are connected events, the stagnant market, which is the intersection, will be added to the other markets with the same venn diagram logic and the sum of the probability set will be 1. is equal to. I hope that we can renew the number generators in the very important parameters of machine learning such as Markov Process with generators dependent on dependent variables, which bring us closer to reality. This function is open to development and can be made of various ideas on machine learning. Best wishes.
This code is open source under the MIT license. If you have any improvements or corrections to suggest, please send me a pull request via the github repository github.com/user-Noldo
A few years ago I played a small role in a machine learning study.
It was about engineering, but for finance, we just think that our variables are different, the basic logic is the same.
I will share the commands publicly.
First, I'm going to try some major indicators and oscillators.
Then I will look for ways to use it effectively in this system.
I decided on this new property :
And I realize that there is not a completely proper Deep Learning Script in Tradingview.
I hope I can make it and share it with everyone.
Because the idea is superior to the idea and is open to development.So deep learning scripts will be open source.
I will just get MIT license.
- Dow factor used instead of Bollinger Band Width then script is more accurate and rapid now.
- Indicator design updates.
More information for Dow Factor , here is my script about Dow Theory :
- All bugs fixed.
- Method option added. You can calculate odds with Money Flow Index (MFI) and Relative Strength Index (RSI) methods now !!
- Periods minimum value descreased to 2 .
The index factor in the Dow theory was taken into account .
It now takes into account the effect of volume and index - independent variable, the price(source-close) - dependent variable.
Before starting the analysis, select the relevant market from the menu, the system will set the appropriate index itself.
Factor loads were adjusted.
All bugs fixed.
In true TradingView spirit, the author of this script has published it open-source, so traders can understand and verify it. Cheers to the author! You may use it for free, but reuse of this code in a publication is governed by House Rules. You can favorite it to use it on a chart.