Dependent Variable Odd Generator For Machine Learning Techniques
CAUTION : Not suitable for strategy, open to development.
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
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
Notes de version:
This is the first update. The stationary market in the cluster was recognized and taken for the probability set. On the other hand, band squeeze is added as an external factor, but I will work on how to participate deeper. On the other hand, the upper and lower limits have been removed and made according to the condition of being greater than 50 or less than, and in this way, crossovers are taken into account both simpler and much more effective. Compared to the prices and the generator right now is much more reasonable .Updates will continue. !
Notes de version:
The width of the bollinger band has been added to the optimum level for all market situations. Now is the time to optimize the trend factor.
Notes de version:
As of today, I have started my deep learning research.
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 :
www.tradingview.com/...t-data-in-csv-14395/
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.
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 :
www.tradingview.com/...t-data-in-csv-14395/
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.
Notes de version:
Dependent Variable Odd Generator v2.0 :
- 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 :
- 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 :
Notes de version:
Mistake fixed : Missed Dow Factor added to bear market probability.
Notes de version:
Mistake 2 : Stagnant market shares that were forgotten to be redistributed.
Notes de version:
UPDATES :
- 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 .
- 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 .
Notes de version:
Version 2.1 has been released.
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.
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.