Dependent Variable Odd Generator Risk Detector
In fact, I wrote this script for detect Bollinger and Linear Regression Bands squeeze.
It's a side script.
Logic works like this:
Only the stagnant market probability is drawn from the Bollinger bandwidth by Dependent Variable Odd Generator and MFI index is calculated taking into account the volume.
This value ranges from 0 to 100.
To be sure, this value is averaged over a small period.
If you break the average and exceed 50, the bollinger band is too narrow and the risk is too high.
This means more commissions, more transactions, and vain work.
Or, when in position, the warning is not ignored due to unnecessary signals.
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
Stay tuned , best regards.
Probability
Dependent Variable Odd Generator For Machine Learning TechniquesCAUTION : 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
Function Markov ProcessEXPERIMENTAL:
this is very experimental and INCOMPLETE, use at your own discretion.
thanks glaz for the help :)
DownAfterLowProba// The script is useful to inspect probability:
// If previous day closed at lowest price for several days
// how often next day would be red bar
// As one can see gray lines indicate bars with lowest close. If next bar is green, increment diff_hi, overwise increment diff_lo
// Probability is counted as diff_lo / (diff_hi+diff_lo)
// One can copy script and change conditions to count other interesting probabilities
[RS]Study into sequential probabilitys V0EXPERIMENTAL:
just some experimentation to check results, putting it out there. :P
odds of the next bar being up or down bar.
Moving CO-covariance (covariance on covariance)This is Covariance on Covariance. It shows you how much a given covariance period has deviated from it mean over another defined period. Because it is a time series, It can allow you to spot changes in how covariance changes. You can apply trend lines, Fibonacci retracements, etc. This is also volume weighting covariance.
This is not a directional indicator nor is moving covariance. This is used for forecasting volatility. This must be used in conjunction with moving covariance.
Moving CovarianceCo-variance is a representation of the average percent data points deviate from there mean. A standard calculation of Co-variance uses One standard Deviation. Using the empirical rule, we can assume that about 68.26% of Data points lie in this range.
The advantage to plotting co variance as a time series is that it will show you how volatility of a trailing period changes. Therefore trend lines and other methods of analysis such as Fibonacci retracements could be applied in order to generate volatility targets.
For the purpose of this indicator I have the mean using a vwma derived from vwap. This makes this measurement of co-variance more sensitive to changes in volume, likewise are more representative a change in volatility, thus giving this indicator a "leading aspect".
