Probability
Cumulative distribution function - Probability Cumulative distribution function (tScore and zScore)
This script provides the calculation of the cumulative distribution function (i.e., probability). The measure allows you to calculate the chances of a value of interest being above or below a hypothesized value over the measurement period—nothing fancy here, just good old statistics and mathematics. The closer you are to 0 or 1, the more significant your measurement. We’ve included a significance level highlighting feature. The ability to turn price and/or volume off.
We have included both the Z and T statistics. Where the ‘Z’ is looking at the difference of the current value, minus the mean, and divided by the standard deviation. This is usually pretty noisy on a single value, so a smoother is included. Nice shoutout to the Pinecoders Github Page with this function also. The t-statistic is measuring the difference between a short measurement, an extended measurement, and divided by the standard error (sigma/sqrt(n)). Both of these are neatly wrapped into a function, so please feel free to use them in your code. Add a bit of science to your guessing game. For the purists out there, we have chosen to use sigma in the t-statistic because we know the population's behavior (as opposed to the s-measure). We’ve also included two levels of the t-statistic cumulative distribution function if you are using a short sample period below 6.
Finally, because everyone loves choices, we’ve included the ability to measure the probability of:
the current value (Price and volume)
change
percent change
momentum (change over a period of time)
Acceleration (change of the change)
contribution (amount of the current bar over the sum)
volatility (natural log ratio of today and the previous bar)
Here is a chart example explaining some of the data for the function.
Here are the various options you have the print the different measurements
A comparison of the t-statistic and z-statistic (t-score and z-score)
And the coloring options
Momentum adjusted Moving Average by DGTA brand new Moving Average , calculated using Momentum, Acceleration and Probability (Psychological Effect).
Momentum adjusted Moving Average(MaMA) is an indicator that measures Price Action by taking into consideration not only Price movements but also its Momentum, Acceleration and Probability. MaMA, provides faster responses comparing to the regular Moving Average
Here is the math of the MaMA idea
Momentum measures change in price over a specified time period
momentum = source – source(length)
where,
source, indicates current bar’s price value
source(length), indicates historical price value of length bars earlier
Lets play with this formula and rewrite it by moving source(length) to other side of the equation
source = source(length) + momentum
to avoid confusion let’s call the source that we aim to predict as adjustedSource
adjustedSource = source(length) + momentum
looks nice the next value of source simply can be calculated by summing of historical value of the source value and value of the momentum. I wish it was so easy, the formula holds true only when the momentum is conserved/constant/steady but momentum move up or down with the price fluctuations (accelerating or decelerating)
Let’s add acceleration effects on our formula, where acceleration is change in momentum for a given length. Then the formula will become as (skipped proof part of acceleration effects, you may google for further details)
adjustedSource = source(length) + momentum + 1/2 * acceleration
here again the formula holds true when the acceleration is constant and once again it is not the case for trading, acceleration also changes with the price fluctuations
Then, how we can benefit from all of this, it has value yet requires additional approaches for better outcome
Let’s simulate behaviour with some predictive approach such as using probability (also known as psychological effect ), where probability is a measure for calculating the chances or the possibilities of the occurrence of a random event. As stated earlier above momentum and acceleration are changing with the price fluctuations, by using the probability approach we can add a predictive skill to determine the likelihood of momentum and acceleration changes (remember it is a predictive approach). With this approach, our equations can be expresses as follows
adjustedSource = source(length) + momentum * probability
adjustedSource = source(length) + ( momentum + 1/2 * acceleration ) * probability , with acceleration effect
Finally, we plot MaMA with the new predicted source adjustedSource, applying acceleration effect is made settable by the used from the dialog box, default value is true.
What to look for:
• Trend Identification
• Support and Resistance
• Price Crossovers
Recommended settings are applied as default settings, if you wish to change the length of the MaMA then you should also adjust length of Momentum (and/or Probability). For example for faster moving average such as 21 period it would be suggested to set momentum length to 13
Alternative usage , set moving average length to 1 and keep rest lengths with default values, it will produce a predictive price line based on momentum and probability. Experience acceleration factor by enabling and disabling it
Conclusion
MaMA provide an added level of confidence to a trading strategy and yet it is important to always be aware that it implements a predictive approach in a chaotic market use with caution just like with any indicator
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
Disclaimer : The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
multi pack fisher's and EMACross and Probabilty densityFisher dönüşümün farklı türlerini en çok kullanılan indikartörlerle yeniden sentezlenmesi sonucu ve farklı ema kesimlerine olasılık dağılım yoğunluğu eklenerek içinde bulunan piyasanın trend gücünü görseleştirme amaçlanmıştır.Çalışma tamamen eğitim amaçlı olup, farklı indikatörlerin bir arada kullanımını göstermek için hazırlanmıştır.Kesinlikle yatırım tavsiyesi değildir.
Saygılarımla...
LazEngineer ,Elecrical Engineer
// English explanation
It is aimed to visualize the trend of the market containing the result of re-synthesis with the most used indicator in different types of fisher trasform and by adding the density of the distribution Z transform, required for cutting different ema.
Yours truly ...
LazEngineer, Electrical Engineer
Minkovski Distance Period DVOGThis script was created by building my Dependent Variable Odd Generator script on the Minkovski Distance Adaptive Period.
I have tried this on MACD before.
Script related to MACD :
I used an older version that does not use Dow Factor to suit multi timeframe analysis.
In this way, market situations provide the opportunity to see histograms in an adaptive period as a Multi Timeframe.
Minkowski Distance Function Original Script by RicardoSantos :
Regards.
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.
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".
