## ATR Ratio v1a

Indicators based on Indicator of the average true range … ATR_Ratio_v1a .

астам жүктеп 2000 MT4 көрсеткіштер мен кеңесшісі. MetaTrader 4 Expert Advisors … ATR_Ratio_v1a.mq4 ATR_Ratio_v2.mq4 ATREA.mq4 ATR-MQLS.mq4 AutoDayFibs.mq4 AutoDayFibs1.mq4

ATR_Ratio_v1a.mq4 . ATR_Ratio_v2.mq4 . ATR_Separate_Labeled.mq4 . Bandwidth Indicator.mq4 . BS_RSI.mq4 . CCI woodies.mq4 . CCI_MA.mq4 . CCI_MA_Smoothed.mq4 . CCI_Woodies_Lnx_v1_1.mq4

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**Екілік Options көрсеткіштері – Жүктеу нұсқаулары**

ATR Ratio v1a is a Metatrader 4 (MT4) индикатор және форекс индикаторы мәні жинақталған тарихы деректерді өзгертуді болып табылады.

ATR Ratio v1a provides for an opportunity to detect various peculiarities and patterns in price dynamics which are invisible to the naked eye .

Осы ақпарат негізінде, трейдерлер тиісінше одан әрі баға қозғалысын болжауға және олардың стратегиясын реттеуге болады.

**How to install ATR Ratio v1a.mq4 ?**

- Download ATR Ratio v1a.mq4
- Copy ATR Ratio v1a.mq4 to your Metatrader Directory / сарапшылар / көрсеткіштері /
- Егер Metatrader Client бастаңыз немесе қайта іске қосыңыз
- Диаграмманы таңдаңыз және Сіз өз көрсеткішін тексеру үшін келеді мерзімі
- Іздеу “Custom көрсеткіштері” Сіздің Navigator негізінен сіздің Metatrader Клиенттің қалды
- Right click on ATR Ratio v1a.mq4
- Диаграмма бекітіңіз
- Параметрлерін немесе ОК түймесін басыңыз өзгерту
- Indicator ATR Ratio v1a.mq4 is available on your Chart

**How to remove ATR Ratio v1a.mq4 from your Metatrader Chart ?**

- Индикатор Сіздің Metatrader тұтынғышында Диаграмма таңдаңыз
- Диаграмма тінтуірдің оң жақ түймешігін басыңыз
- “Индикаторлар тізімі”
- Көрсеткішті таңдаңыз және жою

**Бинарлық опциялары индикаторлары жүктеп алу үшін төменде мұнда басыңыз:**

## Some R Packages for ROC Curves

In a recent post, I presented some of the theory underlying ROC curves, and outlined the history leading up to their present popularity for characterizing the performance of machine learning models. In this post, I describe how to search CRAN for packages to plot ROC curves, and highlight six useful packages.

Although I began with a few ideas about packages that I wanted to talk about, like ROCR and pROC, which I have found useful in the past, I decided to use Gábor Csárdi’s relatively new package pkgsearch to search through CRAN and see what’s out there. The package_search() function takes a text string as input and uses basic text mining techniques to search all of CRAN. The algorithm searches through package text fields, and produces a score for each package it finds that is weighted by the number of reverse dependencies and downloads.

After some trial and error, I settled on the following query, which includes a number of interesting ROC-related packages.

Then, I narrowed down the field to 46 packages by filtering out orphaned packages and packages with a score less than 190.

To complete the selection process, I did the hard work of browsing the documentation for the packages to pick out what I thought would be generally useful to most data scientists. The following plot uses Guangchuang Yu’s dlstats package to look at the download history for the six packages I selected to profile.

### ROCR – 2005

ROCR has been around for almost 14 years, and has be a rock-solid workhorse for drawing ROC curves. I particularly like the way the performance() function has you set up calculation of the curve by entering the true positive rate, tpr , and false positive rate, fpr , parameters. Not only is this reassuringly transparent, it shows the flexibility to calculate nearly every performance measure for a binary classifier by entering the appropriate parameter. For example, to produce a precision-recall curve, you would enter prec and rec . Although there is no vignette, the documentation of the package is very good.

The following code sets up and plots the default ROCR ROC curve using a synthetic data set that comes with the package. I will use this same data set throughout this post.

### pROC – 2020

It is clear from the downloads curve that pROC is also popular with data scientists. I like that it is pretty easy to get confidence intervals for the Area Under the Curve, AUC , on the plot.

### PRROC – 2020

Although not nearly as popular as ROCR and pROC , PRROC seems to be making a bit of a comeback lately. The terminology for the inputs is a bit eclectic, but once you figure that out the roc.curve() function plots a clean ROC curve with minimal fuss. PRROC is really set up to do precision-recall curves as the vignette indicates.

### plotROC – 2020

plotROC is an excellent choice for drawing ROC curves with ggplot() . My guess is that it appears to enjoy only limited popularity because the documentation uses medical terminology like “disease status” and “markers”. Nevertheless, the documentation, which includes both a vignette and a Shiny application, is very good.

The package offers a number of feature-rich ggplot() geoms that enable the production of elaborate plots. The following plot contains some styling, and includes Clopper and Pearson (1934) exact method confidence intervals.

### precrec – 2020

precrec is another library for plotting ROC and precision-recall curves.

Parameter options for the evalmod() function make it easy to produce basic plots of various model features.

### ROCit – 2020

ROCit is a new package for plotting ROC curves and other binary classification visualizations that rocketed onto the scene in January, and is climbing quickly in popularity. I would never have discovered it if I had automatically filtered my original search by downloads. The default plot includes the location of the Yourden’s J Statistic.

Several other visualizations are possible. The following plot shows the cumulative densities of the positive and negative responses. The KS statistic shows the maximum distance between the two curves.

In this attempt to dig into CRAN and uncover some of the resources R contains for plotting ROC curves and other binary classifier visualizations, I have only scratched the surface. Moreover, I have deliberately ignored the many packages available for specialized applications, such as survivalROC for computing time-dependent ROC curves from censored survival data, and cvAUC, which contains functions for evaluating cross-validated AUC measures. Nevertheless, I hope that this little exercise will help you find what you are looking for.

## ci.auc

##### Compute the confidence interval of the AUC

This function computes the confidence interval (CI) of an area under the curve (AUC). By default, the 95% CI is computed with 2000 stratified bootstrap replicates.

##### Usage

##### Arguments

a “roc” object from the roc function, or a “smooth.roc” object from the smooth function.

an “auc” object from the auc function.

arguments for the roc function.

a formula (and possibly a data object) of type response

predictor for the roc function.

the width of the confidence interval as [0,1], never in percent. Default: 0.95, resulting in a 95% CI.

the method to use, either “delong” or “bootstrap”. The first letter is sufficient. If omitted, the appropriate method is selected as explained in details.

the number of bootstrap replicates. Default: 2000.

should the bootstrap be stratified (default, same number of cases/controls in each replicate than in the original sample) or not.

if TRUE (default) and the “roc” object contains an “auc” field, re-use these specifications for the test. If false, use optional … arguments to auc . See details.

the name of progress bar to display. Typically “none”, “win”, “tk” or “text” (see the name argument to create_progress_bar for more information), but a list as returned by create_progress_bar is also accepted. See also the “Progress bars” section of this package’s documentation.

if TRUE, the bootstrap is processed in parallel, using parallel backend provided by plyr (foreach).

further arguments passed to or from other methods, especially arguments for roc and roc.test.roc when calling roc.test.default or roc.test.formula . Arguments for auc and txtProgressBar (only char and style ) if applicable.

##### Details

This function computes the CI of an AUC. Two methods are available: “delong” and “bootstrap” with the parameters defined in “roc$auc” to compute a CI. When it is called with two vectors (response, predictor) or a formula (response

predictor) arguments, the roc function is called to build the ROC curve first.

The default is to use “delong” method except for comparison of partial AUC and smoothed curves, where bootstrap is used. Using “delong” for partial AUC and smoothed ROCs is not supported.

With method=»bootstrap» , the function calls auc boot.n times. For more details about the bootstrap, see the Bootstrap section in this package’s documentation.

For smoothed ROC curves, smoothing is performed again at each bootstrap replicate with the parameters originally provided. If a density smoothing was performed with user-provided density.cases or density.controls the bootstrap cannot be performed and an error is issued.

With method=»delong» , the variance of the AUC is computed as defined by DeLong *et al.* (1988) using the algorithm by Sun and Xu (2020) and the CI is deduced with qnorm .

CI of multiclass ROC curves and AUC is not implemented yet. Attempting to call these methods returns an error.

##### Value

A numeric vector of length 3 and class “ci.auc”, “ci” and “numeric” (in this order), with the lower bound, the median and the upper bound of the CI, and the following attributes:

the width of the CI, in fraction.

the method employed.

the number of bootstrap replicates.

whether or not the bootstrapping was stratified.

an object of class “auc” stored for reference about the compued AUC details (partial, percent, . )

The aucs item is not included in this list since version 1.2 for consistency reasons.

##### AUC specification

The comparison of the CI needs a specification of the AUC. This allows to compute the CI for full or partial AUCs. The specification is defined by:

the “auc” field in the “roc” object if reuse.auc is set to TRUE (default). It is naturally inherited from any call to roc and fits most cases.

passing the specification to auc with … (arguments partial.auc , partial.auc.correct and partial.auc.focus ). In this case, you must ensure either that the roc object do not contain an auc field (if you called roc with auc=FALSE ), or set reuse.auc=FALSE .

If reuse.auc=FALSE the auc function will always be called with … to determine the specification, even if the “roc” object do contain an auc field.

As well if the “roc” object do not contain an auc field, the auc function will always be called with … to determine the specification.

Warning: if the roc object passed to ci contains an auc field and reuse.auc=TRUE , auc is not called and arguments such as partial.auc are silently ignored.

##### Warnings

If method=»delong» and the AUC specification specifies a partial AUC, the warning “Using DeLong’s test for partial AUC is not supported. Using bootstrap test instead.” is issued. The method argument is ignored and “bootstrap” is used instead.

If boot.stratified=FALSE and the sample has a large imbalance between cases and controls, it could happen that one or more of the replicates contains no case or control observation, or that there are not enough points for smoothing, producing a NA area. The warning “NA value(s) produced during bootstrap were ignored.” will be issued and the observation will be ignored. If you have a large imbalance in your sample, it could be safer to keep boot.stratified=TRUE .

##### Errors

If density.cases and density.controls were provided for smoothing, the error “Cannot compute the statistic on ROC curves smoothed with density.controls and density.cases.” is issued.

##### References

James Carpenter and John Bithell (2000) «Bootstrap condence intervals: when, which, what? A practical guide for medical statisticians». *Statistics in Medicine* **19**, 1141–1164. DOI: 10.1002/(SICI)1097-0258(20000515)19:9 3.0.CO;2-F.

Elisabeth R. DeLong, David M. DeLong and Daniel L. Clarke-Pearson (1988) «Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach». *Biometrics* **44**, 837–845.

Xu Sun and Weichao Xu (2020) «Fast Implementation of DeLongs Algorithm for Comparing the Areas Under Correlated Receiver Operating Characteristic Curves». *IEEE Signal Processing Letters*, **21**, 1389–1393. DOI: 10.1109/LSP.2020.2337313.

Xavier Robin, Natacha Turck, Alexandre Hainard, *et al.* (2020) «pROC: an open-source package for R and S+ to analyze and compare ROC curves». *BMC Bioinformatics*, **7**, 77. DOI: 10.1186/1471-2105-12-77.