Moving Average Cross Price Alert MT5
Moving Average Price Crossover alert Indicator: is an indicator that calculates the Precise Entry and Exits for your strategy, using an automated Complex algorithm to alert you at the crossing of the price and the moving average line.
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1- It offers visual indication of entries and exits.
2- It Moving average(移动平均线)指标基本用法 sends push notifications to your phone, to save you screen time and save you energy, so you don't need to sit and watch the chart and wait for the crossover.
3- It offers Sound and Screen message Alerts so you don't miss any opportunity.
How does this Indicator works?
This Indicator work in this main strategies:
1. MA Lines Crossover: In this configuration the MA crosses the Price to the upside that's a buy signal Moving average(移动平均线)指标基本用法 and when the opposite happen or when the MA crosses Price to the down side that's a sell signal, this strategy is fully explained with examples in the screenshots.
Can I use the MA Lines cross signals with my Expert Adviser?
the basic idea is that the indicator is able to return data that can be used directly by the Expert Adviser like the entries signals and the exit signals with high accuracy.
Calculate Moving Average, Maximum, Median & Sum of Time Series in R (6 Examples)
This tutorial shows how to calculate moving averages, maxima, medians, and sums in the R programming Moving average(移动平均线)指标基本用法 language.
The article looks as follows:
Let’s dive right into the examples!
Creation of Example Data
We’ll use the following data as basement for this R programming tutorial:
Have a look at the previous output of the RStudio console. It shows that our example data is a series of numeric values with a length of 100. In a real application, this could be a time series.
Example 1: Compute Moving Average Using User-Defined Function
In Example 1, I’ll explain how to create a user-defined function to calculate a moving average (also called rolling average or running average) in R.
We can create a new function called moving_average as shown below (credit to Matti Pastell’s response in this thread):
Now, we can apply this function to our time series data:
The output are the moving averages of our time series.
Example 2: Compute Moving Average Using rollmean() Function of zoo Package
In case you don’t want to create your own function to compute rolling averages, this example is for you. Example 2 shows how to use the zoo package to calculate a moving average in R.
If we want to use the functions of the zoo package, we first need to install and load zoo:
install.packages("zoo") # Install zoo package library("zoo") # Load zoo
Now, we can use the rollmean function of the zoo package to calculate our running mean:
The output values are the same as in Example 1 (without the NA values at the beginning and at the end of the output vector).
Example 3: Compute Moving Maximum Using rollmax() Function of zoo Package
The following R programming code illustrates how to use the rollmax function of the zoo package to calculate the moving maximum in R.
Example 4: Compute Moving Median Using rollmedian() Function of zoo Package
The R programming code below illustrates how to use the rollmedian function of the zoo package to return the moving median to the RStudio console.
Example 5: Compute Moving Sum Using rollsum() Function of zoo Package
In this Example, I’ll illustrate how to apply Moving average(移动平均线)指标基本用法 Moving average(移动平均线)指标基本用法 the rollsum function of the zoo package to calculate the rolling sum in Moving average(移动平均线)指标基本用法 R.
Example 6: Draw Plot of Time Series, Moving Average, Maximum, Median & Sum
In the previous examples, I have explained how to compute different moving metrics in R. This Example explains how to draw all these values to a Moving average(移动平均线)指标基本用法 graphic.
plot(1:length(my_series), my_series, type = "l", # Plotting series & moving metrics ylim = c(min(my_series), max(my_moving_sum)), xlab = "Time Series", ylab = "Values") lines(1:length(my_series), c(NA, NA, my_moving_average_2, NA, NA), type = "l", col = 2) lines(1:length(my_series), c(NA, NA, Moving average(移动平均线)指标基本用法 my_moving_max, NA, NA), type = "l", col = 3) lines(1:length(my_series), c(NA, NA, my_moving_median, NA, NA), type = "l", col = 4) lines(1:length(my_series), c(NA, NA, my_moving_sum, NA, NA), type = "l", col = 5) legend("topleft", c("Time Series", "Moving Average", "Moving Maximum", "Moving Median", "Moving Sum"), lty = 1, col = 1:5)
Figure 1 shows the output of the previous R programming syntax: A plot showing the different moving metrics.
Video, Further Resources & Summary
Have a look at the following video of my YouTube channel. I explain the topics of this tutorial in the video:
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In addition, you might want to have a look at the related articles on this website. A selection of tutorials is shown below:
Summary: This post illustrated simple ways on how to compute moving averages, maxima, medians, and sums in the R programming language. Don’t hesitate to let me know in the comments, in case you have any further questions.
Moving Average in R
When working with time series, we often want to view the average over a certain number of days. For example, we can view a 7-day rolling average to give us an idea of change from week to week. In this article, we will learn how to conduct a moving average in R.
Let’s load a data set of monthly milk production. We will load it from the url below. The data consists of monthly intervals and kilograms of milk produced.
Now, we convert our data to a time series object then to an zoo object to have access to many indexing methods Moving average(移动平均线)指标基本用法 explored below.
Conducting a moving average
To conduct a moving average, we can use the rollapply function from the zoo package. This function takes three variables: the time series, the number of days to apply, and the function to apply. In the example below, we run a 2-day mean (or 2 day avg).Moving average(移动平均线)指标基本用法
We can also plot the data over our orignal time series to see Moving average(移动平均线)指标基本用法 how the avg smoothed out the data.
Let’s do another example with a 7-day avg which is a common task in disease outbreaks and stocks.
Again, let’s plot the data.
Other Rolling Functions
You may have noticed from the above that we can do more than a rolling average with the rollapply function. We can actually apply any math function. Let’s run a couple of more examples, sum and median.
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