Moving average(移动平均线)指标基本用法

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.

ATTENTION:THIS INDICATOR IS NOT RELIABLE FOR TRADING IF ITS USED ALONE I RECOMMEND USING ONE OF MY THREE BELOW BEST STRATEGIES INSTEAD.

My mission is to help you become a Professional Trader, In the shortest period of time possible, by giving you a complete FREE Trading system, This system has 3 Main strategies Moving average Strategy, Support and Resistance with Candlestick Strategy and MACD Strategy. All these strategies are tested and profitable.

1. Learn one of these strategies.
2. Open a trading account with the Broker I trust Here.

How is this MA Different from the Others?

1- It offers visual indication of entries and exits.

2- It 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 Moving average(移动平均线)指标基本用法 and wait for the crossover.

3- It offers Sound and Screen message Alerts Moving average(移动平均线)指标基本用法 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 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 Moving average(移动平均线)指标基本用法 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, Moving average(移动平均线)指标基本用法 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 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 Moving average(移动平均线)指标基本用法 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 Moving average(移动平均线)指标基本用法 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 the rollsum function of the zoo package to calculate the rolling sum in 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, 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 Moving average(移动平均线)指标基本用法 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:

Please accept YouTube cookies to play this video. By accepting you will be accessing content from YouTube, a service provided by an external third party.

If you accept this notice, your choice will be saved and the page will refresh.

In addition, you might want to have a look at the related articles on this website. A selection of tutorials is shown Moving average(移动平均线)指标基本用法 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 Moving average(移动平均线)指标基本用法 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 Moving average(移动平均线)指标基本用法 in R.

Let’s load a data set of monthly milk production. We will Moving average(移动平均线)指标基本用法 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 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).

We can also plot the data over our orignal time series to see 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.

Moving average(移动平均线)指标基本用法

My new book ASP.NET Core in Action, Second Edition is available now! It supports .NET 5.0, and is available as an eBook or paperback. You even get a free copy of the first edition of ASP.NET Core in Action!