Articles on manual and algorithmic trading in MetaTrader 5

The development of trading strategies primarily focuses on searching for patterns for entering and exiting the market, as well as maintaining positions. If we are able to formalize some patterns into rules for automated trading, then the trader faces the question of calculating the volume of positions, the size of the margins, as well as maintaining a safe level of mortgage funds for assuring open positions in an automated mode. In this article we will use the MQL5 language to construct simple examples of conducting these calculations.

Is it possible to trade on a real MetaTrader 5 account today? How to organize such trading? The article contains the theory of these questions and the working codes used for copying trades from the MetaTrader 5 terminal to MetaTrader 4. The article will be useful both for the developers of Expert Advisors and for practicing traders.

The MetaTrader 5 Client Terminal offers a wide range of opportunities for optimization of Expert Advisor parameters. In addition to the optimization criteria included in the strategy tester, developers are given the opportunity of creating their own criteria. This leads to an almost limitless number of possibilities of testing and optimizing of Expert Advisors. The article describes practical ways of creating such criteria - both complex and simple ones.

In early 2011 we released the first version of the MQL5 Wizard. This new application provides a simple and convenient tool to automatically generate trading robots. Any MetaTrader 5 user can create a custom Expert Advisor without even knowing how to program in MQL5.

The article thoroughly explains the idea behind the Hurst exponent, as well as the meaning of its values and the calculation algorithm. A number of financial market segments are analyzed and the method of working with MetaTrader 5 products implementing the fractal analysis is described.

In this article, we will learn about Bollinger Bands which is one of the most popular indicators in the trading world. We will consider technical analysis and see how to design an algorithmic trading system based on the Bollinger Bands indicator.

In this article, I will continue the development of the topic by improving the flexibility of the previously created algorithm. The algorithm became more stable with an increase in the number of candles in the analysis window or with an increase in the threshold percentage of the overweight of falling or growing candles. I had to make a compromise and set a larger sample size for analysis or a larger percentage of the prevailing candle excess.

The article aims to describe the main features of Forex trading as simply and quickly as possible, as well as share some basic ideas with beginners. It also attempts to answer the most tantalizing questions in the trading community along with showcasing the development of a simple indicator.

The number of trading robots used on the currency markets has significantly increased recently. They employ various concepts and strategies, however, none of them has yet succeeded to create a win-win sample of artificial intelligence. Therefore, many traders remain committed to manual trading. But even for such specialists, robotic assistants or, so called, trading panels, are created. This article is yet another example of creating a trading panel from scratch.

In this article, we finish testing the patterns that can be detected when trading currency pair baskets. Here we present the results of testing the patterns tracking the movement of pair's currencies relative to each other.

The article describes the Fibo levels-based trading system developed by Joe DiNapoli. The idea behind the system and the main concepts are explained, as well as a simple indicator is provided as an example for more clarity.

Automation of trading strategies involving graphical patterns requires the ability to search for extreme points on the charts for further processing and interpretation. Existing tools do not always provide such an ability. The algorithms described in the article allow finding all extreme points on charts. The tools discussed here are equally efficient both during trends and flat movements. The obtained results are not strongly affected by a selected timeframe and are only defined by a specified scale.

This article is dedicated to the Three Line Break chart, suggested by Steve Nison in his book "Beyond Candlesticks". The greatest advantage of this chart is that it allows filtering minor fluctuations of a price in relation to the previous movement. We are going to discuss the principle of the chart construction, the code of the indicator and some examples of trading strategies based on it.

In this article we will view seasonal characteristics of financial time series using Boxplot diagrams. Each separate boxplot (or box-and-whiskey diagram) provides a good visualization of how values are distributed along the dataset. Boxplots should not be confused with the candlestick charts, although they can be visually similar.

Support Vector Machines have long been used in fields such as bioinformatics and applied mathematics to assess complex data sets and extract useful patterns that can be used to classify data. This article looks at what a support vector machine is, how they work and why they can be so useful in extracting complex patterns. We then investigate how they can be applied to the market and potentially used to advise on trades. Using the Support Vector Machine Learning Tool, the article provides worked examples that allow readers to experiment with their own trading.

The article describes the automation of trend lines plotting based on the Fractals indicator using MQL4 and MQL5. The article structure provides a comparative view of the solution for two languages. Trend lines are plotted using two last known fractals.

In the previous article, we considered application of Merrill patterns to various data, such as to a price value on a currency symbol chart and values of standard MetaTrader 5 indicators: ATR, WPR, CCI, RSI, among others. Now, let us try to create a strategy construction set based on Merrill patterns.

The knowledge of programming languages is no longer a prerequisite for creating trading robots. Earlier lack of programming skills was an impassable obstacle to the implementation of one's own trading strategies, but with the emergence of the MQL5 Wizard, the situation radically changed. Novice traders can stop worrying because of the lack of programming experience - with the new Wizard, which allows you to generate Expert Advisor code, it is not necessary.

This is the second part of the article showing the development of a multi-symbol signal Expert Advisor for manual trading. We have already created the graphical interface. It is now time to connect it with the program's functionality.

Using limit orders instead of conventional take profits has long been a topic of discussions on the forum. What is the advantage of this approach and how can it be implemented in your trading? In this article, I want to offer you my vision of this topic.

The article considers three methods which can be used to increase the classification quality of bagging ensembles, and their efficiency is estimated. The effects of optimization of the ELM neural network hyperparameters and postprocessing parameters are evaluated.

This article demonstrates how to utilize Depth of Market (DOM) programmatically and describes the operation principle of CMarketBook class, that can expand the Standard Library of MQL5 classes and offer convenient methods of using DOM.

The article offers basic tools for the OLAP analysis of tester reports relating to single passes and optimization results. The tool can work with standard format files (tst and opt), and it also provides a graphical interface. MQL source codes are attached below.

There are lots of chart types that provide information on the current market situation. Many of them, such as Point and Figure chart, are the legacy of the remote past. The article describes an example of Point and Figure charting using a real time indicator.

The aim of this article is to investigate the possibilities of trade automation and the analysis, on the basis of some ideas from a book by James Hyerczyk "Pattern, Price & Time: Using Gann Theory in Trading Systems" in the form of indicators and Expert Advisor. Without claiming to be exhaustive, here we investigate only the Model - the first part of the Gann theory.

This article considers new capabilities of the darch package (v.0.12.0). It contains a description of training of a deep neural networks with different data types, different structure and training sequence. Training results are included.

The widget provides websites with a detailed release schedule of 500 indicators and indices, of the world's largest economies. Thus, traders quickly receive up-to-date information on all important events with explanations and graphs in addition to the main website content.

This article concludes the series devoted to trading currency pair baskets. Here we test the remaining pattern and discuss applying the entire method in real trading. Market entries and exits, searching for patterns and analyzing them, complex use of combined indicators are considered.

We continue to build ensembles. This time, the bagging ensemble created earlier will be supplemented with a trainable combiner — a deep neural network. One neural network combines the 7 best ensemble outputs after pruning. The second one takes all 500 outputs of the ensemble as input, prunes and combines them. The neural networks will be built using the keras/TensorFlow package for Python. The features of the package will be briefly considered. Testing will be performed and the classification quality of bagging and stacking ensembles will be compared.

The article contains descriptions of the new features available in the updated MQL5 Wizard. The modified architecture of signals allow creating trading robots based on the combination of various market patterns. The example contained in the article explains the procedure of interactive creation of an Expert Advisor.

This is the continuation of Another MQL5 OOP class article which showed you how to build a simple OO EA from scratch and gave you some tips on object-oriented programming. Today I am showing you the technical basics needed to develop an EA able to trade the news. My goal is to keep on giving you ideas about OOP and also cover a new topic in this series of articles, working with the file system.

In this article we will consider in detail the martingale system. We will review whether this system can be applied in trading and how to use it in order to minimize risks. The main disadvantage of this simple system is the probability of losing the entire deposit. This fact must be taken into account, if you decide to trade using the martingale technique.

Following up our previous article on the currency baskets trading principles, here we are going to analyze the patterns traders can detect. We will also consider the advantages and the drawbacks of each pattern and provide some recommendations on their use. The indicators based on Williams' oscillator will be used as analysis tools.

In the upcoming series of articles, I will demonstrate the development of self-adapting algorithms considering most market factors, as well as show how to systematize these situations, describe them in logic and take them into account in your trading activity. I will start with a very simple algorithm that will gradually acquire theory and evolve into a very complex project.

In this article, we will discuss how to develop a trading robot with minimum programming. Of course, MetaTrader 5 provides a high level of control over trading positions. However, using only the manual ability to place orders can be quite difficult and risky for less experienced users.

This article centers around strategies that actively use pending orders, a metalanguage that can be created to formally describe such strategies and the use of a multi-purpose Expert Advisor whose operation is based on those descriptions

The article describes the basic principles and methods that allow you to analyze any strategy using spreadsheets (Excel, Calc, Google). The obtained results are compared with MetaTrader 5 tester.

In this article, we will learn a new tool from our series: we will learn how to design a trading system based on one of the most popular technical indicators Moving Average Convergence Divergence (MACD).

This article presents Pivot Mean Oscillator (PMO), an implementation of the cumulative moving average (CMA) as a trading indicator for the MetaTrader platforms. In particular, we first introduce Pivot Mean (PM) as a normalization index for timeseries that computes the fraction between any data point and the CMA. We then build PMO as the difference between the moving averages applied to two PM signals. Some preliminary experiments carried out on the EURUSD symbol to test the efficacy of the proposed indicator are also reported, leaving ample space for further considerations and improvements.