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.
In this article, we demonstrate features of botbrains.app - a no-code platform for trading robots development. To create a trading robot you don't need to write any code - just drag and drop the necessary blocks onto the scheme, set their parameters, and establish connections between them.
In this article, I decided to conduct a study related to the possibility of reducing multiple states to double-state systems. The main purpose of the article is to analyze and to come to useful conclusions that may help in the further development of scalable trading algorithms based on the probability theory. Of course, this topic involves mathematics. However, given the experience of previous articles, I see that generalized information is more useful than details.
In this article, I decided to highlight the well-known Bernoulli scheme and to show how it can be used to describe trading-related data arrays. All this will then be used to create a self-adapting trading system. We will also look for a more generic algorithm, a special case of which is the Bernoulli formula, and will find an application for it.
This article is a logical continuation of the previous one. It highlights the facts that confirm the conclusions made in the first article. These facts were revealed within ten years after its publication. They are centered around three detected dynamic transient functions describing the patterns in market price changes.
Any trader's trading activity involves various mechanisms and interrelationships including relations among orders. This article suggests a solution of OCO orders processing. Standard library classes are extensively involved, as well as new data types are created herein.
How to write correctly the Requirement Specifications? What should and should not be expected from a programmer when ordering an Expert Advisor or an indicator? How to keep a dialog, what moments to pay special attention to? This article gives the answers to these, as well as to many other questions, which often don't seem obvious to many people.
The article describes the examples of creating directories, copying data, filing, working with the symbols in Market Watch or the common list, as well as the examples of handling errors, etc. All these elements can eventually be gathered in a single script for filing the data in a user-defined format.
A logical continuation of the earlier discussed topic would be the development of multifunctional mathematical models for trading tasks. In this article, I will describe the entire process related to the development of the first mathematical model describing fractals, from scratch. This model should become an important building block and be multifunctional and universal. It will build up our theoretical basis for further development of this idea.
In this series of article, we will try to find a practical application of probability theory to describe trading and pricing processes. In the first article, we will look into the basics of combinatorics and probability, and will analyze the first example of how to apply fractals in the framework of the probability theory.
In this article, we will continue to study fractals and will pay special attention to summarizing all the material. To do this, I will try to bring all earlier developments into a compact form which would be convenient and understandable for practical application in trading.
In this article I will try to expand the classic concept of swap trading methods. I will explain why I have come to the conclusion that this concept deserves special attention and is absolutely recommended for study.
The article provides the description of the technology aimed at increasing the effectiveness of any automated trading system. It provides a brief explanation of the idea, as well as its underlying basics, possibilities and disadvantages.
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.
This article describes the machine learning technique applied to grid and martingale trading. Surprisingly, this approach has little to no coverage in the global network. After reading the article, you will be able to create your own trading bots.
I continue filling the algorithm with the minimum necessary functionality and testing the results. The profitability is quite low but the articles demonstrate the model of the fully automated profitable trading on completely different instruments traded on fundamentally different markets.
It is impossible to get a truly stable algorithm if we use optimization based on historical data to select parameters. A stable algorithm should be aware of what parameters are needed when working on any trading instrument at any time. It should not forecast or guess, it should know for sure.
The use of computer vision allows training neural networks on the visual representation of the price chart and indicators. This method enables wider operations with the whole complex of technical indicators, since there is no need to feed them digitally into the neural network.
The article considers the creation of machine learning models with time filters and discusses the effectiveness of this approach. The human factor can be eliminated now by simply instructing the model to trade at a certain hour of a certain day of the week. Pattern search can be provided by a separate algorithm.
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.
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 consider active machine learning methods utilizing real data, as well discuss their pros and cons. Perhaps you will find these methods useful and will include them in your arsenal of machine learning models. Transduction was introduced by Vladimir Vapnik, who is the co-inventor of the Support-Vector Machine (SVM).
The article considers the methodology for developing trading algorithms, in which a consistent scientific approach is used to analyze possible price patterns and to build trading algorithms based on these patterns. Development ideals are demonstrated using examples.
In this article, we will analyze the step-by-step implementation of a trading system based on the programming of deep neural networks in Python. This will be performed using the TensorFlow machine learning library developed by Google. We will also use the Keras library for describing neural networks.
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 article is devoted to the programmatic generation of custom symbols which are used to demonstrate some popular methods for displaying quotes. It describes a suggested variant of minimally invasive adaptation of Expert Advisors for trading a real symbol from a derived custom symbol chart. MQL source codes are attached to this article.
Traders often talk about trends and flats but very few of them really understand what a trend/flat really is and even fewer are able to clearly explain these concepts. Discussing these basic terms is often beset by a solid set of prejudices and misconceptions. However, if we want to make profit, we need to understand the mathematical and logical meaning of these concepts. In this article, I will take a closer look at the essence of trend and flat, as well as try to define whether the market structure is based on trend, flat or something else. I will also consider the most optimal strategies for making profit on trend and flat markets.
Training the CatBoost classifier in Python and exporting the model to mql5, as well as parsing the model parameters and a custom strategy tester. The Python language and the MetaTrader 5 library are used for preparing the data and for training the model.
In this article, we consider encryption/decryption of objects in MetaTrader and in external applications. Our purpose is to determine the conditions under which the same results will be obtained with the same initial data.
In this article, we will expand the capabilities of the toolkit: we will add the ability to close trade positions upon specific conditions and will create tables for controlling market and pending orders, with the ability to edit these orders.
The Signals service introduces social trading with MetaTrader 4 and MetaTrader 5. The Service is integrated into the trading platform, and allows anyone to easily copy trades of professional traders. Select any of the thousands of signal providers, subscribe in a few clicks and the provider's trades will be copied on your account.
Today, many traders switch to automated trading systems which can require additional setup or can be fully automated and ready to use. However, there is a considerable part of traders who prefer trading manually, in the old fashioned way. In this article, we will create toolkit for quick manual trading, using hotkeys, and for performing typical trading actions in one click.
This is the first article in a series, in which I am going to describe a toolkit which enables manual application of chart graphics by utilizing keyboard shortcuts. It is very convenient: you press one key and a trendline appears, you press another key — this will create a Fibonacci fan with the necessary parameters. It will also be possible to switch timeframes, to rearrange layers or to delete all objects from the chart.
In the fifth article related to the creation of a trading signal monitor, we will consider composite signals and will implement the necessary functionality. In earlier versions, we used simple signals, such as RSI, WPR and CCI, and we also introduced the possibility to use custom indicators.
In the previous article, we created the application framework, which we will use as the basis for all further work. In this part, we will proceed with the development: we will create the visual part of the application and will configure basic interaction of interface elements.
In this part, we expand the trading signal searching and editing system, as well as introduce the possibility to use custom indicators and add program localization. We have previously created a basic system for searching signals, but it was based on a small set of indicators and a simple set of search rules.
In the previous article, we developed the visual part of the application, as well as the basic interaction of GUI elements. This time we are going to add internal logic and the algorithm of trading signal data preparation, as well us the ability to set up signals, to search them and to visualize them in the monitor.
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.
The MetaTrader 5 platform allows developing and testing trading robots that simultaneously trade multiple financial instruments. The built-in Strategy Tester automatically downloads required tick history from the broker's server taking into account contract specifications, so the developer does not need to do anything manually. This makes it possible to easily and reliably reproduce trading environment conditions, including even millisecond intervals between the arrival of ticks on different symbols. In this article we will demonstrate the development and testing of a spread strategy on two Moscow Exchange futures.