![Trader's Statistical Cookbook: Hypotheses](https://c.mql5.com/2/12/Trader_Statistics_Recipes_MetaTrader5_Alglib_MQL5__1.png)
![Trader's Statistical Cookbook: Hypotheses](https://c.mql5.com/i/articles/overlay.png)
Trader's Statistical Cookbook: Hypotheses
This article considers hypothesis - one of the basic ideas of mathematical statistics. Various hypotheses are examined and verified through examples using methods of mathematical statistics. The actual data is generalized using nonparametric methods. The Statistica package and the ported ALGLIB MQL5 numerical analysis library are used for processing data.
![Creating an EA that works automatically (Part 10): Automation (II)](https://c.mql5.com/2/50/aprendendo_construindo_010_600x314.jpg)
Creating an EA that works automatically (Part 10): Automation (II)
Automation means nothing if you cannot control its schedule. No worker can be efficient working 24 hours a day. However, many believe that an automated system should operate 24 hours a day. But it is always good to have means to set a working time range for the EA. In this article, we will consider how to properly set such a time range.
![Graphics in DoEasy library (Part 93): Preparing functionality for creating composite graphical objects](https://c.mql5.com/2/44/MQL5-avatar-doeasy-library3-2__5.png)
![Graphics in DoEasy library (Part 93): Preparing functionality for creating composite graphical objects](https://c.mql5.com/i/articles/overlay.png)
Graphics in DoEasy library (Part 93): Preparing functionality for creating composite graphical objects
In this article, I will start developing the functionality for creating composite graphical objects. The library will support creating composite graphical objects allowing those objects have any hierarchy of connections. I will prepare all the necessary classes for subsequent implementation of such objects.
![Developing a Replay System — Market simulation (Part 21): FOREX (II)](https://c.mql5.com/2/57/replay_p21_600x314.jpg)
Developing a Replay System — Market simulation (Part 21): FOREX (II)
We will continue to build a system for working in the FOREX market. In order to solve this problem, we must first declare the loading of ticks before loading the previous bars. This solves the problem, but at the same time forces the user to follow some structure in the configuration file, which, personally, does not make much sense to me. The reason is that by designing a program that is responsible for analyzing and executing what is in the configuration file, we can allow the user to declare the elements he needs in any order.
![Neural networks made easy (Part 21): Variational autoencoders (VAE)](https://c.mql5.com/2/49/Neural_Networks_Easy_013_600x314.jpg)
Neural networks made easy (Part 21): Variational autoencoders (VAE)
In the last article, we got acquainted with the Autoencoder algorithm. Like any other algorithm, it has its advantages and disadvantages. In its original implementation, the autoenctoder is used to separate the objects from the training sample as much as possible. This time we will talk about how to deal with some of its disadvantages.
![MQL5 Market Results for Q1 2013](https://c.mql5.com/2/0/MQL5_Market_Results.png)
![MQL5 Market Results for Q1 2013](https://c.mql5.com/i/articles/overlay.png)
MQL5 Market Results for Q1 2013
Since its founding, the store of trading robots and technical indicators MQL5 Market has already attracted more than 250 developers who have published 580 products. The first quarter of 2013 has turned out to be quite successful for some MQL5 Market sellers who have managed to make handsome profit by selling their products.
![Automated grid trading using limit orders on Moscow Exchange (MOEX)](https://c.mql5.com/2/49/Automated-grid-trading-using-limit-orders-on-Moscow-Exchange-6MOEXe_600x314.jpg)
Automated grid trading using limit orders on Moscow Exchange (MOEX)
The article considers the development of an MQL5 Expert Advisor (EA) for MetaTrader 5 aimed at working on MOEX. The EA is to follow a grid strategy while trading on MOEX using MetaTrader 5 terminal. The EA involves closing positions by stop loss and take profit, as well as removing pending orders in case of certain market conditions.
![Interview with Enbo Lu (ATC 2012)](https://c.mql5.com/2/0/luenbo_avatar614.png)
![Interview with Enbo Lu (ATC 2012)](https://c.mql5.com/i/articles/overlay.png)
Interview with Enbo Lu (ATC 2012)
"Be sure to participate in the Automated Trading Championships, where you can get a truly invaluable experience!" - this is the motto of contestant Enbo Lu (luenbo) from China. He appeared in the TOP-10 of Automated Trading Championship 2012 last week and is now consistently trying to reach the podium.
![Timeseries in DoEasy library (part 53): Abstract base indicator class](https://c.mql5.com/2/49/doeasy_053_600x314.jpg)
Timeseries in DoEasy library (part 53): Abstract base indicator class
The article considers creation of an abstract indicator which further will be used as the base class to create objects of library’s standard and custom indicators.
![Developing a Replay System — Market simulation (Part 06): First improvements (I)](https://c.mql5.com/2/53/replay-p6_600x314.jpg)
Developing a Replay System — Market simulation (Part 06): First improvements (I)
In this article, we will begin to stabilize the entire system, without which we might not be able to proceed to the next steps.
![Neural networks made easy (Part 16): Practical use of clustering](https://c.mql5.com/2/49/Neural_networks_made_easy_006_600x314.jpg)
Neural networks made easy (Part 16): Practical use of clustering
In the previous article, we have created a class for data clustering. In this article, I want to share variants of the possible application of obtained results in solving practical trading tasks.
![Developing a trading Expert Advisor from scratch (Part 13): Time and Trade (II)](https://c.mql5.com/2/49/Developing_a_trading_Expert_Advisor_from_scratch_004_600x314.jpg)
Developing a trading Expert Advisor from scratch (Part 13): Time and Trade (II)
Today we will construct the second part of the Times & Trade system for market analysis. In the previous article "Times & Trade (I)" we discussed an alternative chart organization system, which would allow having an indicator for the quickest possible interpretation of deals executed in the market.
![Brute force approach to patterns search (Part VI): Cyclic optimization](https://c.mql5.com/2/57/bruteforce_approach_cyclic_optimization_600x314.jpg)
Brute force approach to patterns search (Part VI): Cyclic optimization
In this article I will show the first part of the improvements that allowed me not only to close the entire automation chain for MetaTrader 4 and 5 trading, but also to do something much more interesting. From now on, this solution allows me to fully automate both creating EAs and optimization, as well as to minimize labor costs for finding effective trading configurations.
![Forecasting with ARIMA models in MQL5](https://c.mql5.com/2/55/Forecasting_with_ARIMA_models_in_MQL5_600x314.jpg)
Forecasting with ARIMA models in MQL5
In this article we continue the development of the CArima class for building ARIMA models by adding intuitive methods that enable forecasting.
![MQL5 Market Turns One Year Old](https://c.mql5.com/2/0/mql5-market-1year-avatar.png)
![MQL5 Market Turns One Year Old](https://c.mql5.com/i/articles/overlay.png)
MQL5 Market Turns One Year Old
One year has passed since the launch of sales in MQL5 Market. It was a year of hard work, which turned the new service into the largest store of trading robots and technical indicators for MetaTrader 5 platform.
![DoEasy. Controls (Part 6): Panel control, auto resizing the container to fit inner content](https://c.mql5.com/2/49/doeasy_006_600x314.jpg)
DoEasy. Controls (Part 6): Panel control, auto resizing the container to fit inner content
In the article, I will continue my work on the Panel WinForms object and implement its auto resizing to fit the general size of Dock objects located inside the panel. Besides, I will add the new properties to the Symbol library object.
![Cascade Order Trading Strategy Based on EMA Crossovers for MetaTrader 5](https://c.mql5.com/2/84/Cascade_Order_Trading_Strategy_Based_on_EMA_Crossovers_600x314.jpg)
Cascade Order Trading Strategy Based on EMA Crossovers for MetaTrader 5
The article guides in demonstrating an automated algorithm based on EMA Crossovers for MetaTrader 5. Detailed information on all aspects of demonstrating an Expert Advisor in MQL5 and testing it in MetaTrader 5 - from analyzing price range behaviors to risk management.
![Continuous walk-forward optimization (Part 8): Program improvements and fixes](https://c.mql5.com/2/49/Continuous-Walk-Forward-Optimization_008_600x314.jpg)
Continuous walk-forward optimization (Part 8): Program improvements and fixes
The program has been modified based on comments and requests from users and readers of this article series. This article contains a new version of the auto optimizer. This version implements requested features and provides other improvements, which I found when working with the program.
![Backpropagation Neural Networks using MQL5 Matrices](https://c.mql5.com/2/51/ljsnhuhb0-oo9q-wpjy41jz4-qm54hcjep42jwc1-eptmus-qs-mvfbuysh_600x314.jpg)
Backpropagation Neural Networks using MQL5 Matrices
The article describes the theory and practice of applying the backpropagation algorithm in MQL5 using matrices. It provides ready-made classes along with script, indicator and Expert Advisor examples.
![Interview with Sergey Abramov (ATC 2012)](https://c.mql5.com/2/0/26405_avatarr1o.png)
![Interview with Sergey Abramov (ATC 2012)](https://c.mql5.com/i/articles/overlay.png)
Interview with Sergey Abramov (ATC 2012)
The trading robot of Sergey Abramov (26405) is staying in TOP-10 since the second week. However, it caused much anxiety for its developer. As it turned out, the robot contains a small error in position close block. The robot has been developed almost exclusively on the basis of the past years' results.
![Developing a trading Expert Advisor from scratch (Part 10): Accessing custom indicators](https://c.mql5.com/2/49/Developing_a_trading_Expert_Advisor_from_scratch_001_600x314.jpg)
Developing a trading Expert Advisor from scratch (Part 10): Accessing custom indicators
How to access custom indicators directly in an Expert Advisor? A trading EA can be truly useful only if it can use custom indicators; otherwise, it is just a set of codes and instructions.
![The price movement model and its main provisions. (Part 3): Calculating optimal parameters of stock exchange speculations](https://c.mql5.com/2/56/The_price_movement_model_and_its_main_points_Part_3_600x314.jpg)
The price movement model and its main provisions. (Part 3): Calculating optimal parameters of stock exchange speculations
Within the framework of the engineering approach developed by the author based on the probability theory, the conditions for opening a profitable position are found and the optimal (profit-maximizing) take profit and stop loss values are calculated.
![Andrey Voitenko (avoitenko): "Developers benefit from the ideas that they code? Nonsense!"](https://c.mql5.com/2/0/Avoitenko.png)
![Andrey Voitenko (avoitenko): "Developers benefit from the ideas that they code? Nonsense!"](https://c.mql5.com/i/articles/overlay.png)
Andrey Voitenko (avoitenko): "Developers benefit from the ideas that they code? Nonsense!"
A Ukrainian developer Andrey Voitenko (avoitenko) is an active participant of the "Jobs" service at mql5.com, helping traders from all over the world to implement their ideas. Last year Andrey's Expert Advisor was on the fourth place in the Automated Trading Championship 2010, being slightly behind the bronze winner. This time we are discussing the Jobs service with Andrey.
![Data Science and Machine Learning (Part 07): Polynomial Regression](https://c.mql5.com/2/49/Data_Science_and_Machine_Learning_Part_07_Polynomial_Regression_600x314.jpg)
Data Science and Machine Learning (Part 07): Polynomial Regression
Unlike linear regression, polynomial regression is a flexible model aimed to perform better at tasks the linear regression model could not handle, Let's find out how to make polynomial models in MQL5 and make something positive out of it.
![Tips from a professional programmer (Part III): Logging. Connecting to the Seq log collection and analysis system](https://c.mql5.com/2/49/10475_tips_logging_600x314.jpg)
Tips from a professional programmer (Part III): Logging. Connecting to the Seq log collection and analysis system
Implementation of the Logger class for unifying and structuring messages which are printed to the Experts log. Connection to the Seq log collection and analysis system. Monitoring log messages online.
![Creating a ticker tape panel: Improved version](https://c.mql5.com/2/49/ticker_tape_002_600x314.jpg)
Creating a ticker tape panel: Improved version
How do you like the idea of reviving the basic version of our ticker tape panel? The first thing we will do is change the panel to be able to add an image, such as an asset logo or some other image, so that the user could quickly and easily identify the displayed symbol.
![Structures in MQL5 and methods for printing their data](https://c.mql5.com/2/57/formatro_series_mqlformat_600x314.jpg)
Structures in MQL5 and methods for printing their data
In this article we will look at the MqlDateTime, MqlTick, MqlRates and MqlBookInfo strutures, as well as methods for printing data from them. In order to print all the fields of a structure, there is a standard ArrayPrint() function, which displays the data contained in the array with the type of the handled structure in a convenient tabular format.
![Timeseries in DoEasy library (part 52): Cross-platform nature of multi-period multi-symbol single-buffer standard indicators](https://c.mql5.com/2/49/doeasy_052_600x314.jpg)
Timeseries in DoEasy library (part 52): Cross-platform nature of multi-period multi-symbol single-buffer standard indicators
In the article, consider creation of multi-symbol multi-period standard indicator Accumulation/Distribution. Slightly improve library classes with respect to indicators so that, the programs developed for outdated platform MetaTrader 4 based on this library could work normally when switching over to MetaTrader 5.
![Interview with Sergey Pankratyev (ATC 2012)](https://c.mql5.com/2/0/s75-avatar31t.png)
![Interview with Sergey Pankratyev (ATC 2012)](https://c.mql5.com/i/articles/overlay.png)
Interview with Sergey Pankratyev (ATC 2012)
The Championship is coming to an end leaving us with vivid impressions of many unusual trading strategies. However, the trading robot of Sergey Pankratyev (s75) is showing really peculiar things - it is trading all 12 currency pairs opening only long positions. It is not an error but just a response to some certain market conditions.
![How to connect MetaTrader 5 to PostgreSQL](https://c.mql5.com/2/53/How_to_connect_MetaTrader_5_to_PostgreSQL_600x314.jpg)
How to connect MetaTrader 5 to PostgreSQL
This article describes four methods for connecting MQL5 code to a Postgres database and provides a step-by-step tutorial for setting up a development environment for one of them, a REST API, using the Windows Subsystem For Linux (WSL). A demo app for the API is provided along with the corresponding MQL5 code to insert data and query the respective tables, as well as a demo Expert Advisor to consume this data.
![Neural networks made easy (Part 49): Soft Actor-Critic](https://c.mql5.com/2/56/Neural_Networks_are_Easy_Part_49_600x314.jpg)
Neural networks made easy (Part 49): Soft Actor-Critic
We continue our discussion of reinforcement learning algorithms for solving continuous action space problems. In this article, I will present the Soft Actor-Critic (SAC) algorithm. The main advantage of SAC is the ability to find optimal policies that not only maximize the expected reward, but also have maximum entropy (diversity) of actions.
![Studying PrintFormat() and applying ready-made examples](https://c.mql5.com/2/56/printformat_600x314.jpg)
Studying PrintFormat() and applying ready-made examples
The article will be useful for both beginners and experienced developers. We will look at the PrintFormat() function, analyze examples of string formatting and write templates for displaying various information in the terminal log.
![Population optimization algorithms: Gravitational Search Algorithm (GSA)](https://c.mql5.com/2/0/Gravitational_Search_Algorithm_GSA_600x314.jpg)
Population optimization algorithms: Gravitational Search Algorithm (GSA)
GSA is a population optimization algorithm inspired by inanimate nature. Thanks to Newton's law of gravity implemented in the algorithm, the high reliability of modeling the interaction of physical bodies allows us to observe the enchanting dance of planetary systems and galactic clusters. In this article, I will consider one of the most interesting and original optimization algorithms. The simulator of the space objects movement is provided as well.
![Ready-made templates for including indicators to Expert Advisors (Part 2): Volume and Bill Williams indicators](https://c.mql5.com/2/58/Volume_Bill_Williams_indicators_600x314.jpg)
Ready-made templates for including indicators to Expert Advisors (Part 2): Volume and Bill Williams indicators
In this article, we will look at standard indicators of the Volume and Bill Williams' indicators category. We will create ready-to-use templates for indicator use in EAs - declaring and setting parameters, indicator initialization and deinitialization, as well as receiving data and signals from indicator buffers in EAs.
![Population optimization algorithms: Grey Wolf Optimizer (GWO)](https://c.mql5.com/2/50/grey_wolf_optimizer_600x314.jpg)
Population optimization algorithms: Grey Wolf Optimizer (GWO)
Let's consider one of the newest modern optimization algorithms - Grey Wolf Optimization. The original behavior on test functions makes this algorithm one of the most interesting among the ones considered earlier. This is one of the top algorithms for use in training neural networks, smooth functions with many variables.
![Neural networks made easy (Part 25): Practicing Transfer Learning](https://c.mql5.com/2/49/Neural_Networks_Easy_017_600x314.jpg)
Neural networks made easy (Part 25): Practicing Transfer Learning
In the last two articles, we developed a tool for creating and editing neural network models. Now it is time to evaluate the potential use of Transfer Learning technology using practical examples.
![Who Is Who in MQL5.community?](https://c.mql5.com/2/0/whoiswho.png)
![Who Is Who in MQL5.community?](https://c.mql5.com/i/articles/overlay.png)
Who Is Who in MQL5.community?
The MQL5.com website remembers all of you quite well! How many of your threads are epic, how popular your articles are and how often your programs in the Code Base are downloaded – this is only a small part of what is remembered at MQL5.com. Your achievements are available in your profile, but what about the overall picture? In this article we will show the general picture of all MQL5.community members achievements.
![Data Science and Machine Learning (Part 06): Gradient Descent](https://c.mql5.com/2/49/gradient_descent_600x314.jpg)
Data Science and Machine Learning (Part 06): Gradient Descent
The gradient descent plays a significant role in training neural networks and many machine learning algorithms. It is a quick and intelligent algorithm despite its impressive work it is still misunderstood by a lot of data scientists let's see what it is all about.
![Data Science and Machine Learning (Part 13): Improve your financial market analysis with Principal Component Analysis (PCA)](https://c.mql5.com/2/52/pca_600x314.jpg)
Data Science and Machine Learning (Part 13): Improve your financial market analysis with Principal Component Analysis (PCA)
Revolutionize your financial market analysis with Principal Component Analysis (PCA)! Discover how this powerful technique can unlock hidden patterns in your data, uncover latent market trends, and optimize your investment strategies. In this article, we explore how PCA can provide a new lens for analyzing complex financial data, revealing insights that would be missed by traditional approaches. Find out how applying PCA to financial market data can give you a competitive edge and help you stay ahead of the curve
![Matrices and vectors in MQL5: Activation functions](https://c.mql5.com/2/54/matrix_vector_600x314.jpg)
Matrices and vectors in MQL5: Activation functions
Here we will describe only one of the aspects of machine learning - activation functions. In artificial neural networks, a neuron activation function calculates an output signal value based on the values of an input signal or a set of input signals. We will delve into the inner workings of the process.