![Developing a self-adapting algorithm (Part II): Improving efficiency](https://c.mql5.com/2/41/50_percents__2.png)
![Developing a self-adapting algorithm (Part II): Improving efficiency](https://c.mql5.com/i/articles/overlay.png)
Developing a self-adapting algorithm (Part II): Improving efficiency
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.
![Brute force approach to pattern search (Part III): New horizons](https://c.mql5.com/2/49/Brute_force_approach_to_pattern_search_003_600x314.jpg)
Brute force approach to pattern search (Part III): New horizons
This article provides a continuation to the brute force topic, and it introduces new opportunities for market analysis into the program algorithm, thereby accelerating the speed of analysis and improving the quality of results. New additions enable the highest-quality view of global patterns within this approach.
![Finding seasonal patterns in the forex market using the CatBoost algorithm](https://c.mql5.com/2/49/finding_seasonal_patterns_catboost_600x314.jpg)
Finding seasonal patterns in the forex market using the CatBoost algorithm
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.
![The market and the physics of its global patterns](https://c.mql5.com/2/40/5a55ed9f370f2c15608b457b.png)
![The market and the physics of its global patterns](https://c.mql5.com/i/articles/overlay.png)
The market and the physics of its global patterns
In this article, I will try to test the assumption that any system with even a small understanding of the market can operate on a global scale. I will not invent any theories or patterns, but I will only use known facts, gradually translating these facts into the language of mathematical analysis.
![Neural networks made easy (Part 9): Documenting the work](https://c.mql5.com/2/49/Neural_networks_made_easy_008_600x314.jpg)
Neural networks made easy (Part 9): Documenting the work
We have already passed a long way and the code in our library is becoming bigger and bigger. This makes it difficult to keep track of all connections and dependencies. Therefore, I suggest creating documentation for the earlier created code and to keep it updating with each new step. Properly prepared documentation will help us see the integrity of our work.
![Developing a self-adapting algorithm (Part I): Finding a basic pattern](https://c.mql5.com/2/41/50_percents__1.png)
![Developing a self-adapting algorithm (Part I): Finding a basic pattern](https://c.mql5.com/i/articles/overlay.png)
Developing a self-adapting algorithm (Part I): Finding a basic pattern
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.
![Neural networks made easy (Part 8): Attention mechanisms](https://c.mql5.com/2/49/Neural_Networks_Easy_008_600x314.jpg)
Neural networks made easy (Part 8): Attention mechanisms
In previous articles, we have already tested various options for organizing neural networks. We also considered convolutional networks borrowed from image processing algorithms. In this article, I suggest considering Attention Mechanisms, the appearance of which gave impetus to the development of language models.
![Prices in DoEasy library (part 59): Object to store data of one tick](https://c.mql5.com/2/41/MQL5-avatar-doeasy-library__3.png)
![Prices in DoEasy library (part 59): Object to store data of one tick](https://c.mql5.com/i/articles/overlay.png)
Prices in DoEasy library (part 59): Object to store data of one tick
From this article on, start creating library functionality to work with price data. Today, create an object class which will store all price data which arrived with yet another tick.
![Using spreadsheets to build trading strategies](https://c.mql5.com/2/41/spread_sheets_strategy.png)
![Using spreadsheets to build trading strategies](https://c.mql5.com/i/articles/overlay.png)
Using spreadsheets to build trading strategies
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.
![Timeseries in DoEasy library (part 58): Timeseries of indicator buffer data](https://c.mql5.com/2/49/doeasy_058_600x314.jpg)
Timeseries in DoEasy library (part 58): Timeseries of indicator buffer data
In conclusion of the topic of working with timeseries organise storage, search and sort of data stored in indicator buffers which will allow to further perform the analysis based on values of the indicators to be created on the library basis in programs. The general concept of all collection classes of the library allows to easily find necessary data in the corresponding collection. Respectively, the same will be possible in the class created today.
![Manual charting and trading toolkit (Part II). Chart graphics drawing tools](https://c.mql5.com/2/49/7908_manual_charting_trading_toolkit_002_600x314.jpg)
Manual charting and trading toolkit (Part II). Chart graphics drawing tools
This is the next article within the series, in which I show how I created a convenient library for manual application of chart graphics by utilizing keyboard shortcuts. The tools used include straight lines and their combinations. In this part, we will view how the drawing tools are applied using the functions described in the first part. The library can be connected to any Expert Advisor or indicator which will greatly simplify the charting tasks. This solution DOES NOT use external dlls, while all the commands are implemented using built-in MQL tools.
![Brute force approach to pattern search (Part II): Immersion](https://c.mql5.com/2/49/Brute_force_approach_to_pattern_search_002_600x314.jpg)
Brute force approach to pattern search (Part II): Immersion
In this article we will continue discussing the brute force approach. I will try to provide a better explanation of the pattern using the new improved version of my application. I will also try to find the difference in stability using different time intervals and timeframes.
![How to make $1,000,000 off algorithmic trading? Use MQL5.com services!](https://c.mql5.com/2/68/earn-million_600x314.jpg)
How to make $1,000,000 off algorithmic trading? Use MQL5.com services!
All traders visit the market with the goal of earning their first million dollars. How to do that without excessive risk and start-up budget? MQL5 services provide such opportunity for developers and traders from around the world.
![Neural networks made easy (Part 7): Adaptive optimization methods](https://c.mql5.com/2/49/Neural_Networks_Easy_007_600x314.jpg)
Neural networks made easy (Part 7): Adaptive optimization methods
In previous articles, we used stochastic gradient descent to train a neural network using the same learning rate for all neurons within the network. In this article, I propose to look towards adaptive learning methods which enable changing of the learning rate for each neuron. We will also consider the pros and cons of this approach.
![Analyzing charts using DeMark Sequential and Murray-Gann levels](https://c.mql5.com/2/41/steps.png)
![Analyzing charts using DeMark Sequential and Murray-Gann levels](https://c.mql5.com/i/articles/overlay.png)
Analyzing charts using DeMark Sequential and Murray-Gann levels
Thomas DeMark Sequential is good at showing balance changes in the price movement. This is especially evident if we combine its signals with a level indicator, for example, Murray levels. The article is intended mostly for beginners and those who still cannot find their "Grail". I will also display some features of building levels that I have not seen on other forums. So, the article will probably be useful for advanced traders as well... Suggestions and reasonable criticism are welcome...
![Gradient boosting in transductive and active machine learning](https://c.mql5.com/2/49/gradient_boosting_catboost_ml_600x314.jpg)
Gradient boosting in transductive and active machine learning
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).
![Neural networks made easy (Part 6): Experimenting with the neural network learning rate](https://c.mql5.com/2/49/Neural_Networks_Easy_006_600x314.jpg)
Neural networks made easy (Part 6): Experimenting with the neural network learning rate
We have previously considered various types of neural networks along with their implementations. In all cases, the neural networks were trained using the gradient decent method, for which we need to choose a learning rate. In this article, I want to show the importance of a correctly selected rate and its impact on the neural network training, using examples.
![Optimal approach to the development and analysis of trading systems](https://c.mql5.com/2/40/optimal-approach.png)
![Optimal approach to the development and analysis of trading systems](https://c.mql5.com/i/articles/overlay.png)
Optimal approach to the development and analysis of trading systems
In this article, I will show the criteria to be used when selecting a system or a signal for investing your funds, as well as describe the optimal approach to the development of trading systems and highlight the importance of this matter in Forex trading.
![Timeseries in DoEasy library (part 57): Indicator buffer data object](https://c.mql5.com/2/49/doeasy_057_600x314.jpg)
Timeseries in DoEasy library (part 57): Indicator buffer data object
In the article, develop an object which will contain all data of one buffer for one indicator. Such objects will be necessary for storing serial data of indicator buffers. With their help, it will be possible to sort and compare buffer data of any indicators, as well as other similar data with each other.
![Timeseries in DoEasy library (part 56): Custom indicator object, get data from indicator objects in the collection](https://c.mql5.com/2/49/doeasy_056_600x314.jpg)
Timeseries in DoEasy library (part 56): Custom indicator object, get data from indicator objects in the collection
The article considers creation of the custom indicator object for the use in EAs. Let’s slightly improve library classes and add methods to get data from indicator objects in EAs.
![Practical application of neural networks in trading. Python (Part I)](https://c.mql5.com/2/49/Practical_application_of_neural_networks_in_trading_003_600x314.jpg)
Practical application of neural networks in trading. Python (Part I)
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.
![Neural networks made easy (Part 5): Multithreaded calculations in OpenCL](https://c.mql5.com/2/49/Neural_Networks_Easy_005_600x314.jpg)
Neural networks made easy (Part 5): Multithreaded calculations in OpenCL
We have earlier discussed some types of neural network implementations. In the considered networks, the same operations are repeated for each neuron. A logical further step is to utilize multithreaded computing capabilities provided by modern technology in an effort to speed up the neural network learning process. One of the possible implementations is described in this article.
![Timeseries in DoEasy library (part 55): Indicator collection class](https://c.mql5.com/2/49/doeasy_055_600x314.jpg)
Timeseries in DoEasy library (part 55): Indicator collection class
The article continues developing indicator object classes and their collections. For each indicator object create its description and correct collection class for error-free storage and getting indicator objects from the collection list.
![Neural networks made easy (Part 4): Recurrent networks](https://c.mql5.com/2/49/Neural_Networks_Easy_004_600x314.jpg)
Neural networks made easy (Part 4): Recurrent networks
We continue studying the world of neural networks. In this article, we will consider another type of neural networks, recurrent networks. This type is proposed for use with time series, which are represented in the MetaTrader 5 trading platform by price charts.
![Timeseries in DoEasy library (part 54): Descendant classes of abstract base indicator](https://c.mql5.com/2/49/doeasy_054_600x314.jpg)
Timeseries in DoEasy library (part 54): Descendant classes of abstract base indicator
The article considers creation of classes of descendant objects of base abstract indicator. Such objects will provide access to features of creating indicator EAs, collecting and getting data value statistics of various indicators and prices. Also, create indicator object collection from which getting access to properties and data of each indicator created in the program will be possible.
![Grid and martingale: what are they and how to use them?](https://c.mql5.com/2/40/mql5_martin_grid.png)
![Grid and martingale: what are they and how to use them?](https://c.mql5.com/i/articles/overlay.png)
Grid and martingale: what are they and how to use them?
In this article, I will try to explain in detail what grid and martingale are, as well as what they have in common. Besides, I will try to analyze how viable these strategies really are. The article features mathematical and practical sections.
![Brute force approach to pattern search](https://c.mql5.com/2/49/Brute_force_approach_to_pattern_search_001_600x314.jpg)
Brute force approach to pattern search
In this article, we will search for market patterns, create Expert Advisors based on the identified patterns, and check how long these patterns remain valid, if they ever retain their validity.
![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.
![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.
![Parallel Particle Swarm Optimization](https://c.mql5.com/2/49/Parallel-Particle-Swarm-Optimization_600x314.jpg)
Parallel Particle Swarm Optimization
The article describes a method of fast optimization using the particle swarm algorithm. It also presents the method implementation in MQL, which is ready for use both in single-threaded mode inside an Expert Advisor and in a parallel multi-threaded mode as an add-on that runs on local tester agents.
![Neural networks made easy (Part 3): Convolutional networks](https://c.mql5.com/2/49/Neural_Networks_Easy_003_600x314.jpg)
Neural networks made easy (Part 3): Convolutional networks
As a continuation of the neural network topic, I propose considering convolutional neural networks. This type of neural network are usually applied to analyzing visual imagery. In this article, we will consider the application of these networks in the financial markets.
![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.
![Basic math behind Forex trading](https://c.mql5.com/2/40/56.png)
![Basic math behind Forex trading](https://c.mql5.com/i/articles/overlay.png)
Basic math behind Forex trading
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.
![Advanced resampling and selection of CatBoost models by brute-force method](https://c.mql5.com/2/49/Advanced-resampling-and-selection-of-CatBoost-models-by-brute-force-method_600x314.jpg)
Advanced resampling and selection of CatBoost models by brute-force method
This article describes one of the possible approaches to data transformation aimed at improving the generalizability of the model, and also discusses sampling and selection of CatBoost models.
![Timeseries in DoEasy library (part 51): Composite multi-period multi-symbol standard indicators](https://c.mql5.com/2/49/doeasy_051_600x314.jpg)
Timeseries in DoEasy library (part 51): Composite multi-period multi-symbol standard indicators
In the article, complete development of objects of multi-period multi-symbol standard indicators. Using Ichimoku Kinko Hyo standard indicator example, analyze creation of compound custom indicators which have auxiliary drawn buffers for displaying data on the chart.
![A scientific approach to the development of trading algorithms](https://c.mql5.com/2/40/algorithm_2.png)
![A scientific approach to the development of trading algorithms](https://c.mql5.com/i/articles/overlay.png)
A scientific approach to the development of trading algorithms
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.
![CatBoost machine learning algorithm from Yandex with no Python or R knowledge required](https://c.mql5.com/2/49/CatBoost-machine-learning-algorithm-from-Yandex-with-no-Python-or-R-knowledge-required_600x314.jpg)
CatBoost machine learning algorithm from Yandex with no Python or R knowledge required
The article provides the code and the description of the main stages of the machine learning process using a specific example. To obtain the model, you do not need Python or R knowledge. Furthermore, basic MQL5 knowledge is enough — this is exactly my level. Therefore, I hope that the article will serve as a good tutorial for a broad audience, assisting those interested in evaluating machine learning capabilities and in implementing them in their programs.
![Timeseries in DoEasy library (part 50): Multi-period multi-symbol standard indicators with a shift](https://c.mql5.com/2/49/doeasy_050_600x314.jpg)
Timeseries in DoEasy library (part 50): Multi-period multi-symbol standard indicators with a shift
In the article, let’s improve library methods for correct display of multi-symbol multi-period standard indicators, which lines are displayed on the current symbol chart with a shift set in the settings. As well, let’s put things in order in methods of work with standard indicators and remove the redundant code to the library area in the final indicator program.
![Custom symbols: Practical basics](https://c.mql5.com/2/49/Custom_symbols_Practical_basics_600x314.jpg)
Custom symbols: Practical basics
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.
![Neural networks made easy (Part 2): Network training and testing](https://c.mql5.com/2/49/Neural_Networks_Easy_001_600x314.jpg)
Neural networks made easy (Part 2): Network training and testing
In this second article, we will continue to study neural networks and will consider an example of using our created CNet class in Expert Advisors. We will work with two neural network models, which show similar results both in terms of training time and prediction accuracy.