The system analyzes data like the human brain. With decision-making strategies such as trial and error, segregation, and generalization, the network improves its effectiveness. Neural networks are essential for productive artificial intelligence systems.
The use of this technology is currently being applied to the Forex market. The process of developing neural networks is important for improving the accuracy of artificial intelligence. Artificial intelligence is used all over the globe, from smartphones to cars. In many ways, artificial intelligence makes everyday life easier and more convenient.
With all of this advancement, it is important to keep sight of their faults. For this reason, the Forex market needs to see more development on neural networks before they can be used across the board. We have 2 most common types of problems: 1 Forex regression problem where we try to predict future prices in trading.
Neural Network Forex Trading Example:. Target Variable: Tomorrow Close price. Forex trading classification problem: Input: Yesterday open price, Yesterday high price, Yesterday low price, Last 7 days high price, LAst 7 days low price, Relative Strength Index for Daily chart time frame. Target Variable: 1 or 0, where 1 is profit trade, 0 is loss trade. Neural network systems utilize data and analyze it.
The process in which neural networks analyze information is similar to the cause-effect relationship in human thinking. The probabilities of a situation are analyzed before making a final decision. This is also true for neural network systems. The neural network analyzes past information to make a more informed decision in the future.
This is similar to when a child makes an error when doing a puzzle and corrects it with their next move. This is how the system processes information and makes educated decisions. The biological neural network operates very similarly to the nerves in the human body. For example, all system elements communicate with each other to determine a final answer.
The neural network is multi-layered and detail-oriented. There are two main databases involved with neural networks. There is a training base as well as a test base. Database improvements are completed through trial and error. The network maintains permanent progress.
The system is always using new information to improve the result. The Forex market has been increasingly expanding its technology to improve trading outcomes. Tech developers have the ability to improve the effectiveness of all forms of artificial intelligence greatly. The most important feature of neural networks is their ability to gather data and analyze it. This information is then stored and used when it comes time to make predictions. The system takes time to recognize and learn patterns before it can be used consistently with guaranteed success.
The process of system learning does not take long, which is another benefit of this quick network. The different features of the network include immersion, extraction, neural training, and decision-making. These are the steps involved in creating an accurate prediction. Neural networks have the ability to benefit the forex market significantly.
The main reason for this is due to their accuracy and intuitive instincts. They have the ability to analyze fundamental data as well as technical data. Mechanical systems are not well-equipped to analyze this type of data. Human errors are even more common when faced with analyzing this data. This is why neural networks have the ability to benefit traders greatly.
Another major benefit of neural networks is their quick adaptability. Neural networks do not take a long time to train. This is beneficial as it saves time and resources. Neural networks can help bridge the gap between human intelligence and computers.
Neural networks are already in use today. Popular search engines such as Google already use neural networks to improve their system. Google uses neural networks to analyze and classify images, text, and other data. The neural network has the ability to sort images and distinguish certain features from others. Google translate also utilizes neural networks in part.
For example, the translations have become more accurate with the use of neural networks. If you need to print the contents of the two-dimensional matrix to check the accuracy of input and output data, use the "imprimeDatosEntra " function. These data are then passed to a.
We will not dwell on that since this is a secondary function for the current article's subject. You can use Excel to verify. Usually, before we start optimizing the network, it is considered appropriate for the input data to be located within a certain range in other words, to be normalized. To achieve this, use the following function that performs normalization of input or output data at your choice located in the "arDatos" array of the CMatrixDouble class:.
Reiteration is performed if the decision is made to normalize NN training input data within a certain interval. Make sure that real data to be used for requesting the NN forecast are also inside the range. Otherwise, normalization is not required. Remember that "intervEntrada" and "intervSalida" are string type variables defined as inputs see the beginning of the "Implementation in MQL5" section. They may look as follows, for example "0;1" or "-1;1", i.
The "StringSplit " function passes the string to the array that is to contain these relative extreme values. The following should be done for each column:. In order to avoid potential value inheritance inside the data array, we can arbitrarily change iterate the sequence of strings inside the array. To do this, apply the "barajaDatosEntra" function that iterates over the CMatrixDouble array strings and defines a new target string for each string considering data position of each line and moving data using the bubble method "filaTmp" variable.
After resetting the random "MathSrand GetTcikCount " descendant series, the "randomEntero " function becomes responsible for where exactly the strings are randomly moved to. As we have already mentioned, we are to use:. The second algorithm is to be used to optimize the network with the number of weights exceeding We also define cycles, or training epochs "ciclosEntrena"; number of times the algorithm performs fitting looking for the least probable "training error" ; the value recommended in the documentation is 2.
Our tests did not demonstrate improved results after increasing the number of training epochs. We also mentioned the "Training ratio" "tasaAprende" parameter. Let's define the "infoEntren" object of the CMLPReportShell class at the beginning of the function that will collect training result data. The average training error mean square error of all output data relative to the output data obtained after processing by the algorithm is obtained using the "MLPRMSError " function.
Besides, a user is informed on time spent for optimization. Initial and end times in tmpIni and tmpFin variables are used for that. These optimization functions return the execution error code "codResp" that can take the following values:. Thus, the correct execution will return 2 or 6 according to the number of weights of the optimized network. These algorithms perform configuration so that reiteration of training cycles "ciclosEntrena" variable has almost no effect on the error obtained unlike the "back propagation" algorithm where reiteration can significantly change the obtained accuracy.
For security reasons, the network should be saved to the disk in case unexpected errors occur during the EA operation. To do this, we should use the functions provided by ALGLIB to receive the network characteristics and internal values number of layers and neurons in each layer, value of weights, etc. As we can see the sixth code string,. I have spent hours debugging this function but it works! If the work should be continued after an event that stopped the EA, we restore the network from the file on the disk using the function opposite to the one described above.
This function creates the network object and fills it with data reading them from the text file where we stored the NN. This function assumes the ability to change normalization applied to the output data in the training matrix. After the EA optimizes the neural network as well as the inputs applied to the EA during its operation with no optimization built into the strategy tester, the basic algorithm described in the section 1 should be repeated.
In addition, we have an important task: the EA should continuously monitor the market without losing its control during the NN optimization involving great amount of computing resources. We should also set another enumeration allowing users to decide whether the EA acts as the network "optimizer" or the strategy "actuator".
Therefore, let's launch the EA in the execution mode on the first chart and run it in the optimization mode on the second one. On the first chart, the EA manages the strategy, while on the second, it only optimizes the neural networks. Thus, the second described issue is solved. On the first chart, the EA "uses" the neural network "reading" it from the text file that it generates in the "optimizer" mode each time it optimizes the NN.
We have already noted that optimization tests took about minutes of computational time. This method slightly increases the process taking minutes depending on Asian or European market activity time but the strategy management never stops. The time should correspond with the low or zero market activity: for example, in Europe it should be early morning h as the default value or on weekends. If you want to perform optimization every time the EA is launched without waiting for a defined time and day, specify as follows:.
In order to define if the network is to be considered optimized "optimizer" mode and define the last network file reading time "actuator" mode , we should define the following open variables:. To solve the first issue, the specified method processing block is set in the OnTimer function that is to be executed according to "tmp" period, which in turn is set via EventSetTimer tmp in OnInit at least every hour. Thus, every tmp seconds, the "optimizer" EA checks if the network should be re-optimized, while the "actuator" EA checks if the network file should be read again because it has been updated by the "optimizer".
This algorithm is currently used in the tested EA allowing us to manage the entire strategy. Every night, from a. In order to check the system, we should solve the task that already has a known solution there is an appropriate algorithm and compare it to the one provided by the neural network. Let's create the binary-decimal converter. The following script is provided for test:.
After creating the NN, we should train it with the first natural numbers in binary form 10 characters, 10 input neurons and 1 output neuron. After that, we should transform the next natural numbers to binary form from to in binary form and compare the real result with the one predicted by the NN.
For example, if we set to the network in binary form; 10 characters, 10 input neurons , the network should receive or some other figure close to it. The For method described above is responsible for receiving the network forecast concerning these numbers and comparing the expected result with the estimated one. After executing the script with the specified parameters the NN without hidden layers, 10 input neurons and 1 output neuron , we obtain a great result.
As we can see, the script has printed out the training matrix up to in binary input and decimal output forms. The NN has been trained and we printed out the answer starting with The third column contains 'true'. This is the result of a comparison between the expected and the actual results.
As already mentioned, this is a good result. However, if we define the NN structure as "10 input neurons, 20 first hidden layer neurons, 8 second hidden layer neurons, 1 output neuron", we obtain the following:. This is an unacceptable result! Here we face a serious issue while processing the neural network: What is the most suitable internal configuration number of layers, neurons and activation functions?
This issue can be solved only by solid experience, thousands of tests conducted by users and reading appropriate articles, for example " Evaluation and selection of variables for machine learning models ". Besides, we applied the training matrix data in Rapid Miner statistical analysis software in order to find the most efficient structure before implementing it in MQL5.
Let's consider a similar task. This time, the NN will define whether it is a prime number or not. The training matrix will contain 10 columns with 10 characters of each natural number in binary form up to and one column indicating whether it is a prime number "1" or not "0". In other words, we will have lines and 11 columns. Next, we should make the NN analyze the next natural numbers in binary form from to and define which number is prime and which is not.
Since this task is more difficult, let's print out the statistics of obtained matches. After executing the script with the specified parameters the NN without hidden layers, 10 input neurons, 20 neurons in the first hidden layer and 1 in the output layer , the result is worse than the one in the previous task. Below is the statistical summary:. However, out of 29 prime numbers present within the interval, it has managed to detect only 13 If we conduct a test with the following network structure: "10 input layer neurons, 35 first hidden layer neurons, 10 second hidden layer neurons and 1 output layer neuron", the script displays the following data during its execution:.
As we can see on the image below, the results have worsened with the time of 0. Thus, we find ourselves asking again: How to define the network internal structure in the best way? We have proposed a solution for the issue that prevents the EA from managing a trading strategy when it performs the network configuration involving considerable computing resources.
After that, we used a part of the proposed code to solve two tasks from the MQL5 program: binary-decimal conversion, detecting prime numbers and tracking the results according to the NN internal structure. Will this material provide a good basis for implementing an efficient trading strategy?
We do not know yet, but we are working on it. At this stage, this article seems to be a good start. Just to help, I work with AlgLib is so many platforms, and MQL is one of the most defective, like it's almost impossible to work with training tasks due to extremely poor resource management.
It is corroborated by the fact that neural forex networks are used in many spheres of our life including trading. However, you have to decide for yourself whether they are useful for you or not. Not a single neural forex network even the best one can predict future price by means of simply pressing of a button.
However, you can use neural forex network to make predictions with a certain probability and thus it will help you to make better trading decisions. The limited capabilities of neural forex networks do not prevent them from being effective tools of market analysis, especially, when there are a lot of noise and non-linear connections. The Hlaiman EA Generator application must be up and running. Neural network teaching preparation.
Prior to running the 'TeachHNN' script, you should make sure it has all the appropriate settings. It has the following parameters:. The TeachHNN script parameters. If everything is ready, click 'OK' to start the process of teaching the Expert Advisor. This will initiate the automatic generation of graphical patterns for each of the available signals in the chart.
The relevant information is displayed in the 'Experts' tab on the 'Toolbox' panel of the terminal, while the corresponding objects appear in the Hlaiman EA Generator window. Upon completion of the pattern generation, the teaching process starts. It is displayed in the teaching progress bar that appears on the screen. Wait until the process is completed.
The teaching process can be terminated before it is completed by right-clicking on the teaching progress bar and selecting the appropriate option in the context menu. Upon completion of the teaching process and script operation, the relevant message will be added to the log in the 'Experts' tab, e. On patterns' indicates that the teaching of the Expert Advisor was successfully completed using signals.
These messages show how many patterns were involved in the teaching process and find out the numbers of those patterns. The TeachHNN script messages in the course of teaching. The reasons why the process of teaching the Expert Advisor may start with an error are as follows:.
When the teaching of the Expert Advisor is completed, you can view the relevant results by switching to GUI Hlaiman and selecting the appropriate objects and visualization panels. The 'Text' tab of the Hlaiman application. The 'Graph' tab of the Hlaiman application. To do this, select the name of the trained Expert Advisor, symbol, time frame, interval and other testing parameters in the Strategy Tester.
Set the external variables, if necessary, and run the test. Below is an example of the Expert Advisor operation report in the Strategy Tester. The Expert Advisor has been taught using automatically generated signals, with all the external parameters of the teaching script being set by default.
The teaching period: The class is inherited from the CExpertSignal base class and includes all the necessary data fields and methods for the operation and integration of Hlaiman, as well as for working with Expert Advisors generated using the MQL5 Wizard. Following the creation of the class instance using the constructor, this object can work in two main modes:.
The mode is identified upon calling the InitHNN initialization mode using the Boolean parameter openn. The true value of this parameter initiates the search and opening of, as well as loading and operation of, the data file of the taught neural network in the indicator mode 2.
This mode is considered to be the operating mode and is used in the Expert Advisor for trading. As can be seen from the code, the first initialization step covers an attempt to open a named pipe for connectivity with the Hlaiman application. If this attempt fails e. At the second step upon the successful completion of the first step and operating indicator mode , local and common folders of the terminal are searched for the required file name with the neural network data.
The third step deals with the preparation of the code in ObjectPascal Delphi for the initialization directly in the Hlaiman application. The text of the code is then moved to the source string. As defined in the text, the object-based environment of the MetaTrader 5 Hlaiman plug-in represents tree architecture, with the object of the plug-in lying at the root.
In case of successful translation and execution of the source code passed via the named pipe, the returned Result value will contain the number of elements of the neural network input vector. As the code suggests, this value is used to initialize the pattern array and the method execution is completed with a positive status.
The first one returns the result of the neural network calculation in the indicator mode. The other two methods are used in the teaching mode when collecting patterns and initiating the neural network teaching process, respectively. As can be seen from the code, the method body primarily consists of the source lines, whose text is arranged similarly to the texts considered above in the InitHNN method description.
The only difference being that the object-based hierarchy of the plug-in has two more levels for the pattern representation - order and tick. Furthermore, the code contains additional object properties and methods. The CalculateHNN method is also different from other methods in that the type of the 'main' value returned by the function in this case is 'double'.
This value is the neural network output - the signal, whereby the BUY level lies in the range This signal is used by the Expert Advisor in taking decisions regarding opening or closing of trading positions and is controlled by the Direction method.
This method performs recalculation in case of the new bar and returns its value expressed as percentage. To set the signal response threshold of the Expert Advisor in respect of signals for opening and closing trading positions, you can use the following external variables:.
In practice, signal levels depend on the neural network teaching quality and intensity which, as a rule, can be assessed visually by monitoring the decrease dynamics of the computational error displayed in the indicator in the course of teaching. Hlaiman EA Generator provides components and a transparent controlled object-based environment for the integration with MQL5, whereby:. The use of the script interpreter causing the integrated computing system to appear not very high performing can be considered as one of the disadvantages of the implementation provided above.
First off, however, it should be noted that the script code interpretation, as well as the operation of the Hlaiman plug-in is asynchronous with EX5, i. Second, to increase the speed of time-consuming calculations, e. Launching a trading terminal on a separate computer, you will not only gain in performance but you may also increase its security. When put in perspective, we can look into development of Expert Advisors that can self-learn in the course of trading.
At this point the easiest way to do this is by combining the code of the Expert Advisor with the teaching script code as both of them use the same CSignalHNN class that provides the required functionality. This material can become the subject of the follow-through article or form the basis of a completely new one, if it appears to be of interest.
MetaTrader 5 — Expert Advisors. Ivan Negreshniy. Introduction Virtually every trader knows about the existence of neural networks. The choice of tools to solve the task at hand is far from being random: MQL5 Wizard is an efficient and the fastest mechanism of automatic MQL5 code generation to date that allows you to scale the generated code using additional modules. Hlaiman EA Generator is a neural network engine with a flexible mechanism of object integration, programmable directly in the MQL5 code of an Expert Advisor.
General Description Due to the reason outlined in the article's objective, you will not find here any theoretical information, classifications and structure of neural networks or research data related to financial markets.
The SampleHNN Expert Advisor Before we proceed to teaching the generated Expert Advisor, we need to open a chart with the required symbol and time frame in the terminal. Neural network teaching preparation To teach the Expert Advisor, select 'TeachHNN' under 'Scripts' in the Navigator panel of the terminal and activate it for the specified chart.
They also have a unique quality to track barely detectable interconnections in accessible data; other methods do not allow you to do this. The ability to create patterns based on analysis data makes neural forex network method absolutely unique among other methods and tools. You can effectively use neural forex network for: - Evaluating probability of trend continuation - Classification of market phases - Temporary prediction of maximum and minimum formation for different timeframes - Predicting the probability of fluctuating movements after trends and following corrections - Inter-market interconnections tracking In other words, you will get the tool that is much more effective than classic methods of technical analysis for cases when there is a lot of noise on the market or when data interconnection is not obvious and linear.
Home Expert Advisors Forex Neural. Login or create an account to earn Points! Forex Robots based on FOREX Neural Neural forex network is an algorithm, which imitates nervous activity of living beings with some part of inaccuracy.
The introduction of additional non-linear filters and their combination into multilayer neural networks allows to consider more factors and increases the accuracy of the forecast. When optimizing, the higher-order surfaces are used. Neural network advisor: opinions of skeptics and optimists. Skeptics bring several good arguments that, in their opinion, make predicting the behavior of prices meaningless:.
Neural network advisor, according to the optimists, is the future of trading. Their main argument is that on large timeframes the pronounced trend areas are seen. If the price movement was really chaotic, the chart would have been approaching to a straight line with the timeframe increase. Nevertheless, at the present stage of development, the effectiveness of neural networks can in the best case be comparable with the results of the technical analysis and indicator strategies.
Join us:. Forex About the site. During the creation of any trading robot, the trader goes through such steps as: identification of patterns in the currency market and the formulation of clear strategy rules; description of the resulting trading strategy in the language understandable to the computer; advisor backtesting and optimization if necessary ; trading on demo account; trading with real money; periodic optimization to maximize profits.
How the neural network advisor works All existing neural advisors using neural networks of any complexity and various filters solve the same problem — the assignment of the object to a particular class. Risk Disclosure: Dewinforex. All information is provided for reference and cannot be considered as a recommendation.
Website administration is not responsible for damages resulting from the use of the information provided. Settlement of transactions in the foreign exchange and stock markets involves taking concomitant, high risks by the trader.
Share ideas, debate tactics, and swap war stories with forex traders from around the world. auri.jashe.xyz › › Metatrader Expert Advisor. Neural Networks FX EA Real Test started with help of Investor Access. Neural Networks FX EA Metatrader Expert Advisors based on FOREX Neural.