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Fuzzy logic forex

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A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Using Fuzzy Inference Systems for the Creation of Forex Market Predictive Models Abstract: This paper presents a method for creating Forex market predictive models using multi-agent and fuzzy systems, which have the objective of simulating the interactions that provoke changes in the price.

Agents in the system represent traders performing buy and sell orders in a market, and fuzzy systems are used to model the rules followed by traders performing trades in a live market and intuitionistic fuzzy logic to model their decisions' indeterminacy. We use functions to restrict the agents' decisions, which make the agents become specialized at particular market conditions. We have performed experiments and compared against the state of the art.

Results demonstrate that our method obtains predictive errors using mean absolute error that are in the same order of magnitude than those errors obtained by models generated using deep learning and models generated by random forest, AdaBoost, XGBoost, and support-vector machines. During the development process, we create custom rules for fuzzy inference, based on our expert judgment of the trading system.

Setting correspondence between the numerical value of the input variable of the fuzzy inference system and the value of the membership function of the corresponding term of the linguistic variable. The procedure of determining the degree of truth of conditions for each rule of the fuzzy inference system.

The process of finding the truth degree of each of the elementary propositions subclauses constituting the consequents of kernels of all fuzzy production rules. It should be noted that only the points 1 and 2 need to be performed, all others will be done by the system without intervention. Those interested in the subtleties of the fuzzy logic operation at all stages can find more details here. Let us continue with the creation of the model. Define objects of three inputs and one output, as well as auxiliary objects of dictionary to facilitate the work with the logic:.

Three RSI with different periods will be used as inputs. Since the RSI oscillator is always in the range of 0—, it is necessary to create a variable for it with the same dimension. But for convenience, the indicator values will be normalized to a range of 0—1. Simply keep in mind that the created variable must have a dimension equal to the dimension of the input vector, i.

A range from 0 to 1 is set at the output as well. According to point 1 of fuzzy logic creation, it is also necessary to define and configure the membership functions. This will be done in the OnInit event handler:. Three terms have been created for each input and one output variable: "buy", "neutral", "sell", each with its own membership function.

In other words, the oscillator values can now be divided into 3 fuzzy groups, and each group can be assigned a range of values using the membership function. Speaking in the language of fuzzy logic, 4 term sets have been created, each of which has 3 terms. To illustrate the above, we will write a simple script that can be used for visualization of the terms and their membership functions:.

These membership functions have been selected, because they have only 2 optimizable input parameters this will be done later, during the system testing stage. They also describe the extreme and central positions of the system well.

You can apply any membership function from the ones available in the Fuzzy library. Let us adopt a rule that the extreme values of the oscillator indicate an upcoming change in its direction and, consequently, an upcoming trend reversal. Therefore, the oscillator approaching to zero hints at a possible beginning of growth. Movement of the oscillator to the 0. The same principle is used in all inputs and output, which check if the indicator values belong to a particular zone with fuzzy boundaries.

There are no restrictions on the number of membership functions for each variable. You can set 5, 7, 15 functions instead of three, but, of course, within the limits of common sense and in the name of fuzzy logic. At least one logical condition must be added to the knowledge base: it is considered incomplete if at least one term is not involved in logical operations. There can be an indefinite amount of logical conditions. The provided example sets 12 logical conditions, which influence the fuzzy inference when met.

Thus, all terms participate in logical operations. By default, all logical operations are assigned the same weight coefficients equal to 1. They will not be changed in this example. If all 3 indicators are within the fuzzy area for buying, a fuzzy buy signal will be output.

The same applies to sell and neutral signals. If 2 indicators show buy and one shows sell, the output value will be neutral, that is, uncertain. If 2 indicators show buy or sell, and one is neutral, then buy or sell is assigned to the output value.

Obviously, this is not the only variant for creating a rule base, you are free to experiment. This rule base is founded merely on my "expert" judgment and vision of how the system should function. It remains to calculate the model and obtain the result as a value from 0 to 1.

Values close to 0 will indicate a strong buy signal, those close to 0. This function gets the values of three RSI oscillators with different periods, normalizes them to a range from 0 to 1 values can be simply divided by , updates the list with objects of the Fuzzy dictionary the latest indicator values , sends it to calculations, creates a list for the output variable and takes the result in the 'res' variable. Since machine learning or at least its basics are also being considered, some parameters will be moved to inputs and optimized.

The parameters of the Gaussian membership function will undergo optimization at the output of the fuzzy logic. It will have its center along the X axis shifted parameter Gposition , its sigma changed its bell narrowed and compressed, parameter Gsigma. This will give a better fine-tuning of the system in case the RSI signals for buying and selling are asymmetric.

Additionally, optimize the conditions for opening deals: the minimum value of a neutral signal and the maximum value new positions will not be opened in the range between these values, as the signal is not defined. Calculations will be carried out on new bar to accelerate the demonstration. You are free to customize the logic at your discretion, for example, trade on every tick by simply removing the check for a new bar.

If there are open positions and the signal contradicts the current position or is not defined, close the position. If there is a condition for opening an opposite position, open it. The resulting membership functions at the output, after optimization the inputs remain unchanged since they were not optimized :. In both cases, the Gaussian neutral zone was shifted towards buys and the number of long positions prevails over the number of short positions. This means that the buy and sell signals turned out to be asymmetrical on this particular segment of history, which could not be discovered without such an experiment.

It is possible that the system consisting of three RSI was in the oversold zone area 1 more often than in the overbought zone area 0 , and optimization of the Gaussian helped smooth out this imbalance. As for the crispest output, it is analytically hard to imagine why such an output configuration contributed to the improvement of the trading system results, because the process of defuzzification using the center of gravity method, in conjunction with all the mapping of inputs to fuzzy sets, is already a complex system by itself.

The system proved to be quite stable for 8 months, even though only 4 parameters were optimized. And they can easily be reduced to two Gsigma and Gposition , since the remaining 2 had little impact on the result and are always in the vicinity of 0. This is assumed a satisfactory result for an experimental system, aimed at showing how the number of optimized parameters can be reduced by introducing an element of fuzzy logic into the trading system.

In contrast, it would have been necessary to create numerous optimization criteria for strict rules, which would increase the complexity of system development and the number of optimized parameters. It should also be noted that this is still a very crude example of building a trading system based on fuzzy logic, as it uses a primitive RSI-based strategy without even using stop losses. However, this should be sufficient to understand the applicability of fuzzy logic to creation of trading systems.

Fuzzy logic allows for a quick creation of systems with fuzzy rules that are very simple to optimize. At the same time, the complex process of selecting the trading system parameters passes through genetic optimization, freeing the developer from the routine of searching for a trading strategy, developing and algorithmizing numerous rules of the trading system. Together with other machine learning methods for example, neural networks , this approach allows achieving impressive results. It reduces the chance of overfitting and the dimension of the input data 3 RSI indicators with different periods narrowed down to a single signal, which describes the market situation more fully and more generalized than each indicator on its own.

If you still have troubles understanding how the fuzzy logic works, ask yourself how you think, what terms you operate and what rule bases your decision-making relies on. Here is a reinforcement example. For instance, you have 3 wishes: go to a party, watch a movie at home or save the world. The term "watch a movie at home" has the greatest weight, because you are already at home and no further effort is necessary.

Going to a party is viable if someone invites you and picks you up, but since it had not happened yet, the chances of going are average. And, finally, to save the world, you need to mobilize all your supernatural abilities, put on a superman costume and fight an alien monster. It is unlikely that you decide to do it today and not leave it until tomorrow, so the chances are slim. The fuzzy inference will be something like this: I will most likely stay at home, and perhaps I will go to the party, but I am definitely not going to save the world today.

After defuzzification, our chances could be evaluated on a scale of 0 to 10, where 0 is "stay at home", 5 is "go to the party", 10 is "fight a monster". Obviously, the crisp output would lie in the range of 0 to 3, i. The same principle is used in the presented trading system: it compares the values of three indicators and uses logical conditions to determine the most preferable option at the current moment — buying, selling or doing nothing.

If there is enough interest in the article, and I receive sufficient feedback, I could consider the possibility of writing a new article devoted to combination of fuzzy logic and a neural network. Below are the source codes of the experts and a test script for the membership functions. For the expert to compile and work, it is necessary to download the MT4Orders library and the updated Fuzzy library.

Value we get a double value that correspond to Fuzzy result. How I can convert this value to corresponding text term? I'm pretty new to Trading, specifically Algo Trading. It is simply the ease with which I can read and make sense of the concepts you have explained. P Chan and Quantra for that. This article will surely put me ahead in the race. You agree to website policy and terms of use. Do you like the article? Share it with others - post a link to it!

Use new possibilities of MetaTrader 5. Similar articles Graphics in DoEasy library Part : Making improvements in handling extended standard graphical objects Data Science and Machine Learning part Matrix Regressions Graphics in DoEasy library Part 99 : Moving an extended graphical object using a single control point Graphics in DoEasy library Part 98 : Moving pivot points of extended standard graphical objects Multiple indicators on one chart Part 03 : Developing definitions for users.

MetaTrader 5 — Examples. Maxim Dmitrievsky.

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So you see there are never crisp yes and no answers in our daily life. The same think happens in trading. This is one of the main causes of getting false signals. Boolean Logic always talks about only True or False. There are no crisp answers most of the time. In real life decision making is always grey. We are always dealing with shades of grey. This applies in trading as well. We can reduce false signals in our trading using fuzzy logic by learning how to deal with grey decision making.

The important question is how we are going to deal with uncertainty in our decision making. This important question was solved by Dr. Lotfi Zadeh in s when he proposed the Fuzzy Logic System. In Fuzzy Logic there are always shades. In classical logic or what we call Boolean Logic, there is always True or False.

However, in fuzzy logic we can say it is 0. So there is no clear cut answer. This is much closer to our real life decision making when we are most of the time not sure about a thing. We can only guess it is 0. As a trader you know how this situation happens every time we are ready to pull the trigger. MACD is giving a buy signal, but the other indicators are not sure so how to deal with that. In fuzzy logic first we map the input set onto the range [0,1] using a membership function. After that we use if then inference rules to make the decision.

Once the decision has been made we use defuzzification to calculate the crisp value. Sounds complicated? First watch the videos below that explain in simple terms how we use fuzzy logic. Watch the video below that explains what is fuzzy logic! Fuzzy logic has major applications in industrial controllers. Air conditioning controllers use fuzzy logic a lot. One of the most famous applications of fuzzy logic is that of the Sendai Subway system in Sendai, Japan. This control of the Nanboku line, developed by Hitachi, used a fuzzy controller to run the train all day long.

This made the line one of the smoothest running subway systems in the world and increased efficiency as well as stopping time. So you can see fuzzy logic is being used in major ways in real world applications. Below is another good introductory video on fuzzy logic. Watch this third video that explains what is fuzzy logic.

In this video you can see how we can apply the fuzzy logic by first using a membership to map the input set onto the range [0,1]. Then how to use the inference rules and finally how to defuzzify the results. There are 2 main inference systems that you can use. These inference systems are known as Type 1 system and Type 2 system. Now these were a few introductory videos on fuzzy logic. In trading there are some traders who have tried to apply fuzzy logic.

More Filters. The paper presents the application of a fuzzy logic in building the trading agents of the A-Trader system. The … Expand. View 1 excerpt, cites methods. Fuzzy logic in the multi-agent financial decision support system. Optimal artificial neural network topology for foreign exchange forecasting.

ACM-SE View 2 excerpts, references background. Software maintenance productivity assessment using fuzzy logic. Designing safe, profitable automated stock trading agents using evolutionary algorithms. GECCO ' View 1 excerpt, references background. Australasian Database Conference. Software cost estimation using fuzzy logic.

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What Is Fuzzy Logic? - Fuzzy Logic, Part 1

Combined with genetic algorithms, it is able to expand the capabilities of creating self-learning or easily optimizable trading systems. At the same time, fuzzy. This type of analysis can be applied to all types of security, not only to stocks, and it's prevalent in the ForEx market (FOReign EXchange, the platform for. In this paper, we present the topology of an Expert Advisor that serves as a robot for Foreign Exchange trading, using fuzzy logic.