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What is the Role of Fuzzy Logic in Algorithmic Trading?

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Do you know what Fuzzy logic is? Did you know fuzzy logic can do wonders in problem-solving? And How do you deploy fuzzy logic in solving glitches in algorithmic trading? Coding the financial market isn’t easy, but there are tools and techniques to master the art of developing trading robots. Fuzzy logic is one of the crucial technique to resolve the most ambiguous decision-making process in trading activities. How does fuzzy logic helps is all about we are going to discuss here.


Fuzzy logic is the basic concept behind the human decision-making process. It could be explained with the decision tree method and rule-based programming methods. It checks with every possibility and probability of the end results by evaluating each of its benefits and drawbacks. Fuzzy logic is the base for developing Artificial Intelligence through rule-based inferences. As of trading, it is used to evaluate and process multiple input variables to achieve desired results. It is largely implemented in machine learning concepts of trading systems. It helps in decision-making based on the multiple variables. The variables that is greater than zero and less than one.

What is Fuzzy Logic exactly?

Fuzzy logic is nothing but the concept that is invented to resemble the human’s sense of reasoning. How would a human think in a risky situation? Maybe the emotions could interfere with the intelligence, right? But fuzzy logic is a concept of making a wise decision in an ambiguous situation without the distortions from emotions. Fuzzy logic manipulates data with multiple variables between 1 and 0 to arrive at a most authentic solution. So this fuzzy logic concept is much used in stock market buying and selling. It is considered to be essential in high-frequency trading and accumulating maximum profits.

How Does Fuzzy Logic Simplify Trading Activity?

  • Fuzzy logic simplifies trading activity by minimising the risk involved with human emotions and correctly manipulating data with the facts and figures.
  • With fuzzy logic principles, trading decisions are made in a fraction of seconds without any human errors. With Relative Strength Index(RSI) as a technical signal, common fuzzy sets are created so as to execute trade operations. RSI is a technical indicator to measure the strength of the stock over a period of time. It is actually very simple to implement a fuzzy set with RSI parameters.
  • Fuzzy logic helps to trade with the reasonable choices of picking up the stocks and selling them at maximum profits.
  • To develop Fuzzy logic protocols, we have to integrate rule-based programming. And these rules or conditions would act as fuzzy sets which therein helps in evaluating trading decisions.
  • Fuzzy logic Solving Glitches in Algo Trading.
  • Fuzzy logic’s Fuzzy Inference System(FIS) is the one that solves the complexities in the algorithms.
  • FIS uses the fuzzy set theory or the membership functions to map the multiple blurred input to that of the output. There are two types of FIS in the system. They are Mamdani and Takagi-Sugeno.

7 Steps To Compute the FIS(Fuzzy Inference System) Names To Arrive at an Output

  • Set of fuzzy rules has to be inferred- Rule Base.
  • Fuzzyifying input membership functions- Database.
  • Establishing fuzzy rule strength- Decision-Making Unit.
  • Combining rule strength and output membership function-Decision making unit.
  • Getting output distribution from the consequences-Fuzzification interface unit.
  • Activation of kernel through fuzzy production rules – Rule-Based Programming.
  • Defuzzyfying output distribution with centre of mass – Defuzzyfication Interface Unit.

Note: FIS Takagi-Sugeno is also similar to that of Mamdani inference system.

How to Implement Fuzzy Logic in the Trading Algorithm?

Trading System can be easily integrated with Metatrader 5 terminal. Fuzzy logic is available in MQL5 library function that can be executed in the standard Meta Trader 5 terminal. The function would return trading advice on an expert level that can also be customised according to the practical trading activity. Sounds simple, right?!.

FuzzyNet is the most prominent mathematical model for establishing Fuzzy Models and prototypes in the trading system. The trading system works on Implication(min value), Aggregation(max value) and the defuzzyfication that operates on centre of gravity.

Factors That Contribute to Fuzzy Logic

  • The ever-changing market scenario contributes to the fuzzy logic prototypes in the trading system. It is widely used over the trading platforms for more accurate trading and attaining maximum gains.
  • Fuzzy logic along with neural net technology is used greatly in trading and finance to quantify the operational risk involved in market transactions.Fuzzy logic has been implemented in the area of machine learning and investment intelligence specifically towards trading systems.
  • Here, the Expert Advisor(EAs) or the algorithmic trading bots use the MT4Orders of order language system in MT4 library function to enable the task with orders and to formulate the code effortlessly switchable into MQL4.
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Last Updated : 12 Jan, 2020
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