What is Fuzzy Logic?

Fuzzy Logic is an approach to variable processing that allows for multiple values to be processed through the same variable. Fuzzy logic attempts to solve problems with an open, imprecise spectrum of data that makes it possible to obtain an array of accurate conclusions. Fuzzy logic is designed to solve problems by considering all available information and making the best possible decision given the input.

Key Takeaways

  • Fuzzy logic allows for more advanced decision-tree processing and better integration with rules-based programming.
  • Theoretically, this gives the approach more opportunity to mimic real-life circumstances.
  • Fuzzy logic may be used by quantitative analysts to improve execution of their algorithms.

Understanding Fuzzy Logic

Fuzzy logic stems from the mathematical study of fuzzy concepts which also involves fuzzy sets of data. Mathematicians may use a variety of terms when referring to fuzzy concepts and fuzzy analysis. Broadly and comprehensively these terms are classified as fuzzy semantics.

In practice, these constructs all allow for multiple values of the "true" condition. Instead of True being numerically equivalent to 1 and False being equivalent to 0 (or vice versa), the True condition could be any number of values less than one and greater than zero. This creates opportunity for algorithms to make decisions based on ranges of price data as opposed to one discreet data point.

Fuzzy Logic Considerations

Fuzzy logic in its most basic sense is developed through decision tree type analysis. Thus, on a broader scale it forms the basis for artificial intelligence systems programmed through rules-based inferences.

Generally, the term fuzzy refers to the vast number of scenarios that can be developed in a decision tree like system. Developing fuzzy logic protocols can require the integration of rules-based programming. These programming rules may be referred as fuzzy sets since they are developed at the discretion of comprehensive models.

Fuzzy sets may also be more complex. In more complex programming analogies, programmers may have the capability to widen the rules used to determine inclusion and exclusion of variables. This can result in a wider range of options with less precise rules-based reasoning.

Fuzzy Semantics in Artificial Intelligence

The concept of fuzzy logic and fuzzy semantics is a central component to programing of artificial intelligence solutions. Artificial intelligence solutions and tools continue to expand in the economy across a range of sectors as the programming capabilities from fuzzy logic also expand.

IBM’s Watson is one of the most well-known artificial intelligence systems using variations of fuzzy logic and fuzzy semantics. Specifically in financial services, fuzzy logic is being used in machine learning and technology systems supporting outputs of investment intelligence.

In some advanced trading models, integration of fuzzy logic mathematics can also be used to help analysts create automated buy and sell signals. These systems help investors to react to a broad range of changing market variables that affect their investments.

In advanced software trading models, systems can use programmable fuzzy sets to analyze thousands of securities in real time and present the investor with the best available opportunity. Fuzzy logic is often used when a trader seeks to make use of multiple factors for consideration. This can result in a narrowed analysis for trading decisions. Traders may also have the capability to program a variety of rules for enacting trades. Two examples include the following:

Rule 1: If moving average is low and Relative Strength Index is low, then sell.

Rule 2: If moving average is high and Relative Strength Index is high, then buy.

Fuzzy logic allows a trader to program their own subjective inferences on low and high in these basic examples to arrive at their own automated trading signals.