Fuzzy Logic

Fuzzy logic is a mathematical logic that 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.

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

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.

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.