Embedding technical indicators into fuzzy inference system for trading stocks with high volatility
Fuzzy Inference System, which applies the concept of fuzzy logic and fuzzy set theory, has become popular in trading nowadays due to its ability to control uncertainty in financial data and simulate the decision making process of human traders. While Fuzzy Inference System is prevalent in foreign ex...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/175158 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Fuzzy Inference System, which applies the concept of fuzzy logic and fuzzy set theory, has become popular in trading nowadays due to its ability to control uncertainty in financial data and simulate the decision making process of human traders. While Fuzzy Inference System is prevalent in foreign exchange trading, its application in stock trading remains underexplored. Fuzzy Inference Systems for stock trading proposed by existing studies fail to demonstrate their effectiveness and mainly focuses on stocks from specific sectors, which lacks generalizability. This research proposes a novel Mamdani type fuzzy inference system that integrates multiple technical indicators and risk management techniques to trade stocks with high volatility. Moreover, the system is validated on stocks from different sectors and optimized. The objective is to outperform existing Fuzzy stock trading systems and help traders generate better risk adjusted returns. |
|---|