Computational intelligence for evolving trading rules

Copyright © 2008 IEEEThis paper describes an adaptive computational intelligence system for learning trading rules. The trading rules are represented using a fuzzy logic rule base, and using an artificial evolutionary process the system learns to form rules that can perform well in dynamic market conditions. A comprehensive analysis of the results of applying the system for portfolio construction using portfolio evaluation tools widely accepted by both the financial industry and academia is provided.Adam Ghandar, Zbigniew Michalewicz, Martin Schmidt, Thuy-Duong Tô, and Ralf Zurbrug

Evolving temporal association rules with genetic algorithms

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty

Predicting IPO underpricing with genetic algorithms

This paper introduces a rule system to predict first-day returns of initial public offerings based on the structure of the offerings. The solution is based on a genetic algorithm using a Michigan approach. The performance of the system is assessed comparing it to a set of widely used machine learning algorithms. The results suggest that this approach offers significant advantages on two fronts: predictive performance and robustness to outlier patterns. The importance of the latter should be emphasized as the results in this domain are very sensitive to their presence.We acknowledge financial support granted by the Spanish Ministry of Science under contract TIN2008-06491-C04-03 (MSTAR) and Comunidad de Madrid (CCG10-UC3M/TIC-5029)

Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms

The crude oil futures market plays a critical role in energy finance. To gain greater investment return, scholars and traders use technical indicators when selecting trading strategies in oil futures market. In this paper, the authors used moving average prices of oil futures with genetic algorithms to generate profitable trading rules. We defined individuals with different combinations of period lengths and calculation methods as moving average trading rules and used genetic algorithms to search for the suitable lengths of moving average periods and the appropriate calculation methods. The authors used daily crude oil prices of NYMEX futures from 1983 to 2013 to evaluate and select moving average rules. We compared the generated trading rules with the buy-and-hold (BH) strategy to determine whether generated moving average trading rules can obtain excess returns in the crude oil futures market. Through 420 experiments, we determine that the generated trading rules help traders make profits when there are obvious price fluctuations. Generated trading rules can realize excess returns when price falls and experiences significant fluctuations, while BH strategy is better when price increases or is smooth with few fluctuations. The results can help traders choose better strategies in different circumstances

Generating long-term trading system rules using a genetic algorithm based on analyzing historical data

In current times, trading success depends on choosing a correct strategy. Algorithmic trading is often based on technical analysis - an approach where the values of one or several technical indicators are translated into buy or sell signals. Thus, every trader's main challenge is the choice and use of the most fitting trading rules. In our work, we suggest an evolutionary algorithm for generating and selecting the most fitting trading rules for interday trading, which are presented in the form of binary decision trees. A distinctive feature of this approach is the interpretation of the evaluation of the current state of technical indicators with the help of dynamic ranges that are recalculated on a daily basis. This allows to create long-term trading rules. We demonstrate the effectiveness of this system for the Top-5 stocks of the United States IT sector and discuss the ways to improve it

A Review of Natural Language Processing Research

Natural language processing (NLP) is a theory-motivated range of computational techniques for the automatic analysis and representation of human language. NLP research has evolved from the era of punch cards and batch processing (in which the analysis of a sentence could take up to 7 minutes) to the era of Google and the likes of it (in which millions of webpages can be processed in less than a second). This review paper draws on recent developments in NLP research to look at the past, present, and future of NLP technology in a new light. Borrowing the paradigm of ‘jumping curves’ from the field of business management and marketing prediction, this survey article reinterprets the evolution of NLP research as the intersection of three overlapping curves-namely Syntactics, Semantics, and Pragmatics Curves- which will eventually lead NLP research to evolve into natural language understanding

A Fuzzy Logic Stock Trading System Based On Technical Analysis

Technical analysis of financial markets involves analyzing past price movements in order to identify favorable trading opportunities. The objective of this research was to demonstrate that a fuzzy logic stock trading system based on technical analysis can assist average traders in becoming successful by optimizing the use of technical indicators and trading rules that experts use to identify when to buy and sell stock. Research of relevant literature explored the current state of knowledge in methodologies for developing and validating trading systems using technical indicators and fuzzy logic trading systems, providing guidelines for the development and evaluation of the system. Evaluation of the system confirmed that fuzzy logic can have a positive contribution to a successful trading system, and that once a successful trading system has been developed and verified an average trader can be successful by simply following the trading system\u27s buy and sell signals. The trader need not be an expert at interpreting the underlying technical indicators or react to price movements emotionally. The trading decisions are made by the trading system, so the only decision that the average trader need make is whether there is enough confidence in the system to commit real money in live trading. Suggestions for future research include improvements in accuracy and flexibility, and investigation of additional trading models and filters

Comparison Uncertainty of Different Types of Membership Functions in T2FLS: Case of International Financial Market

This article deals with the determination and comparison of different types of functions of the type-2 interval of fuzzy logic, using a case study on the international financial market. The model is demonstrated on the time series of the leading stock index DJIA of the US market. Type-2 Fuzzy Logic membership features are able to include additional uncertainty resulting from unclear, uncertain or inaccurate financial data that are selected as inputs to the model. Data on the financial situation of companies are prone to inaccuracies or incomplete information, which is why the type-2 fuzzy logic application is most suitable for this type of financial analysis. This paper is primarily focused on comparing and evaluating the performance of different types of type-2 fuzzy membership functions with integrated additional uncertainty. For this purpose, several model situations differing in shape and level or degree of uncertainty of membership functions are constructed. The results of this research show that type-2 fuzzy sets with dual membership functions is a suitable expert system for highly chaotic and unstable international stock markets and achieves higher accuracy with the integration of a certain level of uncertainty compared to type-1 fuzzy logic

Evolutionary Algorithms Based on Effective Search Space Reduction for Financial Optimization Problems

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 문병로.This thesis presents evolutionary algorithms incorporated with effective search space reduction for financial optimization problems. Typical evolutionary algorithms try to find optimal solutions in the original, or unrestricted search space. However, they can be unsuccessful if the optimal solutions are too complex to be discovered from scratch. This can be relieved by restricting the forms of meaningful solutions or providing the initial population with some promising solutions. To this end, we propose three evolution approaches including modular, grammatical, and seeded evolutions for financial optimization problems. We also adopt local optimizations for fine-tuning the solutions, resulting in hybrid evolutionary algorithms. First, the thesis proposes a modular evolution. In the modular evolution, the possible forms of solutions are statically restricted to certain combinations of module solutions, which reflect more domain knowledge. To preserve the module solutions, we devise modular genetic operators which work on modular search space. The modular genetic operators and statically defined modules help genetic programming focus on highly promising search space. Second, the thesis introduces a grammatical evolution. We restrict the possible forms of solutions in genetic programming by a context-free grammar. In the grammatical evolution, genetic programming works on more extended search space than modular one. Grammatically typed genetic operators are introduced for the grammatical evolution. Compared with the modular evolution, grammatical evolution requires less domain knowledge. Finally, the thesis presents a seeded evolution. Our seeded evolution provides the initial population with partially optimized solutions. The set of genes for the partial optimization is selected in terms of encoding complexity. The partially optimized solutions help genetic algorithm find more promising solutions efficiently. Since they are not too excessively optimized, genetic algorithm is still able to search better solutions. Extensive empirical results are provided using three real-world financial optimization problems: attractive technical pattern discovery, extended attractive technical pattern discovery, and large-scale stock selection. They show that our search space reductions are fairly effective for the problems. By combining the search space reductions with systematic evolutionary algorithm frameworks, we show that evolutionary algorithms can be exploited for realistic profitable trading.1. Introduction 1 1.1 Search Methods 3 1.2 Search Space Reduction 4 1.3 Main Contributions 5 1.4 Organization 7 2. Preliminaries 8 2.1 Evolutionary Algorithms 8 2.1.1 Genetic Algorithm 10 2.1.2 Genetic Programing 11 2.2 Evolutionary Algorithms in Finance 12 2.3 Search Space Reduction 12 2.3.1 Modular Evolution 12 2.3.2 Grammatical Evolution 13 2.3.3 Seeded Evolution 14 2.3.4 Summary 14 2.4 Terminology 15 2.4.1 Technical Pattern and Technical Trading Rule 15 2.4.2 Forecasting Model and Trading Model 16 2.4.3 Portfolio and Rebalancing 17 2.4.4 Data Snooping Bias 17 2.5 Financial Optimization Problems 19 2.5.1 Attractive Technical Pattern Discovery and Its Extension 19 2.5.2 Stock Selection 20 2.6 Issues 21 2.6.1 General Assumptions 21 2.6.2 Performance Measure 22 3. Modular Evolution 23 3.1 Modular Genetic Programming 24 3.2 Hybrid Genetic Programming 28 3.3 Attractive Technical Pattern Discovery 29 3.3.1 Introduction 29 3.3.2 Problem Formulation 31 3.3.3 Modular Search Space 33 3.3.4 Experimental Results 35 3.3.5 Summary 41 4. Grammatical Evolution 44 4.1 Grammatical Type System 45 4.2 Hybrid Genetic Programming 47 4.3 Extended Attractive Technical Pattern Discovery 51 4.3.1 Introduction 51 4.3.2 Problem Formulation 54 4.3.3 Experimental Results 56 4.3.4 Summary 73 5. Seeded Evolution 76 5.1 Heuristic Seeding 77 5.2 Hybrid Genetic Algorithm 78 5.3 Large-Scale Stock Selection 81 5.3.1 Introduction 81 5.3.2 Problem Formulation 83 5.3.3 Ranking with Partitions 85 5.3.4 Experimental Results 87 5.3.5 Summary 96 6. Conclusions 104Docto