A Technical Analysis Indicator Based On Fuzzy Logic

AbstractIn this paper an indicator for technical analysis based on fuzzy logic is proposed, which unlike traditional technical indicators, is not a totally objective mathematical model, but incorporates subjective investor features such as the risk tendency. The fuzzy logic approach allows representing in a more “human” way the decision making reasoning that a non-expert investor would have in a real market. Such an indicator takes as input, general market information like profitability and volatility of the stock prices, while the outputs are the buy and sell signals. In addition to present the detailed formulation of the indicator, in this paper a validation for the same is presented, which makes use of a multi-agent based simulation platform within which the behavior and profits obtained by agents that used traditional technical indicators such as MA, RSI and MACD, are compared against those obtained by agents that use the fuzzy indicator for the decision making process

Fund managers - why the best might be the worst: On the evolutionary vigor of risk-seeking behavior

This article explores the influence of competitive conditions on the evolutionary fitness of different risk preferences. As a practical example, the professional competition between fund managers is considered. To explore how different settings of competition parameters, the exclusion rate and the exclusion interval, affect individual investment behavior, an evolutionary model based on a genetic algorithm is developed. The simulation experiments indicate that the influence of competitve conditions on investment behavior and attitudes towards risk is significant. What is alarming is that intense competitive pressure generates riskseeking behavior and undermines the predominance of the most skilled. --risk preferences,competition,genetic programming,fund managers,portfolio theory

An Exploration of Simple Optimized Technical Trading Strategies

Honors (Bachelor's)StatisticsEconomicsUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/91813/1/chben.pd

Deriving Trading Rules Using Gene Expression Programming

This paper presents how buy and sell trading rules are generated using gene expression programming with special setup. Market concepts are presented and market analysis is discussed with emphasis on technical analysis and quantitative methods. The use of genetic algorithms in deriving trading rules is presented. Gene expression programming is applied in a form where multiple types of operators and operands are used. This gives birth to multiple gene contexts and references between genes in order to keep the linear structure of the gene expression programming chromosome. The setup of multiple gene contexts is presented. The case study shows how to use the proposed gene setup to derive trading rules encoded by Boolean expressions, using a dataset with the reference exchange rates between the Euro and the Romanian leu. The conclusions highlight the positive results obtained in deriving useful trading rules

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

Revisión del Estado del Arte en Métodos de Redes Neuronales, Máquinas de Kernel y Computación Evolutiva para Predicción de Precios Financieros

A review of the representative models of machine learning research applied to the foreign exchange rate and stock price prediction problem is conducted. The article is organized as follows: The first section provides a context on the definitions and importance of foreign exchange rate and stock markets. The second section reviews machine learning models for financial prediction focusing on neural networks, SVM and evolutionary methods. Lastly, the third section draws some conclusions.El siguiente artículo revisa algunos de los trabajos de investigación mas representativos relacionados con aprendizaje computacional aplicado al problema de predicción de tipos de cambio y precios de acciones. El artículo esta organizado de la siguiente forma: La primera sección se concentra en contextualizar definiciones relevantes y la importancia del problema de predicción en el mercado de acciones y de tasa de cambio. La segunda sección contiene la revisión de modelos de aprendizaje computacional para predicción de precios financieros enfocándose en tres subareas: Redes Neuronales, SVM y métodos evolutivos. La tercera sección presenta las conclusiones

Development of a Genetic Programming-based GA Methodology for the Prediction of Short-to-Medium-term Stock Markets

This research presents a specialised extension to the genetic algorithms (GA) known as the genetic programming (GP) and gene expression programming (GEP) to explore and investigate the outcome of the GEP criteria on the stock market price prediction. The aim of this research is to model and predict short-to-medium term stock value fluctuations in the market via genetically tuned stock market parameters. The technology proposes a fractional adaptive mutation rate Elitism (GEPFAMR) technique to initiate a balance between varied mutation rates and between varied-fitness chromosomes, thereby improving prediction accuracy and fitness improvement rate. The methodology is evaluated against different dataset and selection methods and showed promising results with a low error-rate in the resultant pattern matching with an overall accuracy of 95.96% for short-term 5-day and 95.35% for medium-term 56-day trading periods