In this work, we are proposing a trading system where fuzzy logic is applied not only for defining the trading rules, but also for managing the capital to invest. In fact, two fuzzy decision support systems are developed. The first one uses fuzzy logic to design the trading rules and to apply the stock market technical indicators. The second one enhances this fuzzy trading system adding a fuzzy strategy to manage the capital to trade. Additionally, a new technical market indicator that produces short and long entry signals is introduced. It is based on the moving average convergence divergence indicator. Its parameters have been optimized by genetic algorithms. The proposals are compared to a classical nonfuzzy version of the proposed trading systems and to the buy-and-hold strategy. Results favor our fuzzy trading system in the two markets considered, NASDAQ100 and EUROSTOXX. Conclusions suggest that the use of fuzzy logic for capital management is promising and deserves further exploration.

/pdf/an-intelligent-trading-system-with-fuzzy-rules-and-fuzzy-3tc9yuwb68.pdf

An Intelligent Trading System with Fuzzy Rules and Fuzzy Capital Management

Technical indicators are normally used to monitor the stock prices and assist investors to set up trading rules to make the buy-sell-hold decision. Although some trading rules are clear, most of them are vague and fuzzy. Therefore, an investor cannot be the winner all the time with the same set of trading rules. The weight of trading rules should be varied with time. A Genetic Fuzzy Expert Trading System (GFETS) was designed to simulate the vague and fuzzy trading rules and give the buy-sell signal. Fuzzy trading rules are optimized and selected using genetic algorithm in GFETS. Experimental evaluations showed that trading with the optimized fuzzy trading rules obtains a good profitable return. To maintain the quality of the fuzzy trading rules being in-used, GFETS must be re-trained from time-to-time. In this paper, an incremental training approach was studied and evaluated with all Hang Seng China Enterprises Index (HSCEI) stocks. The risk and the profit return compared with other trading strategies were reported.

https://ieeexplore.ieee.org/iel5/8512/26901/01196291.pdf

Incremental genetic fuzzy expert trading system for derivatives market timing

This paper summarizes the collective work done in the application of evolutionary computation for financial time series forecasting. These are mainly stock market indices and foreign exchange rate prediction. The time series corresponding to these indices is a non-linear dynamic stochastic system different from other static patterns which are independent of time. Evolutionary techniques have capabilities of efficient search space exploration with population models corresponding to the problem. Their ability to capture the non linear dependencies among the system variables has invited economic analysts towards their use in the field of financial time series prediction. In this paper, previous research done in the application of evolutionary techniques for economic time series prediction and resolving the issues involved has been presented.

Evolutionary computation and economic time series forecasting

The principle objective of this paper is to obtain trading rules with a low risk level which are also capable of obtaining high returns. To that purpose a methodology has been defined, based on the design of a genetic algorithm GAP and an incremental training technique adapted to the learning of series of stock market values. The GAP technique consists in a fusion of GP and GA. In GAP a chromosome is composed of a tree with language operators and a vector with numeric values. The GAP algorithm implements the automatic search for trading rules taking as objectives of the training both the optimization of the return obtained and the minimization of the assumed risk. In order to diminish high over-fitting, a technique of incremental training has been used. Applying the proposed methodology, rules have been obtained for a period of eight years of the S&P500 index. The achieved adjustment of the relation return-risk has generated rules with returns very superior in the testing period to those obtained applying habitual methodologies and even clearly superior to Buy&Hold. Insert your abstract here. Include keywords, PACS and mathematical subject classification numbers as needed.

Improving return using risk-return adjustment and incremental training in technical trading rules with GAPs

Learning and Technical Market -Effects of In-Sample Data Selection-

Technical indicators are developed for monitoring the movement of stock prices from different perspective. They are widely used to define trading rules to assist investors to make the buy-sell-hold decision. Most of these trading rules are vague and fuzzy in nature. Since the stock prices are affected by the artificial news factors from time to time, investors cannot be the winner all the time on using the same set of trading rules. The weight (i.e., the significance) of a trading rule to investors is varying with time. The problem of determining the weight of the trading rules can be modelled as an optimization problem. In this paper, we propose a Genetic Fuzzy Expert Trading System (GFETS) for market timing. We apply the fuzzy expert system to simulate vague and fuzzy trading rules and give the buy-sell signal. The set of trading rules adopted in the system will vary with time and is optimized using Genetic Algorithm (GA). Two training approaches—incremental and dynamic—are designed and studied. The system was evaluated with the stocks in NASDAQ market. Experimental results showed that the system can give reliable buy-sell signals and using the system to perform buy-sell can produce significant profit.

Genetic fuzzy expert trading system for nasdaq stock market timing

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Genetic fuzzy expert trading system for nasdaq stock market timing