Genetic Programming with Memory For Financial Trading
نویسندگان
چکیده
Amemory-enabled program representation in strongly-typed Genetic Programming (GP) is compared against the standard representation in a number of financial time-series modelling tasks. The paper first presents a survey of GP systems that utilise memory. Thereafter, a number of simulations show thatmemory-enabled programs generalise better than their standard counterparts in most datasets of this problem domain.
منابع مشابه
Global Supply Chain Management under Carbon Emission Trading Program Using Mixed Integer Programming and Genetic Algorithm
In this paper, the transportation problem under the carbon emission trading program ismodelled by mathematical programming and genetic algorithm. Since green supply chain issuesbecome important and new legislations are taken into account, carbon emissions costs are included inthe total costs of the supply chain. The optimisation model has the ability to minimise the total costsand provides the ...
متن کاملBankruptcy Prediction: Dynamic Geometric Genetic Programming (DGGP) Approach
In this paper, a new Dynamic Geometric Genetic Programming (DGGP) technique is applied to empirical analysis of financial ratios and bankruptcy prediction. Financial ratios are indeed desirable for prediction of corporate bankruptcy and identification of firms’ impending failure for investors, creditors, borrowing firms, and governments. By the time, several methods have been attempted in...
متن کاملEvolving short-term trading strategies using Genetic Programming
We have used a linear Genetic Programming system with a multitude of different quotes on financial securities as input in order to evolve an intraday trading strategy for an individual stock, attempting to outperform a simple buy and hold strategy over the same period of time.
متن کاملDiscovering Financial Technical Trading Rules Using Genetic Programming with Lambda Abstraction
Keywords: We applied genetic programming with a lambda abstraction module mechanism to learn technical trading rules based on S&P 500 index from 1982 to 2002. The results show strong evidence of excess returns over buy-and-hold after transaction cost. The discovered trading rules can be interpreted easily; each rule uses a combination of one to four widely used technical indicators to make trad...
متن کاملCartesian Genetic Programming for Trading: A Preliminary Investigation
In this paper, a preliminary investigation of Cartesian Genetic Programming (CGP) for algorithmic intraday trading is conducted. CGP is a recent new variant of genetic programming that differs from traditional approaches in a number of ways, including being able to evolve programs with limited size and with multiple outputs. CGP is used to evolve a predictor for intraday price movements, and tr...
متن کامل