Evolutionary Single-Position Automated Trading
نویسندگان
چکیده
Automated Trading is the activity of buying and selling financial instruments for the purpose of gaining a profit, through the use of automated trading rules. This work presents an evolutionary approach for the design and optimization of artificial neural networks to the discovery of profitable automated trading rules. Experimental results indicate that, despite its simplicity, both in terms of input data and in terms of trading strategy, such an approach to automated trading may yield significant returns.
منابع مشابه
Fuzzy-Evolutionary Modeling for Single-Position Day Trading
This chapter illustrates a data-mining approach to single-position day trading which uses an evolutionary algorithm to construct a fuzzy predictive model of a financial instrument. The model is expressed as a set of fuzzy IF-THEN rules. The model takes as inputs the open, high, low, and close prices, as well as the values of a number of popular technical indicators on day t and produces a go sh...
متن کاملAlgorithmic Currency Trading using NEAT - based Evolutionary Computation
This paper introduces NEAT-based Evolutionary Computation as the basis for a fully automated trading system application. The system is designed to trade FX markets by detecting profitable currency cycles in the most widely traded currencies and forecasting future exchange rates. To do this, it relies on a fitness function that measures profitability, as well as a fitness function that measures ...
متن کاملHorizontal Generalization Properties of Fuzzy Rule-Based Trading Models
We investigate the generalization properties of a data-mining approach to single-position day trading which uses an evolutionary algorithm to construct fuzzy predictive models of financial instruments. The models, expressed as fuzzy rule bases, take a number of popular technical indicators on day t as inputs and produce a trading signal for day t+ 1 based on a dataset of past observations of wh...
متن کاملEvolutionary Algorithms in Optimization of Technical Rules for Automated Stock Trading
.......................................................................................vi List of Tables.................................................................................x List of Figures...............................................................................xi Chapter
متن کاملEvolutionary Dynamics of Multi-Agent Learning: A Survey
The interaction of multiple autonomous agents gives rise to highly dynamic and nondeterministic environments, contributing to the complexity in applications such as automated financial markets, smart grids, or robotics. Due to the sheer number of situations that may arise, it is not possible to foresee and program the optimal behaviour for all agents beforehand. Consequently, it becomes essenti...
متن کامل