Abstract
In trading in currency markets, reducing the mean of absolute or squared errors of predicted values is not valuable unless it results in profits. A trading rule is a set of conditions that describe when to buy or sell a currency or to close a position, which can be used for automated trading. To optimize the rule to obtain a profit in the future, a probabilistic method such as a genetic algorithm (GA) or genetic programming (GP) is utilized, since the profit is a discrete and multimodal function with many parameters. Although the rules optimized by GA/GP reportedly obtain a profit in out-of-sample testing periods, it is hard to believe that they yield a profit in distant out-of-sample periods. In this paper, we first consider a framework where we optimize the parameters of the trading rule in an in-sample training period, and then execute trades according to the rule in its succeeding out-of-sample period. We experimentally show that the framework very often results in a profit. We then consider a framework in which we conduct optimization as above and then execute trades in distant out-of-sample periods. We empirically show that the results depend on the similarity of the trends in the training and testing periods.
Original language | English |
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Pages (from-to) | 86-98 |
Number of pages | 13 |
Journal | Procedia Computer Science |
Volume | 13 |
DOIs | |
Publication status | Published - 2012 Jan 1 |
Event | 3rd International Neural Network Society Winter Conference, INNS-WC 2012 - Bangkok, Thailand Duration: 2012 Oct 3 → 2012 Oct 5 |
Keywords
- Financial prediction
- Foreign exchange
- Genetic algorithm
- Optimization algorithm
- Robustness test
- Technical analysis
ASJC Scopus subject areas
- Computer Science(all)