Many evolutionary computation methods applied to the financial field have been reported. A new evolutionary method named ‘‘Genetic Network Programming” (GNP) has been developed and applied to the stock market recently. The efficient trading rules created by GNP has been confirmed in our previous research. In this paper a multi-brands portfolio optimization model based on Genetic Network Program...

In this paper we study whether the commodity futures market predicts the commodity spot market. Using historical daily data on four commodities—oil, gold, platinum, and silver—we find that they do. We then show how investors can use this information on the futures market to devise trading strategies and make profits. In particular, dynamic trading strategies based on a mean–variance investor fr...

We apply Inductive Logic Programming (ILP) for inducing trading rules formed out of combinations of technical indicators from historical market data. To do this, we first identify ideal trading opportunities in the historical data, and then feed these as examples to an ILP learner, which will try to induce a description of them in terms of a given set of indicators. The main contributions of th...

We report evidence on the profitability and statistical significance among 2,127 technical trading rules. The best rules are found to be significantly profitable based on standard tests. We then employ White’s (2000) Reality Check to evaluate these rules and find that data-snooping biases do not change the basic conclusions for the full sample. A sub-sample analysis indicates that the data-snoo...

This study uncovers trading styles in the transaction records of US Treasury bond futures. We use statistical clustering techniques to group together trades that are similar. Trade profit was held back in the clustering process. Results show that clusters differ significantly in their profit and risk characteristics. Some clusters uncover "technical" trading rules. Using the information about t...

This research investigates the ability of genetic programming (GP) to build profitable trading strategies for the Foreign Exchange Market (FX) of three major currency pairs (EURUSD, USDCHF and EURCHF) using one hour prices from 2008 to 2011. We recognize that such environments are likely to be non-stationary. Thus, we do not require a single training partition to capture all likely future behav...

Chong and Ng (2008) find that the Moving Average Convergence–Divergence (MACD) and Relative Strength Index (RSI) rules can generate excess return in the London Stock Exchange. This paper revisits the performance of the two trading rules in the stock markets of five other OECD countries. It is found that the MACD(12,26,0) and RSI(21,50) rules consistently generate significant abnormal returns in...

Grammatical Evolution (GE) is a novel, data-driven, model-induction tool, inspired by the biological gene-to-protein mapping process. This study provides an introduction to GE, and applies the methodology in an attempt to uncover useful technical trading rules which can be used to trade foreign exchange markets. In this study, each of the evolved rules (programs) represents a market trading sys...