Expert Systems with Applications: An International Journal
Domain-Driven actionable knowledge discovery in the real world
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Multi-strategy Integration for Actionable Trading Agents
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Knowledge actionability: satisfying technical and business interestingness
International Journal of Business Intelligence and Data Mining
Evolutionary Optimization of Trading Strategies
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
Domain-Driven Data Mining: Methodologies and Applications
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
Fuzzy genetic algorithms for pairs mining
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Intelligence metasynthesis in building business intelligence systems
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
Agent-based evolutionary optimisation of trading strategies
International Journal of Intelligent Information and Database Systems
International Journal of Business Intelligence and Data Mining
Hi-index | 0.00 |
Stock trading plays an important role for supporting profitable stock investment. In particular, more and more data mining-based technical trading rules have been developed and used in stock trading systems to assist investors with their smart trading decisions. However, many mined trading rules are of no interest to traders and brokers because they are discovered based on statistical significance without checking traders' interestingness concerns. To this end, this paper proposes in-depth data mining technologies to overcome the disadvantages of current data mining methods. We implement a decision support in-depth trading pattern discovery system with Robust Genetic Algorithms (RGA). The system integrates expert knowledge and considers domain constraints into the trading rule development. We further utilise this technique to mine actionable stock-rule pairs targeting behaviour with high return at low risk. The proposed approaches are tested in real stock orderbook data with varying investment strategies.