Authors:
Nikitas Goumatianos
1
;
Ioannis T. Christou
2
and
Peter Lindgren
3
Affiliations:
1
Athens Information Technology and Aalborg University, Greece
;
2
Athens Information Technology, Greece
;
3
Aalborg University, Denmark
Keyword(s):
Pattern Mining, Stock Market, Trading Systems, Time-series Forecasting, Distributed Computing, Databases.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Economics, Business and Forecasting Applications
;
Knowledge Acquisition and Representation
;
Pattern Recognition
;
Theory and Methods
Abstract:
We present the architecture of a “useful pattern” mining system that is capable of detecting thousands of different candlestick sequence patterns at the tick or any higher granularity levels. The system architecture is highly distributed and performs most of its highly compute-intensive aggregation calculations as complex but efficient distributed SQL queries on the relational databases that store the time-series. We present initial results from mining all frequent candlestick sequences with the characteristic property that when they occur then, with an average at least 60% probability, they signal a 2% or higher increase (or, alternatively, decrease) in a chosen property of the stock (e.g. close-value) within a given time-window (e.g. 5 days). Initial results from a first prototype implementation of the architecture show that after training on a large set of stocks, the system is capable of finding a significant number of candlestick sequences whose output signals (measured against
an unseen set of stocks) have predictive accuracy which varies between 60% and 95% depended on the type of pattern.
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