Piotr Lipinski
ABSTRACT
This paper proposes a method of distinguishing stock market states, classifying them based on price variations of securities, and using an evolutionary algorithm for improving the quality of classification. The data represents buy/sell order queues obtained from rebuild order book, given as price-volume pairs. In order to put more emphasis on certain features before the classifier is used, we use a weighting scheme, further optimized by an evolutionary algorithm.
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Digital Library
- R. Engle, M. J. Fleming, E. Ghysels, and G. Nguyen. Liquidity and volatility in the us treasury market: Evidence from a new class of dynamic order book models. Federal Reserve Bank of New York Working Paper, 2011.Google Scholar
- P. Lipinski and A. Brabazon. Pattern mining in ultra-high frequency order books with self-organizing maps. In Applications of Evolutionary Computation, pages 288--298. Springer, 2014.Google Scholar
- R. Storn and K. Price. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4):341--359, 1997. Google ScholarDigital Library
Index Terms
- Improving Classification of Patterns in Ultra-High Frequency Time Series with Evolutionary Algorithms
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