Genetic algorithms for robust optimization in financial applications

In stock market or other financial market systems, the technical trading rules are used widely to generate buy and sell alert signals. In each rule, there are many parameters. The users often want to get the best signal series from the in-sample sets, (Here, the best means they can get the most profit, return or Sharpe Ratio, etc), but the best one will not be the best in the out-of-sample sets. Sometimes, it does not work any more. In this paper, the authors set the parameters a sub-range value instead of a single value. In the sub-range, every value will give a better prediction in the out-of-sample sets. The improved result is robust and has a better performance in experience

NEW METHODS FOR MINING SEQUENTIAL AND TIME SERIES DATA

Data mining is the process of extracting knowledge from large amounts of data. It covers a variety of techniques aimed at discovering diverse types of patterns on the basis of the requirements of the domain. These techniques include association rules mining, classification, cluster analysis and outlier detection. The availability of applications that produce massive amounts of spatial, spatio-temporal (ST) and time series data (TSD) is the rationale for developing specialized techniques to excavate such data. In spatial data mining, the spatial co-location rule problem is different from the association rule problem, since there is no natural notion of transactions in spatial datasets that are embedded in continuous geographic space. Therefore, we have proposed an efficient algorithm (GridClique) to mine interesting spatial co-location patterns (maximal cliques). These patterns are used as the raw transactions for an association rule mining technique to discover complex co-location rules. Our proposal includes certain types of complex relationships – especially negative relationships – in the patterns. The relationships can be obtained from only the maximal clique patterns, which have never been used until now. Our approach is applied on a well-known astronomy dataset obtained from the Sloan Digital Sky Survey (SDSS). ST data is continuously collected and made accessible in the public domain. We present an approach to mine and query large ST data with the aim of finding interesting patterns and understanding the underlying process of data generation. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a predefined time. One approach to processing a “flock query” is to map ST data into high-dimensional space and to reduce the query to a sequence of standard range queries that can be answered using a spatial indexing structure; however, the performance of spatial indexing structures rapidly deteriorates in high-dimensional space. This thesis sets out a preprocessing strategy that uses a random projection to reduce the dimensionality of the transformed space. We use probabilistic arguments to prove the accuracy of the projection and to present experimental results that show the possibility of managing the curse of dimensionality in a ST setting by combining random projections with traditional data structures. In time series data mining, we devised a new space-efficient algorithm (SparseDTW) to compute the dynamic time warping (DTW) distance between two time series, which always yields the optimal result. This is in contrast to other approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series: the more the similarity between the time series, the less space required to compute the DTW between them. Other techniques for speeding up DTW, impose a priori constraints and do not exploit similarity characteristics that may be present in the data. Our experiments demonstrate that SparseDTW outperforms these approaches. We discover an interesting pattern by applying SparseDTW algorithm: “pairs trading” in a large stock-market dataset, of the index daily prices from the Australian stock exchange (ASX) from 1980 to 2002

Meta-heurísticas aplicadas ao problema de projeção do preço de ações na bolsa de valores

The stock prices prediction in the stock exchange is an attractive field for research due to its commercial applications and financial benefits offered. The objective of this work is to analyze the performance of two meta-heuristic algorithms, Bat Algorithm and Genetic Algorithm to the problem of stock prices prediction. The individuals in the population of the algorithms were modeled using 7 technical indicators. The profit at the end of a period is maximized by choosing the right time to buy and sell stocks. To evaluate the proposed methodology, experiments were performed using real historical data (2006-2012) of 92 stocks listed on the stock exchange in Brazil. Cross-validation was applied in the experiments to avoid the overfiting using 3 periods for training and 4 for testing. The results of the algorithms were compared among them and also the performance indicator BuyandHold (B&H).For 91.30% of the stocks, the algorithms obtained profit higher than the B&H, and in 79.35% of them Bat Algorithm had the best performance, while for 11.95% of the stocks Genetic Algorithm was better. The results indicate that it is promising to apply meta-heuristics with the proposed model to the problem of stock prices prediction in the stock exchange.A projeção do preço de ações na bolsa de valores é um campo atraente para a investigação devido às suas aplicações comerciais e os benefícios financeiros oferecidos. O objetivo deste trabalho é analisar o desempenho de dois algoritmos meta-heurísticos, o Algoritmo do Morcego e o Algoritmo Genético, para o problema de projeção do preço de ações. Os indivíduos da população dos algoritmos foram modelados utilizando os parâmetros de 7 indicadores técnicos. O lucro final ao fim de um período é maximizado através da escolha do momento adequado para compra e venda de ações. Para avaliar a metodologia proposta foram realizados experimentos utilizando dados históricos reais (2006-2012) de 92 ações listadas na bolsa de valores do Brasil. A validação cruzada foi aplicada nos experimentos para evitar o overfiting, utilizando 3 períodos para treinamento e 4 para teste. Os resultados dos algoritmos foram comparados entre si e com o indicador de desempenho Buy and Hold (B&H). Para 91,30% das ações os algoritmos obtiveram lucro superior ao B&H, sendo que em 79,35% delas o Algoritmo do Morcego teve o melhor desempenho, enquanto que para 11,95% das ações o Algoritmo Genético foi melhor. Os resultados alcançados indicam que é promissora a aplicação de meta-heurísticas com a modelagem proposta para o problema de projeção do preço de ações na bolsa de valores

Using Genetic Algorithms for Robust Optimization in Financial Applications

In this study, optimal indicators and strategies for foreign exchange trading models are investigated in the framework of genetic algorithms. We first explain how the relevant quantities of our application can be encoded in "genes" so as to fit the requirements of the genetic evolutionary optimization technique. In financial problems, sharp peaks of high fitness are usually not representative of a general solution but, rather, indicative of some accidental fluctuations. Such fluctuations may arise out of inherent noise in the time series or due to threshold effects in the trading model performance. Peaks in such a discontinuous, noisy and multimodal fitness space generally correspond to trading models which will not perform well in out-of-sample tests. In this paper we show that standard genetic algorithms will be quickly attracted to one of the accidental peaks of the fitness space whereas genetic algorithm for multimodal functions employing clustering and a specially desi..