Combining Technical Trading Rules Using Parallel Particle Swarm Optimization based on Hadoop

Technical trading rules have been utilized in the stock markets to make profit for more than a century. However, no single trading rule can ever be expected to predict the stock price trend accurately. In fact, many investors and fund managers make trading decisions by combining a bunch of technical indicators. In this paper, we consider the complex stock trading strategy, called Performance-based Reward Strategy (PRS), proposed by [1]. Instead of combining two classes of technical trading rules, we expand the scope to combine the seven most popular classes of trading rules in financial markets, resulting in a total of 1059 component trading rules. Each component rule is assigned a starting weight and a reward/penalty mechanism based on rules' recent profit is proposed to update their weights over time. To determine the best parameter values of PRS, we employ an improved time variant particle swarm optimization (TVPSO) algorithm with the objective of maximizing the annual net profit generated by PRS. Due to a large number of component rules and swarm size, the optimization time is significant. A parallel PSO based on Hadoop, an open source parallel programming model of MapReduce, is employed to optimize PRS more efficiently. The experimental results show that PRS outperforms all of the component rules in the testing period.published_or_final_versio

Complex stock trading strategy based on particle swarm optimization

Technical Session 1B - Advanced Algorithmic Trading – I: no. 41Trading rules have been utilized in the stock market to make profit for more than a century. However, only using a single trading rule may not be sufficient to predict the stock price trend accurately. Although some complex trading strategies combining various classes of trading rules have been proposed in the literature, they often pick only one rule for each class, which may lose valuable information from other rules in the same class. In this paper, a complex stock trading strategy, namely weight reward strategy (WRS), is proposed. WRS combines the two most popular classes of trading rules-moving average (MA) and trading range break-out (TRB). For both MA and TRB, WRS includes different combinations of the rule parameters to get a universe of 140 component trading rules in all. Each component rule is assigned a start weight and a reward/penalty mechanism based on profit is proposed to update these rules’ weights over time. To determine the best parameter values of WRS, we employ an improved time variant Particle Swarm Optimization (PSO) algorithm with the objective of maximizing the annual net profit generated by WRS. The experiments show that our proposed WRS optimized by PSO outperforms the best moving average and trading range break-out rules.postprin

Improving Query Performance by Clustering XML Documents

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Improving Query Performance by Clustering XML Documents

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Efficient mining of frequent item sets on large uncertain databases

The data handled in emerging applications like location-based services, sensor monitoring systems, and data integration, are often inexact in nature. In this paper, we study the important problem of extracting frequent item sets from a large uncertain database, interpreted under the Possible World Semantics (PWS). This issue is technically challenging, since an uncertain database contains an exponential number of possible worlds. By observing that the mining process can be modeled as a Poisson binomial distribution, we develop an approximate algorithm, which can efficiently and accurately discover frequent item sets in a large uncertain database. We also study the important issue of maintaining the mining result for a database that is evolving (e.g., by inserting a tuple). Specifically, we propose incremental mining algorithms, which enable Probabilistic Frequent Item set (PFI) results to be refreshed. This reduces the need of re-executing the whole mining algorithm on the new database, which is often more expensive and unnecessary. We examine how an existing algorithm that extracts exact item sets, as well as our approximate algorithm, can support incremental mining. All our approaches support both tuple and attribute uncertainty, which are two common uncertain database models. We also perform extensive evaluation on real and synthetic data sets to validate our approaches. © 1989-2012 IEEE.published_or_final_versio

GLProbs: Aligning multiple sequences adaptively

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Depression literacy among Australians of Chinese-speaking background in Melbourne, Australia

<p>Abstract</p> <p>Background</p> <p>This study investigated the knowledge of depression and preference for professional help, medications and treatment methods among Australians of Chinese-speaking background, and the perceptions of this population of the causes of mental illness.</p> <p>Methods</p> <p>Adopting a cluster convenience sampling method, the study recruited 200 Chinese-speaking subjects from four major areas in metropolitan Melbourne where many Chinese live. The respondents were presented with a vignette describing an individual with depression and then asked questions to assess their understanding of depression and preference for professional help, medications and treatment methods. A comparative approach was used to compare the findings with those of a previous study of the mental health literacy of Australian and Japanese adults.</p> <p>Results</p> <p>Compared to the Australian and Japanese samples, a much lower percentage of Chinese-speaking Australians (14%) could correctly identify major depression described in the vignette, and a higher percentage believed that counseling professionals could be helpful. Higher percentages of those who believed that close family members could be helpful were found in the Chinese-speaking Australian and Japanese samples, and these two groups also expressed more uncertainty about the usefulness or harmfulness of certain medications compared to the Australian sample. Higher percentages of respondents in both the Chinese-speaking Australian and the Australian sample considered "lifestyle changes" to be helpful compared to the Japanese sample. In the Chinese-speaking sample, 30%, 17.4%, 33% and 27% of the respondents rated "traditional Chinese medicine doctors," "Chinese herbal medications," "taking Chinese nutritional foods/supplements" and "<it>qiqong</it>" as helpful. Many perceived "changing <it>fungshui</it>" and "traditional Chinese worship" to be harmful. Regarding the perception of causes of mental illness, items related to psychosocial perspectives including "life stress" and "interpersonal conflict" were rated highly by the respondents, whereas traditional beliefs including "punishment for misdeeds conducted by ancestors" and "demon possession" had the lowest ratings.</p> <p>Conclusions</p> <p>Campaigns to increase the mental health literacy of Chinese-speaking Australians are needed. The abovementioned socially and culturally driven beliefs need to be taken into consideration in the development of culturally relevant education programs.</p

Lipid (per) oxidation in mitochondria:an emerging target in the ageing process?

Lipids are essential for physiological processes such as maintaining membrane integrity, providing a source of energy and acting as signalling molecules to control processes including cell proliferation, metabolism, inflammation and apoptosis. Disruption of lipid homeostasis can promote pathological changes that contribute towards biological ageing and age-related diseases. Several age-related diseases have been associated with altered lipid metabolism and an elevation in highly damaging lipid peroxidation products; the latter has been ascribed, at least in part, to mitochondrial dysfunction and elevated ROS formation. In addition, senescent cells, which are known to contribute significantly to age-related pathologies, are also associated with impaired mitochondrial function and changes in lipid metabolism. Therapeutic targeting of dysfunctional mitochondrial and pathological lipid metabolism is an emerging strategy for alleviating their negative impact during ageing and the progression to age-related diseases. Such therapies could include the use of drugs that prevent mitochondrial uncoupling, inhibit inflammatory lipid synthesis, modulate lipid transport or storage, reduce mitochondrial oxidative stress and eliminate senescent cells from tissues. In this review, we provide an overview of lipid structure and function, with emphasis on mitochondrial lipids and their potential for therapeutic targeting during ageing and age-related disease