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<title>Automated Trader lottery RSS feed results</title>
<link>http://automatedtrader.net</link>
<description>
Automated Trader delivers immediate in-depth coverage of automated and algorithmic trading across all asset classes. Our global resource base utilises both online and print media to support market participants from both a business and a technological perspective. Give yourself an edge. Subscribe today.

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<language>en-uk</language>
<copyright>Copyright 2008 Algorithmic Media ltd</copyright>
<pubDate>Wed,  3 Dec 2008 12:21:30 -0600</pubDate>
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<title><![CDATA[Pretests for genetic-programming evolved trading programs: “zero-intelligence” strategies and lottery trading - Part 1]]></title>
<link>http://automatedtrader.net/algorithmic-trading-online-578.xhtm</link>
<description><![CDATA[In this paper, we discuss a series of pretests, based on several variants of random search, aiming at giving more clearcut answers as to whether a GP scheme, or any other machine-learning technique, can be effective with the training data at hand. Precisely, pretesting allows us to distinguish between a failure due to the market being efficient of due to GP being inefficient. The analysis is illustrated with GP-evolved strategies for three stock exchanges exhibiting different trends. ]]></description>
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<title><![CDATA[Pretests for genetic-programming evolved trading programs: “zero-intelligence” strategies and lottery trading - Part 2]]></title>
<link>http://automatedtrader.net/algorithmic-trading-online-579.xhtm</link>
<description><![CDATA[Part 2 of Pretests for genetic-programming evolved
trading programs: “zero-intelligence” strategies
and lottery trading bootstrap paper. By Shu-Heng Chen and Nicolas Navet
]]></description>
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<title><![CDATA[Event processing in the world of electronic and algorithmic trading ]]></title>
<link>http://automatedtrader.net/automated-trader-technology-workshop-596.xhtm</link>
<description><![CDATA[Event processing is a set of concepts and accompanying technologies that have been building momentum in the world of finance over the past few years. These ideas and technologies are rapidly changing the way in which automated trading systems and related parts of electronic trading systems are built and run. As with any new concept or technology, a glut of confusing “market speak” often arises from vendors promising to save the world. Chris Donnan, who works in equity derivatives trading technology at a top Wall Street firm, provides a translation.]]></description>
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<title><![CDATA[Entropy Rate and Profitability of Technical Analysis: Experiments on the NYSE US 100 Stocks]]></title>
<link>http://automatedtrader.net/algorithmic-trading-online-688.xhtm</link>
<description><![CDATA[ The entropy rate of a dynamic process measures the uncertainty that  remains in the next information produced by the process given complete  knowledge of the past. It is thus a natural measure of the difficulty to  predict the evolution of the process. The first question investigated here is  whether stock price time series exhibit temporal dependencies that can be  measured through entropy estimates. Then we study the extent to which  the return of financial trading rules is correlated with the entropy rates  of the price time series. Experiments are conducted on EOD data of the  stocks composing the NYSE US 100 index during period 2000-2006, with  the use of genetic programming to induce the trading rules. By Nicolas NAVET and Shu-Heng CHEN]]></description>
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<title><![CDATA[Financial Data Mining with Genetic Programming: a Survey and Look Forward]]></title>
<link>http://automatedtrader.net/algorithmic-trading-online-689.xhtm</link>
<description><![CDATA[ Genetic Programming (GP) is an appealing machine-learning  technique for tackling financial engineering problems: it belongs to the  family of evolutionary algorithms that have proven to be remarkably  successful at handling complex optimization problems, and possesses  the unique feature of producing solutions under a symbolic form that  can be understood and analyzed by humans. Over the last decade,  GP has been applied to generate financial trading strategies, forecast  stocks and options prices, or grasp some insight into the dynamics of  the markets and the behavior of the agents. In this paper, we first  provide a brief survey of the existing studies, then highlight fields of  investigations that, we believe, should lead to enhance the applicability  and efficiency of GP in the financial domain. By Nicolas NAVET and Shu-Heng CHEN]]></description>
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<title><![CDATA[Data-Mining Bias: The Fool’s Gold of Objective TA]]></title>
<link>http://automatedtrader.net/algorithmic-trading-online-1087.xhtm</link>
<description><![CDATA[The following excerpt is from Chapter 6 of David Aronson's recently published book "Evidence-Based Technical Analysis". Together with Chapters 4 and 5 of the book it addresses aspects of statistics that are particularly relevant to evidence-based (as opposed to subjective) technical analysis.]]></description>
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