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<title>Automated Trader Predictive 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 14:29:16 -0600</pubDate>
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<title><![CDATA[Yipes Introduces New Market Data Distribution Service]]></title>
<link>http://automatedtrader.net/algo-trading-news-192.xhtm</link>
<description><![CDATA[The introduction of the Yipes FinancialConnect! suite of services establishes the company as a direct source for exchange data over its network, and comes as the rapid rise of electronic trading places ever-greater communications infrastructure demands on ]]></description>
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<title><![CDATA[Strategies: Building a Better Bear Trap]]></title>
<link>http://automatedtrader.net/automated-trader-strategies-271.xhtm</link>
<description><![CDATA[One of the most critical elements in algorithmic trading lies in accurately modelling trading costs, yet this still remains a rather inexact science. While certain cost elements are relatively stable and/or easy to predict, others are not. As a result, models for estimating trading costs have tended to be reasonably predictive when viewed across a very large sample of trades, but decidedly indifferent performers on individual ones. This has in turn made the task of minimising these costs through the selection, tuning and scheduling of appropriate execution algorithms difficult. Dan diBartolomeo, president of Northfield Information Services, discusses the current limitations and suggests some additional elements that can be used to improve forecasting of trading costs and trade scheduling.]]></description>
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<title><![CDATA[AT Round Table: Pre-trade analytics - expectations and reality]]></title>
<link>http://automatedtrader.net/automated-trader-at-roundtable-503.xhtm</link>
<description><![CDATA[Pre-trade analytics have become an integral part of the workflow for algorithmic traders. We asked four major sellside banks for their views on some of the current and emerging themes in analytics.
With:
- Chris Biscoe, Head of US Ecommerce,
Barclays Capital
- Mike Duff, executive director, UBS
- Andrew Freyre-Sanders, Head of
Algorithmic Trading for EMEA and Asia,
JP Morgan
- Timothy Reilly, Co-Head of Alternative
Execution at Citibank, Citigroup Global
Equities
]]></description>
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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[Transaction Cost Research]]></title>
<link>http://automatedtrader.net/algorithmic-trading-online-631.xhtm</link>
<description><![CDATA[An excerpt from Kendall Kim's forthcoming book "Electronic and Algorithmic Trading Technology: The Complete Guide"

Chapter 10: Transaction Cost Research]]></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[Sybase Announces New Financial Services Real-Time Analytics and Complex Event Processing Platform with StreamBase]]></title>
<link>http://automatedtrader.net/algo-trading-news-712.xhtm</link>
<description><![CDATA[June 19, 2007 — Sybase, Inc., today announced that Sybase’s Real-time Analytics Platform, a highly optimized real-time data processing service platform, now integrates with StreamBase’s high-performance Complex Event Processing (CEP) platform. The joint solution will support real-time applications having large storage requirements, such as back-testing for algorithmic trading, risk analysis and historical trade auditing.]]></description>
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<title><![CDATA[Using Trading Dynamics to Boost Strategy Performance]]></title>
<link>http://automatedtrader.net/automated-trader-strategies-924.xhtm</link>
<description><![CDATA[In the first part of a two-part article, David Aronson, President of Hood River 
Research, introduces the concept of performance boosting strategies and explains the selection process for their predictor inputs.]]></description>
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<title><![CDATA[Survival of the Fastest]]></title>
<link>http://automatedtrader.net/automated-trader-sponsored-articles-976.xhtm</link>
<description><![CDATA[In historical terms,the speed and movement of sensitive data can clearly be seen to have delivered execution advantages. Looking back nearly 200 years, Nathan Rothschild proved the benefit of having the quickest communications system in Europe in 1815 by knowing prior to the British government the outcome of the Battle of Waterloo.The communication and anticipation of market momentum continued to develop from the first stock ticker launched on the NYSE in 1867,through Louis Bachelier’s thesis on market movement predictability at the turn of the 20th Century to the dawn of the electronic era and the development of Instinet’s DOT system. The current environment,with its proliferation of order management systems and execution management systems supported by numerous instantaneous execution choices, underlines the success of market entry strategies based on timing.]]></description>
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