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	<title>Comments on: A filter with negative lag</title>
	<link>http://ai-quant.com/2008/01/22/a-filter-with-negative-lag/</link>
	<description>Signal Processing applied to the financial markets.</description>
	<pubDate>Sat, 17 May 2008 00:44:46 +0000</pubDate>
	<generator>http://wordpress.org/?v=2.3.1</generator>
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		<title>By: aiQUANT &#187; Blog Archive &#187; Negative lag filter - new algorithm</title>
		<link>http://ai-quant.com/2008/01/22/a-filter-with-negative-lag/#comment-553</link>
		<dc:creator>aiQUANT &#187; Blog Archive &#187; Negative lag filter - new algorithm</dc:creator>
		<pubDate>Sat, 15 Mar 2008 00:20:31 +0000</pubDate>
		<guid>http://ai-quant.com/2008/01/22/a-filter-with-negative-lag/#comment-553</guid>
		<description>[...] an earlier post I mentioned a filtering algorithm which demonstrated negative lag for low frequencies (trending [...]</description>
		<content:encoded><![CDATA[<p>[&#8230;] an earlier post I mentioned a filtering algorithm which demonstrated negative lag for low frequencies (trending [&#8230;]</p>
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		<title>By: Juan Carlos Christensen</title>
		<link>http://ai-quant.com/2008/01/22/a-filter-with-negative-lag/#comment-512</link>
		<dc:creator>Juan Carlos Christensen</dc:creator>
		<pubDate>Sun, 09 Mar 2008 04:03:49 +0000</pubDate>
		<guid>http://ai-quant.com/2008/01/22/a-filter-with-negative-lag/#comment-512</guid>
		<description>Hi, nice article!
Besides particle filters, markov chain montecarlo and uncented kalman filters work pretty well too.
I also wanted to point out that there's a very good indicator for trend identification called CFB by Jurik Research, and there's a version of the indicator with code for the Metatrader platform. It identifies trend start and strength really good, but the most amazing thing is it's ability to pinpoint trend end really fast.
As for the algorithm you used in the above article, there are many indicators that use 2nd or 3rd degree powers to ascelerate the indicator response. From kalman filters, polynomial regressions, and many others.
You are on a good track, but you also face the same problem most of those filters face, the all have a lot of overshoot (personally i would consider some technique of adaptive filtering to solve the overshoot problem).
Good luck,
Juan.</description>
		<content:encoded><![CDATA[<p>Hi, nice article!<br />
Besides particle filters, markov chain montecarlo and uncented kalman filters work pretty well too.<br />
I also wanted to point out that there&#8217;s a very good indicator for trend identification called CFB by Jurik Research, and there&#8217;s a version of the indicator with code for the Metatrader platform. It identifies trend start and strength really good, but the most amazing thing is it&#8217;s ability to pinpoint trend end really fast.<br />
As for the algorithm you used in the above article, there are many indicators that use 2nd or 3rd degree powers to ascelerate the indicator response. From kalman filters, polynomial regressions, and many others.<br />
You are on a good track, but you also face the same problem most of those filters face, the all have a lot of overshoot (personally i would consider some technique of adaptive filtering to solve the overshoot problem).<br />
Good luck,<br />
Juan.</p>
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		<title>By: aiquant</title>
		<link>http://ai-quant.com/2008/01/22/a-filter-with-negative-lag/#comment-313</link>
		<dc:creator>aiquant</dc:creator>
		<pubDate>Tue, 29 Jan 2008 21:57:32 +0000</pubDate>
		<guid>http://ai-quant.com/2008/01/22/a-filter-with-negative-lag/#comment-313</guid>
		<description>Hi Curt,

Yes I follow ehlers work closely, and have even managed to beat some of his best indicators - and at present waiting for a verdict from ehlers himself on some of my work.

On filtering 5 second ticks, you might want to look into particle filtering.  The book "Introduction to High Frequency Finance" provides examples - but it all depends on how much smoothing you want to achieve.  I'd imagine changing the sampling rate i.e. to 10 seconds and doing some kind of (spline) interpolation between the points can help.  Is this data very noisy?</description>
		<content:encoded><![CDATA[<p>Hi Curt,</p>
<p>Yes I follow ehlers work closely, and have even managed to beat some of his best indicators - and at present waiting for a verdict from ehlers himself on some of my work.</p>
<p>On filtering 5 second ticks, you might want to look into particle filtering.  The book &#8220;Introduction to High Frequency Finance&#8221; provides examples - but it all depends on how much smoothing you want to achieve.  I&#8217;d imagine changing the sampling rate i.e. to 10 seconds and doing some kind of (spline) interpolation between the points can help.  Is this data very noisy?</p>
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		<title>By: Curt Smith</title>
		<link>http://ai-quant.com/2008/01/22/a-filter-with-negative-lag/#comment-309</link>
		<dc:creator>Curt Smith</dc:creator>
		<pubDate>Tue, 29 Jan 2008 20:16:16 +0000</pubDate>
		<guid>http://ai-quant.com/2008/01/22/a-filter-with-negative-lag/#comment-309</guid>
		<description>Hi, I just discovered your site.  I've been reading John Ehlers books on this same subject

Looks like you must have crossed paths with Ehlers at some point?  You can google Ehlers and find discussion of his Z based algorithms mentioned in his ebooks (complete with code) in the 2003,4 time frame but discussion dried up and recent trading shops selling his algorithms for trading your account I hear are underwater.  

Do you have recent successes, links, systems you can describe that are working in todays wip-saw markets?  I'm especially interested in tick/5 second bar intraday algorithms??

Thanks, Curt</description>
		<content:encoded><![CDATA[<p>Hi, I just discovered your site.  I&#8217;ve been reading John Ehlers books on this same subject</p>
<p>Looks like you must have crossed paths with Ehlers at some point?  You can google Ehlers and find discussion of his Z based algorithms mentioned in his ebooks (complete with code) in the 2003,4 time frame but discussion dried up and recent trading shops selling his algorithms for trading your account I hear are underwater.  </p>
<p>Do you have recent successes, links, systems you can describe that are working in todays wip-saw markets?  I&#8217;m especially interested in tick/5 second bar intraday algorithms??</p>
<p>Thanks, Curt</p>
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		<title>By: aiquant</title>
		<link>http://ai-quant.com/2008/01/22/a-filter-with-negative-lag/#comment-280</link>
		<dc:creator>aiquant</dc:creator>
		<pubDate>Fri, 25 Jan 2008 17:12:54 +0000</pubDate>
		<guid>http://ai-quant.com/2008/01/22/a-filter-with-negative-lag/#comment-280</guid>
		<description>There may well be statistical ways of determining the mode of the market -- one that incorporates not only the past values of the time series, but also other measures such as volatility, volume, and the one you mention.  It would be reasonable to use such information because the market is not just reflected by the price itself but rather, a multitude of measures.

My approach here is from a purely signal processing perspective, where one attempts to deduce characteristics of a time series given the time series itself and nothing else.  But your findings are interesting -- maybe this points to an opportunity in the future to improve our models by combining the benefits of two different methods that do the same thing. Thanks for posting!</description>
		<content:encoded><![CDATA[<p>There may well be statistical ways of determining the mode of the market &#8212; one that incorporates not only the past values of the time series, but also other measures such as volatility, volume, and the one you mention.  It would be reasonable to use such information because the market is not just reflected by the price itself but rather, a multitude of measures.</p>
<p>My approach here is from a purely signal processing perspective, where one attempts to deduce characteristics of a time series given the time series itself and nothing else.  But your findings are interesting &#8212; maybe this points to an opportunity in the future to improve our models by combining the benefits of two different methods that do the same thing. Thanks for posting!</p>
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		<title>By: foquant</title>
		<link>http://ai-quant.com/2008/01/22/a-filter-with-negative-lag/#comment-279</link>
		<dc:creator>foquant</dc:creator>
		<pubDate>Fri, 25 Jan 2008 14:26:34 +0000</pubDate>
		<guid>http://ai-quant.com/2008/01/22/a-filter-with-negative-lag/#comment-279</guid>
		<description>The main issue you face with 1 is something I plan to tackle myself in the not-too distant future, specifically when is a market trending or not.  While my initial thought is more remedial than the more advanced method you are working with, I wonder if using some simple approach, such as a short-term regression slope or price pattern recognition, as a trend indicator would help in determining when to change alpha.

While its a popular method, I've also found that combining fixed interval time periods (ie 5 min interval) with inconsistent interval periods (ie 500 contract interval) yields interesting results... although I'm still trying to fully comprehend it all.  Depending upon the setup used, the inconsistent method, if small enough compared to the fixed time method, can often predict &lt;em&gt;very near-term moves&lt;/em&gt;, much like local predictability, which in high-frequency can be used to determine short-term trends.  Just $0.02 but may be off the mark.  Either way, very interesting post!  Thanks!</description>
		<content:encoded><![CDATA[<p>The main issue you face with 1 is something I plan to tackle myself in the not-too distant future, specifically when is a market trending or not.  While my initial thought is more remedial than the more advanced method you are working with, I wonder if using some simple approach, such as a short-term regression slope or price pattern recognition, as a trend indicator would help in determining when to change alpha.</p>
<p>While its a popular method, I&#8217;ve also found that combining fixed interval time periods (ie 5 min interval) with inconsistent interval periods (ie 500 contract interval) yields interesting results&#8230; although I&#8217;m still trying to fully comprehend it all.  Depending upon the setup used, the inconsistent method, if small enough compared to the fixed time method, can often predict <em>very near-term moves</em>, much like local predictability, which in high-frequency can be used to determine short-term trends.  Just $0.02 but may be off the mark.  Either way, very interesting post!  Thanks!</p>
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