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	<title>
	Comments on: The AutoTune filter	</title>
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	<description>A new view on algorithmic trading</description>
	<lastBuildDate>Thu, 27 Aug 2026 12:42:46 +0000</lastBuildDate>
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		<title>
		By: P K		</title>
		<link>https://financial-hacker.com/the-autotune-filter/#comment-142645</link>

		<dc:creator><![CDATA[P K]]></dc:creator>
		<pubDate>Thu, 27 Aug 2026 12:42:46 +0000</pubDate>
		<guid isPermaLink="false">https://financial-hacker.com/?p=4989#comment-142645</guid>

					<description><![CDATA[Thanks for putting the C version out,  the EasyLanguage-to-Zorro ports are the part nobody else does, and they&#039;re what makes any of this checkable. ChatGPT still produces bugs, we still need to keep it verified.

One thing I&#039;d add rather than dispute. The AutoTune machinery assumes a dominant cycle is there to be found, and I couldn&#039;t see anywhere in the post where that assumption gets its own test - no shuffled control, no random-period arm. So the 25% is measured against buy-and-hold, but not against &quot;the same filter, tuned to a period that means nothing&quot;.

I ran that premise separately, on hourly bars of three majors, 2000–2026, against a null of the same returns shuffled — which destroys serial structure and leaves the fat tails alone, so a peak that survives is structure and not kurtosis. Two hundred shuffles for the band.

In the returns, nothing stood up: no bin reached 1.2× the 99th percentile of the shuffled band, and about ten bins per instrument cleared it at all, which is what a thousand bins and a 99% band give you by chance. The bins that cleared repeated on no second instrument. Period estimates didn&#039;t survive the move to the next window either — six instrument-estimator cells, all inside ±1.6 sd of shuffled.

The part I&#039;d stress is the positive control, because it&#039;s what stops this being a null from a blunt instrument. In absolute returns a peak stands up enormously — 24 bars at six to twenty-nine times the band on all three, and again at 98 on M15 and 293 on M5. That&#039;s the trading day at each scale. Fed a real cycle, Welch and Burg find it and find it loudly. They just don&#039;t find one in price.

Two honest limits. My A/B was a channel breakout whose only parameter is the lookback, chosen because a second free parameter lets either arm win by tuning rather than by adapting, so it says nothing directly about a band-pass tuned for mean reversion, which asks something different of the same estimate. And your result comes out of walk-forward optimisation, which is the right method; my worry is only that if the period itself isn&#039;t persistent, walk-forward hides that rather than catching it.

Numbers and method are at https://turnmarks.com/filters/adaptive if useful. I&#039;d be glad to be wrong about the premise: a shuffled-period arm on your ES setup would settle it faster than anything I ran.]]></description>
			<content:encoded><![CDATA[<p>Thanks for putting the C version out,  the EasyLanguage-to-Zorro ports are the part nobody else does, and they&#8217;re what makes any of this checkable. ChatGPT still produces bugs, we still need to keep it verified.</p>
<p>One thing I&#8217;d add rather than dispute. The AutoTune machinery assumes a dominant cycle is there to be found, and I couldn&#8217;t see anywhere in the post where that assumption gets its own test &#8211; no shuffled control, no random-period arm. So the 25% is measured against buy-and-hold, but not against &#8220;the same filter, tuned to a period that means nothing&#8221;.</p>
<p>I ran that premise separately, on hourly bars of three majors, 2000–2026, against a null of the same returns shuffled — which destroys serial structure and leaves the fat tails alone, so a peak that survives is structure and not kurtosis. Two hundred shuffles for the band.</p>
<p>In the returns, nothing stood up: no bin reached 1.2× the 99th percentile of the shuffled band, and about ten bins per instrument cleared it at all, which is what a thousand bins and a 99% band give you by chance. The bins that cleared repeated on no second instrument. Period estimates didn&#8217;t survive the move to the next window either — six instrument-estimator cells, all inside ±1.6 sd of shuffled.</p>
<p>The part I&#8217;d stress is the positive control, because it&#8217;s what stops this being a null from a blunt instrument. In absolute returns a peak stands up enormously — 24 bars at six to twenty-nine times the band on all three, and again at 98 on M15 and 293 on M5. That&#8217;s the trading day at each scale. Fed a real cycle, Welch and Burg find it and find it loudly. They just don&#8217;t find one in price.</p>
<p>Two honest limits. My A/B was a channel breakout whose only parameter is the lookback, chosen because a second free parameter lets either arm win by tuning rather than by adapting, so it says nothing directly about a band-pass tuned for mean reversion, which asks something different of the same estimate. And your result comes out of walk-forward optimisation, which is the right method; my worry is only that if the period itself isn&#8217;t persistent, walk-forward hides that rather than catching it.</p>
<p>Numbers and method are at <a href="https://turnmarks.com/filters/adaptive" rel="nofollow ugc">https://turnmarks.com/filters/adaptive</a> if useful. I&#8217;d be glad to be wrong about the premise: a shuffled-period arm on your ES setup would settle it faster than anything I ran.</p>
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		<title>
		By: Recent Quant Links from Quantocracy as of 04/18/2026 - Quantocracy		</title>
		<link>https://financial-hacker.com/the-autotune-filter/#comment-135234</link>

		<dc:creator><![CDATA[Recent Quant Links from Quantocracy as of 04/18/2026 - Quantocracy]]></dc:creator>
		<pubDate>Sun, 19 Apr 2026 05:30:09 +0000</pubDate>
		<guid isPermaLink="false">https://financial-hacker.com/?p=4989#comment-135234</guid>

					<description><![CDATA[[&#8230;] The AutoTune filter [Financial Hacker] [&#8230;]]]></description>
			<content:encoded><![CDATA[<p>[&#8230;] The AutoTune filter [Financial Hacker] [&#8230;]</p>
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