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One thing I’d add rather than dispute. The AutoTune machinery assumes a dominant cycle is there to be found, and I couldn’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 “the same filter, tuned to a period that means nothing”.

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’t survive the move to the next window either — six instrument-estimator cells, all inside ±1.6 sd of shuffled.

The part I’d stress is the positive control, because it’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’s the trading day at each scale. Fed a real cycle, Welch and Burg find it and find it loudly. They just don’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’t persistent, walk-forward hides that rather than catching it.

Numbers and method are at https://turnmarks.com/filters/adaptive if useful. I’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.

What I like most is the focus on the core broker functions: login, asset data, price retrieval, and order execution. That is usually where most of the practical work is, especially when dealing with signatures, timestamps, JSON parsing, rate limits, and partial fills.

I have been looking at BYDFi’s API recently, and this kind of lean REST implementation is exactly the approach I prefer for testing small crypto strategies. Before automating anything serious, I think developers should first handle authentication properly, test read-only access, log every response, and make sure order execution behaves as expected in edge cases.

The main lesson is still the same: the trading logic may be simple, but API reliability and error handling decide whether the system is actually usable.

Yes, sorry. I’ve now uploaded it.

I am glad you mentioned our work. In case you are interested, I have written an article about the regime and how it worked this year , together with Python implementation (not 9 pages 😉 ).
https://fabiobaruffa.com/articles/market-regime-framework/

Thank you