MiniRocket

MiniRocket ported to native MQL5: a convolution transform that is never trained, and an audit harness that states how weak an edge it could have detected before it reports finding none.

Companion code for the MQL5 article: https://www.mql5.com/en/articles/24273

What it does

MiniRocket turns a window of prices into roughly ten thousand features using 84 fixed convolution kernels spread over a dilation schedule. Nothing about the kernels is learned, which is what makes the transform cost about two milliseconds a window and need no ONNX, no gradient descent and no Python at runtime. CMiniRocket implements it; CRidgeClassifier solves the linear model in the dual, so a single eigendecomposition serves the whole penalty grid and leave-one-out error falls out in closed form.

The port is checked for bit equality against an independent float64 reimplementation rather than assumed correct. A transform that is subtly wrong still returns plausible features, so MiniRocketVerify.mq5 writes the input and the output at seventeen significant digits and minirocket_reference.py diffs them exactly. compare_sktime.py repeats the check against sktime's own source as a third party.

The harder half is that a null result is ambiguous: point a model at a classic setup, find nothing, and you cannot tell whether the setup is worthless or the pipeline is broken. SetupLab.mqh settles that with controls instead of assurances. A planted label the features can provably recover, that same label with a quarter of its answers flipped, and a coin flip all travel the identical path as the seven real setups.

That calibration is the point. The planted label reaches AUC 0.803 and the coin flip sits at 0.499, so the harness detects what is there and nothing when there is nothing. The corrupted label lands at 0.546, which puts the resolution of this test bed at roughly AUC 0.55 at these sample sizes. Read against that floor, all seven setups on six years of EURUSD H1 come back null, and the article is explicit about which rows have the sample size to support that claim and which only support "not enough signals to say".

Layout

Include/MiniRocket/MiniRocket.mqh         the transform: 84 kernels, dilations, PPV pooling
Include/MiniRocket/RidgeClassifier.mqh    ridge in the dual, closed-form leave-one-out
Include/MiniRocket/SetupLab.mqh           setups, controls, labelling and the purged split
Scripts/MiniRocket/MiniRocketVerify.mq5   fixture, schedule, timings, and the CSV dump
Scripts/MiniRocket/SetupAudit.mq5         every setup and control through one pipeline
Scripts/MiniRocket/MiniRocketFit.mq5      fits one setup and writes the model file
Indicators/MiniRocket/MiniRocketProb.mq5  scores each trigger on a live chart
Files/MiniRocket/minirocket_reference.py  independent float64 reference
Files/MiniRocket/compare_sktime.py        diffed against sktime's own source
Files/MiniRocket/level_invariance.py      what the features can and cannot see

Run MiniRocketVerify.mq5 first, then minirocket_reference.py on the two CSVs it writes. If the features do not match exactly, nothing downstream is worth reading.

Disclaimer

Educational code. Past behaviour of any model or dataset says nothing about future results. Test on your own data and broker conditions before drawing conclusions.