Discussing the article: "Hyperdimensional Computing: Learning in One Integer Add, and the Nulls That Judge It"

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Check out the new article: Hyperdimensional Computing: Learning in One Integer Add, and the Nulls That Judge It.

A complete Hyperdimensional Computing library for MQL5 implements bind, bundle and permute over 4096‑bit hypervectors, graded level ladders for quantities, an encoder for bars and sequences, and a classifier trained by integer addition. It includes a purged, ordered validation split, online updates without refitting, a market scan with two nulls, and a Python reference that reproduces the MQL5 model bit for bit, so you can validate results and deploy a reproducible indicator and test suite.

Most machine learning methods that reach MetaTrader arrive with some version of the same bill attached. Training is expensive, so you retrain on a cadence and hope nothing important happened in between. The model is a block of floating point weights, so reproducing a result on another machine means reproducing a sequence of floating point operations in the same order. And when the model finally prints a number, you have no idea what that number would look like if the model knew nothing at all.

Hyperdimensional Computing, set out by Pentti Kanerva in 2009 and also known as Vector Symbolic Architecture, addresses all three at once, by giving up something most would not think they could: no single weight in the model means anything. Every concept, feature name and quantity is one very long random vector, and there are exactly three operations. Bind is XOR, bundle is an element-wise majority, permute is a cyclic shift. That is the whole algebra.

What you get in exchange is worth having in a trading terminal specifically. Learning is an integer add into an accumulator, so a model absorbs one more labeled row in a few hundred microseconds rather than waiting for a refit. The classifier core is integer and bitwise end to end, so a reference implementation of that core does not produce a similar model, it produces the same model. And because two unrelated hypervectors have a known distance distribution, the noise floor a reading is measured against is a closed-form expression rather than a permutation run that costs seconds per bar.

Each of those three has a boundary, and this article marks the boundary where it arises. A row carries a label that reads five bars ahead, so the newest row that can be folded in sits five bars behind the close. Feature engineering upstream of the encoder is ordinary double arithmetic, and only what happens from the quantizer onwards is exact. And the closed-form band describes unrelated prototypes, which the prototypes of a trained model are not.

Author: Hammad Dilber