# The level that remembered nothing

> My first real finding said price levels remember their own history. At sixteen years and 183,689 revisits, the memory was recency wearing a costume.

- Canonical: https://hadalinstruments.com/research/the-level-that-remembered-nothing/
- Published: 2026-08-22
- Author: Hadal Research
- Answers the question: "do price levels remember what happened there before"
- Coins the term: **Recency Mirage** — The appearance of durable structure produced entirely by short-range recency — a level looks as though it remembers its own history, when every part of the signal is carried by what happened there most recently.

---
Ask whether a price level remembers what happened there before, and the honest
answer from my own data is: it remembers roughly a fortnight, and calling that
memory oversells it. What predicts a level's next behaviour is what happened
there most recently. Reach further back, hold the recent outcome fixed, and the
distant past adds nothing measurable at all. I call the pattern a **Recency
Mirage** — durable structure that turns out, on inspection, to be short-range
recency wearing a costume.

This is a post-mortem rather than a study of somebody else's idea. The memory
hypothesis was my first real finding, I published it internally as the
engine's most promising result, and I spent the following weeks killing it.

## What I found, and why it looked right

Split a level's history into two halves and correlate a pool's bias across the
split. I reported r = +0.31 at p = 0.014, and read it as structure living
exactly where the theory said it would: levels carrying persistent identity.

That figure is quoted here as a record of what I said at the time, not as a
measurement I would stand on now. It is the claim this post retracts, and the
separator below is the receipt for the retraction.

I also recorded, at the time, the reason not to believe it yet. A correlation
across a time split is produced just as readily by short-range trend as by
durable identity, because the two halves meet at a boundary and trend carries
across it. The only thing that separates the two explanations is a revisit with
a long gap — far enough back that recent momentum cannot reach. Over the
thirteen-month window I had forty-four of those, and the long-gap subset
looked *better* than the short-gap one, at +39% against +28%.

A tiny sample strengthening the preferred reading is the exact shape of a
result that should not be trusted. I wrote that down before I ran the test
that settled it.

## The test that settled it

The full history is sixteen years: 97,693 bars, 200,369 pool arrivals, and
enough long-gap revisits to make the separator meaningful. The specification
is a single logistic model — predict the outcome at a level from its most
recent prior outcome and a genuinely distant prior outcome together, then ask
whether the distant term adds anything once the recent one is present.

It does not.

The recent term carries an odds ratio of 1.55. The distant term comes in at
1.01, with a partial likelihood-ratio χ²(1) of 0.39 and p = 0.53, on 183,689
eligible arrivals. Adding the distant outcome changes held-out log-loss by
0.0000. The entire split is decided by what happened at that level last time.

The decay curve is the clearest way to see it. Measured by the gap since the
previous visit, the effect runs about eleven per cent at zero to fifty bars,
five per cent from fifty to two hundred and forty, three per cent out to a
thousand, and then zero. Beyond roughly a thousand bars — about two months —
there is nothing left to measure.

## The part that makes the null trustworthy

A null result is only as good as its ability to have come out the other way, so
one detail matters more than the headline. Running the walk over the full
history builds a much denser ladder of levels than the shorter window did,
because levels never expire. Density inflates trend, and inflated trend would
tend to *manufacture* a spurious distant effect rather than suppress one.

The test was therefore biased against the null it returned, and it returned the
null anyway. That is the direction of error you want when you are trying to kill
your own hypothesis, and it is why I treat this one as settled rather than
provisional.

## What survives

Something does. A level's most recent outcome genuinely predicts its next one —
the probability of absorption given that the level last absorbed is 61%, against
33% when it last swept, an odds ratio of 3.49 that holds on held-out data. That
is a real, out-of-sample-valid effect and I am not throwing it away.

I am also not dressing it up. "Recent behaviour persists for a few weeks" is a
general property of price series. It is not evidence for anything about
liquidity, institutional positioning, or levels as objects, and it was available
to anyone before I measured it. The finding here is not that I discovered
momentum. It is that momentum accounts for all of what I had mistaken for
memory.

## Limits, printed rather than buried

The test covers one instrument. A single-pair null is a null about that pair, and I
have not tested whether it generalises.

It is also a one-dimensional test, which matters more than the pair count. The
broader thesis this work sits inside claims a *conjunction* — crowding and
dealer positioning and thin liquidity together — and a signal that only fires
at the intersection of three conditions would return null on each one measured
alone, by construction. That conjunction has not been run, and two of its
inputs are not available to an individual at any price. So this result kills a
specific, popular, one-variable version of the idea. It does not settle the
general question, and I would be overselling a null if I said otherwise.

## Why I published it

The claim that levels remember is taught as fact, and it is the load-bearing
assumption under a large amount of retail trading education. I believed it,
measured it properly, and found that my own best evidence for it was a
forty-four-sample artefact that dissolved at scale.

A firm that only publishes results flattering to its thesis is not running an
instrument, it is running a marketing department. This one is in the register
with its kill criteria attached, counted against the full denominator of every
hypothesis I have registered — including, and especially, the ones that died.
---

## Claims examined

### Claim 01 — canonical: https://hadalinstruments.com/refutations/#claim-2d76a6d7

> "That level is loaded — price remembers what happened there." — our reading: Unproven

I measured this on my own data and I was the ones who believed it first. Splitting a level's history in two and correlating the halves gave me r = +0.31, which is exactly what durable identity would produce — and is also exactly what short-range trend produces, because the two halves of any split share a boundary. I read it the first way; that reading is what this retracts. Separating them needs revisits with a long gap, and over a thirteen-month window there were forty-four of those. At sixteen years there are enough. Controlling for a level's most recent outcome and asking whether a genuinely distant outcome still adds anything returns an odds ratio of 1.01 with p = 0.53. The distant past contributes nothing once the recent past is accounted for.

**What is true:** A level's behaviour is predicted by what happened there most recently, and that predictive content decays to nothing within roughly a thousand bars — so the useful signal is short-range recency, which is a general property of price series rather than anything specific to the level.

Evidence: https://hadalinstruments.com/research/the-level-that-remembered-nothing/#claim-2d76a6d7

### Claim 02 — canonical: https://hadalinstruments.com/refutations/#claim-742e4dce

> "The more times a level gets tested, the stronger it becomes." — our reading: Unproven

The claim above is the memory claim in its strongest and most falsifiable form, which is why it is worth stating separately. If levels accumulate significance, distant touches should still carry weight after recent ones are controlled for. They do not. The decay is monotonic and it reaches zero: the gradient runs from about eleven per cent at the closest gaps down through five and three per cent, and is flat by the thousand-bar mark. What looks like accumulation is the most recent touch doing all the work.

**What is true:** The measured effect of a prior reaction on the next one falls away with distance in time — around eleven per cent for the most recent visits, and indistinguishable from zero beyond roughly a thousand bars — so accumulated touch history adds no measurable predictive weight beyond the latest one.

Evidence: https://hadalinstruments.com/research/the-level-that-remembered-nothing/#claim-742e4dce

### Claim 03 — canonical: https://hadalinstruments.com/refutations/#claim-c3e7a498

> "You proved this on one pair, so it proves nothing." — our reading: True

The objection is fair, and I am recording it as true rather than arguing with it. Everything below rests on one pair, and a single-instrument null is a null about that instrument. What it does establish is narrower and still useful: the belief is not a law, because a law would not have somewhere it fails to hold. Anyone can run the same separator on their own instrument, and the method is written down here precisely so that it can be.

**What is true:** This result is established on a single instrument and states nothing about any other, which is why the scope is printed beside the number rather than left for a reader to discover.

Evidence: https://hadalinstruments.com/research/the-level-that-remembered-nothing/#claim-c3e7a498

## Cite This Article

APA BibTeX HTML

Hadal Research. (2026). The level that remembered nothing. Hadal Research. https://hadalinstruments.com/research/the-level-that-remembered-nothing/ Version 5762f56, 2026-08-29.

@misc{hadal_2026_the-level-that-remembered-nothing,
author = {Hadal Research},
title = {The level that remembered nothing},
year = {2026},
url = {https://hadalinstruments.com/research/the-level-that-remembered-nothing/},
howpublished = {Hadal Research},
version = {5762f56},
note = {Published: 2026-08-22; version dated 2026-08-29}
}

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