FAILED

40-day average by the direction of the line · entry only in low volatility (0.8σ)

Not every trade a trend system signals is worth taking. Waiting for the market to be in the right volatility regime before entering discards more bad trades than good ones — and the result improves despite there being fewer trades.

Measured in forex — EUR/USD and GBP/USD · daily bars built from 15m · 1 pip spread (~0.009% per round trip) · no survivorship bias

Net per trade

+0.07%

after fees

The fee is charged on both legs: every trade pays to open and pays to close.

Died at

invariance

the result is not physically plausible

Worst drawdown

−0%

897 days underwater

How far the account fell below its own best previous moment.

Timeframe

1 day

The time grid this was measured on. The same technique on a coarser or finer grid is a different measurement, and can earn a different verdict.

invariance
costs
placebo
benchmark
out of sample
multiple testing

Equity curve

Risking 1.0% per trade, marked to market every day — not only when a trade closes. The line at 1.0 is the capital it started with.

At its worst the account was worth 0% less than its own best previous moment, and it spent 897 days below that peak.

The technique against chance

Same number of trades, same holding time, same assets — only the dates were drawn at random.

The average trade and the typical one

The mean sits above the median: the lottery signature — the typical trade loses and a rare handful pays for everything.

What was measured

We coded the rule exactly as it is described and let it trade on its own from 2020-01-01 to 2026-06-26. It found trades in 2 coins, 220 in total. Each one enters and exits at the price that existed on that day — the program never sees what comes next, which is the most common mistake people make testing strategies at home. Wherever the description was ambiguous we took the reading least favourable to the technique; whatever was left open is stated in the hypothesis, at the foot of this page.

How many of those trades actually count

Only 121 of the 220. Cryptocurrencies rise and fall almost in unison, so the same rule fires on dozens of coins on the same day — and that is one bet repeated, not dozens of different bets. Counting them all separately is the trick that makes a bad test look impressive.

What it paid, before any deductions

+0.08% per trade. This is the number the technique claims, and the only one on this page that has not yet been through a single control.

Why it did not pass — it died here · where the result came from

We look at where the result came from: whether it is spread across the trades or packed into a handful of them. It concentrated. That is the shape of a lottery: the typical trade loses, and a rare handful pays for everything. Anyone following the rule for real would face a long run of losses before any gain, and would almost certainly quit before it arrived. To give the measure: 60% of the profit came from the top 5% of trades, and the same technique with its dates shuffled would concentrate 43%. The control fails above 50%.

What would have happened to the money

Risking 1.0% of capital per trade — the most widely taught rule — across the whole period: The capital would have ended at 1.00× what it started with. At its worst the account was worth 0% less than its own best previous moment, and it spent 897 days below that peak. In none of the 12 paths tested did it fall to less than half of what it started with.

What this result does NOT say

For an edge to be assertable here it would have to reach 0.65% per trade — that is the size that survives this archive's multiple-testing correction, and it rises as the archive grows. So FAILED means “we found nothing above that size”, and never “it cannot possibly work”. The instrument is far more sensitive than that: on synthetic data, with a clean effect, it separates from 0.09% upwards. The distance between the two numbers is the price of a real market and the price of publishing many claims. The difference matters, and it is the rule of this house: the card confronts the claim, never the person who made it.

What this card does not measure

Every verdict holds for the conditions it was measured under. These are this card's — and outside them the result does not apply.

One market, one universe: Measured on 2 spot currency pairs. It says nothing about futures, equities or indices, nor about how the same technique behaves in another market.
One window of time, not every window: The measured period runs from 2020-01-01 to 2026-06-26. A market moves through regimes, and a technique can work in one and fail in another — the card measures the regimes that fit inside this window, not the ones still to come.
One cost structure: The cost charged is a 1.0 pip spread, crossed once. Anyone paying more than that gets a worse result, and anyone paying less gets a better one — the verdict holds for this fee.
One exit rule: The trade was closed by: the technique itself (held until the opposite signal). The same entry measured with a different exit is a different strategy, and can earn a different verdict — it happens in this archive.
The number of trades is not the sample size: There are 220 trades, but only 121 independent market episodes: trades that overlap in time are not independent observations, and counting them as if they were inflates any result. It is the smaller number that governs the arithmetic. With 121 episodes, what the data supports is a range from -0.21% to +0.36% per trade — the published average is the centre of it, not the exact measurement.
Daily bars: Measured at the daily close. Nothing here measures what happens inside the day, and an intraday technique is not auditable with this data.

Numbers and reproducibility

The six controls

control	status	t	threshold	episodes
invariance	failed	—	—	—
costs	inconclusive	0.47	1.98	121
placebo	inconclusive	0.62	1.98	121
benchmark	passed	—	—	—
out of sample	inconclusive	—	—	—
multiple testing	failed	—	—	—

invariance60% of the gross profit comes from 11 trades (5% of the total) — lotteryconcentration: +59.92% of the profit sits in the top 5% of trades — with the dates shuffled, +43.15% (fails above +50.00%)
costsgross +0.075% · cost 0.008% · net +0.067% (t=0.47) · the range runs from -0.214% to +0.348%
placeboactual +0.067% · placebo -0.022% · excess +0.089% ± 0.143% (t=0.62 against a threshold of 1.98, 121 real groups, 11,000 sham dates, draw error ±0.018%)
benchmarktechnique +0.07% · buy and hold (same horizon) +0.04% · excess +0.02%
out of sampleasset half A: +0.103% (t=0.48, 79 episodes) · asset half B: +0.032% (t=0.15, 80 episodes) · liquid half (>= US$ 0/day): +0.067% (t=0.47, 121 episodes) · period 1/4 (2020-02-17 a 2021-06-23): -0.287% (t=-1.17, 33 episodes) · period 2/4 (2021-08-04 a 2023-02-20): +0.500% (t=1.50, 28 episodes) · period 3/4 (2023-02-23 a 2024-05-16): -0.073% (t=-0.41, 25 episodes) · period 4/4 (2024-05-23 a 2026-05-28): +0.138% (t=0.46, 36 episodes)
multiple testing20 variation(s) tested · t=0.47 across 121 episodes (equivalent to t=0.47) · p≈0.6404 · false positives expected by chance ≈ 12.81

Equity — outside the six controls, and here is why

The t of the trade series is invariant to bet size: 0.5%, 1% and 3% agree to the sixth decimal. Nothing here moves the verdict — it moves what the account would have lived through.

risking 1.0% per trade: ×1.00
worst drawdown from the peak: −0%
days below the previous peak: 897
signals refused for lack of capital: 0%
paths where the account halved (out of 12): 0

Reproducibility

period: 2020-01-01 to 2026-06-26
assets that traded: 2
variations tested before this one: 20
gross per trade: +0.08%
net per trade: +0.07%
exit rule: the technique itself (held until the opposite signal)
median duration bars: 5
mean duration bars: 17.0
max duration bars: 126
spread pips: 1.0
episode days: 7
seed: 20260728
slope period: 40
volatility regime filter: bearish
volatility filter threshold: 0.8
volatility filter window: 20

Twelve Data forex (15m aggregated to 1d) · collected on 2026-07-25 · 2 assets · 4,055 bars · 2020-01-01 to 2026-06-26

Hypothesis, filed before the result

The entry layer of pp. 862–864: the base only enters on a day where the day's move is SMALLER than k times the standard deviation of the returns of the previous 20 days. THE FILTER DEFERS, IT DOES NOT ELIMINATE — «a volatility entry filter that DELAYS entry until the threshold conditions are met» (p. 863) —, and that is what reconciles Table 20.2: with a threshold of 3.0 on the S&P, 88 of the 378 trades remain, 23%, and not the 0.13% a pure elimination would leave. If the line turns before the right day arrives, the trade never happens. THE THRESHOLD IS A SEARCH OPTIMUM BY CONFESSION: «found by OPTIMIZING the filter times the standard deviation of the returns of the past 20 days over the range 0.05 to 1.0 in steps of 0.05» — 20 values, and that is the N control 6 charges. The claim is strong and comes without an error bar: «all markets show significant increases in performance when only low-volatility trades were taken», and «this test confirms the popular notion that entering on low volatility is preferable» — over thresholds the previous page admits were optimized, with no out-of-sample test at all. Family-level prediction, recorded before measuring: (1) NONE survives the corpus-wide Benjamini-Hochberg — a filter that only decides WHEN NOT TO ENTER does not create an effect, it redistributes one; (2) the gravedigger will be the BENCHMARK, and not the placebo, and this is where this family parts from `fibonacci`: there the claim was that one specific level matters, and the placebo is what asks that; here the claim is that REMOVING TRADES IMPROVES THE RESULT, and what asks that is the control that compares against doing nothing — the protocol already records that a filter almost always cuts `n` without separating anything; (3) the LOW volatility filter and the HIGH one will give results IN THE SAME DIRECTION, despite the source claiming the low one is superior with two numbers and no error bar (information ratio 1.143 against 0.867) — if both improve, what improves is cutting trades, not picking a regime; (4) the RESET rule will have the family's best `t`, being the only layer that gives exposure back instead of taking it away, and even so it will not pass the BH; (5) the BASE will do better than any layer built on top of it. MEASURED IN FOREX (EUR/USD and GBP/USD, daily aggregated from 15m), and not in crypto. This is a pre-registered REPLICATION — not a new discovery —, and the multiple-testing count treats it as such. And there is one difference that matters especially to this family: the threshold is measured in standard deviations of the asset itself, so it adapts to the market by construction — but what counts as «a busy day» in forex is a move an order of magnitude smaller than in crypto, and the 10σ exit, already extreme in a fat-tailed market, may be unreachable in one with thinner tails.

filed on 2026-08-04, before the number existed

The original, as it was filed

A camada de entrada da p. 862–864: a base só entra num dia em que o movimento do dia for MENOR que k vezes o desvio-padrão dos retornos dos 20 dias anteriores. ⚠️ O FILTRO ADIA, NÃO ELIMINA — «um filtro de entrada por volatilidade que ADIA a entrada até as condições de limiar serem atingidas» (p. 863) —, e é isso que reconcilia a Tabela 20.2: com limiar 3,0 no S&P sobram 88 das 378 operações, 23%, e não os 0,13% que uma eliminação pura deixaria. Se a linha virar antes do dia certo chegar, a operação nunca acontece. ⚠️ O LIMIAR É ÓTIMO DE BUSCA POR CONFISSÃO: «encontrados OTIMIZANDO o filtro vezes o desvio-padrão dos retornos dos últimos 20 dias sobre a faixa de 0,05 a 1,0 em passos de 0,05» — 20 valores, e é esse o N que o controle 6 cobra. ⚠️ A alegação é forte e vem sem barra de erro: «todos os mercados mostram aumentos significativos de desempenho quando só operações de baixa volatilidade foram tomadas», e «este teste confirma a noção popular de que entrar em baixa volatilidade é preferível» — sobre limiares que a página anterior admite terem sido otimizados, sem nenhum teste fora da amostra. Previsão da família, registrada antes de medir: (1) NENHUMA sobrevive ao Benjamini-Hochberg do corpus — um filtro que só decide QUANDO NÃO ENTRAR não cria efeito, redistribui; (2) o coveiro será o BENCHMARK, e não o placebo, e é aqui que esta família se separa de `fibonacci`: lá a alegação era que um nível específico importa, e o placebo é quem pergunta isso; aqui a alegação é que REMOVER OPERAÇÕES MELHORA O RESULTADO, e quem pergunta isso é o controle que compara com não fazer nada — o protocolo já registra que filtro quase sempre corta `n` sem separar nada; (3) o filtro de volatilidade BAIXA e o de ALTA darão resultados NA MESMA DIREÇÃO, apesar de a fonte alegar superioridade do baixo com dois números e nenhuma barra de erro (razão de informação 1,143 contra 0,867) — se os dois melhoram, o que melhora é cortar operação, não escolher regime; (4) a regra de RESET terá o melhor `t` da família, por ser a única camada que devolve exposição em vez de tirar, e ainda assim não passará no BH; (5) a BASE irá melhor que qualquer camada sobre ela. ⚠️ MEDIDO EM FOREX (EUR/USD e GBP/USD, diário agregado de 15m), e não em cripto. Isto é uma REPLICAÇÃO pré-registrada — não uma descoberta nova —, e a conta de múltiplos testes a trata como tal. ⚠️ E há uma diferença que importa especialmente a esta família: o limiar é medido em desvios-padrão do próprio ativo, então ele se adapta ao mercado por construção — mas o que conta como «um dia agitado» em forex é um movimento uma ordem de grandeza menor que em cripto, e a saída de 10σ, que já é extrema num mercado de caudas gordas, pode ser inalcançável num de caudas menores.

Quotations from the source were translated from the Portuguese record and back into English — they are not the author's exact words.

Pre-registration exists to keep prediction apart from rationalisation: written after the number, every hypothesis is right.

This claim has been audited once — there is no history to compare against.