FAILED

VIDYA (9/30, accelerates on volatility, as the formula says)

The trend's speed should respond to relative volatility: comparing the distribution of recent moves against a longer historical yardstick tells you whether the market is more agitated than its own normal. Volatility above normal slows the trend and volatility below it speeds it up, so the line is not turned by passing agitation and still keeps up with the market when it settles.

Measured in crypto — Binance spot · 540 pairs, delisted ones included · 0.2% per round trip

Net per trade

+0.26%

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

−67%

1,786 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 67% less than its own best previous moment, and it spent 1,786 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 2017-08-17 to 2026-07-28. It found trades in 540 coins, 111,263 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 456 of the 111,263. 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

+0.46% 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: 64% of the profit came from the top 5% of trades, and the same technique with its dates shuffled would concentrate 44%. 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 1.82× what it started with. At its worst the account was worth 67% less than its own best previous moment, and it spent 1,786 days below that peak. In none of the 12 paths tested did it fall to less than half of what it started with.

The account would not fit every signal

68% of the trades had to be turned down: by the time they appeared, the money was already tied up in other positions. That matters because the average return per trade the technique claims is computed over trades that nobody could have taken all of.

The effect sits at the start of the period, not the end

Cutting the period into four equal pieces: in the first, 2017-09-16 a 2022-04-07, the technique returned +1.56% per trade — a number this engine can assert. In the last, 2025-03-27 a 2026-07-27, it returned -0.60%, which cannot be told apart from zero. The average over the whole period mixes the two and hides the difference: it is what remains of an edge that no longer shows up in the most recent slice. What this does not say: that the edge is gone. The last slice holds 71 independent episodes, and with that much data it could not tell even a reasonable effect from zero. What can be asserted is narrower, and it is the part that matters to anyone trading today: the result of the whole comes from a period that has already passed, and the recent slice does not confirm it.

What this result does NOT say

For an edge to be assertable here it would have to reach 4.54% 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 0.69% 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 540 spot cryptocurrency pairs, delisted ones included. 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 2017-08-17 to 2026-07-28. 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 0.10% per leg, in and out. 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 111,263 trades, but only 456 independent market episodes: a single move fires the technique across dozens of assets at once, and counting those as separate observations inflates any result. It is the smaller number that governs the arithmetic. With 456 episodes, what the data supports is a range from -0.21% to +1.13% 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	0.76	1.97	456
placebo	inconclusive	1.27	1.97	456
benchmark	passed	—	—	—
out of sample	inconclusive	—	—	—
multiple testing	inconclusive	—	—	—

invariance64% of the gross profit comes from 5563 trades (5% of the total) — lotteryconcentration: +64.27% of the profit sits in the top 5% of trades — with the dates shuffled, +43.68% (fails above +50.00%)
costsgross +0.459% · cost 0.200% · net +0.259% (t=0.76) · the range runs from -0.411% to +0.929%
placeboactual +0.259% · placebo -0.180% · excess +0.439% ± 0.346% (t=1.27 against a threshold of 1.97, 456 real groups, 5,563,150 sham dates, draw error ±0.009%)
benchmarktechnique +0.26% · buy and hold (same horizon) +0.04% · excess +0.22%
out of sampleasset half A: +0.323% (t=0.81, 439 episodes) · asset half B: +0.201% (t=0.61, 455 episodes) · liquid half (>= US$ 2,066,613/day): +0.518% (t=1.47, 456 episodes) · illiquid half: -0.030% (t=-0.09, 414 episodes) · period 1/4 (2017-09-16 a 2022-04-07): +1.560% (t=2.58, 231 episodes) · period 2/4 (2022-04-08 a 2023-11-20): -0.140% (t=-0.26, 86 episodes) · period 3/4 (2023-11-21 a 2025-03-26): +0.219% (t=0.37, 71 episodes) · period 4/4 (2025-03-27 a 2026-07-27): -0.600% (t=-1.53, 71 episodes)The edge decayed: +1.56% (t=2.58) in the first quarter of the period, −0.60% (t=-1.53) in the last. The partitions that replicate above are across assets, not across time.
multiple testing1 variation(s) tested · t=0.76 across 456 episodes (equivalent to t=0.76) · p≈0.4488 · false positives expected by chance ≈ 0.45

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: ×1.82
worst drawdown from the peak: −67%
days below the previous peak: 1,786
signals refused for lack of capital: 68%
paths where the account halved (out of 12): 0

Reproducibility

period: 2017-08-17 to 2026-07-28
assets that traded: 540
variations tested before this one: 1
gross per trade: +0.46%
net per trade: +0.26%
exit rule: the technique itself (held until the opposite signal)
median duration bars: 3
mean duration bars: 6.5
max duration bars: 140
fee per leg: 0.001
episode days: 7
seed: 20260728
short deviation: 9
long deviation: 30
smoothing period: 9
fixed constant: 0.2
reading: formula

Binance spot klines (delisted pairs included) · collected from 2026-07-27 23:31 to 2026-08-03 10:49 · 540 assets · 750,934 bars · 2017-08-17 to 2026-07-28

Hypothesis, filed before the result

Corrects this declaration BEFORE measuring. It said the prose contradicts the formula and that both readings would be measured; on a careful re-reading of the source, this is a DEFECT OF DESCRIPTION and not an ambiguity of specification — the same case as the KAMA's '25 to 900'. The chain in the text has three links and the first two are right: higher volatility ⇒ higher ratio ⇒ higher constant. Only the third errs, concluding 'a slower trend' when a higher constant is FASTER. Since the source describes the ratio and the constant correctly, the formula is the specified technique and there is ONE card, not two. The evidence that settles it: inverting k to obtain the behaviour the text states produces a recursion that DIVERGES with the source's own parameters — measured over 17,154 bars, k·s never exceeds 0.37 in the formula, and inverted it exceeds 1 on 6.62% of the bars (maximum 4.31, the series blowing up to infinity), because inverted k grows when recent volatility is LOW. Nobody specifies a technique that explodes when the market calms down. The prediction that stands: the VIDYA accelerates on volatility, so it turns over more than the KAMA and is the one that depends most on cost. A measured property to record on the card: the specified k uses the standard deviation of PRICES, which grows with the window even without volatility — on a zero-noise ramp k=0.3111, pure arithmetic of the window. In BTC the median k over prices is 0.538 against 0.927 over returns. The source suggests returns ('the result may possibly benefit'), but does not specify it; we audit the specified version. Family prediction, filed before measuring: (1) none of the adaptive averages survives the family's Benjamini-Hochberg in crypto; (2) the control that kills the most will be COST, not the benchmark — unlike the chart-pattern census, where the benchmark was the gravedigger, because these are always-in-the-market systems and they turn over a lot; (3) each adaptive average will have HIGHER turnover than its fixed-period counterpart already in the archive, and will die more at cost than it does. The mechanism is in the source itself: Table 17.1 reports a profit factor 'even before costs' and states that success is 'inversely related to the average number of trades' (KAMA 159 trades, factor 1.53; VIDYA 443, factor 1.17). That is a cost story told as a quality story. If I am wrong and one survives cost with higher turnover, the chapter's thesis gains evidence it did not present.

filed on 2026-07-30, before the number existed

The original, as it was filed

Corrige esta declaração ANTES de medir. Ela dizia que a prosa contradiz a fórmula e que as duas leituras seriam medidas; relendo a fonte com cuidado, é DEFEITO DE DESCRIÇÃO e não ambiguidade de especificação — mesmo caso do '25 a 900' da KAMA. A cadeia do texto tem três elos e os dois primeiros estão certos: volatilidade maior ⇒ razão maior ⇒ constante maior. Só o terceiro erra, ao concluir 'tendência mais lenta' quando constante maior é mais RÁPIDA. Como a fonte descreve corretamente a razão e a constante, a fórmula é a técnica especificada e há UM card, não dois. Evidência que fecha a questão: inverter k para obter o comportamento que o texto enuncia produz recursão que DIVERGE com os parâmetros da própria fonte — medido em 17.154 velas, k·s nunca passa de 0,37 na fórmula, e invertido passa de 1 em 6,62% das velas (máximo 4,31, série estourando para infinito), porque invertido k cresce quando a volatilidade recente é BAIXA. Ninguém especifica uma técnica que explode quando o mercado se acalma. Previsão que fica de pé: a VIDYA acelera na volatilidade, então gira mais que a KAMA e é a que mais depende do custo. ⚠️ Propriedade medida a registrar no card: o k especificado usa o desvio dos PREÇOS, que cresce com a janela mesmo sem volatilidade — numa rampa de ruído zero k=0,3111, pura aritmética de janela. Em BTC a mediana de k sobre preços é 0,538 contra 0,927 sobre retornos. A fonte sugere retornos ('é possível que o resultado se beneficie'), mas não especifica; auditamos a versão especificada. Previsão da família, registrada antes de medir: (1) nenhuma das adaptativas sobrevive ao Benjamini-Hochberg da família em cripto; (2) o controle que mais mata será o CUSTO, e não o benchmark — diferente do censo de padrões gráficos, onde o benchmark foi o coveiro, porque estas são sistemas sempre-no-mercado e giram muito; (3) cada adaptativa terá giro MAIOR que a sua contraparte de período fixo já no corpus, e morrerá mais no custo do que ela. O mecanismo está na própria fonte: a Tabela 17.1 relata fator de lucro 'even before costs' e afirma que o sucesso é 'inversely related to the average number of trades' (KAMA 159 operações, fator 1,53; VIDYA 443, fator 1,17). Isso é uma história de custo contada como história de qualidade. Se eu estiver errado e alguma sobreviver ao custo com giro maior, a tese do capítulo ganha uma evidência que ele não apresentou.

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

Earlier audits of the same technique

Each variation an author teaches enters as its own test, so that whatever might work in the strategy gets covered. The verdict held in all of them.

2026-08-03FAILEDopen ↗
this measurement →FAILED
2026-07-31FAILEDopen ↗
2026-07-30FAILEDopen ↗
2026-07-30FAILEDopen ↗