FAILED

Adaptive stochastic (2–30/3, 20/80)

Varying the calculation period need not be confined to a trend formula: the period of any technique can be adaptive. A fixed-window stochastic measures exhaustion against a range that may be far too wide or far too narrow for the moment; adjusting the window to the market's noise means the overbought and oversold reading is taken against the range that actually matters now.

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

Net per trade

−1.92%

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

−100%

3,219 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 100% less than its own best previous moment, and it spent 3,219 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 BELOW the median: most trades win a little and the tail loses a lot. It is the lottery inverted, and no less dangerous — what decides the outcome is the rare loss.

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 511 coins, 8,099 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 70 of the 8,099. 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

-1.72% per trade. Look at the sign: the average is already negative before any fee is taken out. The controls below still hold, but here they are not knocking down an edge — they are confirming there never was one.

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: 51% of the profit came from the top 5% of trades, and the same technique with its dates shuffled would concentrate 58%. 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 account would have been wiped out. At its worst the account was worth 100% less than its own best previous moment, and it spent 3,219 days below that peak. In 12 of the 12 paths tested, the account fell to less than half of what it started with.

The account would not fit every signal

93% 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.

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 511 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 8,099 trades, but only 70 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 70 episodes, what the data supports is a range from -21.61% to +18.17% 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	-0.19	2.00	70
placebo	failed	-0.38	2.00	70
benchmark	failed	—	—	—
out of sample	failed	—	—	—
multiple testing	inconclusive	—	—	—

invariance51% of the gross profit comes from 404 trades (5% of the total) — lotteryconcentration: +50.86% of the profit sits in the top 5% of trades — with the dates shuffled, +58.38% (fails above +50.00%)
costsgross -1.720% · cost 0.200% · net -1.920% (t=-0.19) · the range reaches +17.970%
placeboactual -1.920% · placebo +1.954% · excess -3.874% ± 10.266% (t=-0.38 against a threshold of 2.00, 70 real groups, 404,950 sham dates, draw error ±0.280%)
benchmarktechnique -1.92% · buy and hold (same horizon) +8.44% · excess -10.36%
out of sampleasset half A: -0.497% (t=-0.03, 69 episodes) · asset half B: -3.307% (t=-0.61, 70 episodes) · liquid half (>= US$ 2,136,479/day): -0.489% (t=-0.02, 70 episodes) · illiquid half: -4.141% (t=-0.73, 64 episodes) · period 1/4 (2017-10-02 a 2022-03-24): -7.937% (t=-0.41, 36 episodes) · period 2/4 (2022-03-25 a 2024-01-24): -6.999% (t=-1.41, 16 episodes) · period 4/4 (2024-12-20 a 2026-07-13): -0.897% (t=-0.19, 13 episodes) · period 3/4 (2024-01-25 a 2024-12-19): +8.310% (8 episodes — too small, does not count)
multiple testing1 variation(s) tested · t=-0.19 across 70 episodes (equivalent to t=-0.19) · p≈0.8499 · false positives expected by chance ≈ 0.85

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: ×0.00
worst drawdown from the peak: −100%
days below the previous peak: 3,219
signals refused for lack of capital: 93%
paths where the account halved (out of 12): 12

Reproducibility

period: 2017-08-17 to 2026-07-28
assets that traded: 511
variations tested before this one: 1
gross per trade: −1.72%
net per trade: −1.92%
exit rule: the technique itself (held until the opposite signal)
median duration bars: 46
mean duration bars: 77.8
max duration bars: 1429
fee per leg: 0.001
episode days: 46
seed: 20260728
stochastic efficiency ratio period: 10
stochastic fast: 2
stochastic slow: 30
smoothing: 3
oversold: 20.0
overbought: 80.0

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

Code given in full in the chapter (10-period efficiency ratio moving the period between 2 and 30, K smoothed over 3). Fixed counterparts already in the corpus: Stochastic 14/3 and 14/5 — a direct confrontation. The chapter does not give the overbought and oversold thresholds; they will be the auditor's, declared as such. 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 2026-07-29, before the number existed

The original, as it was filed

Código dado por inteiro no capítulo (razão de eficiência de 10 períodos movendo o período entre 2 e 30, K suavizado em 3). Contrapartes fixas já no corpus: Estocástico 14/3 e 14/5 — é confronto direto. ⚠️ O capítulo não dá os limiares de sobrecompra e sobrevenda; serão os do auditor, declarados como tais. 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 ↗