Parabolic Time/Price (0.02–0.2)
Time is the trade's enemy: once in, it must keep making money or it is liquidated. Making the liquidation point accelerate towards price as the profit grows cuts the lag intrinsic to any trend system without giving back what has already been earned — in short, consistent trends the level converges on price and extracts excellent profits. And the starting point is not a computer-generated number: it is the real extreme of the previous move.
Measured in crypto — Binance spot · 540 pairs, delisted ones included · 0.2% per round trip
- ✓invariance
- ?costs
- ?placebo
- ✓benchmark
- ?out of sample
- ?multiple testing
At its worst the account was worth 61% less than its own best previous moment, and it spent 867 days below that peak.
What was measured
How many of those trades actually count
What it paid, before any deductions
Where it stalled · the broker's fee
What would have happened to the money
The account would not fit every signal
The effect sits at the start of the period, not the end
What this result does NOT say
- 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 54,208 trades, but only 270 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 270 episodes, what the data supports is a range from -0.58% to +2.02% 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 | passed | — | — | — |
| costs | inconclusive | 0.79 | 1.97 | 270 |
| placebo | inconclusive | 1.06 | 1.97 | 270 |
| benchmark | passed | — | — | — |
| out of sample | inconclusive | — | — | — |
| multiple testing | inconclusive | — | — | — |
- invariance54208 signals across 750934 barsconcentration: +43.87% of the profit sits in the top 5% of trades — with the dates shuffled, +37.47% (fails above +50.00%)
- costsgross +0.722% · cost 0.200% · net +0.522% (t=0.79) · the range runs from -0.778% to +1.822%
- placeboactual +0.522% · placebo -0.185% · excess +0.707% ± 0.670% (t=1.06 against a threshold of 1.97, 270 real groups, 2,710,400 sham dates, draw error ±0.016%)
- benchmarktechnique +0.52% · buy and hold (same horizon) +0.26% · excess +0.27%
- out of sampleasset half A: +0.598% (t=0.72, 269 episodes) · asset half B: +0.454% (t=0.64, 269 episodes) · liquid half (>= US$ 2,066,613/day): +0.911% (t=1.34, 270 episodes) · illiquid half: +0.083% (t=0.12, 250 episodes) · period 1/4 (2017-08-27 a 2022-05-14): +2.965% (t=2.61, 142 episodes) · period 2/4 (2022-05-15 a 2023-11-25): -0.509% (t=-0.55, 48 episodes) · period 3/4 (2023-11-26 a 2025-03-29): +0.347% (t=0.36, 41 episodes) · period 4/4 (2025-03-30 a 2026-07-27): -0.715% (t=-0.76, 41 episodes)The edge decayed: +2.96% (t=2.61) in the first quarter of the period, −0.71% (t=-0.76) in the last. The partitions that replicate above are across assets, not across time.
- multiple testing1 variation(s) tested · t=0.79 across 270 episodes (equivalent to t=0.79) · p≈0.4314 · false positives expected by chance ≈ 0.43
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
- ×2.74
- worst drawdown from the peak
- −61%
- days below the previous peak
- 867
- 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
- +0.72%
- net per trade
- +0.52%
- exit rule
- the technique itself (held until the opposite signal)
- median duration bars
- 12
- mean duration bars
- 13.6
- max duration bars
- 258
- fee per leg
- 0.001
- episode days
- 12
- seed
- 20260728
- AF min
- 0.02
- AF step
- 0.02
- AF max
- 0.2
- warm-up bars
- 10
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
The most completely specified technique in the chapter — acceleration factor from 0.02 to 0.20, always in the market, reversal at the level. Two things are on record: Wilder's reversal is INTRA-BAR and we measure at the daily close; and the rule that 'the SAR can never be above today's or yesterday's low' LOOSENS the stop, which our engine's management guard refuses. That is why it goes as a held position, not as a managed stop: in a system that reverses there is no fixed entry to be at a loss from. 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
A técnica mais completamente especificada do capítulo — fator de aceleração de 0,02 a 0,20, sempre no mercado, reversão no nível. ⚠️ Duas coisas ficam registradas: a reversão de Wilder é INTRA-VELA e nós medimos no fechamento diário; e a regra 'o SAR nunca pode ficar acima da mínima de hoje ou de ontem' AFROUXA o stop, o que o guarda de gestão do nosso motor recusa. Por isso vai como posição mantida, não como stop gerido: num sistema que reverte não há entrada fixa da qual se esteja no prejuízo. 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.