+1 sessions
Median return -1.22%Completed observations: 22
- Middle 50%
- -3.51% … +1.75%
- Positive returns
- 41%
Shopify · Sigma Moves
Closed-period price moves, measured against their own history. One engine. Five markets. No forecasts.
Latest closed period
Shopify's latest move against its own history.
SHOP Rare
-7.37%
-3.00σ
About once every 60 trading sessions
21 comparable moves / 1,257 observations
What happened afterwards
All history-qualified moves of at least 2.5σ. Results after the event are descriptive statistics, not trading instructions.
+7 sessions
Median return -5.40%Completed observations: 22
- Middle 50%
- -12.52% … +4.84%
- Positive returns
- 41%
+30 sessions
Median return -3.14%Completed observations: 22
- Middle 50%
- -21.25% … +11.10%
- Positive returns
- 45%
Every rare move, in context
Instrument: SHOP (price return)
| Date | σ | Move | Observed rarity | +1 sessions | +7 sessions | +30 sessions |
|---|---|---|---|---|---|---|
| 25 Oct 2023 | -3.00σ | -7.37% | About once every 60 trading sessions21 comparable moves / 1,257 observations | -3.71% | +26.77% | +48.20% |
| 31 Aug 2023 | +3.77σ | +10.80% | About once every 180 trading sessions7 comparable moves / 1,257 observations | +0.33% | -5.35% | -22.47% |
| 2 Aug 2023 | -3.02σ | -7.44% | About once every 66 trading sessions19 comparable moves / 1,257 observations | -5.43% | -11.47% | +1.55% |
| 4 May 2023 | +8.72σ | +23.84% | Not previously observed in 5 years0 comparable moves / 1,258 observations | +8.25% | +7.47% | +12.93% |
| 16 Feb 2023 | -4.28σ | -15.88% | 1 times in 5 years1 comparable moves / 1,258 observations | -2.89% | -8.39% | +6.75% |
| 25 Jan 2023 | +2.53σ | +10.87% | About once every 32 trading sessions39 comparable moves / 1,258 observations | +2.11% | +11.83% | -9.15% |
| 10 Nov 2022 | +3.44σ | +18.16% | About once every 157 trading sessions8 comparable moves / 1,258 observations | +8.03% | -3.56% | -7.67% |
| 27 Oct 2022 | +3.62σ | +17.34% | About once every 210 trading sessions6 comparable moves / 1,258 observations | +0.26% | -5.45% | +12.55% |
| 4 Oct 2022 | +2.76σ | +13.58% | About once every 52 trading sessions24 comparable moves / 1,258 observations | -2.22% | -16.78% | +26.93% |
| 26 Jul 2022 | -2.55σ | -14.06% | About once every 35 trading sessions35 comparable moves / 1,209 observations | +11.70% | +30.02% | -1.81% |
| 5 May 2022 | -2.75σ | -14.91% | About once every 46 trading sessions25 comparable moves / 1,154 observations | -8.62% | -12.86% | -23.29% |
| 20 Apr 2022 | -2.52σ | -13.31% | About once every 31 trading sessions37 comparable moves / 1,143 observations | -8.38% | -18.70% | -24.02% |
| 16 Feb 2022 | -3.17σ | -16.04% | About once every 100 trading sessions11 comparable moves / 1,100 observations | -11.63% | -7.04% | -9.49% |
| 21 Jan 2022 | -3.90σ | -13.85% | 4 times in 4 years4 comparable moves / 1,082 observations | +6.39% | +11.57% | -36.43% |
| 13 Jan 2022 | -2.75σ | -8.93% | About once every 49 trading sessions22 comparable moves / 1,077 observations | +3.62% | -16.16% | -34.77% |
| 4 Jan 2022 | -3.74σ | -10.36% | About once every 214 trading sessions5 comparable moves / 1,070 observations | -2.61% | -12.89% | -38.88% |
| 12 Nov 2021 | +4.17σ | +12.02% | 3 times in 4 years3 comparable moves / 1,035 observations | -0.78% | -5.78% | -17.60% |
| 28 Oct 2021 | +3.02σ | +7.04% | About once every 102 trading sessions10 comparable moves / 1,024 observations | +0.67% | +5.27% | +0.31% |
| 22 Oct 2021 | -2.56σ | -4.73% | About once every 38 trading sessions27 comparable moves / 1,020 observations | -0.05% | +3.56% | +0.16% |
| 28 Sep 2021 | -3.02σ | -5.16% | About once every 91 trading sessions11 comparable moves / 1,002 observations | -1.66% | +2.13% | +13.55% |
| 28 Apr 2021 | +3.25σ | +11.40% | About once every 149 trading sessions6 comparable moves / 896 observations | -4.32% | -13.98% | -4.48% |
| 9 Nov 2020 | -4.44σ | -13.63% | 1 times in 3 years1 comparable moves / 780 observations | -1.87% | +3.19% | +41.49% |
The number has a method behind it
Sigma measures the size of a log return relative to volatility estimated before the period. Rarity is counted from actual historical standardized moves, not inferred from a normal distribution.
σt = ln(Ct / Ct−1) / vtvt ← t−1, t−2, …
Volatility adapts to the market regime with EWMA (λ = 0.94 for daily and weekly observations). Current-period returns are excluded. Each historical σ uses its own prior volatility.
Both directions count toward rarity. Reference history spans up to five years. A period with no prior exceedances is labelled unprecedented in the observed sample; it is never given an invented recurrence interval.
Stock observations use split-adjusted price returns, without dividend reinvestment. Commodities are explicitly labelled ETF price proxies; they do not represent spot commodity prices.
Questions behind the sigma
What does a 3σ move mean?
The period log return is three times the volatility estimated from earlier periods. Sigma is signed: negative for declines and positive for rises.
Why not calculate rarity from a normal distribution?
Market returns have heavy tails and changing volatility. We count actual past absolute sigma exceedances for the same instrument and period.
Does “once every 200 days” predict the next event?
No. It is the number of historical observed periods divided by comparable moves. Events can cluster and the market regime can change.
How much history is required?
At least three years of valid standardized observations, after a one-year volatility warm-up. This normally requires approximately four years of source history.
Are all instruments directly comparable?
The engine is shared, but each instrument has its own prices, calendar, volatility and reference sample. Commodity cards use explicitly labelled ETF proxies.
What happens on quiet days?
The page shows the largest covered standardized move, the complete rarity scale and recent records. Missing data is reported separately from market calm.
For information only. Historical observations are not investment advice and do not predict future moves. A rare move can be followed by another rare move.