Long/Short Ratio

The Bitcoin long/short ratio plots the daily account-weighted long-to-short ratio on the highest-volume Bitcoin perpetual-futures venue, sampled at the 00:00 UTC close. The y-axis is linear, anchored on a balanced book at 1.0; two dashed references at 0.7 and 1.3 mark the canonical crowding thresholds. A muted Bitcoin price line sits behind the ratio for cycle context. Pale-rust shading marks the long-crowded regime, pale-cool shading the short-crowded one.

Today’s reading is 0.580 — Shorts crowded on the canonical bands. Long accounts hold 36.8% of top-trader open positions, shorts hold 63.2%. The ratio refreshes overnight; the spot price in the reading row above auto-refreshes a few times a day in the browser.

One input drives the chart: the venue’s daily snapshot of the account-weighted long-to-short ratio for its top 20% of traders by margin balance. Provenance is documented on the data sources page; the formula and the venue convention are spelled out on methodology. The canonical construction is published verbatim in the venue’s API documentation:

Long Account % = Accounts of top traders with net long positions / Total accounts of top traders with open positions
L/S = Long Account % / Short Account %

Two methodology choices worth flagging. First, the venue publishes two surfaces — the account-weighted ratio plotted here, and a position-weighted sibling that sums notional instead of counting heads. The two often disagree. A market with seventy long accounts holding small positions against thirty short accounts holding leveraged ones can print account-weighted 2.3 and position-weighted 0.6 on the same day. We plot the account-weighted version because it is the more widely reproduced number and because retail positioning — the contrarian signal — lives in account counts, not notional. The position-weighted divergence lives in the page caveats.

Second, the chart shows one venue. Aggregating long/short across venues to a single “global” reading is not possible upstream — each venue defines its own ratio surface (top-X-percent by margin on one, all open-position holders on another, top traders by recent volume on a third), snapshots on its own cadence, and exposes only the precomputed result. There is no shared account-classification spec to sum across. The cleanest construction is to pick the venue carrying the largest perp-volume share and live with the single-venue caveat.

Three regimes resolve cleanly. Below 0.7, short-account share has pushed past 60% and the book is shorts-crowded — historically a setup for a squeeze, since the under-capitalised side covers fast on any rally. Above 1.3, long-account share has pushed past 56% and the book is longs-crowded — historically a setup for a leverage flush, since stops cascade when the trend stalls. The middle band, between 0.7 and 1.3, is descriptive: it tells you who is positioned, not where price is going next.

Six anchor windows since the start of our daily series make the indicator’s character visible. Spot prices are nightly closes from our own pipeline; ratios are pulled from the same daily snapshot that powers the chart above. Every cell recomputes overnight, so the table is a current read of the indicator’s historical record — not a frozen snapshot.

The chart shows one venue. It has been that way since the first Bitcoin perpetual swap launched in May 2016: every venue computes its own ratio on its own user subset, and no upstream feed reconciles them. So the ratio reflects whichever venue currently carries the largest perp-volume share, and the cycle-to-cycle survivor shifts. The original perpetual’s home venue dominated through 2018; a different one rose through 2020–2022 and then collapsed; a handful of offshore books then split the post-2022 book with one venue consistently on top. A long/short series spliced across that history is not a like-for-like comparison even within the same dashboard.

Two specific consequences. The survivorship effect means the indicator “works” differently in each cycle; a 2021-vintage 1.3 reading on the then-dominant venue captured a different slice of trader behaviour than a 2025 reading on today’s. The account-vs-position fork means the same venue’s reading can flip sign depending on which surface a dashboard pulls. We plot the account-weighted version because retail positioning — the contrarian story — sits in account counts; the position-weighted divergence is a caveat, not a competing chart.

The clearest failure window is November 2022. On 2 November 2022, reporting on the Alameda balance sheet exposed an FTT-collateralised hole at Bitcoin’s second-largest perp venue; by 11 November FTX had filed Chapter 11. Long/short feeds on FTX stopped — the venue went from live to dark over a weekend. The dominant survivor’s reading then became, by default, the new “global” reading without any change in the underlying methodology, even though the user base feeding it had been swelled by a venue migration that was nothing like organic positioning.

The post-FTX liquidity gap framework — the Alameda Gap coined by independent researchers a year later — documented that in the week after the collapse, “global crypto liquidity had halved,” and that depth had still recovered to only half its pre-FTX level eleven months in. FTX’s peak derivatives market share was around 15%; that share didn’t move smoothly — it disappeared into one weekend, and the ratios on the remaining venues drifted to extremes that reflected the migration as much as any real positioning shift. Our daily series starts after that window precisely because the pre-FTX reading is not comparable to today’s.

For a dollar-cost-averaging investor. Functionally none. The ratio is a tactical-positioning lens that resolves on day-to-week horizons, below the timescale a steady weekly buy operates on. Skim the regime line for cycle texture; otherwise this chart is not for you.

For a cycle-timing trader. Treat extremes as setups, not triggers. A reading below 0.7 is the kind of book that squeezes when spot breaks higher; a reading above 1.3 is the kind that flushes when spot stalls. Pair with funding rate to see the price of carrying that crowded book and with the liquidation heatmap to see where the cascade lives. A single-venue account-count read on its own is not a trade.

For a researcher. The ratio, the long-share, the short-share, and the daily timestamp are the only inputs. The methodology page lists every choice — venue, subset, snapshot cadence, account-vs-position selection — and flags every place the historical series isn’t comparable across cycles.

Account counts ignore size. The ratio is the most common criticism the metric attracts, and it is fair: a hundred small retail longs stacked against five large institutional shorts will print long-crowded even though the dollar imbalance runs the other way. Always cross-read against the same venue’s position-weighted ratio if a directional decision rests on the call. The two regularly disagree on direction.

The dominant venue rotates. November 2022 is the canonical example, but the broader pattern is that perp market share migrates between successive dominant venues on regulatory, geographic, and product-design timescales. A 2021 long/short reading and a 2026 long/short reading are sampled from different user bases, under different rule sets, in different volume regimes. The line on the chart hides that. The post-FTX liquidity-gap analysis put the share lost across that one weekend at roughly 15% of all derivatives flow. That kind of shift moves a ratio without anyone trading anything.

It is the most gameable metric on the site. Funding rates reflect a real cash payment and open interest reflects a settled book, so both are hard to manipulate without leaving evidence. Long/short ratios are a derived classification of accounts; even small population changes — new sub-accounts opening, dormant accounts closing, regional bans culling part of the user base — can shift the readout without any trader changing their view. Treat extremes as suggestive and absolute levels with suspicion.

Canonical questions readers bring to the long/short ratio, answered against the daily series powering this page.