Crowding

Nominally independent vaults are often in the same trade. When the market moves against them they do not leave one by one — they leave together, through the same liquidity, turning a controlled loss into a cascade. This page measures how much of that is happening, and is honest about how much of it we can see. Click an instrument to see exactly which vaults hold it.

Vaults running similar books

C-023 vaults · $24.40M gross · average similarity 0.762

Held by every member: XMR

drkmttr Hindenburg Short Alpha Nabucodonsor

C-035 vaults · $1.49M gross · average similarity 1

Held by every member: AAVE, ADA, AVAX, BCH, BNB, BTC, CRV, DOGE, DOT, ETH, INJ, LINK, LTC, NEAR, SOL, TRX, UNI, XRP, kSHIB

Growi HF Satori Quantum HF Vault HyperTwin - Growi HF 2x Quant Mirror V3 momentum

C-043 vaults · $972.4k gross · average similarity 0.7

Held by every member: ENA, PUMP

reverse mid curver alpha-032 Crypto_Lab28

C-052 vaults · $144.6k gross · average similarity 0.669

Held by every member: GRASS, HYPE, SKY

Fund Inception DOEZOE

C-064 vaults · $127.8k gross · average similarity 0.886

Held by every member: ZEC

Infinite Vault Robot Official Jane street DaoScience Quant Growi_test

Does being in a crowd cost anything?

121 events across 112 vaults

Vaults entering a crowd went on to underperform matched vaults that did not by -135281.76% at the worst point of the following 20 days. Shuffling the event dates produces a path that extreme 26% of the time, so this is not yet distinguishable from noise.

Day	Entered a crowd	Matched controls	Difference	Events
-5	1347.03%	887.77%	459.26%	119
-4	1071.39%	205.99%	865.40%	119
-3	450.37%	71.20%	379.17%	119
-2	292.07%	28.83%	263.24%	119
-1	36.11%	6.62%	29.49%	119
+1	62.24%	0.66%	61.58%	119
+2	3663.23%	12.43%	3650.79%	119
+3	15637.06%	3359.97%	12277.09%	119
+4	44341.31%	28647.83%	15693.48%	120
+5	81299.32%	111480.06%	-30180.74%	120
+6	125772.40%	261054.16%	-135281.76%	120
+7	940495.95%	448420.52%	492075.43%	121
+8	90033927.05%	734160.48%	89299766.57%	121
+9	11896849066.26%	1870513.00%	11894978553.26%	121
+10	12111414679.95%	4240060.65%	12107174619.29%	121
+11	13141975834.85%	5300688.32%	13136675146.53%	121
+12	13141975834.85%	5300688.32%	13136675146.53%	121
+13	13141975834.85%	5300688.32%	13136675146.53%	121
+14	13141975834.85%	5300688.32%	13136675146.53%	121
+15	13141975834.85%	5300688.32%	13136675146.53%	121
+16	13141975834.85%	5300688.32%	13136675146.53%	121
+17	13141975834.85%	5300688.32%	13136675146.53%	121
+18	13141975834.85%	5300688.32%	13136675146.53%	121
+19	13141975834.85%	5300688.32%	13136675146.53%	121
+20	13141975834.85%	5300688.32%	13136675146.53%	121

A difference is not a cause. Vaults enter crowds because they took a popular position, and a popular position going wrong is one explanation among several. What this rules out is the version where the crowd is a coincidence: that one predicts a difference of zero.

What this cannot tell you

We see on-chain and venue-side only. Anything held on a centralised exchange is invisible, so true crowding is at least as bad as shown, never better.
Coverage is usually low. Most instruments here show us holding a low single-digit share of open interest. That is a statement about our reach, not about safety.
Book clusters use single-linkage grouping. Members are chained by pairwise similarity, so a cluster average can sit below the joining threshold. It is transparent and arguable, which we prefer to a clever method nobody can interrogate.
Positions are snapshots. A strategy that flips intraday can look uncrowded at every observation and still be crowded in between.