Clumsy-Sanctity
0x172c3bbc62748a38c1830f25e54d41b55ddb9602
Activity score
52/100
Performance: thin sample
Open positions
3
Open notional
$0.91
Total PnL
$-1.0K
Realised
$0.00
Win rate
n/a
too few closed
Largest open positions
Recent activity
- TRADEBUYSpread: 1. FC Köln (-2.5)$1.0013h ago
- REDEEMSpread: Qingdao Xihaian FC (-2.5)$1.0113h ago
- TRADEBUYSpread: Qingdao Xihaian FC (-2.5)$1.0015d ago
- TRADEBUYPistons vs. Hornets: O/U 223.5$1.0K30d ago
- REDEEMBeijing Royal Fighters vs. Beijing Ducks$200.5130d ago
- REDEEMShanghai Sharks vs. Fujian Sturgeons$811.0230d ago
- TRADEBUYShanghai Sharks vs. Fujian Sturgeons$800.0030d ago
- REDEEMCanucks vs. Kings$1.0330d ago
- TRADEBUYBeijing Royal Fighters vs. Beijing Ducks$100.0030d ago
- TRADEBUYBeijing Royal Fighters vs. Beijing Ducks$100.0030d ago
- TRADEBUYCanucks vs. Kings$1.0131d ago
- REDEEMRider Broncs vs. Sacred Heart Pioneers$1.5931d ago
- REDEEMNets vs. Thunder$2.2531d ago
- TRADEBUYRider Broncs vs. Sacred Heart Pioneers$1.3084d ago
- TRADEBUYUTSA Roadrunners vs. Charlotte 49ers$1.1084d ago
- TRADEBUYNets vs. Thunder$2.0084d ago
- REDEEMWill Levante UD win on 2026-01-17?$1.0584d ago
- REDEEMWill Burnley FC win on 2026-01-17?$1.0684d ago
- REDEEMWill FC St. Pauli 1910 win on 2026-01-17?$1.0984d ago
- REDEEMWill AJ Auxerre win on 2026-01-17?$1.1284d ago
Profile dimensions
Trade count + how recently they were active. Low = dormant.
How trustworthy the win-rate number is, based on sample size of closed markets.
Share of trades concentrated in their top category.
Share of positions taken while the market was still uncertain (30–70¢) rather than after direction was obvious.
How risky to blindly copy. Higher = riskier — large size, single-position exposure, or thin win-rate sample.
- Trades (all time)
- 28
- Avg trade size
- $99.74
- Top category
- —
- Category concentration
- 0%
- First seen
- 145d ago
- Last active
- 13h ago
- Win rate sample
- 0 closed
The single Activity score is kept for the leaderboard sort. The five dimensions above are the canonical read — copy-risk bar is inverted so green is always "better for the user".
