Intent-Starter
0xd3ec513012d3c16c1d6cc63417120af97c430939
Activity score
45/100
Performance: thin sample
Open positions
3
Open notional
$0.15
Total PnL
$-343.13
Realised
$0.00
Win rate
n/a
too few closed
Largest open positions
- OVER
Games Total: O/U 2.5
300 shares @ 33.0¢·now 0.1¢·exp May 10, 2026$0.15
$-98.85
- TEAM LIQUID
Valorant: Team Liquid vs Gentle Mates (BO3) - VCT EMEA Playoffs
236 shares @ 61.0¢·now 0.0¢·exp May 9, 2026$0.00
$-143.96
- NONGSHIM RED FORCE
LoL: Nongshim Red Force vs HANJIN BRION (BO3) - LCK Rounds 1-2
176 shares @ 57.0¢·now 0.0¢·exp May 9, 2026$0.00
$-100.32
Recent activity
- REDEEMWill Nagoya Grampus win on 2026-05-10?$50.255h ago
- REDEEMChiba Jets vs. Gunma Crane Thunders$92.055h ago
- TRADEBUYGames Total: O/U 2.5$100.9918h ago
- TRADEBUYWill Nagoya Grampus win on 2026-05-10?$23.4918h ago
- TRADEBUYChiba Jets vs. Gunma Crane Thunders$57.0718h ago
- TRADESELLLoL: Kiwoom DRX vs Hanwha Life Esports - Game 1 Winner$1.901d ago
- TRADEBUYLoL: Kiwoom DRX vs Hanwha Life Esports - Game 1 Winner$1.691d ago
- TRADEBUYValorant: Team Liquid vs Gentle Mates (BO3) - VCT EMEA Playoffs$145.642d ago
- REDEEMGriekspoor vs. Blockx: Match O/U 22.5$1.012d ago
- TRADEBUYLoL: Nongshim Red Force vs HANJIN BRION (BO3) - LCK Rounds 1-2$101.612d ago
- TRADEBUYGriekspoor vs. Blockx: Match O/U 22.5$1.002d 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)
- 8
- Avg trade size
- $54.18
- Top category
- —
- Category concentration
- 0%
- First seen
- 2d ago
- Last active
- 5h 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".
