ErGN…ocgR trades like a high-volume position trader rather than a pure scalper. Over the last 30 days, this wallet made 931 trades across 21 unique tokens, with an average holding time of 981706 seconds. That mix suggests broad rotation and repeated sizing into a relatively small token set. The profile is active, but not especially selective: turnover is high, while the token count stays contained. A 38.1% win rate also points to a style that depends on a smaller number of stronger outcomes offsetting many losing trades, which did not happen in this period.
Recent performance was weak on both absolute and percentage terms. This wallet posted -30124.37 in PnL with ROI of -12.15% over the last 30 days. Total buys reached 247997.1, while total sells came in at 202064.36, showing a sizable gap between deployed capital and realized exits during the window. The trade count is large enough that this is not a result driven by one or two isolated decisions. Instead, the loss appears spread across an active book, with heavy repetition in several names. That makes the recent record easier to read as a consistent trading outcome rather than noise from low sample size.
The clearest drag came from Agency, which lost -23100.42 across 144 trades. QUEEF was another major detractor at -10959.85 across 212 trades. Those two positions alone explain most of the net drawdown. On the positive side, BULLSHIT was the standout winner, generating 8556.6 across 131 trades, while HIGGS added 1319.96 over 40 trades and SI contributed 184.99 across 134 trades. Outside those gains, several smaller losses accumulated in AUTON, LILANSEM, ser, Catinu, Bear, KET, and shard, reinforcing the picture of broad but uneven execution.
This wallet is best suited to traders who specifically want exposure to an active, high-frequency position-trading approach with concentrated repetition in a handful of tokens. It is less suitable for anyone looking for high hit rate behavior, tight risk control, or a recent track record of efficient capital rotation. The current data fits a copier who can tolerate frequent losses, extended holds, and meaningful dependence on a few names to recover broader underperformance.