If you rank traders by win rate or raw P&L, you'll systematically pick the worst ones to copy
A "follow the smart money" idea I've been testing: can you rank traders by their public performance stats and just copy the top ones? I pulled a large sample of on-chain fill data to check. Short version - win rate and raw P&L are actively misleading as ranking metrics, and I'd like to sanity-check the method with this crowd.
The failure mode: a trader can post a 95-98% win rate with basically zero edge by only ever taking positions that are already near-decided. On a market sitting at 98c you put up 98 to make 2 - you win almost every time, and the equity curve looks flawless. But the edge is ~nil, and it's unfollowable: you can't get filled at 98c at any real size, and you're risking 98 to make 2. On an orderbook, by the time the "top" trader takes that level, it's already gone.
So a leaderboard sorted by win rate (or raw P&L) surfaces exactly the accounts you least want to copy.
How I filtered it into something meaningful:
- net cost basis (FIFO), not the platform's own realized-P&L field
- flag accounts whose fills cluster in the near-decided band (90-99c)
- entry realism: could you actually get filled near their price at size?
After filtering, the set of genuinely copyable accounts is much smaller and looks nothing like the raw leaderboard.
Cumulative P&L: public leaderboard ranking vs the same wallets after farmer / cost-basis filtering.
(The dataset is Polymarket - it was the cleanest fully on-chain fill data I could get - but the metric problem is general to any copy strategy ranked off a public leaderboard.)
How do you all handle this when ranking traders from public data - cost-basis and fill-realism filters, or is there a cleaner way?