The engine, exposed.

Deterministic multi-timeframe technical read (SMA trend + momentum confluence). The engine trades long-only in confirmed uptrends and holds cash otherwise.

Realized P&L from every closed round-trip across the swarm, reconciled on-chain. ROI is shown as the median vs SOL — a near-zero-cost-basis remainder can read +15,000% and would poison a simple average.

Every close is attributed to the sleeve that opened it. Sharpe is the per-trade ROI mean ÷ stdev (a comparable risk-adjusted read, not annualized). This is how we prove which of the newly-enabled sleeves earn — and tune or cut the rest — before scaling. 185 attributed round-trips so far.

Every level is a different game — the engine changes how it trades as the bag grows (risk scales down, capacity caps kick in, an income base builds). Checkpoints are enforced on-box: at the floor, new risk halts until the book recovers. The SCORE is SOL grown — every deposit (stables counted in SOL at deposit-time price) moves the start line, never the score; withdrawals lower the checkpoint floor instead of counting as a loss.

Every agent reports its closed trades to one shared brain, which learns which hold times and which tokens actually work and feeds that back to the swarm. Figures below are medians — memecoin returns are heavy-tailed, and a mean describes the outlier rather than the typical trade. Realized P&L is not restated here; the chain-derived figure at the top of this page is the single source for it.