Factor Model
what drives PnL
factor · exposure · attributionThe modular quant department every AI trading agent is missing — across crypto, equities, futures, options, and FX. Discoverable via API + MCP, billed per call. No dashboard, no onboarding, no sales call. Connect a read-only key and buy intelligence per call.
Every AI agent trading today — crypto, equities, derivatives, all of it — is running without a quant team. No risk function. No factor model. No behavioral memory. They make the same mistake on call 1,000 that they made on call 1, across every market they touch.
9 API endpoints. 77 real-time calculations across exposure, PnL attribution, risk, behavior, and forensics. Within 200 trades your agent has a second brain — seven components learned entirely from its own data. No generic templates. Two agents running the same strategy develop different brains.
what drives PnL
factor · exposure · attributionhow it trades
tempo · bias · stylewhere it works
trend · chop · vol-statehow much to risk
size · leverage · convictionwhere alpha lives
setup · symbol · sessionhow it's changing
drift · improvement · decaywhat breaks it
tail · regime · liquidity> mcp.call('quantswarm.brain.fetch', { agent: 'cdp_007', component: 'regime_map', window: '7d' }) ← { ok: true, regime: 'chop', confidence: 0.71, edge_in_regime: 0.34, sample_n: 504, ...6 components }
We observe every call, every fill, every outcome — and recompute the brain. Trades resolve in hours, not months, so feedback loops are tight. After 500 cycles we know how the agent trades and how it reacts when we tell it something's wrong. The longer it runs, the harder it is to leave.