The first AI-native hedge fund.

Quantum Hedge was not a fund that adopted AI. It was built as one system from the first line of code: agentic AI orchestrating research, signal analysis, and execution review across every strategy in the book — with humans owning risk.

01  ABOUT

A systematic, market-neutral fund where the operating core is an agentic AI loop — not a research toy, but production infrastructure with real PnL and real failure modes.

Twelve strategies run in production. Each one is monitored, summarized, and stress-questioned continuously by language-model systems wired to live market data. What used to be a daily spreadsheet — delta-neutral exposure across eight venues, with collateral attribution — is now a live feed: an AI-written treasury summary every hour, anomaly alerts in under sixty seconds.

The firm stays deliberately small. Every strategy has a human owner who can explain, line by line, why it makes money and how it fails — and every automated decision path has a kill switch, an audit trail, and a documented worst case.

02  PLATFORM

Agentic orchestration across the book

A single agentic layer coordinates research, signal analysis, and execution review across all twelve live strategies. Models draft; deterministic systems verify; humans decide. Nothing reaches the market on a model's word alone.

Market-neutral by construction

Positions are hedged across eight venues with continuous collateral-risk monitoring and real-time exposure attribution. Returns are engineered from structural inefficiency, not directional conviction, and no single venue or funding regime is allowed to dominate risk.

Infrastructure economics

Because AI is the operating core, its cost structure is engineered like any other trading system: cached context, versioned prompts, reproducible evaluations. Prompt-caching the volatility surface alone cut inference costs by roughly eighty percent — an edge measured in basis points, compounding daily.

03  DISCIPLINE

Operating principles for a fund whose core is a reasoning system.

P—1
Models propose; deterministic systems and humans dispose
Every agentic output is checked against hard constraints before it can move capital.
P—2
Risk-tier everything before it is built
Rule-based work is automated deterministically; judgment goes to agents; high-stakes paths get a human gate.
P—3
Prompts are production code
Versioned, reviewed, rolled back like anything else that touches the book.
P—4
Degrade gracefully, never silently
If the reasoning layer is unavailable, deterministic pipelines keep running and queue their work for human review.
P—5
The deliverable is a workflow, not a model
Systems are only done when the people accountable for the money own them end to end.
04  CONTACT
info@quantumhedge.net

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