virattt-ai-hedge-fund

name	virattt-ai-hedge-fund
description	Run an ai-hedge-fund-inspired multi-lens public-equity research committee using QVeris-routed data. Use for value, quality, growth, technical, sentiment, insider, and risk analysis that produces an evidence-backed research memo without brokerage access or order execution.

AI Hedge Fund-Inspired Research with QVeris

Use the upstream ai-hedge-fund project as a methodology reference. Replace its direct financial-data dependency with QVeris and keep all analytical reasoning local.

Data boundary

Acquire every external price, fundamental, news, insider, company, and analyst input through QVeris. Require QVERIS_API_KEY for real calls. Use Discover → Inspect → Call and the capability plan in qveris.manifest.json. Do not call financial-data providers directly.

This skill is research-only. Never access brokerage accounts or place, modify, or cancel orders.

Workflow

1. Resolve the issuer, listed security, exchange, currency, requested date, and evidence cutoff.
2. Discover and call historical prices, financial ratios, statements, tagged news, insider transactions, and company profile capabilities.
3. Record search_id, tool_id, provider, retrieval time, fiscal period, filing date, and market timestamp.
4. Build independent evidence packets for these lenses:
   • quality and fundamentals;
   • intrinsic and relative value;
   • growth and expectations;
   • technical trend and market reaction;
   • news sentiment and catalysts;
   • insider activity;
   • portfolio and downside risk.
5. Require every lens to state its evidence, counterevidence, missing inputs, confidence, and as-of date before synthesis.
6. Let the risk lens set research exposure constraints and veto unsupported conclusions. Do not translate this into an executable order.
7. Synthesize a committee memo that preserves disagreements instead of averaging them away.

Quality gates

• Keep reported facts, provider labels, local calculations, and model judgments separate.
• Reject mismatched currencies, periods, or duplicate securities.
• Avoid double-counting the same source across lenses.
• Do not fabricate data when a capability is missing or a call fails.

Output contract

Return scope, as-of time, QVeris trace, lens-by-lens evidence, valuation assumptions, catalyst and risk map, disagreements, confidence, missing data, and a research-only conclusion.

Validation

node scripts/qveris_candidate_runner.mjs validate .
node --test tests/smoke.test.mjs
node scripts/qveris_candidate_runner.mjs preflight .
QVERIS_API_KEY=... node scripts/qveris_candidate_runner.mjs live .