AI-Native Hedge Funds

Build an AI-native hedge fund platform designed to generate entirely new sources of alpha using coordinated swarms of AI agents. Objective: Create a system where autonomous AI agents perform the core functions of a hedge fund — from research to thesis formation to trade execution — with humans supervising strategy and risk boundaries. This is not a research assistant. It is an AI-driven investment engine. Core vision: Replace traditional analyst workflows with agent swarms that: - Continuously ingest global financial data - Form and debate investment theses - Generate and test novel trading strategies - Adapt models based on market feedback - Execute trades within defined constraints The system should be capable of discovering new strategies, not just analyzing existing signals. Core system architecture: 1. Global Data Ingestion Layer Continuously ingest: - 10-Ks, 10-Qs, earnings transcripts - Global regulatory filings - News and macroeconomic releases - Alternative datasets (satellite, shipping, sentiment, web traffic) - Market microstructure data - Cross-border and small/mid-cap equities Normalize into structured knowledge graphs linking entities, events, and financial metrics. 2. Agent Swarm Research Engine Deploy specialized agents: - Fundamental analysis agents - Macro agents - Event-driven agents - Statistical arbitrage agents - Sentiment and narrative agents - Risk anomaly detection agents Agents must: - Generate independent theses - Debate bull and bear cases - Challenge each other's assumptions - Propose trade structures - Assign confidence levels - Identify invalidation triggers All outputs must be explainable and traceable to source data. 3. Strategy Discovery Layer Instead of ranking stocks only, the system should: - Discover new strategy archetypes - Combine signals into novel factor exposures - Detect emerging structural inefficiencies - Identify market regime changes - Adapt weighting dynamically Track evolution of strategies over time via model-versioning. 4. Simulation and Self-Improvement Loop For every proposed strategy: - Run historical and regime-based backtests - Stress test under volatility shocks - Simulate liquidity constraints - Measure drawdown characteristics - Compare against benchmark strategies Feed performance outcomes back into agent learning loops. 5. Portfolio Construction Engine Convert validated strategies into portfolios with: - Liquidity-aware position sizing - Sector and geography exposure limits - Max drawdown guardrails - Correlation clustering controls - Risk budgeting allocation - Capital efficiency optimization Allow both long/short and multi-strategy configurations. 6. Autonomous Monitoring and Adaptation Agents continuously: - Monitor thesis invalidation signals - Detect structural breaks - Recommend rebalancing - Flag regime shifts - Propose model updates Generate daily recommendation queues with reasoning. 7. Risk and Compliance Oversight - Full audit log of every model decision - Versioned strategy changes - Human override logging - Risk dashboard (VaR proxy, concentration, PnL attribution) - Compliance timeline per recommendation - Permissioned access controls Human roles: - admin - portfolio-manager - risk-officer - compliance-viewer - research-supervisor 8. Explainability by Design Every trade recommendation must include: - Supporting evidence chain - Contributing signals - Confidence weighting - Competing thesis summary - Explicit invalidation criteria 9. Infrastructure Requirements - Observable background agent jobs - Deterministic replay of strategy decisions - Strict role-based permissions - Model version control - Secure multi-tenant architecture - Latency-aware execution pipelines Data model: - securities - market-feeds - raw-documents - knowledge-graph - agent-theses - strategy-models - signals - portfolios - positions - orders - risk-limits - model-versions - audit-events - performance-metrics Positioning summary: This is not a research dashboard. It is an AI-native hedge fund operating system where swarms of agents: - Discover novel alpha sources - Form and test strategies - Adapt to regime shifts - Execute within risk constraints - Continuously improve from feedback The goal is to build the first hedge fund where AI is not a tool — it is the core investment engine. Create a modern startup design inspired by Y Combinator (YC) companies. Choose one bright primary color and build a clean, minimal color scheme around it. The design should feel bold, simple, and product-focused with strong typography, generous whitespace, and clear hierarchy.