autohedge-swarm

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name: autohedge-swarm description: AutoHedge — enterprise-grade autonomous hedge fund using swarm agent architecture. Sequential pipeline: Director → Quant → Risk Manager → Execution agents. Risk-first design: position sizing before any trade execution. Currently supports Solana; Coin kind: agent category: trading/ai-agents status: active tags: [ai-agents, autohedge, risk-and-portfolio, swarm, trading] related_skills: [ai-trading-crew, freqtrade-bot, openalice-trading-agent, polymarket-prediction-agents, ritmex-crypto-agent]

autohedge-swarm

USE FOR:

• "autonomous hedge fund agent"
• "swarm agents for trading"
• "director + quant + risk manager pipeline"
• "Solana autonomous trading"
• "enterprise AI trading system"
• "risk-first automated trading" tags: [swarm, agents, hedge-fund, autonomous, Solana, risk-and-portfolio, enterprise, multi-agent, quant] kind: framework category: quant-ml-trading

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What Is AutoHedge?

Enterprise-grade autonomous agent hedge fund using swarm intelligence. Specialized agents sequentially handle strategy, analysis, risk, and execution.

• Repo: https://github.com/The-Swarm-Corporation/AutoHedge
• Install: pip install -U autohedge
• Current: Solana trading (Coinbase planned)
• Philosophy: Risk-first — position sizing happens before execution

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Swarm Agent Pipeline

Director Agent
    ↓ Strategy generation + market context
Quant Agent
    ↓ Quantitative analysis + signal generation
Risk Manager Agent
    ↓ Position sizing + risk assessment + approval
Execution Agent
    ↓ Order construction + submission
Trade Output (JSON)

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Installation

pip install -U autohedge

Environment variables:

JUPITER_API_KEY="..."          # Solana DEX aggregator
OPENAI_API_KEY="sk-..."        # or ANTHROPIC_API_KEY
WALLET_PRIVATE_KEY="..."       # Solana wallet

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Usage

autohedge

Or programmatically:

from autohedge import AutoHedge

fund = AutoHedge(
    llm_provider="anthropic",     # Director/Quant use Claude
    risk_threshold=0.02,          # Max 2% portfolio risk per trade
    chain="solana",
)

result = fund.analyze_and_trade("SOL/USDC")
print(result)  # JSON: analysis + decision + risk metrics

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Agent Responsibilities

Agent	Role
Director	Market context, strategy selection
Quant	Price analysis, signals, technicals
Risk Manager	Position sizing, max drawdown limits
Execution	Order construction, submission

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Output Format (JSON)

{
  "ticker": "SOL/USDC",
  "director_analysis": "Bullish momentum...",
  "quant_signals": {"rsi": 58, "macd": "bullish"},
  "risk_assessment": {"position_size": 0.015, "stop_loss": 0.02},
  "decision": "BUY",
  "execution": {"order_type": "market", "size": 10.5}
}

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Key Design Principles

1. Risk-first: Never execute without risk approval
2. Audit trail: Enterprise logging at every step
3. Modular: Swap any agent or add custom stages
4. Structured outputs: All agents return JSON for system integration

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