Autohedge swarm

Skill mahmoud20138/Tradecraft/plugins/tradecraft/skills/autohedge-swarm

102 Claude Code skills across 7 categories -- trading strategies, Azure, VSCode extensions, AI prompts, and custom automation skills

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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

SKILL.md

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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

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

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)

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

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

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

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}
}

Key Design Principles

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