菜单
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
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Entry-point live-market scan. Returns current quote, H1/H4 regime, session state, and watch levels for a default watchlist (gold, US30, US500, US100, EURUSD, GBPUSD, BTCUSD, ETHUSD) — or any custom list. Uses the free yfinance path by default, MT5 if available. USE FOR - scan markets, what's moving, market overview, current prices, what's on the watchlist, show me the markets.
Entry-point ranked-setup feed. Runs the full pair-analyze pipeline against a watchlist and returns the top N highest-grade trade setups right now, sorted by confidence × R:R, with one-line rationale and a copy-pasteable trade plan. USE FOR - what should I trade, recommend trades, best setups, top picks, ranked opportunities, where's the edge.
Entry-point skill discovery. Given a keyword or task description, returns the shortest list of Tradecraft skills that match, with direct invocation recipes. No keyword = show the 5 main entry points. USE FOR - find skill, which skill, how do I, search skills, list skills, what can this do, find command.
Entry-point strategy selector. Lists the available trading strategies with a one-line when-to-use for each, plus a deep-dive drill-down by name. Routes to the right specialist skill (ICT/SMC, breakout, scalping, swing, trend-following, mean-reversion). USE FOR - which strategy, what strategies, strategy selector, strategy list, how should I trade, pick a strategy, ICT or SMC, breakout or trend.
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
Agentic storage architecture — persistent memory for AI agents using MCP, immutable versioning, sandboxing, and intent validation. Covers the stateless problem in LLM agents, RAG limitations (read-only), MCP protocol (JSON-RPC, resources, tools), storage safety layers, and agent file system design. Source: IBM Technology (Martin Keen), March 2026. Use this skill for "agentic storage", "agent memory", "persistent agent state", "MCP storage", "agent file system", "agent hard drive", "stateless agent problem", "agent work product", "immutable versioning", "agent sandboxing", "intent validation", "agent safety layers", "agent context window limits", "storage for AI agents", "agent persistence", "autonomous agent storage", "agent audit trail". Works with mcp-integration, trading-brain, agents, trade-psychology-coach.
| 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 |
USE FOR:
Enterprise-grade autonomous agent hedge fund using swarm intelligence. Specialized agents sequentially handle strategy, analysis, risk, and execution.
pip install -U autohedgeDirector 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)
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
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 | Role |
|---|---|
| Director | Market context, strategy selection |
| Quant | Price analysis, signals, technicals |
| Risk Manager | Position sizing, max drawdown limits |
| Execution | Order construction, submission |
{
"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}
}