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pm-agent-strategy-authoring

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用于把 LLM/工具驱动的想法实现为 AgentStrategy 代码，并通过 paper trading 评估效果。

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Source SKILL.md: https://github.com/YichengYang-Ethan/oracle3/blob/HEAD/skills/pm-agent-strategy-authoring/SKILL.md

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PM Agent Strategy Authoring

当用户要求"写一个用 LLM / MCP 工具 / 外部 API 做决策的策略"时，使用这个技能。

目标

  • 产出可运行的策略类（AgentStrategy 子类）
  • 保证 strategy validate 与 paper run 可执行
  • 不做参数网格搜索（agent 策略不可 auto-tune）

代码入口

  • 基类契约：oracle3/strategy/agent_strategy.py (AgentStrategy)
  • LLM 策略参考：oracle3/strategy/simple_strategy.py
  • 新策略目录：strategies/

实施流程

  1. 创建骨架
  • oracle3 strategy create --output strategies/<name>.py --class-name <ClassName> --type agent
  1. 实现策略
  • 在 process_event 中调用 LLM / MCP 工具 / 外部 API
  • 非确定性 OK：每次调用结果可以不同
  • 用 self.record_decision(reasoning=...) 把 LLM 输出写入 reasoning 字段，便于监控
  • 决策时调用 trader.place_order(...)
  • 用 self.is_paused() 守卫控制平面暂停信号
  1. 快速验证
  • oracle3 strategy validate --strategy-ref strategies/<name>.py:<ClassName> --dry-run --events 10 --json
  1. Paper trading 评估（agent 策略的正确评估方式）
  • oracle3 paper run --strategy-ref strategies/<name>.py:<ClassName> --monitor
  • 通过 oracle3 trade status --json 观察 decisions / positions
  • 用 oracle3 trade get-state --json 获取完整快照
  1. 干预与调整
  • oracle3 trade pause / oracle3 trade resume
  • oracle3 trade swap-strategy --strategy-ref strategies/<new>.py:<NewClass> 热换策略

Hard Rules

  • AgentStrategy 子类不可用于 research discover-alpha --param-grid-json（会报错）。
  • 外部 API 调用必须做好超时与错误处理，避免阻塞事件循环。
  • 必须通过 self.is_paused() 检查再决策，尊重控制平面暂停信号。
  • 不使用未来信息（禁止 look-ahead）。
  • 必须可复现路径：策略文件路径、类名、命令都要明确记录。