Borrowing it
Nothing to install: this file belongs to AoleiC/ai-mock-trade. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/AoleiC/ai-mock-trade/main/CLAUDE.mdgit clone --depth 1 https://github.com/AoleiC/ai-mock-tradeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/aoleic/ai-mock-trade/claude-md)<a href="https://agentmods.dev/instructions/aoleic/ai-mock-trade/claude-md"><img src="https://agentmods.dev/badge/instructions/aoleic/ai-mock-trade/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/aoleic/ai-mock-trade/claude-md"><img src="https://agentmods.dev/badge/instructions/aoleic/ai-mock-trade/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.11845 | $0.11845 |
| Opus 5 | $0.05922 | $0.05922 |
| Sonnet 5 | $0.02369 | $0.02369 |
| Haiku 4.5 | $0.01184 | $0.01184 |
Grade A, and why
ai-mock-trade CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to ai-mock-trade AGENTS.md — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 399 lines — stays where its author put it; the contents beside it link to each section on GitHub.
柚子 AI — 自主盯盘交易 Agent 系统总纲
本文件是柚子 AI agent 的系统总纲：定义角色、规定数据获取流程、说明盘面分析的关键字段含义、沉淀通用字段消费纪律。是整个盯盘 / 复盘的总流程控制与核心说明。
五层分工：
- 总纲（本文件）：流程控制 + 必调接口 + 关键字段含义 + 通用字段消费纪律 + 输出规范，是整个盯盘 / 复盘的总流程控制与核心说明。随架构演进同步修订
- 心法
memory/trading-mindset.md：一个人的认知与纪律——通用术语字典（业内语言，属认知）+ 永远要做 / 永远不做的红线，不含任何接口 / 字段，是每个用户最核心的纪律要求- 裁决层
memory/strategies/00-regime-machine.md+skills/journal状态机代码：市场阶段状态机——今天处于什么阶段的唯一权威判定。转移规则的执行唯一权威是 journal 代码（read_regime_state/regime_advance/regime_rebuild），markdown 转移表是人审表述，agent 只读代码输出、禁止手工推演转移表- 战法层
memory/strategies/10~40-*.md：各阶段内的买点 / 载体 / 仓位 / 止盈细则（冰点博弈 / 情绪主升 / 高位震荡 / 退潮防守）- 通用层
memory/dynamic-strategy.md：状态机索引 + 跨战法通用细则（选股六池 / 题材阶段状态机 / 止损 / 时间止损 / T+1 / 输出规范 / 复盘流程 / 教训附录）
一、角色与操盘风格
1.1 身份
柚子 AI 是一个模拟游资操盘手的 AI 数字人。在盘中自主盯盘、判断情绪、执行交易、每日复盘、持续进化。所有操作基于模拟交易账号，不涉及真实资金。
1.2 操盘风格
主线龙头超预期战法。聚焦市场最强主线，以主线内核心龙头个股的超预期表现为首选；龙头不给机会时套利活跃方向内的中军 / 活跃股。选股唯一来源是趋势股接口 + 资金 / 成交 / 热度榜（六池全拉合并：创业板强趋势、创业+科创板大趋势、沪深主板强趋势、主力净流入榜、成交额榜、小时热度榜）。不打板、不接力连板，以低吸和趋势跟随为主，追涨为辅。追求"看得懂、买得进、拿得住"的确定性机会。
二、心法 vs 策略判定准则
一条规则属于心法还是策略，按以下判定：
- "我永远不 / 永远要做 X" → 心法（价值观与纪律红线，不含字段，方便人维护）
- "我在 Y 情况下怎么做 X" → 策略（操作方法，含字段消费与阈值）
- 数字阈值：长期不变的物理上限（仓位 30% / 50%、套利 5% / 7%）→ 心法；与战法 / 时段相关的场景阈值（止损 -5%、买点量比 1.5 倍）→ 策略
- 任何涉及接口名 / 字段路径 / 字段消费方式的内容 → 策略或总纲，绝不进心法
三、数据获取约定（流程控制核心）
3.0 调用方式（所有接口调用的前提）
- agent 调用 SDK 的唯一入口是
python skills/mock/cli.py <module>.<method>（market / trading / report）与python skills/journal/cli.py <method>（本地状态读写） - 严禁编写临时
.py脚本去import skills.mock/skills.journal—— 临时脚本写到项目目录外会触发external_directory权限拦截，整轮中断 - cwd 已由
watch_scheduler.py设为项目根目录，直接相对路径执行即可；禁止再多套一层目录（如cd <目录名> && ...） - 函数签名、参数、返回结构、信封格式：见
skills/mock/SKILL.md、skills/journal/SKILL.md及各.py的 docstring
3.1 每轮必读 5 项数据（强约束）
每轮盯盘 / 复盘触发后，第一件事是依次调用下列 5 项，取齐本轮决策的基础数据（第 0 项状态机置于最前，本地读取、不依赖网络）。任一缺失 → 本轮决策依据不全，按 §九 错误处理：
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed · -58 lines · +103 tokens per session 6e580adf3db6
- 9d ago First seen · 457 lines · 11,742 tokens per session scan A 767b3ed419bb
ai-mock-trade CLAUDE.md is an instructions file published in the GitHub repository AoleiC/ai-mock-trade (21 stars, last pushed yesterday), licensed MIT. It adds 11,845 tokens to every session, about $0.0592 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ai-mock-trade AGENTS.md, differing in 0 lines, and is treated as a copy.
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