| name | plan-review |
| description | Standardized pre-market preparation, noon review, and evening review workflows plus trading journal management.
三段式交易操作系统：盘前准备→午间复盘→晚间复盘，配合交易日志工具记录每一天。
触发词：盘前准备、午间复盘、晚间复盘、交易计划、复盘、写日志、交易日志、今日计划、纪律打分。
——核心原则：脚本取数据，AI 做分析，交易者负责最终决策。
|
Plan & Review Skill
交易操作系统：没有计划不交易，没有复盘不成长。
This skill provides the process backbone for the mmtickerlab project. It connects the market data skill and sim-trade execution skill into a disciplined daily workflow.
Architecture principle: Scripts are data faucets — they fetch, aggregate, and structure raw market data. The AI is the analyst — it reads the data, applies reasoning, identifies patterns, and generates the final trading plan. This separation ensures analysis is contextual and adaptive, not hard-coded.
There are three daily checkpoints:
| Checkpoint | Who fetches data | Who analyzes |
|---|
| Pre-Market (盘前准备) | pre_market.py | AI synthesizes plan |
| Noon Review (午间复盘) | noon_review.py | AI checks alignment |
| Evening Review (晚间复盘) | evening_review.py | AI performs deep复盘 |
The central artifact is the daily trading journal, stored at data/journal/YYYYMMDD.md.
Environment Setup
uv venv skills/plan-review/.venv
uv pip install --python skills/plan-review/.venv -r skills/plan-review/requirements.txt
Data Faucet Scripts (all in skills/market/scripts/)
These scripts fetch and structure raw data. They do NOT make final trading decisions — that's the AI's job.
pre_market.py — 盘前数据聚合器
Fetches all pre-market data into structured output. Run first, then AI analyzes.
skills/market/.venv/bin/python skills/market/scripts/pre_market.py [--date YYYYMMDD]
Data provided:
| Section | Data |
|---|
| A50期货 | 最新价 + 涨跌幅 |
| 离岸人民币 | USD/CNY 买卖报价 |
| 美股三大指数 | DJIA/SPX/NDX 收盘价 + 涨跌幅 |
| 恒生指数 | 收盘价 + 涨跌幅 |
| 宏观新闻 | 政策/经济相关新闻标题 + 摘要 |
| 7大A股指数 | 收盘价 + 涨跌幅 |
| 市场宽度 | 涨跌家数、涨停/跌停数、成交额 |
| 涨停池 Top 15 | 代码/名称/连板/行业/换手率 |
| 概念资金流向 Top 5 | 净流入/涨幅/领涨股 |
| 行业资金流向 Top 5 | 净流入/涨幅/领涨股 |
| 自选股技术指标 | K线 + MA/MACD/RSI/BOLL/KDJ（每只） |
noon_review.py — 午间数据聚合器
skills/market/.venv/bin/python skills/market/scripts/noon_review.py
Data provided:
- 早盘实时三大指数
- 涨跌分布 + 涨停/跌停/炸板率
- 领涨题材 + 大单成交
- 盘前计划标的实时报价（从 journal 解析）
evening_review.py — 晚间数据聚合器
skills/market/.venv/bin/python skills/market/scripts/evening_review.py [--date YYYYMMDD]
Data provided:
- 全天市场总览（同 pre_market 模块二）
- 主线题材资金流向
- 龙虎榜：机构买卖/游资动向/负反馈标的（
stock_lhb_detail_em + stock_lhb_ggtj_sina）
- 连板高度 + 阶梯完整性
Journal Script
journal.py (skills/plan-review/scripts/journal.py)
| Command | Description |
|---|
create | Create today's journal (reads plan from --plan or stdin) |
append --section {noon,evening} | Append a review section |
view [--date YYYYMMDD] | View a journal |
list [--n N] | List recent entries |
Daily Workflows
Phase 1: Pre-Market Preparation (盘前准备)
Goal: Generate a complete, AI-analyzed trading plan before market opens.
Step 1: Fetch Raw Data
skills/market/.venv/bin/python skills/market/scripts/pre_market.py > /tmp/pre_market_data.md
This produces a structured data dump. The AI reads it.
Step 2: AI Analysis & Plan Synthesis
The AI reads the data and performs the following analysis:
模块一：宏观定调
- Read A50 + US indices + RMB + news
- Judge: 今日高开/平开/低开概率？外资情绪偏多/偏空？有无重大政策催化剂？
- Output: 1-2 句宏观定调 + 对仓位的影响
模块二：情绪判断
- Read breadth data (涨跌比、涨停/跌停数、成交额)
- Read 涨停池（连板分布、行业集中度）
- Read 资金流向（概念/行业 Top 5）
- Judge: 赚钱效应在哪里？亏钱效应在哪里？连板梯队是否健康？
- Output: 情绪定性 + 最强 1-2 条主线
模块三：题材筛选
- Cross-reference 资金流向 + 涨停集中行业 + 宏观新闻
- Judge: 哪些题材有持续性？哪些是一日游？
- Output: ≤3 个焦点题材，每个含：逻辑 + 龙头
模块四：交易预案（核心）
- Read 自选股技术指标数据
- For each stock, the AI judges:
- 当前处于什么技术位置？（趋势/震荡/超跌）
- 有哪些可识别的入场信号？信号的可靠性如何？
- 合理的入场价位、止损价位、仓位比例？
- If no clear setups exist, state "今日无符合条件的交易机会" — this is a valid output.
- Output: ≤3 个 If-Then 预案
模块五：仓位纪律
- Based on 模块一+二, AI judges market temperature
- Determines position limit
- Writes today's prohibited behaviors
Step 3: Save to Journal
cat /tmp/final_plan.md | skills/plan-review/.venv/bin/python skills/plan-review/scripts/journal.py create
✅ Pre-Market Completion Criterion
Journal contains AI-generated plan with all 5 modules. No plan = no trading.
Phase 2: Noon Review (午间复盘)
Goal: Check if the morning is going according to plan.
Step 1: Fetch Noon Data
skills/market/.venv/bin/python skills/market/scripts/noon_review.py > /tmp/noon_data.md
Step 2: AI Analysis
- Read the market: 上午指数表现？成交量 vs 昨日同期？领涨/领跌板块？
- Check against plan: Read today's journal. For each plan stock, check:
- Did trigger conditions fire?
- If not, are conditions still valid?
- Any stock showing unexpected weakness?
- Self-examination check: Any plan-external impulses?
- Afternoon strategy: Based on morning patterns, what's the afternoon play?
Step 3: Save
cat /tmp/noon_final.md | skills/plan-review/.venv/bin/python skills/plan-review/scripts/journal.py append --section noon
Phase 3: Evening Review (晚间复盘)
Goal: Deep复盘 — market playback, self-scoring, and next-day preparation.
Step 1: Fetch Evening Data
skills/market/.venv/bin/python skills/market/scripts/evening_review.py > /tmp/evening_data.md
This provides: full-day breadth, theme flows, 龙虎榜 data, 连板高度.
Step 2: AI Deep Analysis
① Market Narrative: What was today's dominant story? One sentence.
② Theme Lifecycle: The AI judges each leading theme's stage:
- 启动期 / 发酵期 / 高潮 / 分歧 / 退潮
- This is NOT a formula — the AI considers volume, breadth, news context, sector rotation patterns
③ LHB Analysis: Read 龙虎榜 data and identify:
- Which institutions are buying? What's the pattern?
- Any stocks with asymmetric buy/sell ratios?
- Any warning signals (机构集体出逃)?
④ Candidate Pool: The AI generates 3-5 next-day candidates. For each:
- Why: Concrete reason grounded in today's data (LHB buying + technical position + theme alignment)
- Risk: What could go wrong?
- Watch level: 重点关注 / 一般关注
- This is raw material for tomorrow's pre_market.py, not trading instructions
⑤ Self-Scoring:
- P&L attribution (planned vs unplanned)
- Discipline score (0-100)
- One error logged
Step 3: Save
cat /tmp/evening_final.md | skills/plan-review/.venv/bin/python skills/plan-review/scripts/journal.py append --section evening
✅ Evening Review Completion Criterion
Journal contains: 1) market narrative, 2) theme lifecycle judgment, 3) LHB analysis, 4) candidate pool with reasoning, 5) discipline score + error log.
AI Analysis Guidelines
When performing analysis, the AI should:
- Ground every judgment in data: "Because 涨停 131 家 and 半导体净流入 119 亿..." not "半导体 looks strong"
- State uncertainty explicitly: "炸板率 59% suggests high分歧 but 131 涨停 still indicates strong underlying demand — the signal is mixed"
- Quality over quantity: 3 well-analyzed candidates > 10 superficial mentions
- "No trade" is a valid output: If conditions aren't right, say so clearly
- Override script suggestions when needed: The scripts provide baseline analysis for efficiency. If the AI disagrees, explain why and provide its own judgment
Integration with Existing Skills
| Skill | Role |
|---|
market | Data faucet: pre_market.py, noon_review.py, evening_review.py, plus individual scripts (overview.py, limit_up.py, fund_flow.py, stock_profile.py, news.py) |
sim-trade | Execution: portfolio check (pre-market), trade verification (evening) |
Data Locations
| Data | Path |
|---|
| Daily journals | data/journal/YYYYMMDD.md |
| Output templates | skills/plan-review/templates/*.md |
| Data scripts | skills/market/scripts/pre_market.py, noon_review.py, evening_review.py |
| Skill scripts | skills/plan-review/scripts/journal.py |
| Watchlist | data/watchlist.json |
