Stock Picking By Common Stocks Uncommon Profits
simbajigege/book2skills
Apply Philip Fisher's growth stock investment framework from Common Stocks and Uncommon Profits.
智能策略选股 — 基于 QuantDB 数据的条件选股。在 QuantBot / Claude Code 中按自然语言或条件筛选股票、构建股票池、生成策略时使用。触发词：筛选股票、股票池、条件选股、智能策略、按条件选股、自然语言选股、帮我选出
AGPL-3.0Auto-check passed
Install Smart Strategy Stock Picking
$ npx skills add qusong0627/QuantMind --skill smart-strategy-stock-picking -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install qusong0627/QuantMind smart-strategy-stock-picking --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/smart-strategy-stock-picking .claude/skills/smart-strategy-stock-picking && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "smart-strategy-stock-picking" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/smart-strategy-stock-picking into .claude/skills/smart-strategy-stock-picking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "smart-strategy-stock-picking", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/qusong0627/QuantMind/tree/master/skills/smart-strategy-stock-pickingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add qusong0627/QuantMind --skill smart-strategy-stock-picking -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install qusong0627/QuantMind smart-strategy-stock-picking --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/smart-strategy-stock-picking .agents/skills/smart-strategy-stock-picking && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "smart-strategy-stock-picking" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/smart-strategy-stock-picking into .agents/skills/smart-strategy-stock-picking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "smart-strategy-stock-picking", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add qusong0627/QuantMind --skill smart-strategy-stock-picking -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install qusong0627/QuantMind smart-strategy-stock-picking --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/smart-strategy-stock-picking .cursor/skills/smart-strategy-stock-picking && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "smart-strategy-stock-picking" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/smart-strategy-stock-picking into .cursor/skills/smart-strategy-stock-picking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "smart-strategy-stock-picking", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/qusong0627/QuantMind.git --path skills/smart-strategy-stock-picking--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add qusong0627/QuantMind --skill smart-strategy-stock-picking -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install qusong0627/QuantMind smart-strategy-stock-picking --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/smart-strategy-stock-picking .gemini/skills/smart-strategy-stock-picking && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "smart-strategy-stock-picking" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/smart-strategy-stock-picking into .gemini/skills/smart-strategy-stock-picking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "smart-strategy-stock-picking", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install qusong0627/QuantMind smart-strategy-stock-pickingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add qusong0627/QuantMind --skill smart-strategy-stock-picking -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/smart-strategy-stock-picking .github/skills/smart-strategy-stock-picking && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "smart-strategy-stock-picking" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/smart-strategy-stock-picking into .github/skills/smart-strategy-stock-picking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "smart-strategy-stock-picking", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add qusong0627/QuantMind --skill smart-strategy-stock-picking -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install qusong0627/QuantMind smart-strategy-stock-picking --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/smart-strategy-stock-picking .opencode/skills/smart-strategy-stock-picking && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "smart-strategy-stock-picking" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/smart-strategy-stock-picking into .opencode/skills/smart-strategy-stock-picking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "smart-strategy-stock-picking", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
smart-strategy-stock-picking- GitHub stars
- 1.7k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 257 words
- Files
- 1
- Skills in repo
- 27
- Repo updated
- First seen
- Licence
- AGPL-3.0
At a glance
智能策略选股 — 基于 QuantDB 数据的条件选股。在 QuantBot / Claude Code 中按自然语言或条件筛选股票、构建股票池、生成策略时使用。触发词：筛选股票、股票池、条件选股、智能策略、按条件选股、自然语言选股、帮我选出
- Works in 7 steps: 常用选股因子（前 30 高频字段） → 方式一：自然语言解析（推荐给用户用） → 方式二：结构化条件解析（最可控） → …
- SKILL.md covers 数据基础, 认证, 1. 常用选股因子（前 30 高频字段） and 2. 方式一：自然语言解析（推荐给用户用）, plus 5 more sections
- Calls curl and python3
What it does
Smart Strategy Stock Picking is an agent skill from qusong0627/QuantMind. 智能策略选股 — 基于 QuantDB 数据的条件选股。在 QuantBot / Claude Code 中按自然语言或条件筛选股票、构建股票池、生成策略时使用。触发词：筛选股票、股票池、条件选股、智能策略、按条件选股、自然语言选股、帮我选出
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: QuantMind（量化大脑）开源版是一款面向个人开发者与投研团队的 AI 原生多市场量化交易平台。深度集成微软 Qlib、RD-Agent 因子演化与 QuantBot全能工作台，提供从 300+ 维因子挖掘、13 种机器学习与深度学习模型工场、Qlib 高性能回测、截面批量推理、7x24… The licence is AGPL-3.0.
Example prompts
- “/smart-strategy-stock-picking”
Requirements
- Python 3
Workflow steps
7 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 2e93d9a. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
curlpython3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use curl, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Smart Strategy Stock Picking loads about 1.4k tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 257 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from qusong0627/QuantMind at commit 2e93d9a, republished under its AGPL-3.0 licence (© qusong0627). 257 words, ~1,400 tokens.
Download SKILL.mdSave it as
.claude/skills/smart-strategy-stock-picking/SKILL.md (or your agent's skills folder).- name
- smart-strategy-stock-picking
- description
- 智能策略选股 — 基于 QuantDB 数据的条件选股。在 QuantBot / Claude Code 中按自然语言或条件筛选股票、构建股票池、生成策略时使用。触发词：筛选股票、股票池、条件选股、智能策略、按条件选股、自然语言选股、帮我选出
⚙️ 本技能遵循公共运行环境契约（最高优先级，先于本文其余内容执行）： 详见 _shared/env-contract.md，执行前先读它。
智能策略选股技能
基于 QuantDB 数据的条件选股。支持自然语言、结构化条件、DSL 三种方式，选出符合要求的股票池并附带量化指标。
数据基础
选股完全基于 QuantDB 本地 parquet。字段映射以后端字典为准：backend/services/engine/ai_strategy/steps/step1_stock_selection.py（DSL 字段 → QuantDB 表/列），覆盖以下数据源：
| QuantDB 数据源 | 字段 | 覆盖 |
|---|---|---|
l1_factors | 66 | 动量/流动性/概念热度/资金流 |
technical_indicators | 24 | 均线/RSI/KDJ/MACD/波动率 |
financial | 20 | 财务指标 |
valuation | 15 | PE/PB/市值/ROE |
sentiment | 14 | 市场情绪 |
margin | 10 | 融资融券 |
stock_list | 6 | 行业/ST/上市天数 |
daily/turnover | 3 | 量价 |
认证
BASE=http://127.0.0.1:8000
TOKEN=$(curl -s -X POST $BASE/api/v1/auth/login -H "Content-Type: application/json" \
-d '{"username":"admin","password":"admin123","tenant_id":"default"}' \
| python3 -c "import sys,json; print(json.load(sys.stdin).get('access_token',''))")
AUTH="Authorization: Bearer $TOKEN"
CT="Content-Type: application/json"1. 常用选股因子（前 30 高频字段）
| 字段 | 含义 | 单位 |
|---|---|---|
market_cap / total_mv | 总市值 | 亿 |
float_mv | 流通市值 | 亿 |
pe / pe_ttm | 市盈率 | — |
pb | 市净率 | — |
roe | 净资产收益率 | % |
close | 收盘价 | 元 |
pct_change | 当日涨跌幅 | % |
turnover_rate | 换手率 | % |
ma5 / ma10 / ma20 / ma60 | 均线 | 元 |
ma_gap_5 / ma_gap_20 | 均线偏离度 | % |
rsi_6 / rsi_14 | RSI | — |
kdj_k / kdj_d / kdj_j | KDJ | — |
macd_dif / macd_dea / macd_hist | MACD | — |
future_return_1d / future_return_3d / future_return_5d / future_return_20d / future_return_60d | 未来 N 日收益（标签，勿作过滤） | % |
vol_std_5 / vol_std_20 / vol_std_60 | 波动率 | % |
vol_atr_14 | 14日ATR | — |
beta_20 | 20日Beta | — |
volume_ratio_5 / volume_ratio_20 | 量比 | — |
main_flow | 主力资金净流入 | 元 |
flow_net_amount | 资金净流入总额 | 万元（2026-09 起；读入归一为元） |
inst_ownership | 机构持仓 | % |
concept_ai / concept_chip 等 | 概念热度 | — |
industry | 行业 | — |
is_st | 是否ST | — |
listed_days | 上市天数 | 天 |
完整字段清单与映射以后端字段字典为准（backend/services/engine/ai_strategy/steps/step1_stock_selection.py + api/schemas/stock_pool.py）。⚠️ 前端 electron/src/features/strategy-wizard/factors/dictionary.ts 已移除，勿再引用。
2. 方式一：自然语言解析（推荐给用户用）
# 解析自然语言为 DSL（内部：可先用此步确认字段能否被识别）
curl -s -X POST "$BASE/api/v1/strategy/parse-text" -H "$AUTH" -H "$CT" \
-d '{"text":"市值大于500亿且ROE大于15%的沪深300成分股，剔除ST"}' \
-o /tmp/pt.json -w "HTTP %{http_code}\n"
cat /tmp/pt.json | python3 -m json.tool --no-ensure-ascii | head -303. 方式二：结构化条件解析（最可控）
curl -s -X POST "$BASE/api/v1/strategy/parse-conditions" -H "$AUTH" -H "$CT" \
-d '{"conditions":{"type":"numeric","factor":"pe","operator":"<","threshold":15}}' \
-o /tmp/pc.json -w "HTTP %{http_code}\n"
cat /tmp/pc.json | python3 -m json.tool --no-ensure-ascii条件结构：
- 数值条件：
{"type":"numeric","factor":"pe","operator":"<|<=|>|>=|=","threshold":15} - 趋势条件：
{"type":"trend","factor":"ma5","window":5,"direction":"above|below"} - 复合条件：
{"type":"composite","op":"and|or","children":[...]}（可嵌套）
返回：dsl（如 SELECT symbol WHERE pe < 15）+ mapping + quantdb_filters（如 [{field:"pe_ttm", operator:"<", value:15, table:"quantdb_valuation"}]）
4. 方式三：DSL 直接查询（执行选股）
curl -s -X POST "$BASE/api/v1/strategy/query-pool" -H "$AUTH" -H "$CT" \
-d '{"dsl":"SELECT symbol WHERE pe < 15 AND roe > 10","market":"CN","exchange":"SH"}'参数：
dsl：SELECT symbol WHERE 条件 [AND/OR 条件...]（字段用上面的因子名）market：CN / HK / US / CRYPTOexchange：SH / SZ / BJ（仅 A 股）
返回：items（每只股票 symbol/name/metrics，metrics 含市值/PE/ROE 等）+ summary（matchRate/totalCandidates/universeTotal/asOf）
5. 实战示例（可直接复用）
5.1 低估值蓝筹（PE<15 且 市值>500亿）
curl -s -X POST "$BASE/api/v1/strategy/query-pool" -H "$AUTH" -H "$CT" \
-d '{"dsl":"SELECT symbol WHERE pe < 15 AND market_cap > 500 AND roe > 10","market":"CN"}'5.2 动量强势（过去 20 日收益>10% 且 RSI>60）
curl -s -X POST "$BASE/api/v1/strategy/query-pool" -H "$AUTH" -H "$CT" \
-d '{"dsl":"SELECT symbol WHERE mom_ret_20d > 0.10 AND rsi_14 > 60 AND turnover_rate < 20","market":"CN"}'勿用
future_return_*（旧名return_*）做选股过滤——那是未来收益标签，会造成泄漏。
5.3 高波动小盘（波动大 + 市值小）
curl -s -X POST "$BASE/api/v1/strategy/query-pool" -H "$AUTH" -H "$CT" \
-d '{"dsl":"SELECT symbol WHERE vol_std_20 > 5 AND market_cap < 100 AND pct_change > 0","market":"CN"}'5.4 资金流入 + 概念热门（AI/半导体）
curl -s -X POST "$BASE/api/v1/strategy/query-pool" -H "$AUTH" -H "$CT" \
-d '{"dsl":"SELECT symbol WHERE main_flow > 10000000 AND concept_ai > 0.5","market":"CN"}'5.5 剔除 ST + 特定行业 + 换手活跃
curl -s -X POST "$BASE/api/v1/strategy/query-pool" -H "$AUTH" -H "$CT" \
-d '{"dsl":"SELECT symbol WHERE is_st = false AND industry = 半导体 AND turnover_rate BETWEEN 3 AND 15","market":"CN"}'6. 分析建议
选出的股票池可结合其他技能深入分析：
- 查新闻：
/news/articles带 tickers 看利好/利空 → [[quantmind-operations]] 第 7 节 - 查推理分数：
/models/inference/stock/{symbol}/history看模型评分 - 挖新因子：[[rd-agent-factor-mining]] 补充更多筛选维度
- 训练模型：选出的池子可喂给模型训练 → [[quantmind-operations]] 第 1 节
7. 常见问题
| 现象 | 原因 | 处理 |
|---|---|---|
| 422 字段不匹配 | DSL 用了映射表外字段 | 用上面的常用因子名，或查后端字典 step1_stock_selection.py |
| 选股结果为空 | 条件过严 | 放宽阈值，或去掉 AND 条件 |
| summary.matchRate 极低 | 条件偏窄 | 检查 totalCandidates 是否正常 |
| 想要全市场 | dsl 用 SELECT symbol WHERE true | 返回全部候选 |
© qusong0627, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/smart-strategy-stock-picking of qusong0627/QuantMind.
Open the folder on GitHubat commit 2e93d9a
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Questions about Smart Strategy Stock Picking
What does Smart Strategy Stock Picking do?
智能策略选股 — 基于 QuantDB 数据的条件选股。在 QuantBot / Claude Code 中按自然语言或条件筛选股票、构建股票池、生成策略时使用。触发词：筛选股票、股票池、条件选股、智能策略、按条件选股、自然语言选股、帮我选出. Smart Strategy Stock Picking is an agent skill from qusong0627/QuantMind.
How do I install Smart Strategy Stock Picking in Claude Code?
Run `npx skills add qusong0627/QuantMind --skill smart-strategy-stock-picking -a claude-code`. Or copy the skill folder (skills/smart-strategy-stock-picking in qusong0627/QuantMind) into .claude/skills/smart-strategy-stock-picking in your project. Claude Code loads it when a task matches its description.
How do I install Smart Strategy Stock Picking in Codex?
Run `npx skills add qusong0627/QuantMind --skill smart-strategy-stock-picking -a codex`. Or copy the skill folder (skills/smart-strategy-stock-picking in qusong0627/QuantMind) into .agents/skills/smart-strategy-stock-picking in your project. Codex loads it when a task matches its description.
Can I use Smart Strategy Stock Picking in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add qusong0627/QuantMind --skill smart-strategy-stock-picking -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/smart-strategy-stock-picking, .gemini/skills/smart-strategy-stock-picking, .github/skills/smart-strategy-stock-picking and .opencode/skills/smart-strategy-stock-picking in your project.
What does Smart Strategy Stock Picking need to run?
Going by SKILL.md and its folder, Smart Strategy Stock Picking needs the command-line tools its instructions call (curl and python3). Our summary lists: Python 3.
Does Smart Strategy Stock Picking access the network?
SKILL.md contains no URLs. Its commands use curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Smart Strategy Stock Picking safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Smart Strategy Stock Picking use?
Smart Strategy Stock Picking is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Smart Strategy Stock Picking use?
About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Smart Strategy Stock Picking?
Skills that share tags, products or a category with Smart Strategy Stock Picking: Stock Picking By Common Stocks Uncommon Profits (simbajigege/book2skills, 183 stars), Smart Contract Formal Verification (sickn33/agentic-awesome-skills, 47k stars), Smart Contract Upgrade Governance (sickn33/agentic-awesome-skills, 47k stars) and Stock (MagicCube/agentara, 516 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Smart Strategy Stock Picking?
qusong0627 (a GitHub user) maintains it in qusong0627/QuantMind, which has 1,725 GitHub stars. The repository holds 27 skills in this directory. The repository was last updated on October 10, 2026.
Source: qusong0627/QuantMind on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.