Tushare Data
zillionare/zillionare
面向中文自然语言的 Tushare 数据研究技能。用于把“看看这只股票最近怎么样”“帮我查财报趋势”“最近哪个板块最强”“北向资金在买什么”“给我导出一份行情数据”这类请求，转成可执行的数据获取、清洗、对比、筛选、导出与简要分析流程。适用于 A 股、指数、ETF/基金、财务、估值、资金流、公告新闻、板块概念与宏观数据等研究场景。
Ranks stocks by standardized factor scores (momentum, reversal, volatility, volume and valuation) and builds an equal-weight TopN portfolio with periodic rebalancing.
Install Multi-Factor Stock Ranking
$ npx skills add HKUDS/Vibe-Trading --skill multi-factor -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install HKUDS/Vibe-Trading multi-factor --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/HKUDS/Vibe-Trading.git skills-src && mkdir -p .claude/skills && cp -r skills-src/agent/src/skills/multi-factor .claude/skills/multi-factor && 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 "multi-factor" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/multi-factor into .claude/skills/multi-factor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multi-factor", 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/HKUDS/Vibe-Trading/tree/main/agent/src/skills/multi-factorType 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 HKUDS/Vibe-Trading --skill multi-factor -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install HKUDS/Vibe-Trading multi-factor --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .agents/skills && cp -r skills-src/agent/src/skills/multi-factor .agents/skills/multi-factor && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "multi-factor" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/multi-factor into .agents/skills/multi-factor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multi-factor", 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 HKUDS/Vibe-Trading --skill multi-factor -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install HKUDS/Vibe-Trading multi-factor --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/agent/src/skills/multi-factor .cursor/skills/multi-factor && 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 "multi-factor" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/multi-factor into .cursor/skills/multi-factor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multi-factor", 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/HKUDS/Vibe-Trading.git --path agent/src/skills/multi-factor--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 HKUDS/Vibe-Trading --skill multi-factor -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install HKUDS/Vibe-Trading multi-factor --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/agent/src/skills/multi-factor .gemini/skills/multi-factor && 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 "multi-factor" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/multi-factor into .gemini/skills/multi-factor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multi-factor", 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 HKUDS/Vibe-Trading multi-factorInstalls 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 HKUDS/Vibe-Trading --skill multi-factor -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .github/skills && cp -r skills-src/agent/src/skills/multi-factor .github/skills/multi-factor && 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 "multi-factor" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/multi-factor into .github/skills/multi-factor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multi-factor", 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 HKUDS/Vibe-Trading --skill multi-factor -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install HKUDS/Vibe-Trading multi-factor --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/agent/src/skills/multi-factor .opencode/skills/multi-factor && 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 "multi-factor" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/multi-factor into .opencode/skills/multi-factor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multi-factor", 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
multi-factor- GitHub stars
- 35k
- Token cost
- ~1k tokens
- SKILL.md length
- 411 words
- Files
- 3
- Skills in repo
- 89
- Repo updated
- First seen
- Licence
- MIT
At a glance
Ranks stocks by standardized factor scores (momentum, reversal, volatility, volume and valuation) and builds an equal-weight TopN portfolio with periodic rebalancing.
- Works in 4 steps: Factor calculation: calculate N factors… → Cross-sectional standardization:… → Composite scoring: sum the factors with… → …
- Building a TopN stock portfolio from several standardized factors
- SKILL.md covers Purpose, Signal Logic, Built-In Factors and Parameters, plus 4 more sections
- Runs Python scripts from its folder; calls pip
What it does
At each date the skill computes several factors for many stocks, standardizes each one across the cross-section with Z-scores, adds them into a composite score with equal or custom weights, and goes long the TopN names at 1/N each. Built-in factors are momentum, reversal, volatility and volume ratio, plus 1/PE, 1/PB and ROE factors when extra_fields exist for A-shares.
Defaults are a 20-day momentum and volatility window, three selected stocks and a rebalance every 20 trading days. The pitfalls list is practical: standardization needs at least three stocks, signals stay unchanged between rebalance dates, factor directions must be aligned before standardization, and weights must be normalized. Two example engines are included, one of them a zoo signal engine, and the code needs pandas and numpy.
When your agent uses it
- Building a TopN stock portfolio from several standardized factors
- Combining factors with equal weights or IC-based weights
- Setting a rebalancing frequency for a cross-sectional strategy
- Avoiding mistakes in factor direction and standardization
Example prompts
- “Write a multi-factor signal engine with momentum, volatility and 1/PE, selecting the top 5 stocks.”
- “Rebalance only every 20 trading days and hold the previous signal in between.”
- “Why do my Z-scores look meaningless with only two stocks?”
- “Add an ROE factor from extra_fields to the composite score.”
Requirements
- Python with pandas and numpy
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Factor calculation: calculate N factors for each stock (such as momentum, value, and quality)
- Cross-sectional standardization: standardize each factor on the cross-section with Z-score normalization (subtract mean, divide by…
- Composite scoring: sum the factors with equal weights (or custom weights) to obtain a composite score
- Rank and select: go long the TopN names, with weight = 1/N for each
What it can do on your machine
Read from SKILL.md and the folder at commit 7f6908b. 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
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip, 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
Multi-Factor Stock Ranking loads about 1k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 411 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 HKUDS/Vibe-Trading at commit 7f6908b, republished under its MIT licence (© HKUDS). 411 words, ~1,025 tokens.
Download SKILL.mdSave it as
.claude/skills/multi-factor/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- multi-factor
- description
- Multi-factor cross-sectional stock ranking. Combines factor standardization, equal-weight or IC-weighted scoring, and TopN portfolio construction. Suitable for multi-instrument portfolio strategies.
- category
- strategy
Multi-Factor Cross-Sectional Stock Ranking
Purpose
On the same time cross-section, compute multiple factor values for many stocks, standardize them, combine them into a composite score, and select the top-ranked stocks to build a portfolio.
Signal Logic
- Factor calculation: calculate N factors for each stock (such as momentum, value, and quality)
- Cross-sectional standardization: standardize each factor on the cross-section with Z-score normalization (subtract mean, divide by standard deviation)
- Composite scoring: sum the factors with equal weights (or custom weights) to obtain a composite score
- Rank and select: go long the TopN names, with weight = 1/N for each
Built-In Factors
| Factor Name | Calculation Method | Direction |
|---|---|---|
| momentum | Return over the past N days | Positive (higher is better) |
| reversal | Return over the past 5 days | Negative (lower is better) |
| volatility | Standard deviation of returns over the past N days | Negative (lower is better) |
| volume_ratio | Today's volume / N-day average volume | Positive |
If extra_fields are available (China A-shares), you can also add:
pe_factor: 1/PE (the larger, the cheaper)pb_factor: 1/PBroe_factor: ROE (the larger, the better)
Parameters
| Parameter | Default | Description |
|---|---|---|
| momentum_window | 20 | Momentum lookback window |
| vol_window | 20 | Volatility lookback window |
| top_n | 3 | Number of selected stocks |
| rebalance_freq | 20 | Rebalancing frequency (trading days) |
Common Pitfalls
- Cross-sectional standardization requires at least 3 stocks, otherwise Z-scores are meaningless
- Keep the previous signal unchanged between rebalance dates (do not rerank every day)
- Factors have different directions: momentum is positively sorted, volatility is negatively sorted, so directions must be aligned before standardization
- Portfolio weights must be normalized: each TopN stock gets 1/N, all others get 0
Show full SKILL.md (152 more words)Show less
Dependencies
pip install pandas numpySignal Convention
1/N= selected into TopN (equal-weight long),0= not selected
Zoo Signal Engine (new in 0.1.8)
When the user wants to compose 1-N alphas drawn from the Alpha Zoo (450+ pre-built factors) into a multi-factor strategy, use ZooSignalEngine.from_zoo(...) from zoo_signal_engine.py instead of the old per-symbol example_signal_engine.py. The new engine operates on wide-panel dict[str, pd.DataFrame] inputs (the same shape the registry's Alpha.compute(panel) contract uses), redistributes weights when any alpha fails or is skipped, and supports long-only (top_n), short-only (bottom_n), and long-short (top_n + bottom_n) signal modes. It also exposes a generate(data_map) adapter so it drops straight into the existing run_backtest pipelines.
from src.factors.registry import Registry
from zoo_signal_engine import ZooSignalEngine
registry = Registry()
# Browse candidates with registry.list(theme="momentum") -- see the alpha-zoo skill.
alpha_ids = ["alpha101_001", "alpha101_012", "guotai_191_003"]
engine = ZooSignalEngine.from_zoo(alpha_ids, top_n=10, bottom_n=10, standardize=True)
# Feed into a panel-aware backtest, or via .generate(data_map) into the bundled engines.
signal_panel = engine.compute_signal(panel) # DataFrame, same shape as panel["close"]Cross-references:
- See the
alpha-zooskill for browsing the alpha catalogue, filtering bytheme/universe, and inspecting__alpha_meta__records. example_signal_engine.pyis kept for legacy per-symbol workflows that compute factors directly from raw OHLCV; new code should preferzoo_signal_engine.pyso it benefits from the 450+ zoo alphas, registry-level NaN/inf guardrails, and per-alpha skip isolation.
© HKUDS, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 2 other files in agent/src/skills/multi-factor of HKUDS/Vibe-Trading.
- SKILL.md
- example_signal_engine.py
- zoo_signal_engine.py
Open the folder on GitHubat commit 7f6908b
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| Quant Analystmajiayu000/claude-skill-registry | 666 | 1 repos | ~964 | Automated safety check: Pass | MIT | |
| WorldQuant BRAIN Alpha ResearchQuantML-Research/wq-alpha-research | 405 | — | ~4.9k | Automated safety check: Pass | None | |
| Polymarket Tennislivetennisapi/livetennisapi-mcp | 152 | — | ~3k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Multi-Factor Stock Ranking
What does Multi-Factor Stock Ranking do?
Ranks stocks by standardized factor scores (momentum, reversal, volatility, volume and valuation) and builds an equal-weight TopN portfolio with periodic rebalancing. At each date the skill computes several factors for many stocks, standardizes each one across the cross-section with Z-scores, adds them into a composite score with equal or custom weights, and goes long the TopN names at 1/N each. Built-in factors are momentum, reversal, volatility and volume ratio, plus 1/PE, 1/PB and ROE factors when extra_fields exist for A-shares.
When should I use Multi-Factor Stock Ranking?
Multi-Factor Stock Ranking fits situations like: building a TopN stock portfolio from several standardized factors; combining factors with equal weights or IC-based weights; setting a rebalancing frequency for a cross-sectional strategy; avoiding mistakes in factor direction and standardization.
How do I install Multi-Factor Stock Ranking in Claude Code?
Run `npx skills add HKUDS/Vibe-Trading --skill multi-factor -a claude-code`. Or copy the skill folder (agent/src/skills/multi-factor in HKUDS/Vibe-Trading) into .claude/skills/multi-factor in your project. Claude Code loads it when a task matches its description.
How do I install Multi-Factor Stock Ranking in Codex?
Run `npx skills add HKUDS/Vibe-Trading --skill multi-factor -a codex`. Or copy the skill folder (agent/src/skills/multi-factor in HKUDS/Vibe-Trading) into .agents/skills/multi-factor in your project. Codex loads it when a task matches its description.
Can I use Multi-Factor Stock Ranking 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 HKUDS/Vibe-Trading --skill multi-factor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/multi-factor, .gemini/skills/multi-factor, .github/skills/multi-factor and .opencode/skills/multi-factor in your project.
What does Multi-Factor Stock Ranking need to run?
Going by SKILL.md and its folder, Multi-Factor Stock Ranking needs Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python with pandas and numpy.
Does Multi-Factor Stock Ranking access the network?
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Multi-Factor Stock Ranking 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 Multi-Factor Stock Ranking use?
Multi-Factor Stock Ranking is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Multi-Factor Stock Ranking use?
About 1k tokens (SKILL.md is roughly 4.1k 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 Multi-Factor Stock Ranking?
Skills that share tags, products or a category with Multi-Factor Stock Ranking: Tushare Data (zillionare/zillionare, 318 stars), Quant Blog Writing (zillionare/zillionare, 318 stars), Quant Analyst (majiayu000/claude-skill-registry, 666 stars) and WorldQuant BRAIN Alpha Research (QuantML-Research/wq-alpha-research, 405 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Multi-Factor Stock Ranking?
HKUDS (a GitHub organization) maintains it in HKUDS/Vibe-Trading, which has 34,884 GitHub stars. The repository holds 89 skills in this directory. The repository was last updated on October 6, 2026.
Source: HKUDS/Vibe-Trading on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.