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A股事件驱动量化 · aifinlab | DecimalAI

financeby aifinlab

A股事件驱动量化

aifinlab/a-share-event-quant

Source aifinlab/finclaw ↗License Apache-2.0

A股事件驱动量化/事件研究法。当用户说"事件研究"、"event study"、"事件驱动量化"、"公告效应"、"CAR"、"异常收益"、"XX公告后会怎样"、"事件窗口"时触发。基于 cn-stock-data 获取K线数据，运用事件研究法量化分析特定事件对股价的影响。支持研报风格（formal）和快速分析风格（brief）。

SkillScore73Maintained

Automatic

Finance & Accounting pack

Last verified 8/28/2026

What it changes

Passed 15 of 20 test cases with the skill, and 12 of 20 without it.

Without skill59%

With skill73%

Checks it passed that the model alone missed

Includes the argument --freq daily.
Uses cn_stock_data.py with the kline subcommand.
Uses cn_stock_data.py with the quote subcommand.

and 2 more in the full report

4 fixed12 passed either way5 still failing1 broke

Pass-rate lift: +14 pp
Tokens: -3%
Agent turns: 0%
Response time: -23%

Model gemini-3.6-flashMeasured 8/28/2026v116 of 22 cases passed with skill · 20 comparable

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/aifinlab-a-share-event-quant/SKILL.md https://app.decimal.ai/s/aifinlab-a-share-event-quant/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add aifinlab-a-share-event-quant, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/aifinlab-a-share-event-quant/ and pins it in .decimal/skills.lock

decimalai skills add aifinlab-a-share-event-quant

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/aifinlab-a-share-event-quant/SKILL.md https://app.decimal.ai/s/aifinlab-a-share-event-quant/SKILL.md

Raw file: /s/aifinlab-a-share-event-quant/SKILL.md ↗ · machine-readable summary at /s/aifinlab-a-share-event-quant.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

What's inside

The instructions your model reads when the skill loads.

SKILL.md65 lines2 files

A股事件驱动量化

数据源

SCRIPTS="$SKILLS_ROOT/cn-stock-data/scripts"
python "$SCRIPTS/cn_stock_data.py" kline --code [CODE] --freq daily --start [日期]
python "$SCRIPTS/cn_stock_data.py" kline --code SH000300 --freq daily --start [日期]
python "$SCRIPTS/cn_stock_data.py" quote --code [CODE]

补充：通过 web 搜索获取事件日期、公告内容。

Workflow

Step 1: 定义事件与事件日

明确事件类型（业绩预告/定增/回购/高管增持等）
确定精确的事件日（公告日 T=0）

Step 2: 设定窗口

估计窗口：[-250, -11]（用于估计正常收益模型）
事件窗口：[-10, +10] 或 [-5, +20]（观察异常收益）

Step 3: 计算正常收益（市场模型）

R_normal = α + β × R_market
α, β 在估计窗口内通过 OLS 回归得到

Step 4: 计算异常收益

AR_t = R_actual - R_normal（每日异常收益）
CAR = Σ AR_t（累计异常收益）
CAAR = mean(CAR) across events（平均累计异常收益）

Step 5: 统计检验 + 输出

t 检验：CAR / (σ_AR × √T)
|t| > 1.96 → 5% 水平显著

维度	formal	brief
模型	市场模型+Fama-French	市场模型
检验	多种统计量	仅 t 统计量
图表	CAR 时序图+置信区间	CAR 数值

Track record

Benchmark

15 / 20

test cases passed, vs 12 / 20 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

aifinlab / finclaw

skills/a-share-event-quant/SKILL.md

View on GitHub ↗

License Apache-2.0Imported 8/3/2026Version v1Bundled 1 file

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What's inside

A股事件驱动量化

数据源

Workflow

Step 1: 定义事件与事件日

Step 2: 设定窗口

Step 3: 计算正常收益（市场模型）

Step 4: 计算异常收益

Step 5: 统计检验 + 输出

Track record

Benchmark

Adoption

SkillSafety

Source

Other skills for the same job

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