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A股回撤分析/最大回撤量化 · aifinlab | DecimalAI

financeby aifinlab

A股回撤分析/最大回撤量化

aifinlab/a-share-drawdown-analysis

Source aifinlab/finclaw ↗License Apache-2.0

A股回撤分析/最大回撤量化/回撤统计。当用户说"回撤"、"drawdown"、"最大回撤"、"回撤分析"、"回撤统计"、"历史回撤"、"水下曲线"、"亏了多少"、"从高点跌了多少"、"回撤修复"、"回撤天数"时触发。MUST USE when user asks about drawdown metrics, max drawdown calculation, or historical drawdown analysis for stocks/indices/portfolios. 基于 cn-stock-data 获取K线数据，量化分析历史回撤特征（最大回撤、回撤持续期、回撤修复时间、水下曲线）。支持研报风格（formal）和快速分析风格（brief）。

SkillScore83Maintained

Automatic

Finance & Accounting pack

Last verified 8/28/2026

What it changes

Passed 17 of 20 test cases with the skill, and 12 of 20 without it.

Without skill61%

With skill83%

Checks it passed that the model alone missed

Sets brief style as the default report output format.
States the 2008 A-share market drawdown benchmark as -72%.
Invokes python script cn_stock_data.py using the kline subcommand.

and 4 more in the full report

7 fixed12 passed either way2 still failing2 broke

Pass-rate lift: +22 pp
Tokens: -5%
Agent turns: 0%
Response time: -29%

Model gemini-3.6-flashMeasured 8/28/2026v119 of 23 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-drawdown-analysis/SKILL.md https://app.decimal.ai/s/aifinlab-a-share-drawdown-analysis/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add aifinlab-a-share-drawdown-analysis, 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-drawdown-analysis/ and pins it in .decimal/skills.lock

decimalai skills add aifinlab-a-share-drawdown-analysis

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

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

Raw file: /s/aifinlab-a-share-drawdown-analysis/SKILL.md ↗ · machine-readable summary at /s/aifinlab-a-share-drawdown-analysis.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.md62 lines2 files

A股回撤分析/最大回撤量化

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 "$SCRIPTS/cn_stock_data.py" quote --code [CODE]
python "$SCRIPTS/cn_stock_data.py" finance --code [CODE]

Workflow

Step 1: 获取K线数据

获取足够长的历史数据（建议 2 年以上）。

Step 2: 计算回撤序列

滚动最高价 = max(close[:t])
回撤 = (close - 滚动最高价) / 滚动最高价 × 100%
最大回撤 = min(回撤序列)

Step 3: 回撤事件分析

识别所有回撤 > 10% 的事件
每次回撤的起点/终点/最低点/持续天数/恢复天数
回撤期间成交量变化

Step 4: 回撤统计

平均回撤深度/持续时间/恢复时间
回撤频率（年均几次 > 5%/10%/20%）
与大盘回撤的对比（β调整后回撤）

Step 5: 输出

维度	formal	brief
回撤序列	完整回撤图表+事件表	最大回撤值
统计分析	回撤分布+恢复时间	关键统计
风险评估	压力测试情景	当前回撤状态

默认风格：brief。

关键规则

最大回撤是衡量风险的核心指标之一

Track record

Benchmark

17 / 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-drawdown-analysis/SKILL.md

View on GitHub ↗

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

More skills from the same source and nearby in the registry, with their headline benchmark lift.

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What's inside

A股回撤分析/最大回撤量化

数据源

Workflow

Step 1: 获取K线数据

Step 2: 计算回撤序列

Step 3: 回撤事件分析

Step 4: 回撤统计

Step 5: 输出

关键规则

Track record

Benchmark

Adoption

SkillSafety

Source

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