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A Share ML Factor Mining · aifinlab | DecimalAI

financeby aifinlab

A Share ML Factor Mining

aifinlab/a-share-ml-factor-mining

Source aifinlab/finclaw ↗License Apache-2.0

A股机器学习因子挖掘/自动化因子发现。当用户说"因子挖掘"、"ML因子"、"机器学习因子"、"自动因子发现"、"factor mining"、"遗传规划因子"时触发。基于 cn-stock-data 获取数据，使用ML方法自动挖掘有效因子。支持 formal/brief 两种输出风格。

SkillScore70Maintained

Automatic

Finance & Accounting pack

Last verified 8/27/2026

What it changes

Passed 16 of 23 test cases with the skill, and 10 of 23 without it.

Without skill43%

With skill70%

Checks it passed that the model alone missed

Includes table header columns for 候选因子数, 有效因子数, and 筛选率.
Employs Random Forest feature importance ranking for factor evaluation.
Divides the stock universe into 5 equal quantile groups for backtesting.

and 4 more in the full report

6 fixed10 passed either way7 still failing0 broke

Pass-rate lift: +26 pp
Tokens: +8%
Agent turns: 0%
Response time: -16%

Model gemini-3.6-flashMeasured 8/27/2026v116 of 23 cases passed with skill

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-ml-factor-mining/SKILL.md https://app.decimal.ai/s/aifinlab-a-share-ml-factor-mining/SKILL.md

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

decimalai skills add aifinlab-a-share-ml-factor-mining

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

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

Raw file: /s/aifinlab-a-share-ml-factor-mining/SKILL.md ↗ · machine-readable summary at /s/aifinlab-a-share-ml-factor-mining.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 6

Prompt

在 Step 3 因子筛选过程中，除了稀疏线性模型之外，使用哪种机器学习决策树算法来评估因子的特征重要性并进行排序？

What was checked, without and with the skill
Checked	Without skill	With skill
Employs Random Forest feature importance ranking for factor evaluation.	Missed	Passed

case-18 · gemini-3.6-flash · 8/27/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md67 lines2 files

机器学习因子挖掘助手

数据获取

通过 cn-stock-data skill 获取数据：

K线数据: 日线(至少3年历史)
财务数据: 季度财务指标
行业分类: 申万行业用于中性化

分析工作流

Step 1: 因子空间构建

基础算子定义：加减乘除、排名、滞后、滚动统计
遗传规划(GP)：随机组合算子生成候选因子表达式
公式复杂度控制：限制表达式树深度(≤5层)
去重：剔除高相关(>0.9)的冗余因子

Step 2: 因子有效性检验

IC/IR检验：因子值与下期收益的秩相关
分组回测：按因子值分5组，检验多空收益
换手率约束：因子换手率过高则实际不可交易
衰减分析：因子IC随持有期的衰减速度

Step 3: 因子筛选与组合

LASSO/ElasticNet：稀疏化选择有效因子
随机森林特征重要性排序
因子正交化：Gram-Schmidt去除因子间共线性
最终因子池：10-30个低相关有效因子

Step 4: 过拟合防控

样本外检验：训练集/验证集/测试集严格分离
多重检验校正：Bonferroni/BH方法调整p值
因子经济学解释：纯数据挖掘因子需谨慎
滚动窗口验证：因子在不同时期的稳定性

Step 5: 输出报告

输出格式

formal 风格（研报级）

Track record

Benchmark

16 / 23

test cases passed, vs 10 / 23 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-ml-factor-mining/SKILL.md

View on GitHub ↗

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

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