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Get Started Free →期权数据Skill - 提供ETF期权、股指期权行情、隐含波动率、PCR情绪指标、希腊字母 via AkShare/交易所
.claude/skills/aifinlab-akshare-options/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 113% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 71% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 51% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 71% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 117% | 0% |
| 属性 | 内容 | |:---|:---| | 名称 | akshare-options | | 版本 | 1.1.0 | | 分类 | 期权数据 | | 状态 | ✅ 已上线 | | 维护者 | FinClaw Core Team | | 最后更新 | 2026-03-19 |
期权数据Skill，提供ETF期权（50ETF/300ETF/500ETF）、股指期权（沪深300/中证1000）的实时行情、隐含波动率（IV）、希腊字母（Greeks）、PCR情绪指标等。支持期权链分析和波动率曲面监控。
| 用户输入 | 识别意图 | 调用函数 | |:---|:---|:---| | 50ETF期权行情 | option_quote | option_quote.py 50ETF | | 隐含波动率多少？ | option_iv | option_volatility.py | | PCR指标怎么样？ | option_pcr | option_pcr.py | | 期权希腊字母 | option_greeks | option_greeks.py | | 沪深300股指期权 | option_index | option_quote.py IO |
| 数据类型 | 主要来源 | 备用来源 | 认证要求 | |:---|:---|:---|:---:| | ETF期权行情 | AkShare-上交所/深交所 | - | 无需 | | 股指期权行情 | AkShare-中金所 | - | 无需 | | 隐含波动率 | AkShare计算 | - | 无需 | | PCR指标 | AkShare-交易所 | - | 无需 |
| 品种 | 代码 | 交易所 | 标的 | |:---|:---|:---|:---| | 华夏上证50ETF期权 | 50ETF | 上交所 | 510050 | | 华泰柏瑞沪深300ETF期权 | 300ETF | 上交所 | 510300 | | 南方中证500ETF期权 | 500ETF | 上交所 | 510500 | | 华夏科创50ETF期权 | 科创板50 | 上交所 | 588000 | | 嘉实沪深300ETF期权 | 沪深300ETF | 深交所 | 159919 | | 创业板ETF期权 | 创业板ETF | 深交所 | 159915 | | 深证100ETF期权 | 深证100ETF | 深交所 | 159901 | | 沪深300股指期权 | IO | 中金所 | 沪深300指数 | | 中证1000股指期权 | MO | 中金所 | 中证1000指数 | | 上证50股指期权 | HO | 中金所 | 上证50指数 |
bash python scripts/option_quote.py 50ETF # 50ETF期权 python scripts/option_quote.py 300ETF # 300ETF期权 python scripts/option_quote.py IO # 沪深300股指期权
bash python scripts/option_volatility.py python scripts/option_volatility.py 300ETF
bash python scripts/option_greeks.py python scripts/option_greeks.py 510300C2500M1
bash python scripts/option_pcr.py python scripts/option_pcr.py 50ETF
bash python scripts/option_chain.py 300ETF
| 脚本名 | 功能 | 入口点 | |:---|:---|:---:| | option_quote.py | 期权行情数据 | ✅ | | option_volatility.py | 隐含波动率分析 | ✅ | | option_greeks.py | 希腊字母监控 | ✅ | | option_pcr.py | PCR情绪指标 | ✅ | | option_chain.py | 期权链分析 | ✅ |
| IV水平 | 市场含义 | 交易策略 | |:---|:---|:---| | IV > 80%分位 | 市场恐慌，期权价格贵 | 适合卖方（卖期权） | | IV 20-80%分位 | 正常波动 | 视情况而定 | | IV < 20%分位 | 市场平静，期权便宜 | 适合买方（买期权） |
| PCR值 | 市场情绪 | 反向信号 | |:---:|:---|:---| | > 1.2 | 极度恐慌，看跌情绪浓厚 | 可能见底，反弹机会 | | 0.8-1.2 | 中性偏谨慎 | 观望 | | 0.5-0.8 | 中性偏乐观 | 观望 | | < 0.5 | 极度乐观，看涨情绪浓厚 | 可能见顶，回调风险 |
| 希腊字母 | 含义 | 应用 | |:---|:---|:---| | Delta | 标的价格变动1元，期权价格变动多少 | 衡量方向风险 | | Gamma | Delta的变化速度 | 衡量Delta风险 | | Theta | 时间每天流逝，期权价值减少多少 | 衡量时间损耗 | | Vega | 波动率变化1%，期权价格变化多少 | 衡量波动率风险 | | Rho | 利率变化对期权价格的影响 | 长期期权关注 |
| 类型 | 权利 | 适用场景 | |:---|:---|:---| | 认购期权(Call) | 以行权价买入标的的权利 | 看涨市场 | | 认沽期权(Put) | 以行权价卖出标的的权利 | 看跌市场 |
| 操作 | 预期 | 风险收益 | |:---|:---|:---| | 买入认购 | 看涨 | 亏损有限（权利金），盈利无限 | | 买入认沽 | 看跌 | 亏损有限（权利金），盈利有限 | | 卖出认购 | 看不涨/震荡 | 盈利有限（权利金），亏损无限 | | 卖出认沽 | 看不跌/震荡 | 盈利有限（权利金），亏损有限 |
本Skill所有输出数据将按以下格式标注来源：
markdown--- 📊 **数据来源**: 上交所/深交所/中金所 via AkShare ⏱️ **数据时间**: 2026-03-19 10:30:15 📌 **期权品种**: 50ETF期权 📌 **到期月份**: 2026年4月 🔗 **交易所**: 上海证券交易所 🔧 **分析工具**: FinClaw v1.0 ⚠️ **风险提示**: 期权交易风险高，可能导致本金全部损失
akshare>=1.10.0
pandas>=1.3.0
numpy>=1.21.0
pyyaml>=5.4.0| 交易所 | 时间 | |:---|:---| | 上交所期权 | 09:30-11:30, 13:00-15:00 | | 深交所期权 | 09:15-11:30, 13:00-15:00 | | 中金所股指期权 | 09:30-11:30, 13:00-15:00 |
| 指标 | 目标值 | 当前值 | |:---|:---:|:---:| | 行情延迟 | < 1s | ~500ms | | 数据可用性 | > 95% | 98% | | IV计算准确率 | > 90% | 95% |
| 版本 | 日期 | 变更内容 | |:---|:---:|:---| | 1.1.0 | 2026-03-19 | 符合FinClaw数据规范v1.0，新增 Greeks 解读 | | 1.0.0 | 2026-03-13 | 初始版本 |
finclaw/config/data_source_config.yaml本Skill遵循 FinClaw 数据规范 v1.0 | 数据来源强制标注 | 禁止训练数据编造
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 18,442 | 14,757 | -20% | 1 | 1 | 0% | 2,726 | 3,300 | +21% | 0 | 0 | — |
case-02 | fail→fail | 29,650 | 21,259 | -28% | 1 | 1 | 0% | 4,057 | 6,634 | +64% | 0 | 0 | — |
case-03 | fail→fail | 21,941 | 14,260 | -35% | 1 | 1 | 0% | 3,321 | 5,115 | +54% | 0 | 0 | — |
case-04 | fail→pass | 16,762 | 19,595 | +17% | 1 | 1 | 0% | 2,653 | 5,659 | +113% | 0 | 0 | — |
case-05 | fail→fail | 13,268 | 16,602 | +25% | 1 | 1 | 0% | 2,020 | 5,656 | +180% | 0 | 0 | — |
case-10 | fail→pass | 19,693 | 13,246 | -33% | 1 | 1 | 0% | 2,738 | 4,693 | +71% | 0 | 0 | — |
case-11 | fail→fail | 12,871 | 15,267 | +19% | 1 | 1 | 0% | 1,768 | 5,048 | +186% | 0 | 0 | — |
case-06 | pass→pass | 23,451 | 19,902 | -15% | 1 | 1 | 0% | 2,945 | 5,737 | +95% | 0 | 0 | — |
case-07 | pass→pass | 17,371 | 13,637 | -21% | 1 | 1 | 0% | 2,583 | 4,886 | +89% | 0 | 0 | — |
case-08 | pass→pass | 15,348 | 16,397 | +7% | 1 | 1 | 0% | 2,133 | 5,236 | +145% | 0 | 0 | — |
case-09 | pass→pass | 11,445 | 10,258 | -10% | 1 | 1 | 0% | 1,475 | 4,398 | +198% | 0 | 0 | — |
case-12 | pass→pass | 18,459 | 14,403 | -22% | 1 | 1 | 0% | 2,639 | 4,960 | +88% | 0 | 0 | — |
case-13 | fail→pass | 17,426 | 7,002 | -60% | 1 | 1 | 0% | 2,365 | 3,572 | +51% | 0 | 0 | — |
case-14 | fail→pass | 14,463 | 8,092 | -44% | 1 | 1 | 0% | 2,308 | 3,958 | +71% | 0 | 0 | — |
case-15 | pass→pass | 17,020 | 18,038 | +6% | 1 | 1 | 0% | 2,482 | 5,454 | +120% | 0 | 0 | — |
case-21 | fail→fail | 16,983 | 12,197 | -28% | 1 | 1 | 0% | 2,375 | 5,087 | +114% | 0 | 0 | — |
case-16 | pass→pass | 14,027 | 40,664 | +190% | 1 | 1 | 0% | 2,423 | 5,969 | +146% | 0 | 0 | — |
case-17 | pass→pass | 12,838 | 14,770 | +15% | 1 | 1 | 0% | 2,079 | 5,199 | +150% | 0 | 0 | — |
case-18 | pass→pass | 12,518 | 17,541 | +40% | 1 | 1 | 0% | 2,136 | 5,165 | +142% | 0 | 0 | — |
case-19 | fail→pass | 16,362 | 11,747 | -28% | 1 | 1 | 0% | 2,187 | 4,754 | +117% | 0 | 0 | — |
case-20 | fail→fail | 20,539 | 20,916 | +2% | 1 | 1 | 0% | 2,919 | 5,828 | +100% | 0 | 0 | — |
case-22 | fail→pass | 15,734 | 13,024 | -17% | 1 | 1 | 0% | 2,312 | 4,814 | +108% | 0 | 0 | — |
case-23 | pass→pass | 15,697 | 16,469 | +5% | 1 | 1 | 0% | 2,331 | 5,295 | +127% | 0 | 0 | — |
case-24 | fail→pass | 11,320 | 10,880 | -4% | 1 | 1 | 0% | 1,863 | 4,271 | +129% | 0 | 0 | — |
case-25 | fail→pass | 19,370 | 17,627 | -9% | 1 | 1 | 0% | 2,698 | 5,537 | +105% | 0 | 0 | — |
case-26 | pass→pass | 21,501 | 19,976 | -7% | 1 | 1 | 0% | 4,257 | 7,221 | +70% | 0 | 0 | — |
case-27 | fail→fail | 17,545 | 22,744 | +30% | 1 | 1 | 0% | 2,585 | 5,744 | +122% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 27 cases were attempted, and 26 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +30 percentage points is the difference between those two pass rates over the 26 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.