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- Replace monolithic image_message_client with HTTP/OCR/legacy modules; tighten OpenAI transport + tool schemas for multimodal downgrade to OCR specialist path. - Add image/stock workspace prompts (META/SOUL/ROLE_SYSTEM); register experts; tweak specialist agent/direct loop/query_image_attachment. - Add bundled runtime/skills/tushare-finance (references, api_client, SKILL metadata). - Document OCR-related env vars; admin chat tweaks; README; weixin_install Ensure-OfficialPluginRuntimeDeps helper. - Tests: multimodal downgrade + OCR coverage, strict tool pairing in attachment replay guard, workspace contract skips _internal/_system dirs, router/trace/prompt guards. Co-authored-by: Cursor <cursoragent@cursor.com>
171 lines
4.3 KiB
Markdown
171 lines
4.3 KiB
Markdown
# 全球财经事件
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**文档ID**: 233
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**原始链接**: https://tushare.pro/document/2?doc_id=233
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---
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## 财经日历
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接口：eco_cal描述：获取全球财经日历、包括经济事件数据更新限量：单次最大获取100行数据积分：2000积分可调取
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输入参数
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<table>
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<thead>
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<tr>
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<th>名称</th>
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<th>类型</th>
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<th>必选</th>
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<th>描述</th>
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</tr>
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</thead>
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<tbody><tr>
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<td>date</td>
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<td>str</td>
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<td>N</td>
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<td>日期（YYYYMMDD格式）</td>
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</tr>
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<tr>
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<td>start_date</td>
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<td>str</td>
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<td>N</td>
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<td>开始日期</td>
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</tr>
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<tr>
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<td>end_date</td>
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<td>str</td>
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<td>N</td>
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<td>结束日期</td>
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</tr>
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<tr>
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<td>currency</td>
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<td>str</td>
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<td>N</td>
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<td>货币代码</td>
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</tr>
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<tr>
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<td>country</td>
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<td>str</td>
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<td>N</td>
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<td>国家（比如：中国、美国）</td>
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</tr>
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<tr>
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<td>event</td>
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<td>str</td>
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<td>N</td>
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<td>事件 （支持模糊匹配： *非农*）</td>
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</tr>
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</tbody></table>
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输出参数
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<table>
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<thead>
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<tr>
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<th>名称</th>
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<th>类型</th>
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<th>默认显示</th>
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<th>描述</th>
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</tr>
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</thead>
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<tbody><tr>
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<td>date</td>
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<td>str</td>
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<td>Y</td>
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<td>日期</td>
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</tr>
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<tr>
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<td>time</td>
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<td>str</td>
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<td>Y</td>
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<td>时间</td>
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</tr>
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<tr>
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<td>currency</td>
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<td>str</td>
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<td>Y</td>
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<td>货币代码</td>
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</tr>
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<tr>
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<td>country</td>
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<td>str</td>
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<td>Y</td>
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<td>国家</td>
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</tr>
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<tr>
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<td>event</td>
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<td>str</td>
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<td>Y</td>
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<td>经济事件</td>
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</tr>
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<tr>
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<td>value</td>
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<td>str</td>
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<td>Y</td>
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<td>今值</td>
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</tr>
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<tr>
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<td>pre_value</td>
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<td>str</td>
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<td>Y</td>
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<td>前值</td>
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</tr>
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<tr>
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<td>fore_value</td>
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<td>str</td>
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<td>Y</td>
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<td>预测值</td>
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</tr>
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</tbody></table>
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接口示例
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```
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pro = ts.pro_api()
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#获取指定日期全球经济日历
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df = pro.eco_cal(date='20200403')
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#获取中国经济事件
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df = pro.eco_cal(country='中国')
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#获取美国非农数据
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df = pro.eco_cal(event='美国季调后非农*', fields='date,time,country,event,value,pre_value,fore_value')
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```
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数据示例
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```
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date time country event value pre_value fore_value
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0 20200410 09:30 中国 中国PPI年率(%)(年度)(三月) -0.4% -1.1%
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1 20200410 09:30 中国 中国CPI月率(%)(月度)(三月) 0.8% -0.7%
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2 20200410 09:30 中国 中国CPI年率(%)(年度)(三月) 5.2% 4.9%
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3 20200407 15:00 中国 中国外汇储备(美元) 3.107T
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4 20200403 09:45 中国 中国财新服务业PMI(三月) 43.0 26.5
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.. ... ... ... ... ... ... ...
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95 20200229 09:00 中国 中国官方非制造业PMI(二月) 29.6 54.1
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96 20200229 09:00 中国 中国官方制造业PMI(二月) 35.7 50.0 46.0
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97 20200229 09:00 中国 中国官方综合PMI(二月) 28.9 53.0
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98 20200308 00:17 中国 中国贸易帐(美元)(二月) 47.21B 12.75B
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99 20200308 00:17 中国 中国进口年率-美元计价(%)(年度)(二月) 16.5% -9.0%
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```
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美国非农数据：
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```
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date time country event value pre_value fore_value
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0 20200403 20:30 美国 美国季调后非农就业人口变动(三月) -701K 275K -100K
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1 20200403 20:30 美国 美国季调后非农就业人口变动(三月) 273K -100K
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2 20200403 20:30 美国 美国季调后非农就业人口变动(三月) 273K -124K
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3 20200403 20:30 美国 美国季调后非农就业人口变动(三月) 273K -100K
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4 20200403 20:30 美国 美国季调后非农就业人口变动(三月) 273K -123K
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.. ... ... ... ... ... ... ...
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95 20190308 21:30 美国 美国季调后非农就业人口变动(二月) 304K 181K
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96 20190308 21:30 美国 美国季调后非农就业人口变动(二月) 304K 180K
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97 20190308 21:30 美国 美国季调后非农就业人口变动(二月) 304K 185K
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98 20190308 21:30 美国 美国季调后非农就业人口变动(二月) 304K 180K
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99 20190308 21:30 美国 美国季调后非农就业人口变动(二月) 304K 185K
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```
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