# MacroMicro — Zapi reference > Macro and market charts, indicators, calendars and 13F filings from MacroMicro. **Base URL:** `https://api.zpi.web.id` **Auth:** Send `x-api-key: YOUR_KEY` header on every request. Get a free key at https://zpi.web.id/dashboard/keys. **Response envelope:** `{ content, message, errors }` **Rate limit:** 60 req/min on free tier. **Related:** - Detail page: https://zpi.web.id/api/finance/macromicro - Endpoint catalog: https://zpi.web.id/category/finance - Concise index: https://zpi.web.id/llms.txt - Full reference: https://zpi.web.id/llms-full.txt --- ## MacroMicro **Category:** finance · **Slug:** `macromicro` **Detail page:** https://zpi.web.id/api/finance/macromicro Macro and market charts, indicators, calendars and 13F filings from MacroMicro. **Tags:** macro, economics, charts, indicators, calendar, 13f, markets ### Chart Series values, render config and the source stat ids behind them, for up to 12 charts in one call. - **Method:** `GET` - **Endpoint:** `https://api.zpi.web.id/v1/finance:macromicro/chart/:id` - **Cache TTL:** 3600s **Parameters:** | Name | Type | Location | Required | Description | |------|------|----------|----------|-------------| | `id` | string | path | yes | Chart ids, comma-separated, up to 12. Take them from `charts`, `collection` or `market`. | | `from` | string | query | no | Drop observations before this date, YYYY-MM-DD. | | `to` | string | query | no | Drop observations after this date, YYYY-MM-DD. | | `length` | number | query | no | Observations per series, newest first (default 100, max 20000) | **cURL:** ```bash curl "https://api.zpi.web.id/v1/finance:macromicro/chart/:id?from=2020-01-01&to=2026-08-29&length=100" \ -H "x-api-key: YOUR_API_KEY" ``` **JavaScript / TypeScript:** ```javascript const res = await fetch("https://api.zpi.web.id/v1/finance:macromicro/chart/:id?from=2020-01-01&to=2026-08-29&length=100", { headers: { "x-api-key": process.env.ZAPI_KEY } }); const data = await res.json(); ``` **Python:** ```python import requests r = requests.get("https://api.zpi.web.id/v1/finance:macromicro/chart/:id?from=2020-01-01&to=2026-08-29&length=100", headers={"x-api-key": "YOUR_API_KEY"}) data = r.json() ``` **Example response:** ```json { "to": null, "from": "2015-01-01", "count": 2, "items": [ { "url": "https://en.macromicro.me/charts/55744/global-citi-earnings-revision-index", "slug": "global-citi-earnings-revision-index", "type": 1, "nameEn": "World - Citi Earnings Revision Index", "nameSc": "全球-花旗盈利修正指数", "nameTc": "全球-花旗盈利修正指數", "series": [ { "color": "#3bafda", "count": 8, "stats": [ { "units": "idx", "nameEn": "World - Citigroup Earnings Revision Index", "nameSc": "全球-花旗盈利修正指数", "nameTc": "全球-花旗盈利修正指數", "statId": 22951, "category": 21, "frequency": "W", "operation": "lin", "categoryV2": 100003, "aggregation": "eop", "frequencyName": "Weekly", "unitsOriginal": "idx", "frequencyOriginal": "W" } ], "total": 607, "units": "idx", "nameEn": "World", "nameSc": "全球-花旗盈利修正指数", "nameTc": "全球-花旗盈利修正指數", "axisType": "linear", "lastDate": "2026-08-14", "lineType": "column", "firstDate": "2015-01-04", "frequency": "W", "lineWidth": 2, "operation": "lin", "axisOffset": 0, "axisPosition": 1, "axisReversed": false, "observations": [ { "date": "2026-08-14", "value": 0.18 }, { "date": "2026-08-07", "value": 0.26 }, { "date": "2026-07-31", "value": 0.2 }, { "date": "2026-07-24", "value": 0.18 }, { "date": "2026-07-17", "value": 0.14 }, { "date": "2026-07-10", "value": 0.12 }, { "date": "2026-07-03", "value": 0.12 }, { "date": "2026-06-26", "value": 0.19 } ], "frequencyName": "Weekly", "unitsOriginal": "idx", "frequencyOriginal": "W" }, { "color": "#e9573f", "count": 8, "stats": [ { "units": "idx", "nameEn": "DM - Citigroup Earnings Revision Index", "nameSc": "成熟市场-花旗盈利修正指数", "nameTc": "成熟市場-花旗盈利修正指數", "statId": 22952, "category": 21, "frequency": "W", "operation": "lin", "categoryV2": 100003, "aggregation": "eop", "frequencyName": "Weekly", "unitsOriginal": "idx", "frequencyOriginal": "W" } ], "total": 607, "units": "idx", "nameEn": "Developed Markets", "nameSc": "成熟市场-花旗盈利修正指数", "nameTc": "成熟市場-花旗盈利修正指數", "axisType": "linear", "lastDate": "2026-08-14", "lineType": "line", "firstDate": "2015-01-04", "frequency": "W", "lineWidth": 2, "operation": "lin", "axisOffset": 0, "axisPosition": 1, "axisReversed": false, "observations": [ { "date": "2026-08-14", "value": 0.24 }, { "date": "2026-08-07", "value": 0.29 }, { "date": "2026-07-31", "value": 0.24 }, { "date": "2026-07-24", "value": 0.22 }, { "date": "2026-07-17", "value": 0.2 }, { "date": "2026-07-10", "value": 0.16 }, { "date": "2026-07-03", "value": 0.15 }, { "date": "2026-06-26", "value": 0.25 } ], "frequencyName": "Weekly", "unitsOriginal": "idx", "frequencyOriginal": "W" }, { "color": "#f6bb42", "count": 8, "stats": [ { "units": "idx", "nameEn": "EM - Citigroup Earnings Revision Index", "nameSc": "新兴市场-花旗盈利修正指数", "nameTc": "新興市場-花旗盈利修正指數", "statId": 22953, "category": 21, "frequency": "W", "operation": "lin", "categoryV2": 100003, "aggregation": "eop", "frequencyName": "Weekly", "unitsOriginal": "idx", "frequencyOriginal": "W" } ], "total": 607, "units": "idx", "nameEn": "Emerging Markets", "nameSc": "新兴市场-花旗盈利修正指数", "nameTc": "新興市場-花旗盈利修正指數", "axisType": "linear", "lastDate": "2026-08-14", "lineType": "line", "firstDate": "2015-01-04", "frequency": "W", "lineWidth": 2, "operation": "lin", "axisOffset": 0, "axisPosition": 1, "axisReversed": false, "observations": [ { "date": "2026-08-14", "value": 0.09 }, { "date": "2026-08-07", "value": 0.18 }, { "date": "2026-07-31", "value": 0.08 }, { "date": "2026-07-24", "value": 0.09 }, { "date": "2026-07-17", "value": -0.04 }, { "date": "2026-07-10", "value": 0.04 }, { "date": "2026-07-03", "value": 0.07 }, { "date": "2026-06-26", "value": 0.01 } ], "frequencyName": "Weekly", "unitsOriginal": "idx", "frequencyOriginal": "W" }, { "color": "#70ca63", "count": 8, "stats": [ { "units": "idx", "nameEn": "Asia Pacific (ex. Japan) - Citigroup Earnings Revision Index", "nameSc": "亚太(排除日本)-花旗盈利修正指数", "nameTc": "亞太(排除日本)-花旗盈利修正指數", "statId": 22958, "category": 21, "frequency": "W", "operation": "lin", "categoryV2": 100003, "aggregation": "eop", "frequencyName": "Weekly", "unitsOriginal": "idx", "frequencyOriginal": "W" } ], "total": 607, "units": "idx", "nameEn": "Asia Pacific (ex. Japan)", "nameSc": "亚太(排除日本)-花旗盈利修正指数", "nameTc": "亞太(排除日本)-花旗盈利修正指數", "axisType": "linear", "lastDate": "2026-08-14", "lineType": "line", "firstDate": "2015-01-04", "frequency": "W", "lineWidth": 2, "operation": "lin", "axisOffset": 0, "axisPosition": 1, "axisReversed": false, "observations": [ { "date": "2026-08-14", "value": -0.09 }, { "date": "2026-08-07", "value": 0.36 }, { "date": "2026-07-31", "value": -0.18 }, { "date": "2026-07-24", "value": -0.11 }, { "date": "2026-07-17", "value": -0.2 }, { "date": "2026-07-10", "value": -0.14 }, { "date": "2026-07-03", "value": -0.23 }, { "date": "2026-06-26", "value": -0.36 } ], "frequencyName": "Weekly", "unitsOriginal": "idx", "frequencyOriginal": "W" } ], "chartId": 55744, "seriesCount": 4, "sortBarChart": false, "descriptionEn": "The Citi Earnings Revision Index is calculated as “Proportion of Companies Revising EPS (%)” − “Proportion of Companies Revising EPS Down (%)” (compared to last week). When the index is above the zero axis, it means that analysts on average are optimistic about the outlook for corporate earnings, and vice versa, it means that analysts are relatively pessimistic.", "descriptionSc": "花旗盈利修正指数计算方式为「上调 EPS 的公司比例（%）」−「下调 EPS 的公司比例（%）」（与上周相比）。该指数位于零轴之上时，代表平均而言分析师对于企业盈利展望持乐观态度，反之则表示相对而言分析师持较悲观看法。", "descriptionTc": "花旗盈利修正指數計算方式為「上調 EPS 的公司比例（%）」−「下調 EPS 的公司比例（%）」（與上週相比）。該指數位於零軸之上時，代表平均而言分析師對於企業盈利展望持樂觀態度，反之則表示相對而言分析師持較悲觀看法。", "valueDecimals": 2 }, { "url": "https://en.macromicro.me/charts/26279/us-epu", "slug": "us-epu", "type": 1, "nameEn": "US - Economic Policy Uncertainty Index", "nameSc": "美国-经济不确定指数", "nameTc": "美國-經濟不確定指數", "series": [ { "color": "#3bafda", "count": 8, "stats": [ { "units": "idx", "nameEn": "US - Economic Policy Uncertainty Index", "nameSc": "美国-经济不确定指数", "nameTc": "美國-經濟不確定指數", "statId": 7909, "country": "us", "category": 1, "frequency": "D", "operation": "lin", "categoryV2": 100001, "aggregation": "eop", "frequencyName": "Daily", "unitsOriginal": "idx", "frequencyOriginal": "D" } ], "total": 4258, "units": "idx", "nameEn": "US Economic Policy Uncertainty Index", "nameSc": "美国-经济不确定指数", "nameTc": "美國-經濟不確定指數", "axisType": "linear", "lastDate": "2026-08-28", "lineType": "line", "firstDate": "2015-01-01", "frequency": "D", "lineWidth": 2, "operation": "lin", "axisOffset": 0, "operationN": "7", "axisPosition": 1, "axisReversed": false, "observations": [ { "date": "2026-08-28", "value": 252.08 }, { "date": "2026-08-27", "value": 168.85 }, { "date": "2026-08-26", "value": 270.71 }, { "date": "2026-08-25", "value": 164.93 }, { "date": "2026-08-24", "value": 346.67 }, { "date": "2026-08-23", "value": 265.3 }, { "date": "2026-08-22", "value": 174.21 }, { "date": "2026-08-21", "value": 153.47 } ], "frequencyName": "Daily", "unitsOriginal": "idx", "frequencyOriginal": "D" }, { "color": "#e9573f", "count": 8, "stats": [ { "units": "idx", "nameEn": "US - Economic Policy Uncertainty Index (MA30)", "nameSc": "美国-经济不确定指数(MA30)", "nameTc": "美國-經濟不確定指數(MA30)", "statId": 7911, "country": "us", "category": 1, "frequency": "D", "operation": "lin", "categoryV2": 100001, "aggregation": "eop", "frequencyName": "Daily", "unitsOriginal": "idx", "frequencyOriginal": "D" } ], "total": 4258, "units": "idx", "nameEn": "US Economic Policy Uncertainty Index (MA30)", "nameSc": "美国-经济不确定指数(MA30)", "nameTc": "美國-經濟不確定指數(MA30)", "axisType": "linear", "lastDate": "2026-08-28", "lineType": "line", "firstDate": "2015-01-01", "frequency": "D", "lineWidth": 2, "operation": "lin", "axisOffset": 0, "axisPosition": 1, "axisReversed": false, "observations": [ { "date": "2026-08-28", "value": 243.5297 }, { "date": "2026-08-27", "value": 242.9523 }, { "date": "2026-08-26", "value": 246.3347 }, { "date": "2026-08-25", "value": 248.1603 }, { "date": "2026-08-24", "value": 250.773 }, { "date": "2026-08-23", "value": 248.8827 }, { "date": "2026-08-22", "value": 264.1837 }, { "date": "2026-08-21", "value": 264.849 } ], "frequencyName": "Daily", "unitsOriginal": "idx", "frequencyOriginal": "D" } ], "chartId": 26279, "country": "us", "seriesCount": 2, "sortBarChart": false, "descriptionEn": "The primary measure for this index is the number of articles that contain at least one term from each of 3 sets of terms. The first set is economic or economy. The second is uncertain or uncertainty. The third set is legislation or deficit or regulation or congress or federal reserve or white house.", "descriptionSc": "美国经济不确定指数以文本探勘的方式分析美国超过 1000 报纸，若当日报纸提到关于经济、不确定性、法规、国会、美联储、白宫等有关词彙越多，该指数越高。\n\n该指标以新闻报导的角度去衡量美国经济不确定性的状况，若该指数越高，代表美国经济不确定性较高。", "descriptionTc": "美國經濟不確定指數以文本探勘的方式分析美國超過 1000 報紙，若當日報紙提到關於經濟、不確定性、法規、國會、聯準會、白宮等有關詞彙越多，該指數越高。\n\n該指標以新聞報導的角度去衡量美國經濟不確定性的狀況，若該指數越高，代表美國經濟不確定性較高。", "valueDecimals": 2 } ], "total": 2, "charts": [ 55744, 26279 ], "length": 8, "dataset": "chart", "missing": [], "provider": "macromicro", "resolved": [ 55744, 26279 ] } ``` --- ### Charts The full chart catalogue — id, slug and URL for every chart the site publishes. - **Method:** `GET` - **Endpoint:** `https://api.zpi.web.id/v1/finance:macromicro/charts` - **Cache TTL:** 86400s **Parameters:** | Name | Type | Location | Required | Description | |------|------|----------|----------|-------------| | `query` | string | query | no | Keep only charts whose slug contains this text. | | `country` | string | query | no | Keep only charts whose slug starts with this country prefix. | | `page` | number | query | no | Result page (default 1) | | `count` | number | query | no | Charts per page (default 100, max 500) | **cURL:** ```bash curl "https://api.zpi.web.id/v1/finance:macromicro/charts?query=inflation&country=us&page=1&count=100" \ -H "x-api-key: YOUR_API_KEY" ``` **JavaScript / TypeScript:** ```javascript const res = await fetch("https://api.zpi.web.id/v1/finance:macromicro/charts?query=inflation&country=us&page=1&count=100", { headers: { "x-api-key": process.env.ZAPI_KEY } }); const data = await res.json(); ``` **Python:** ```python import requests r = requests.get("https://api.zpi.web.id/v1/finance:macromicro/charts?query=inflation&country=us&page=1&count=100", headers={"x-api-key": "YOUR_API_KEY"}) data = r.json() ``` **Example response:** ```json { "page": 1, "count": 8, "items": [ { "url": "https://en.macromicro.me/charts/152511/us-pce-price-index-change-in-inflation-distribution", "slug": "us-pce-price-index-change-in-inflation-distribution", "chartId": 152511 }, { "url": "https://en.macromicro.me/charts/152510/us-pce-price-index-inflation-distribution", "slug": "us-pce-price-index-inflation-distribution", "chartId": 152510 }, { "url": "https://en.macromicro.me/charts/152487/us-inflation-shock-momentum-index", "slug": "us-inflation-shock-momentum-index", "chartId": 152487 }, { "url": "https://en.macromicro.me/charts/151992/us-trimmed-mean-inflation", "slug": "us-trimmed-mean-inflation", "chartId": 151992 }, { "url": "https://en.macromicro.me/charts/147683/us-money-supply-and-inflation-vs-gold", "slug": "us-money-supply-and-inflation-vs-gold", "chartId": 147683 }, { "url": "https://en.macromicro.me/charts/131622/us-cleveland-fed-expected-inflation", "slug": "us-cleveland-fed-expected-inflation", "chartId": 131622 }, { "url": "https://en.macromicro.me/charts/131621/us-new-york-fed-expected-inflation", "slug": "us-new-york-fed-expected-inflation", "chartId": 131621 }, { "url": "https://en.macromicro.me/charts/116478/us-the-number-of-mentions-of-inflation-in-all-industry", "slug": "us-the-number-of-mentions-of-inflation-in-all-industry", "chartId": 116478 } ], "query": "inflation", "total": 20, "country": "us", "dataset": "charts", "hasMore": true, "nextPage": 2, "provider": "macromicro" } ``` --- ### Series Search Search the indicator directory: 66,000+ series with country, frequency, units and currency. - **Method:** `GET` - **Endpoint:** `https://api.zpi.web.id/v1/finance:macromicro/series-search` - **Cache TTL:** 21600s **Parameters:** | Name | Type | Location | Required | Description | |------|------|----------|----------|-------------| | `query` | string | query | no | Free-text match against the indicator name. | | `country` | string | query | no | Two-letter country code. | | `category` | string | query | no | Category id. | | `categoryParent` | string | query | no | Parent category id. | | `slug` | string | query | no | Exact indicator slug. | | `page` | number | query | no | Result page (default 1) | | `count` | number | query | no | Rows per page (default 100, max 500) | **cURL:** ```bash curl "https://api.zpi.web.id/v1/finance:macromicro/series-search?query=inflation&country=us&category=21&categoryParent=100003&slug=realgdp&page=1&count=100" \ -H "x-api-key: YOUR_API_KEY" ``` **JavaScript / TypeScript:** ```javascript const res = await fetch("https://api.zpi.web.id/v1/finance:macromicro/series-search?query=inflation&country=us&category=21&categoryParent=100003&slug=realgdp&page=1&count=100", { headers: { "x-api-key": process.env.ZAPI_KEY } }); const data = await res.json(); ``` **Python:** ```python import requests r = requests.get("https://api.zpi.web.id/v1/finance:macromicro/series-search?query=inflation&country=us&category=21&categoryParent=100003&slug=realgdp&page=1&count=100", headers={"x-api-key": "YOUR_API_KEY"}) data = r.json() ``` **Example response:** ```json { "page": 1, "count": 8, "items": [ { "name": "US - Gold - Commercial Net Position", "slug": "cot-gold-commercial", "statId": 3070, "country": "us", "frequency": "W", "unitsName": "Number", "countryName": "United States", "frequencyName": "Weekly" }, { "name": "US - Gold - Non Commercial Net Position", "slug": "cot-gold-noncommercial", "statId": 3071, "country": "us", "frequency": "W", "unitsName": "Number", "countryName": "United States", "frequencyName": "Weekly" }, { "name": "Gold COT Index", "slug": "gold-cot-index", "units": "idx", "statId": 3351, "country": "us", "frequency": "W", "unitsName": "Index", "countryName": "United States", "frequencyName": "Weekly" }, { "name": "CBOE - Gold ETF Volatility Index [GVZ]", "slug": "gvz", "units": "idx", "statId": 7147, "country": "us", "frequency": "D", "unitsName": "Index", "countryName": "United States", "frequencyName": "Daily" }, { "name": "CBOE - Equity VIX on Goldman Sachs [VXGS]", "slug": "vxgs", "units": "idx", "statId": 7169, "country": "us", "frequency": "D", "unitsName": "Index", "countryName": "United States", "frequencyName": "Daily" }, { "name": "CFTC - Gold Futures & Options - Managed Money Net Position", "slug": "gold-futures-and-options-manage-money-net-position", "statId": 8308, "country": "us", "frequency": "W", "unitsName": "Number", "countryName": "United States", "frequencyName": "Weekly" }, { "name": "CFTC - Gold Futures & Options - Managed Money Long Position", "slug": "gold-futures-and-options-manage-money-long-position", "statId": 8309, "country": "us", "frequency": "W", "unitsName": "Number", "countryName": "United States", "frequencyName": "Weekly" }, { "name": "CFTC - Gold Futures & Options - Managed Money Short Position", "slug": "gold-futures-and-options-manage-money-short-position", "statId": 8310, "country": "us", "frequency": "W", "unitsName": "Number", "countryName": "United States", "frequencyName": "Weekly" } ], "query": "gold", "total": 225, "country": "us", "dataset": "series-search", "hasMore": true, "category": null, "nextPage": 2, "provider": "macromicro" } ``` --- ### Collection One curated collection and every chart in it, in the site's own order. - **Method:** `GET` - **Endpoint:** `https://api.zpi.web.id/v1/finance:macromicro/collection/:id` - **Cache TTL:** 21600s **Parameters:** | Name | Type | Location | Required | Description | |------|------|----------|----------|-------------| | `id` | string | path | yes | Collection id. Take it from a collection URL. | | `slug` | string | query | no | Collection slug. Optional — the id decides which collection you get. | **cURL:** ```bash curl "https://api.zpi.web.id/v1/finance:macromicro/collection/:id?slug=high-frequency-data" \ -H "x-api-key: YOUR_API_KEY" ``` **JavaScript / TypeScript:** ```javascript const res = await fetch("https://api.zpi.web.id/v1/finance:macromicro/collection/:id?slug=high-frequency-data", { headers: { "x-api-key": process.env.ZAPI_KEY } }); const data = await res.json(); ``` **Python:** ```python import requests r = requests.get("https://api.zpi.web.id/v1/finance:macromicro/collection/:id?slug=high-frequency-data", headers={"x-api-key": "YOUR_API_KEY"}) data = r.json() ``` **Example response:** ```json { "url": "https://en.macromicro.me/collections/3208/high-frequency-data", "name": "High Frequency Data", "slug": "high-frequency-data", "count": 12, "items": [ { "url": "https://en.macromicro.me/charts/22281/flight-tracking", "slug": "flight-tracking", "type": 1, "state": "active", "nameEn": "World - Global Flight Tracking", "chartId": 22281, "updatedAt": "2025-05-19T17:20:18.000Z", "viewCount": 0, "commentCount": 3, "bookmarkCount": 216, "descriptionEn": "The number of daily flights is calculated by Flightradar counting the number of flights on the website.\n\nCommercial flights: Commercial passenger flights + cargo flights + charter flights + some business jet flights.\nTotal flights: Commercial flights above + rest of business jet flights + private flights + gliders + most helicopter flights + most ambulance flights + government flights + some military flights + drones", "isMembersOnly": false }, { "url": "https://en.macromicro.me/charts/26114/us-tsa-checkpoint", "slug": "us-tsa-checkpoint", "type": 1, "state": "active", "nameEn": "US - TSA Checkpoint Travel Numbers", "chartId": 26114, "country": "us", "updatedAt": "2023-08-30T04:29:21.000Z", "viewCount": 0, "commentCount": 0, "bookmarkCount": 42, "descriptionEn": "This chart shows the headcount of travelers everyday at the US borders and the changes in the number of travelers since the pandemic hit.", "isMembersOnly": false }, { "url": "https://en.macromicro.me/charts/947/commodity-ccfi-scfi", "slug": "commodity-ccfi-scfi", "type": 1, "state": "active", "nameEn": "China - Global & Shanghai Export Containerized Freight Index", "chartId": 947, "country": "cn", "updatedAt": "2026-04-04T09:10:17.000Z", "viewCount": 0, "commentCount": 11, "bookmarkCount": 1238, "descriptionEn": "First released in 1998 by the Shanghai Shipping Exchange, the China Export Containerized Freight Index (CCFI) is based on the freight rate and shipped volume of 12 selected trade routes around the world, reflecting changes in freight rates. Updated weekly, the index provides a timely reflection of trends in the costs of outbound container shipping from China.\n\nDeparting ports in China include the ports of Dalian, Tianjin, Qingdao, Shanghai, Nanjing, Ningbo, Xiamen, Fuzhou, Shenzhen and Guangzhou. \n\nAs Shanghai is China's maritime hub and an important port of departure for exports, the Shanghai Export Containerized Freight Index (SCFI) is also a key indicator to watch. The index is also updated weekly.\n\nSince China is the world's largest exporter, these freight rate indices can reflect current global trade conditions.\n\nNote: The data is updated weekly and reflects index readings as of the previous Friday.", "isMembersOnly": false }, { "url": "https://en.macromicro.me/charts/26279/us-epu", "slug": "us-epu", "type": 1, "state": "active", "nameEn": "US - Economic Policy Uncertainty Index", "chartId": 26279, "country": "us", "updatedAt": "2026-08-11T19:43:46.000Z", "viewCount": 0, "commentCount": 1, "bookmarkCount": 60, "descriptionEn": "The primary measure for this index is the number of articles that contain at least one term from each of 3 sets of terms. The first set is economic or economy. The second is uncertain or uncertainty. The third set is legislation or deficit or regulation or congress or federal reserve or white house.", "isMembersOnly": true }, { "url": "https://en.macromicro.me/charts/135277/chinaeconomic-policy-uncertainty-index", "slug": "chinaeconomic-policy-uncertainty-index", "type": 1, "state": "active", "nameEn": "China-Economic Policy Uncertainty Index", "chartId": 135277, "country": "cn", "updatedAt": "2025-08-29T13:50:31.000Z", "viewCount": 0, "commentCount": 0, "bookmarkCount": 5, "isMembersOnly": false }, { "url": "https://en.macromicro.me/charts/45866/global-citi-surprise-index", "slug": "global-citi-surprise-index", "type": 1, "state": "active", "nameEn": "World - Citi Surprise Index", "chartId": 45866, "updatedAt": "2026-08-12T18:25:22.000Z", "viewCount": 0, "commentCount": 12, "bookmarkCount": 209, "descriptionEn": "Citigroup Economic Surprise Index represents the sum of the difference between official economic results and forecasts. With a sum over 0, its economic performance generally beats market expectations. With a sum below 0, its economic conditions are generally worse than expected.\n\nSurprise index readings climb up as economy recovers but declines fast as economy declines.", "isMembersOnly": true }, { "url": "https://en.macromicro.me/charts/55744/global-citi-earnings-revision-index", "slug": "global-citi-earnings-revision-index", "type": 1, "state": "active", "nameEn": "World - Citi Earnings Revision Index", "chartId": 55744, "updatedAt": "2026-03-24T04:01:14.000Z", "viewCount": 0, "commentCount": 2, "bookmarkCount": 123, "descriptionEn": "The Citi Earnings Revision Index is calculated as “Proportion of Companies Revising EPS (%)” − “Proportion of Companies Revising EPS Down (%)” (compared to last week). When the index is above the zero axis, it means that analysts on average are optimistic about the outlook for corporate earnings, and vice versa, it means that analysts are relatively pessimistic.", "isMembersOnly": true }, { "url": "https://en.macromicro.me/charts/55746/us-eu-jp-citi-earnings-revision-index", "slug": "us-eu-jp-citi-earnings-revision-index", "type": 1, "state": "active", "nameEn": "US, Europe, Japan - Citi Earnings Revision Index", "chartId": 55746, "updatedAt": "2026-01-02T12:53:44.000Z", "viewCount": 0, "commentCount": 0, "bookmarkCount": 51, "descriptionEn": "The Citi Earnings Revision Index is calculated as “Proportion of Companies with EPS Upgrades (%)” − “Proportion of Companies with EPS Downgrades (%)” (compared to last week). When the index is above the zero axis, it means that analysts on average are optimistic about the outlook for corporate earnings, and vice versa, it means that analysts are relatively pessimistic.", "isMembersOnly": true } ], "total": 12, "country": null, "dataset": "collection", "chartIds": [ 22281, 26114, 947, 26279, 135277, 45866, 55744, 55746 ], "isPublic": true, "language": "tc", "provider": "macromicro", "createdAt": "2020-10-30T09:33:13.000Z", "updatedAt": "2025-07-06T07:41:58.000Z", "coverStatId": 7250, "description": "High frequency data", "collectionId": 3208, "isSearchable": true, "isMembersOnly": false } ``` --- ### Market Live quotes for one asset class with six percentage-change windows and 380 days of raw values. - **Method:** `GET` - **Endpoint:** `https://api.zpi.web.id/v1/finance:macromicro/market/:category` - **Cache TTL:** 300s **Parameters:** | Name | Type | Location | Required | Description | |------|------|----------|----------|-------------| | `category` | enum(stocks|forex|bonds|etf|crypto|volatility|cds|commodities) | path | yes | Asset class: stocks, forex, bonds, etf, crypto, volatility, cds, commodities. | | `history` | enum(true|false) | query | no | Include the 380-day raw value series on every quote. Default false. | **cURL:** ```bash curl "https://api.zpi.web.id/v1/finance:macromicro/market/:category?history=false" \ -H "x-api-key: YOUR_API_KEY" ``` **JavaScript / TypeScript:** ```javascript const res = await fetch("https://api.zpi.web.id/v1/finance:macromicro/market/:category?history=false", { headers: { "x-api-key": process.env.ZAPI_KEY } }); const data = await res.json(); ``` **Python:** ```python import requests r = requests.get("https://api.zpi.web.id/v1/finance:macromicro/market/:category?history=false", headers={"x-api-key": "YOUR_API_KEY"}) data = r.json() ``` **Example response:** ```json { "count": 54, "items": [ { "name": "Crude Oil (WTI)", "group": 1, "statId": 486, "altName": "Crude Oil (WTI)", "chartId": 888, "history": [ { "date": "2025-08-14", "value": 63.96 }, { "date": "2025-08-15", "value": 62.8 }, { "date": "2025-08-16", "value": 62.8 }, { "date": "2025-08-17", "value": 61.74 }, { "date": "2025-08-18", "value": 63.42 }, { "date": "2025-08-19", "value": 62.35 }, { "date": "2025-08-20", "value": 63.21 }, { "date": "2025-08-21", "value": 63.52 } ], "chartUrl": "https://en.macromicro.me/charts/888/commodity-wti", "position": 3, "statName": "NYMEX - WTI Crude Oil Futures Price", "chartSlug": "commodity-wti", "chartType": 1, "viewCount": 0, "chartState": "active", "pctChanges": { "1D": [ { "date": "2026-08-28", "value": 83.4, "changePercent": -0.16 }, { "date": "2026-08-27", "value": 83.53 }, { "date": "2026-08-26", "value": 82.23 }, { "date": "2026-08-25", "value": 82.36 }, { "date": "2026-08-24", "value": 85.01 }, { "date": "2026-08-23", "value": 86.02 } ], "1M": [ { "date": "2026-08-28", "value": 83.4, "changePercent": -1.26 }, { "date": "2026-07-29", "value": 84.46 }, { "date": "2026-06-29", "value": 70.75 }, { "date": "2026-05-29", "value": 87.36 }, { "date": "2026-04-29", "value": 106.88 }, { "date": "2026-03-29", "value": 102.8 } ], "1W": [ { "date": "2026-08-28", "value": 83.4, "changePercent": -4.2 }, { "date": "2026-08-21", "value": 87.06 }, { "date": "2026-08-14", "value": 82.4 }, { "date": "2026-08-07", "value": 78.18 }, { "date": "2026-07-31", "value": 84.67 }, { "date": "2026-07-24", "value": 85.15 } ], "1Y": [ { "date": "2026-08-28", "value": 83.4, "changePercent": 30.29 }, { "date": "2025-08-29", "value": 64.01 }, { "date": "2024-08-29", "value": 75.91 }, { "date": "2023-08-29", "value": 81.16 }, { "date": "2022-08-29", "value": 97.01 }, { "date": "2021-08-29", "value": 69.33 } ], "6M": [ { "date": "2026-08-28", "value": 83.4, "changePercent": 15.85 }, { "date": "2026-03-01", "value": 71.99 }, { "date": "2025-09-01", "value": 64.64 }, { "date": "2025-02-28", "value": 69.76 }, { "date": "2024-09-01", "value": 72.98 }, { "date": "2024-03-01", "value": 79.97 } ], "YTD": [ { "date": "2026-08-28", "value": 83.4, "changePercent": 45.07 }, { "date": "2026-01-01", "value": 57.49 } ] }, "chartNameEn": "WTI Crude Oil", "chartCountry": "us", "commentCount": 1, "statCategory": 22, "bookmarkCount": 168, "isMembersOnly": false, "chartUpdatedAt": "2026-06-08T06:43:17.000Z", "imageUpdatedAt": "2026-08-28T22:59:38.000Z", "chartDescription": "Market: New York Mercantile Exchange (NYMEX)\nContract Size: 1000 barrels\nPrice Quotation: USD/barrel" }, { "name": "Crude Oil (Brent)", "group": 1, "statId": 389, "altName": "Crude Oil (Brent)", "chartId": 889, "history": [ { "date": "2025-08-14", "value": 66.84 }, { "date": "2025-08-15", "value": 65.85 }, { "date": "2025-08-16", "value": 65.85 }, { "date": "2025-08-17", "value": 65.59 }, { "date": "2025-08-18", "value": 66.01 }, { "date": "2025-08-19", "value": 65.3 }, { "date": "2025-08-20", "value": 66.31 }, { "date": "2025-08-21", "value": 67.12 } ], "chartUrl": "https://en.macromicro.me/charts/889/commodity-brent", "position": 3, "statName": "NYMEX - Crude Oil (Brent)", "chartSlug": "commodity-brent", "chartType": 1, "viewCount": 0, "chartState": "active", "pctChanges": { "1D": [ { "date": "2026-08-28", "value": 88.1, "changePercent": -0.47 }, { "date": "2026-08-27", "value": 88.52 }, { "date": "2026-08-26", "value": 86.94 }, { "date": "2026-08-25", "value": 87.27 }, { "date": "2026-08-24", "value": 90.54 }, { "date": "2026-08-23", "value": 91.59 } ], "1M": [ { "date": "2026-08-28", "value": 88.1, "changePercent": -2.91 }, { "date": "2026-07-29", "value": 90.74 }, { "date": "2026-06-29", "value": 73.15 }, { "date": "2026-05-29", "value": 92.05 }, { "date": "2026-04-29", "value": 118.03 }, { "date": "2026-03-29", "value": 108.48 } ], "1W": [ { "date": "2026-08-28", "value": 88.1, "changePercent": -4.93 }, { "date": "2026-08-21", "value": 92.67 }, { "date": "2026-08-14", "value": 88.52 }, { "date": "2026-08-07", "value": 83.55 }, { "date": "2026-07-31", "value": 90.12 }, { "date": "2026-07-24", "value": 91.68 } ], "1Y": [ { "date": "2026-08-28", "value": 88.1, "changePercent": 29.33 }, { "date": "2025-08-29", "value": 68.12 }, { "date": "2024-08-29", "value": 79.94 }, { "date": "2023-08-29", "value": 85.49 }, { "date": "2022-08-29", "value": 102.93 }, { "date": "2021-08-29", "value": 72.57 } ], "6M": [ { "date": "2026-08-28", "value": 88.1, "changePercent": 12.43 }, { "date": "2026-03-01", "value": 78.36 }, { "date": "2025-09-01", "value": 68.15 }, { "date": "2025-02-28", "value": 73.18 }, { "date": "2024-09-01", "value": 76.32 }, { "date": "2024-03-01", "value": 83.55 } ], "YTD": [ { "date": "2026-08-28", "value": 88.1, "changePercent": 44.78 }, { "date": "2025-12-31", "value": 60.85 } ] }, "chartNameEn": "Brent Crude Oil", "chartCountry": "gb", "commentCount": 0, "statCategory": 22, "bookmarkCount": 201, "isMembersOnly": false, "chartUpdatedAt": "2026-07-07T03:14:24.000Z", "imageUpdatedAt": "2026-08-28T22:59:31.000Z", "chartDescription": "Market: New York Mercantile Exchange (NYMEX)\nContract Size: 1000 Barrels\nPrice Quotation: USD/barrel" }, { "name": "Natural Gas", "group": 1, "statId": 1607, "altName": "Natural Gas", "chartId": 890, "history": [ { "date": "2025-08-14", "value": 2.841 }, { "date": "2025-08-15", "value": 2.916 }, { "date": "2025-08-16", "value": 2.916 }, { "date": "2025-08-17", "value": 2.872 }, { "date": "2025-08-18", "value": 2.992 }, { "date": "2025-08-19", "value": 2.862 }, { "date": "2025-08-20", "value": 2.842 }, { "date": "2025-08-21", "value": 2.826 } ], "chartUrl": "https://en.macromicro.me/charts/890/commodity-natural-gas", "position": 3, "statName": "NYMEX - Henry Hub Natural Gas Futures Price", "chartSlug": "commodity-natural-gas", "chartType": 1, "viewCount": 0, "chartState": "active", "pctChanges": { "1D": [ { "date": "2026-08-28", "value": 2.888, "changePercent": -0.89 }, { "date": "2026-08-27", "value": 2.914 }, { "date": "2026-08-26", "value": 2.874 }, { "date": "2026-08-25", "value": 2.821 }, { "date": "2026-08-24", "value": 2.835 }, { "date": "2026-08-23", "value": 2.774 } ], "1M": [ { "date": "2026-08-28", "value": 2.888, "changePercent": 6.1 }, { "date": "2026-07-29", "value": 2.722 }, { "date": "2026-06-29", "value": 3.181 }, { "date": "2026-05-29", "value": 3.29 }, { "date": "2026-04-29", "value": 2.647 }, { "date": "2026-03-29", "value": 2.956 } ], "1W": [ { "date": "2026-08-28", "value": 2.888, "changePercent": 2.74 }, { "date": "2026-08-21", "value": 2.811 }, { "date": "2026-08-14", "value": 2.733 }, { "date": "2026-08-07", "value": 2.662 }, { "date": "2026-07-31", "value": 2.747 }, { "date": "2026-07-24", "value": 2.888 } ], "1Y": [ { "date": "2026-08-28", "value": 2.888, "changePercent": -3.64 }, { "date": "2025-08-29", "value": 2.997 }, { "date": "2024-08-29", "value": 2.529 }, { "date": "2023-08-29", "value": 2.556 }, { "date": "2022-08-29", "value": 9.353 }, { "date": "2021-08-29", "value": 4.471 } ], "6M": [ { "date": "2026-08-28", "value": 2.888, "changePercent": 0.38 }, { "date": "2026-03-01", "value": 2.877 }, { "date": "2025-09-01", "value": 2.982 }, { "date": "2025-02-28", "value": 3.834 }, { "date": "2024-09-01", "value": 2.181 }, { "date": "2024-03-01", "value": 1.835 } ], "YTD": [ { "date": "2026-08-28", "value": 2.888, "changePercent": -20.72 }, { "date": "2026-01-01", "value": 3.643 } ] }, "chartNameEn": "Natural Gas", "chartCountry": "us", "commentCount": 0, "statCategory": 22, "bookmarkCount": 43, "isMembersOnly": false, "chartUpdatedAt": "2025-03-02T12:43:10.000Z", "imageUpdatedAt": "2026-08-28T22:59:31.000Z", "chartDescription": "Market: New York Mercantile Exchange (NYMEX)\nContract Size: 10000 MMBtu\nPrice Quotation: USD/MMBtu" }, { "name": "Heating Oil", "group": 1, "statId": 1608, "altName": "Heating Oil", "chartId": 891, "history": [ { "date": "2025-08-14", "value": 2.235 }, { "date": "2025-08-15", "value": 2.225 }, { "date": "2025-08-16", "value": 2.225 }, { "date": "2025-08-17", "value": 2.216 }, { "date": "2025-08-18", "value": 2.237 }, { "date": "2025-08-19", "value": 2.244 }, { "date": "2025-08-20", "value": 2.273 }, { "date": "2025-08-21", "value": 2.32 } ], "chartUrl": "https://en.macromicro.me/charts/891/commodity-heating-oil", "position": 3, "statName": "NYMEX - Heating Oil Futures Price", "chartSlug": "commodity-heating-oil", "chartType": 1, "viewCount": 0, "chartState": "active", "pctChanges": { "1D": [ { "date": "2026-08-28", "value": 4.3432, "changePercent": 1.51 }, { "date": "2026-08-27", "value": 4.2787 }, { "date": "2026-08-26", "value": 4.26 }, { "date": "2026-08-25", "value": 4.2438 }, { "date": "2026-08-24", "value": 4.2677 }, { "date": "2026-08-23", "value": 4.436 } ], "1M": [ { "date": "2026-08-28", "value": 4.3432, "changePercent": 2.54 }, { "date": "2026-07-29", "value": 4.2357 }, { "date": "2026-06-29", "value": 3.3317 }, { "date": "2026-05-29", "value": 3.4886 }, { "date": "2026-04-29", "value": 4.1987 }, { "date": "2026-03-29", "value": 4.3593 } ], "1W": [ { "date": "2026-08-28", "value": 4.3432, "changePercent": -3.37 }, { "date": "2026-08-21", "value": 4.4948 }, { "date": "2026-08-14", "value": 4.2829 }, { "date": "2026-08-07", "value": 3.8231 }, { "date": "2026-07-31", "value": 4.0955 }, { "date": "2026-07-24", "value": 4.0954 } ], "1Y": [ { "date": "2026-08-28", "value": 4.3432, "changePercent": 91.61 }, { "date": "2025-08-29", "value": 2.2667 }, { "date": "2024-08-29", "value": 2.284 }, { "date": "2023-08-29", "value": 3.2095 }, { "date": "2022-08-29", "value": 3.8516 }, { "date": "2021-08-27", "value": 2.1092 } ], "6M": [ { "date": "2026-08-28", "value": 4.3432, "changePercent": 52.78 }, { "date": "2026-03-01", "value": 2.8427 }, { "date": "2025-09-01", "value": 2.3102 }, { "date": "2025-02-28", "value": 2.3549 }, { "date": "2024-09-01", "value": 2.2694 }, { "date": "2024-03-01", "value": 2.7042 } ], "YTD": [ { "date": "2026-08-28", "value": 4.3432, "changePercent": 104.76 }, { "date": "2026-01-01", "value": 2.1211 } ] }, "chartNameEn": "Heating Oil", "chartCountry": "us", "commentCount": 0, "statCategory": 22, "bookmarkCount": 5, "isMembersOnly": false, "chartUpdatedAt": "2024-09-19T07:41:17.000Z", "imageUpdatedAt": "2026-08-28T22:59:24.000Z", "chartDescription": "Market: New York Mercantile Exchange (NYMEX)\nContract Size: 42000 Gallons\nPrice Quotation: USD/Gallon\n\nHeating oil is the second refined product produced from crude oil; the first is gasoline. Heating oil if the fuel for heating up homes in the winter, so the weather is the biggest factor that influences the price of heating oil. Heating oil is often a hedge for diesel and engine oil suppliers." }, { "name": "Gasoline", "group": 1, "statId": 1609, "altName": "Gasoline", "chartId": 892, "history": [ { "date": "2025-08-14", "value": 2.109 }, { "date": "2025-08-15", "value": 2.073 }, { "date": "2025-08-16", "value": 2.073 }, { "date": "2025-08-17", "value": 2.069 }, { "date": "2025-08-18", "value": 1.937 }, { "date": "2025-08-19", "value": 1.923 }, { "date": "2025-08-20", "value": 1.959 }, { "date": "2025-08-21", "value": 1.987 } ], "chartUrl": "https://en.macromicro.me/charts/892/commodity-gasoline", "position": 3, "statName": "NYMEX - Gasoline RBOB Futures Price", "chartSlug": "commodity-gasoline", "chartType": 1, "viewCount": 0, "chartState": "active", "pctChanges": { "1D": [ { "date": "2026-08-28", "value": 3.4899, "changePercent": 3.12 }, { "date": "2026-08-27", "value": 3.3842 }, { "date": "2026-08-26", "value": 3.3201 }, { "date": "2026-08-25", "value": 3.2529 }, { "date": "2026-08-24", "value": 3.2708 }, { "date": "2026-08-23", "value": 3.3142 } ], "1M": [ { "date": "2026-08-28", "value": 3.4899, "changePercent": 8.19 }, { "date": "2026-07-29", "value": 3.2258 }, { "date": "2026-06-29", "value": 3.0614 }, { "date": "2026-05-29", "value": 3.0344 }, { "date": "2026-04-29", "value": 3.7411 }, { "date": "2026-03-29", "value": 3.2677 } ], "1W": [ { "date": "2026-08-28", "value": 3.4899, "changePercent": 4.24 }, { "date": "2026-08-21", "value": 3.3479 }, { "date": "2026-08-14", "value": 3.1841 }, { "date": "2026-08-07", "value": 2.9853 }, { "date": "2026-07-31", "value": 3.1142 }, { "date": "2026-07-24", "value": 3.2516 } ], "1Y": [ { "date": "2026-08-28", "value": 3.4899, "changePercent": 77.25 }, { "date": "2025-08-29", "value": 1.9689 }, { "date": "2024-08-29", "value": 2.2477 }, { "date": "2023-08-29", "value": 2.5565 }, { "date": "2022-08-29", "value": 2.8776 }, { "date": "2021-08-29", "value": 2.169 } ], "6M": [ { "date": "2026-08-28", "value": 3.4899, "changePercent": 45.87 }, { "date": "2026-03-01", "value": 2.3925 }, { "date": "2025-09-01", "value": 1.9884 }, { "date": "2025-02-28", "value": 2.2223 }, { "date": "2024-09-01", "value": 2.073 }, { "date": "2024-03-01", "value": 2.6144 } ], "YTD": [ { "date": "2026-08-28", "value": 3.4899, "changePercent": 104.09 }, { "date": "2026-01-01", "value": 1.71 } ] }, "chartNameEn": "Gasoline", "chartCountry": "us", "commentCount": 0, "statCategory": 22, "bookmarkCount": 17, "isMembersOnly": false, "chartUpdatedAt": "2024-09-19T07:41:30.000Z", "imageUpdatedAt": "2026-08-28T22:59:23.000Z", "chartDescription": "Market: New York Mercantile Exchange (NYMEX)\nContract Size: 42,000 Gallons\nPrice Quotation: USD/gallon\n\nGasoline is used in transportation; demand for gasoline surges during high seasons for traveling. The higher the octane rating number is, the better quality the gas is." }, { "name": "Corn", "group": 2, "statId": 3994, "altName": "Corn", "chartId": 1294, "history": [ { "date": "2025-08-14", "value": 2202 }, { "date": "2025-08-15", "value": 2190 }, { "date": "2025-08-16", "value": 2190 }, { "date": "2025-08-17", "value": 2190 }, { "date": "2025-08-18", "value": 2177 }, { "date": "2025-08-19", "value": 2170 }, { "date": "2025-08-20", "value": 2170 }, { "date": "2025-08-21", "value": 2166 } ], "chartUrl": "https://en.macromicro.me/charts/1294/cn-corn-futures", "position": 3, "statName": "China - Corn Futures", "chartSlug": "cn-corn-futures", "chartType": 1, "viewCount": 0, "chartState": "active", "pctChanges": { "1D": [ { "date": "2026-08-28", "value": 2285, "changePercent": -0.09 }, { "date": "2026-08-27", "value": 2287 }, { "date": "2026-08-26", "value": 2281 }, { "date": "2026-08-25", "value": 2276 }, { "date": "2026-08-24", "value": 2280 }, { "date": "2026-08-21", "value": 2264 } ], "1M": [ { "date": "2026-08-28", "value": 2285, "changePercent": 0.79 }, { "date": "2026-07-29", "value": 2267 }, { "date": "2026-06-29", "value": 2337 }, { "date": "2026-05-29", "value": 2322 }, { "date": "2026-04-29", "value": 2431 }, { "date": "2026-03-27", "value": 2369 } ], "1W": [ { "date": "2026-08-28", "value": 2285, "changePercent": 0.93 }, { "date": "2026-08-21", "value": 2264 }, { "date": "2026-08-14", "value": 2226 }, { "date": "2026-08-07", "value": 2245 }, { "date": "2026-07-31", "value": 2269 }, { "date": "2026-07-24", "value": 2277 } ], "1Y": [ { "date": "2026-08-28", "value": 2285, "changePercent": 4.29 }, { "date": "2025-08-29", "value": 2191 }, { "date": "2024-08-29", "value": 2344 }, { "date": "2023-08-29", "value": 2728 }, { "date": "2022-08-29", "value": 2806 }, { "date": "2021-08-27", "value": 2478 } ], "6M": [ { "date": "2026-08-28", "value": 2285, "changePercent": -3.18 }, { "date": "2026-02-27", "value": 2360 }, { "date": "2025-09-01", "value": 2193 }, { "date": "2025-02-28", "value": 2292 }, { "date": "2024-08-30", "value": 2338 }, { "date": "2024-03-01", "value": 2462 } ], "YTD": [ { "date": "2026-08-28", "value": 2285, "changePercent": 2.65 }, { "date": "2025-12-31", "value": 2226 } ] }, "chartNameEn": "China Corn Futures", "relatedLink": "https://en.macromicro.me/collections/53/agri-corn", "chartCountry": "cn", "commentCount": 0, "statCategory": 28, "bookmarkCount": 7, "isMembersOnly": false, "chartUpdatedAt": "2023-03-23T08:58:05.000Z", "imageUpdatedAt": "2026-08-28T23:06:45.000Z", "chartDescription": "Market: Dalian Commodity Exchange\nContract Size: 10 Tons/hand\nPrice Quotation: CNT/ton" }, { "name": "Soybean Meal", "group": 2, "statId": 3986, "altName": "Soybean Meal", "chartId": 1295, "history": [ { "date": "2025-08-14", "value": 3157 }, { "date": "2025-08-15", "value": 3137 }, { "date": "2025-08-16", "value": 3137 }, { "date": "2025-08-17", "value": 3137 }, { "date": "2025-08-18", "value": 3155 }, { "date": "2025-08-19", "value": 3161 }, { "date": "2025-08-20", "value": 3160 }, { "date": "2025-08-21", "value": 3113 } ], "chartUrl": "https://en.macromicro.me/charts/1295/cn-soybean-meal", "position": 3, "statName": "China - Soybean Meal Futures [M]", "chartSlug": "cn-soybean-meal", "chartType": 1, "viewCount": 0, "chartState": "active", "pctChanges": { "1D": [ { "date": "2026-08-28", "value": 3359, "changePercent": 0.12 }, { "date": "2026-08-27", "value": 3355 }, { "date": "2026-08-26", "value": 3297 }, { "date": "2026-08-25", "value": 3245 }, { "date": "2026-08-24", "value": 3232 }, { "date": "2026-08-21", "value": 3228 } ], "1M": [ { "date": "2026-08-28", "value": 3359, "changePercent": 8.04 }, { "date": "2026-07-29", "value": 3109 }, { "date": "2026-06-29", "value": 2967 }, { "date": "2026-05-29", "value": 2991 }, { "date": "2026-04-29", "value": 3020 }, { "date": "2026-03-27", "value": 2937 } ], "1W": [ { "date": "2026-08-28", "value": 3359, "changePercent": 4.06 }, { "date": "2026-08-21", "value": 3228 }, { "date": "2026-08-14", "value": 3165 }, { "date": "2026-08-07", "value": 3154 }, { "date": "2026-07-31", "value": 3071 }, { "date": "2026-07-24", "value": 3225 } ], "1Y": [ { "date": "2026-08-28", "value": 3359, "changePercent": 9.95 }, { "date": "2025-08-29", "value": 3055 }, { "date": "2024-08-29", "value": 2998 }, { "date": "2023-08-29", "value": 4144 }, { "date": "2022-08-29", "value": 3789 }, { "date": "2021-08-27", "value": 3501 } ], "6M": [ { "date": "2026-08-28", "value": 3359, "changePercent": 18.57 }, { "date": "2026-02-27", "value": 2833 }, { "date": "2025-09-01", "value": 3054 }, { "date": "2025-02-28", "value": 2932 }, { "date": "2024-08-30", "value": 3027 }, { "date": "2024-03-01", "value": 3086 } ], "YTD": [ { "date": "2026-08-28", "value": 3359, "changePercent": 22.19 }, { "date": "2025-12-31", "value": 2749 } ] }, "chartNameEn": "China Soybean Meal Futures", "chartCountry": "cn", "commentCount": 0, "statCategory": 22, "bookmarkCount": 2, "isMembersOnly": false, "chartUpdatedAt": "2023-03-23T08:56:22.000Z", "imageUpdatedAt": "2026-08-29T05:49:31.000Z", "chartDescription": "Market: Dalian Commodity Exchange\nContract Size: 10 Tons/hand\nPrice Quotation: CNT/ton" }, { "name": "Rapeseed Meal", "group": 2, "statId": 4002, "altName": "Rapeseed Meal", "chartId": 1296, "history": [ { "date": "2025-08-14", "value": 2606 }, { "date": "2025-08-15", "value": 2546 }, { "date": "2025-08-16", "value": 2546 }, { "date": "2025-08-17", "value": 2546 }, { "date": "2025-08-18", "value": 2590 }, { "date": "2025-08-19", "value": 2604 }, { "date": "2025-08-20", "value": 2627 }, { "date": "2025-08-21", "value": 2561 } ], "chartUrl": "https://en.macromicro.me/charts/1296/cn-rapeseed-meal-futures", "position": 3, "statName": "China - Rapeseed Meal Futures [RM]", "chartSlug": "cn-rapeseed-meal-futures", "chartType": 1, "viewCount": 0, "chartState": "active", "pctChanges": { "1D": [ { "date": "2026-08-28", "value": 2346, "changePercent": -0.09 }, { "date": "2026-08-27", "value": 2348 }, { "date": "2026-08-26", "value": 2314 }, { "date": "2026-08-25", "value": 2243 }, { "date": "2026-08-24", "value": 2235 }, { "date": "2026-08-21", "value": 2238 } ], "1M": [ { "date": "2026-08-28", "value": 2346, "changePercent": 0.39 }, { "date": "2026-07-29", "value": 2337 }, { "date": "2026-06-29", "value": 2296 }, { "date": "2026-05-29", "value": 2301 }, { "date": "2026-04-29", "value": 2417 }, { "date": "2026-03-27", "value": 2315 } ], "1W": [ { "date": "2026-08-28", "value": 2346, "changePercent": 4.83 }, { "date": "2026-08-21", "value": 2238 }, { "date": "2026-08-14", "value": 2191 }, { "date": "2026-08-07", "value": 2173 }, { "date": "2026-07-31", "value": 2316 }, { "date": "2026-07-24", "value": 2467 } ], "1Y": [ { "date": "2026-08-28", "value": 2346, "changePercent": -6.65 }, { "date": "2025-08-29", "value": 2513 }, { "date": "2024-08-29", "value": 2240 }, { "date": "2023-08-29", "value": 3382 }, { "date": "2022-08-29", "value": 2894 }, { "date": "2021-08-27", "value": 2883 } ], "6M": [ { "date": "2026-08-28", "value": 2346, "changePercent": 2.58 }, { "date": "2026-02-27", "value": 2287 }, { "date": "2025-09-01", "value": 2513 }, { "date": "2025-02-28", "value": 2566 }, { "date": "2024-08-30", "value": 2248 }, { "date": "2024-03-01", "value": 2495 } ], "YTD": [ { "date": "2026-08-28", "value": 2346, "changePercent": -0.8 }, { "date": "2025-12-31", "value": 2365 } ] }, "chartNameEn": "China Rapeseed Meal Futures", "chartCountry": "cn", "commentCount": 0, "statCategory": 22, "bookmarkCount": 1, "isMembersOnly": false, "chartUpdatedAt": "2021-07-14T04:38:56.000Z", "imageUpdatedAt": "2026-08-29T05:49:30.000Z", "chartDescription": "Market: Zhengzhou Commodity Exchange\nContract Size: 10 Tons/hand\nPrice Quotation: CNY/ton" } ], "total": 54, "groups": 6, "dataset": "market", "category": "commodities", "provider": "macromicro", "updatedAt": "2022-11-29T10:13:11.000Z" } ``` --- ### Calendar Macro Scheduled macro data releases for the window around a date. - **Method:** `GET` - **Endpoint:** `https://api.zpi.web.id/v1/finance:macromicro/calendar-macro` - **Cache TTL:** 1800s **Parameters:** | Name | Type | Location | Required | Description | |------|------|----------|----------|-------------| | `date` | string | query | no | Any date inside the month you want, YYYY-MM-DD. Defaults to today. | | `country` | string | query | no | Keep only releases for this two-letter country code. | **cURL:** ```bash curl "https://api.zpi.web.id/v1/finance:macromicro/calendar-macro?date=2026-08-29&country=us" \ -H "x-api-key: YOUR_API_KEY" ``` **JavaScript / TypeScript:** ```javascript const res = await fetch("https://api.zpi.web.id/v1/finance:macromicro/calendar-macro?date=2026-08-29&country=us", { headers: { "x-api-key": process.env.ZAPI_KEY } }); const data = await res.json(); ``` **Python:** ```python import requests r = requests.get("https://api.zpi.web.id/v1/finance:macromicro/calendar-macro?date=2026-08-29&country=us", headers={"x-api-key": "YOUR_API_KEY"}) data = r.json() ``` **Example response:** ```json { "to": "2026-09-09T16:00:00.000Z", "date": "2026-08-29", "from": "2026-07-21T16:00:00.000Z", "count": 53, "items": [ { "url": "https://en.macromicro.me/collections/4/us-employ-relative/19/initial-jobless-claims", "name": "US Initial & Continuing Jobless Claims", "statId": 34, "country": "us", "eventId": 16, "groupId": 1190, "onFront": true, "isPublic": true, "createdAt": "2020-04-08T07:36:16.000Z", "updatedAt": "2023-01-12T09:48:23.000Z", "onCalendar": true, "releasedAt": "2026-07-23T12:30:00.000Z", "onQuickview": true, "releasedAtEpoch": 1784809800 }, { "url": "https://en.macromicro.me/collections/7/us-housing-relative/34/new-house-price", "name": "US Supply and Demand of New Homes", "statId": 252, "country": "us", "eventId": 36, "groupId": 86, "onFront": true, "isPublic": true, "createdAt": "2020-04-08T07:36:16.000Z", "updatedAt": "2026-03-23T04:20:44.000Z", "onCalendar": false, "releasedAt": "2026-07-24T14:00:00.000Z", "onQuickview": true, "relatedCharts": [ 31, 32, 34, 30 ], "releasedAtEpoch": 1784901600 }, { "url": "https://en.macromicro.me/collections/8/us-industry-relative/3/durable-goods", "name": "US Durable Goods Orders", "statId": 321, "country": "us", "eventId": 35, "groupId": 55, "onFront": false, "isPublic": true, "createdAt": "2020-04-08T07:36:16.000Z", "updatedAt": "2026-03-23T04:20:44.000Z", "onCalendar": false, "releasedAt": "2026-07-27T12:30:00.000Z", "onQuickview": true, "releasedAtEpoch": 1785155400 }, { "url": "https://en.macromicro.me/collections/7/us-housing-relative/35/house-index", "name": "US S&P House Price Index", "statId": 0, "country": "us", "eventId": 34, "groupId": 79, "onFront": false, "createdAt": "2020-04-08T07:36:16.000Z", "updatedAt": "2023-01-12T09:51:33.000Z", "onCalendar": false, "releasedAt": "2026-07-28T14:00:00.000Z", "onQuickview": true, "relatedCharts": [ 31, 32, 34, 30 ], "releasedAtEpoch": 1785247200 }, { "url": "https://en.macromicro.me/collections/3/us-consumption-relative/22/consumer-confidence", "name": "US Consumer Confidence", "statId": 7054, "country": "us", "eventId": 33, "groupId": 68, "onFront": true, "isPublic": true, "createdAt": "2020-04-08T07:36:16.000Z", "updatedAt": "2023-01-12T09:50:55.000Z", "onCalendar": false, "releasedAt": "2026-07-28T14:00:00.000Z", "onQuickview": true, "releasedAtEpoch": 1785247200 }, { "url": "https://en.macromicro.me/collections/4238/us-federal/17668/us-fed-fund-interest-rate", "name": "US Fed Interest Rate Policy", "statId": 0, "country": "us", "eventId": 244, "groupId": 922, "onFront": false, "createdAt": "2023-01-31T09:54:03.000Z", "updatedAt": "2023-01-31T09:54:03.000Z", "onCalendar": true, "releasedAt": "2026-07-29T18:00:00.000Z", "onQuickview": true, "relatedCharts": [ 48, 4238, 17668, 6287, 77, 1242, 1320, 34394 ], "releasedAtEpoch": 1785348000 }, { "url": "https://en.macromicro.me/collections/3/us-consumption-relative/88/real-disposable-personal-income", "name": "US Disposable Personal Income", "statId": 0, "country": "us", "eventId": 202, "groupId": 940, "onFront": false, "createdAt": "2020-08-22T11:16:05.000Z", "updatedAt": "2026-03-23T04:20:44.000Z", "onCalendar": false, "releasedAt": "2026-07-30T12:30:00.000Z", "onQuickview": true, "releasedAtEpoch": 1785414600 }, { "url": "https://en.macromicro.me/collections/3/us-consumption-relative/23/saving-rate", "name": "US Personal Spending", "statId": 74, "country": "us", "eventId": 197, "groupId": 72, "onFront": true, "isPublic": true, "createdAt": "2020-08-17T07:00:43.000Z", "updatedAt": "2026-03-23T04:20:44.000Z", "onCalendar": false, "releasedAt": "2026-07-30T12:30:00.000Z", "onQuickview": true, "relatedCharts": [ 30444, 25948, 86 ], "releasedAtEpoch": 1785414600 } ], "total": 212, "country": "us", "dataset": "calendar-macro", "provider": "macromicro" } ``` --- ### Calendar Earnings Earnings calendar with ticker, fiscal period, market cap and logo. - **Method:** `GET` - **Endpoint:** `https://api.zpi.web.id/v1/finance:macromicro/calendar-earnings` - **Cache TTL:** 1800s **Parameters:** | Name | Type | Location | Required | Description | |------|------|----------|----------|-------------| | `date` | string | query | no | Any date inside the window you want, YYYY-MM-DD. Defaults to today. | | `symbol` | string | query | no | Keep only this ticker. | **cURL:** ```bash curl "https://api.zpi.web.id/v1/finance:macromicro/calendar-earnings?date=2026-08-29&symbol=AAPL" \ -H "x-api-key: YOUR_API_KEY" ``` **JavaScript / TypeScript:** ```javascript const res = await fetch("https://api.zpi.web.id/v1/finance:macromicro/calendar-earnings?date=2026-08-29&symbol=AAPL", { headers: { "x-api-key": process.env.ZAPI_KEY } }); const data = await res.json(); ``` **Python:** ```python import requests r = requests.get("https://api.zpi.web.id/v1/finance:macromicro/calendar-earnings?date=2026-08-29&symbol=AAPL", headers={"x-api-key": "YOUR_API_KEY"}) data = r.json() ``` **Example response:** ```json { "to": "2026-09-10", "date": "2026-08-29", "from": "2026-07-22", "count": 434, "items": [ { "date": "2026-07-22", "logo": "https://cdn.macromicro.me/stocks/logo/T/TEL-2bac1695.png", "name": "TE CONNECTIVITY PLC", "time": "01:00", "symbol": "TEL", "earningId": 12820, "marketCap": 58974060000, "fiscalYear": 2026, "industryId": 452030, "fiscalPeriod": "Q2" }, { "date": "2026-07-22", "logo": "https://cdn.macromicro.me/stocks/logo/O/OTIS-848b696b.png", "name": "OTIS WORLDWIDE CORP", "time": "01:08", "symbol": "OTIS", "earningId": 6949, "marketCap": 27634247999, "fiscalYear": 2026, "industryId": 201060, "fiscalPeriod": "Q2" }, { "date": "2026-07-22", "logo": "https://cdn.macromicro.me/stocks/logo/G/GEV-18bc3ee8.png", "name": "GE VERNOVA INC", "time": "01:27", "symbol": "GEV", "earningId": 11455, "marketCap": 244397240000, "fiscalYear": 2026, "industryId": 201040, "fiscalPeriod": "Q2" }, { "date": "2026-07-22", "logo": "https://cdn.macromicro.me/stocks/logo/P/PHM-5aff5450.png", "name": "PULTEGROUP INC", "time": "01:30", "symbol": "PHM", "earningId": 7258, "marketCap": 24644384389, "fiscalYear": 2026, "industryId": 252010, "fiscalPeriod": "Q2" }, { "date": "2026-07-22", "logo": "https://cdn.macromicro.me/stocks/logo/T/T-ca084bdc.png", "name": "AT&T INC", "time": "01:30", "symbol": "T", "earningId": 8226, "marketCap": 180388000000, "fiscalYear": 2026, "industryId": 501010, "fiscalPeriod": "Q2" }, { "date": "2026-07-22", "logo": "https://cdn.macromicro.me/stocks/logo/W/WAB-258a6b91.png", "name": "WABTEC CORP", "time": "01:30", "symbol": "WAB", "earningId": 9174, "marketCap": 49532985000, "fiscalYear": 2026, "industryId": 201060, "fiscalPeriod": "Q2" }, { "date": "2026-07-22", "logo": "https://cdn.macromicro.me/stocks/logo/T/TDY-5e11846c.png", "name": "TELEDYNE TECHNOLOGIES INC", "time": "01:55", "symbol": "TDY", "earningId": 8301, "marketCap": 29169924000, "fiscalYear": 2026, "industryId": 452030, "fiscalPeriod": "Q2" }, { "date": "2026-07-22", "logo": "https://cdn.macromicro.me/stocks/logo/P/PM-6f18d12c.png", "name": "PHILIP MORRIS INTERNATIONAL", "time": "01:59", "symbol": "PM", "earningId": 7328, "marketCap": 299889628239, "fiscalYear": 2026, "industryId": 302030, "fiscalPeriod": "Q2" } ], "total": 434, "symbol": null, "dataset": "calendar-earnings", "provider": "macromicro" } ``` --- ### Calendar Holidays Market holidays by date and country. - **Method:** `GET` - **Endpoint:** `https://api.zpi.web.id/v1/finance:macromicro/calendar-holidays` - **Cache TTL:** 86400s **Parameters:** | Name | Type | Location | Required | Description | |------|------|----------|----------|-------------| | `country` | string | query | no | Keep only this two-letter country code. | | `from` | string | query | no | Drop holidays before this date, YYYY-MM-DD. | | `to` | string | query | no | Drop holidays after this date, YYYY-MM-DD. | **cURL:** ```bash curl "https://api.zpi.web.id/v1/finance:macromicro/calendar-holidays?country=us&from=2026-01-01&to=2026-12-31" \ -H "x-api-key: YOUR_API_KEY" ``` **JavaScript / TypeScript:** ```javascript const res = await fetch("https://api.zpi.web.id/v1/finance:macromicro/calendar-holidays?country=us&from=2026-01-01&to=2026-12-31", { headers: { "x-api-key": process.env.ZAPI_KEY } }); const data = await res.json(); ``` **Python:** ```python import requests r = requests.get("https://api.zpi.web.id/v1/finance:macromicro/calendar-holidays?country=us&from=2026-01-01&to=2026-12-31", headers={"x-api-key": "YOUR_API_KEY"}) data = r.json() ``` **Example response:** ```json { "to": "2026-12-31", "from": "2026-01-01", "count": 10, "items": [ { "date": "2026-01-01", "name": "New Year's Day", "country": "us", "holidayId": 198, "countryName": "United States" }, { "date": "2026-01-19", "name": "Martin L. King Day", "country": "us", "holidayId": 199, "countryName": "United States" }, { "date": "2026-02-16", "name": "Presidents' Day", "country": "us", "holidayId": 200, "countryName": "United States" }, { "date": "2026-04-03", "name": "Good Friday (not Fed Hol)", "country": "us", "holidayId": 201, "countryName": "United States" }, { "date": "2026-05-25", "name": "Memorial Day", "country": "us", "holidayId": 202, "countryName": "United States" }, { "date": "2026-06-19", "name": "Juneteenth", "country": "us", "holidayId": 203, "countryName": "United States" }, { "date": "2026-07-03", "name": "Independence Day", "country": "us", "holidayId": 204, "countryName": "United States" }, { "date": "2026-09-07", "name": "Labor Day", "country": "us", "holidayId": 205, "countryName": "United States" } ], "total": 385, "country": "us", "dataset": "calendar-holidays", "provider": "macromicro" } ``` --- ### Institutions 13F filers with CIK, slug and the quarter each one is currently reported through. - **Method:** `GET` - **Endpoint:** `https://api.zpi.web.id/v1/finance:macromicro/institutions` - **Cache TTL:** 86400s **Parameters:** | Name | Type | Location | Required | Description | |------|------|----------|----------|-------------| | `query` | string | query | no | Free-text match against the institution name or slug. | **cURL:** ```bash curl "https://api.zpi.web.id/v1/finance:macromicro/institutions?query=berkshire" \ -H "x-api-key: YOUR_API_KEY" ``` **JavaScript / TypeScript:** ```javascript const res = await fetch("https://api.zpi.web.id/v1/finance:macromicro/institutions?query=berkshire", { headers: { "x-api-key": process.env.ZAPI_KEY } }); const data = await res.json(); ``` **Python:** ```python import requests r = requests.get("https://api.zpi.web.id/v1/finance:macromicro/institutions?query=berkshire", headers={"x-api-key": "YOUR_API_KEY"}) data = r.json() ``` **Example response:** ```json { "count": 1, "items": [ { "cik": "0001067983", "url": "https://en.macromicro.me/13f/berkshire_hathaway", "slug": "berkshire_hathaway", "nameEn": "Berkshire Hathaway Inc", "nameSc": "波克夏·海瑟威", "nameTc": "波克夏·海瑟威", "period": "q", "quarter": "2026-Q2", "createdAt": "2020-12-23T05:07:52.000Z", "institutionId": 2 } ], "query": "berkshire", "total": 20, "dataset": "institutions", "provider": "macromicro" } ``` --- ### Institution Holdings One filer's twenty largest positions for a quarter, plus its biggest increases and decreases. - **Method:** `GET` - **Endpoint:** `https://api.zpi.web.id/v1/finance:macromicro/institution-holdings/:institutionId` - **Cache TTL:** 21600s **Parameters:** | Name | Type | Location | Required | Description | |------|------|----------|----------|-------------| | `institutionId` | string | path | yes | Institution id, from `institutions`. | | `quarter` | string | query | no | Reporting quarter. Defaults to the latest the upstream reports for this filer. | **cURL:** ```bash curl "https://api.zpi.web.id/v1/finance:macromicro/institution-holdings/:institutionId?quarter=2026-Q2" \ -H "x-api-key: YOUR_API_KEY" ``` **JavaScript / TypeScript:** ```javascript const res = await fetch("https://api.zpi.web.id/v1/finance:macromicro/institution-holdings/:institutionId?quarter=2026-Q2", { headers: { "x-api-key": process.env.ZAPI_KEY } }); const data = await res.json(); ``` **Python:** ```python import requests r = requests.get("https://api.zpi.web.id/v1/finance:macromicro/institution-holdings/:institutionId?quarter=2026-Q2", headers={"x-api-key": "YOUR_API_KEY"}) data = r.json() ``` **Example response:** ```json { "name": null, "count": 20, "items": [ { "url": "https://en.macromicro.me/stocks/info/AAPL", "isEtf": false, "nameEn": "Apple Inc", "nameSc": "苹果", "nameTc": "蘋果", "shares": 227917808, "splits": [ { "date": "2020-08-31", "label": "August 31, 20", "numerator": 4, "denominator": 1 }, { "date": "2014-06-09", "label": "June 09, 14", "numerator": 7, "denominator": 1 }, { "date": "2005-02-28", "label": "February 28, 05", "numerator": 2, "denominator": 1 }, { "date": "2000-06-21", "label": "June 21, 00", "numerator": 2, "denominator": 1 }, { "date": "1987-06-16", "label": "June 16, 87", "numerator": 2, "denominator": 1 } ], "ticker": "AAPL", "sectorId": 5, "holdingId": 22, "portfolioPercent": 22.04, "sharesChangePercentYear": -18.6, "sharesChangePercent3Year": -75.11, "portfolioPercentChangeYear": -0.28, "sharesChangePercentQuarter": 0, "portfolioPercentChange3Year": -28.95, "portfolioPercentChangeQuarter": 0.06 }, { "url": "https://en.macromicro.me/stocks/info/AXP", "isEtf": false, "nameEn": "American Express Co", "nameSc": "美国运通", "nameTc": "美國運通", "shares": 151610700, "splits": [ { "date": "2005-10-03", "label": "October 03, 05", "numerator": 10000, "denominator": 8753 }, { "date": "2000-05-11", "label": "May 11, 00", "numerator": 3, "denominator": 1 }, { "date": "1994-05-31", "label": "May 31, 94", "numerator": 400, "denominator": 353 }, { "date": "1987-05-11", "label": "May 11, 87", "numerator": 2, "denominator": 1 }, { "date": "1983-08-11", "label": "August 11, 83", "numerator": 3, "denominator": 2 }, { "date": "1983-02-11", "label": "February 11, 83", "numerator": 4, "denominator": 3 } ], "ticker": "AXP", "sectorId": 7, "holdingId": 16, "portfolioPercent": 17.14, "sharesChangePercentYear": 0, "sharesChangePercent3Year": 0, "portfolioPercentChangeYear": -1.63, "sharesChangePercentQuarter": 0, "portfolioPercentChange3Year": 9.55, "portfolioPercentChangeQuarter": -0.29 }, { "url": "https://en.macromicro.me/stocks/info/KO", "isEtf": false, "nameEn": "Coca-Cola Co", "nameSc": "Coca-Cola Co", "nameTc": "可口可樂", "shares": 400000000, "splits": [ { "date": "2012-08-13", "label": "August 13, 12", "numerator": 2, "denominator": 1 }, { "date": "1996-05-13", "label": "May 13, 96", "numerator": 2, "denominator": 1 }, { "date": "1992-05-12", "label": "May 12, 92", "numerator": 2, "denominator": 1 }, { "date": "1990-05-14", "label": "May 14, 90", "numerator": 2, "denominator": 1 }, { "date": "1986-07-01", "label": "July 01, 86", "numerator": 3, "denominator": 1 }, { "date": "1977-06-01", "label": "June 01, 77", "numerator": 2, "denominator": 1 }, { "date": "1968-06-03", "label": "June 03, 68", "numerator": 2, "denominator": 1 }, { "date": "1965-05-19", "label": "May 19, 65", "numerator": 2, "denominator": 1 } ], "ticker": "KO", "sectorId": 8, "holdingId": 71, "portfolioPercent": 10.87, "sharesChangePercentYear": 0, "sharesChangePercent3Year": 0, "portfolioPercentChangeYear": -0.13, "sharesChangePercentQuarter": 0, "portfolioPercentChange3Year": 3.95, "portfolioPercentChangeQuarter": -0.68 }, { "url": "https://en.macromicro.me/stocks/info/GOOGL", "isEtf": false, "nameEn": "Alphabet, Inc. Class A", "nameSc": "Alphabet公司 (A股)", "nameTc": "Alphabet公司 (A股)", "shares": 78791167, "splits": [ { "date": "2022-07-18", "label": "July 18, 22", "numerator": 20, "denominator": 1 }, { "date": "2014-04-03", "label": "April 03, 14", "numerator": 999, "denominator": 500 } ], "ticker": "GOOGL", "sectorId": 10, "holdingId": 754, "portfolioPercent": 9.41, "sharesChangePercentYear": 0, "sharesChangePercent3Year": 0, "portfolioPercentChangeYear": 0, "sharesChangePercentQuarter": 45.24, "portfolioPercentChange3Year": 0, "portfolioPercentChangeQuarter": 3.49 }, { "url": "https://en.macromicro.me/stocks/info/BAC", "isEtf": false, "nameEn": "Bank of America Corp", "nameSc": "美国银行", "nameTc": "美國銀行", "shares": 483394015, "splits": [ { "date": "2004-08-30", "label": "August 30, 04", "numerator": 2, "denominator": 1 }, { "date": "1997-02-28", "label": "February 28, 97", "numerator": 2, "denominator": 1 }, { "date": "1986-11-21", "label": "November 21, 86", "numerator": 2, "denominator": 1 } ], "ticker": "BAC", "sectorId": 7, "holdingId": 564, "portfolioPercent": 9.2, "sharesChangePercentYear": -20.14, "sharesChangePercent3Year": -53.2, "portfolioPercentChangeYear": -1.92, "sharesChangePercentQuarter": -5.89, "portfolioPercentChange3Year": 0.69, "portfolioPercentChangeQuarter": -0.31 }, { "url": "https://en.macromicro.me/stocks/info/CVX", "isEtf": false, "nameEn": "Chevron Corp", "nameSc": "雪佛龙", "nameTc": "雪佛龍", "shares": 84375856, "splits": [ { "date": "2004-09-13", "label": "September 13, 04", "numerator": 2, "denominator": 1 }, { "date": "1994-06-13", "label": "June 13, 94", "numerator": 2, "denominator": 1 }, { "date": "1981-03-11", "label": "March 11, 81", "numerator": 2, "denominator": 1 }, { "date": "1973-12-11", "label": "December 11, 73", "numerator": 2, "denominator": 1 }, { "date": "1962-12-17", "label": "December 17, 62", "numerator": 21, "denominator": 20 } ], "ticker": "CVX", "sectorId": 11, "holdingId": 66, "portfolioPercent": 4.68, "sharesChangePercentYear": -30.88, "sharesChangePercent3Year": -31.47, "portfolioPercentChangeYear": -2.11, "sharesChangePercentQuarter": 0, "portfolioPercentChange3Year": -0.88, "portfolioPercentChangeQuarter": -1.95 }, { "url": "https://en.macromicro.me/stocks/info/OXY", "isEtf": false, "nameEn": "Occidental Petroleum Corp", "nameSc": "西方石油", "nameTc": "西方石油", "shares": 264941431, "splits": [ { "date": "2014-12-01", "label": "December 01, 14", "numerator": 521, "denominator": 500 }, { "date": "2006-08-16", "label": "August 16, 06", "numerator": 2, "denominator": 1 } ], "ticker": "OXY", "sectorId": 11, "holdingId": 196, "portfolioPercent": 4.3, "sharesChangePercentYear": 0, "sharesChangePercent3Year": 18.21, "portfolioPercentChangeYear": -0.02, "sharesChangePercentQuarter": 0, "portfolioPercentChange3Year": 0.52, "portfolioPercentChangeQuarter": -2.25 }, { "url": "https://en.macromicro.me/stocks/info/CB", "isEtf": false, "nameEn": "Chubb Ltd", "nameSc": "安达保险", "nameTc": "安達保險", "shares": 34249183, "splits": [ { "date": "2006-04-19", "label": "April 19, 06", "numerator": 1, "denominator": 2 }, { "date": "1998-03-03", "label": "March 03, 98", "numerator": 3, "denominator": 1 }, { "date": "1996-05-06", "label": "May 06, 96", "numerator": 1, "denominator": 2 }, { "date": "1990-05-31", "label": "May 31, 90", "numerator": 2, "denominator": 1 }, { "date": "1985-10-25", "label": "October 25, 85", "numerator": 3, "denominator": 2 } ], "ticker": "CB", "sectorId": 7, "holdingId": 863, "portfolioPercent": 3.9, "sharesChangePercentYear": 26.69, "sharesChangePercent3Year": 0, "portfolioPercentChangeYear": 0.86, "sharesChangePercentQuarter": 0, "portfolioPercentChange3Year": 0, "portfolioPercentChangeQuarter": -0.34 } ], "total": 20, "losers": [ { "url": "https://en.macromicro.me/stocks/info/KR", "name": "Kroger Co", "ticker": "KR", "assetId": 155, "current": { "date": "2026-06-30", "shares": 39000000, "portfolioPercent": 0.72 }, "previous": { "date": "2026-03-31", "shares": 50000000, "portfolioPercent": 1.37 }, "sectorName": "EQUITY-Consumer Defensive", "sharesDiff": -11000000, "portfolioPercent": 0.72, "sharesChangePercent": -22, "portfolioPercentChange": -0.65 }, { "url": "https://en.macromicro.me/stocks/info/BAC", "name": "Bank of America Corp", "ticker": "BAC", "assetId": 564, "current": { "date": "2026-06-30", "shares": 483394015, "portfolioPercent": 9.2 }, "previous": { "date": "2026-03-31", "shares": 513624165, "portfolioPercent": 9.51 }, "sectorName": "EQUITY-Financial Services", "sharesDiff": -30230150, "portfolioPercent": 9.2, "sharesChangePercent": -5.89, "portfolioPercentChange": -0.31 }, { "url": "https://en.macromicro.me/stocks/info/COF", "name": "Capital One Financial Corp", "ticker": "COF", "assetId": 56, "current": { "date": "2026-06-30", "shares": 3000000, "portfolioPercent": 0.2 }, "previous": { "date": "2026-03-31", "shares": 7150000, "portfolioPercent": 0.49 }, "sectorName": "EQUITY-Financial Services", "sharesDiff": -4150000, "portfolioPercent": 0.2, 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