Skip to content

Viomi Technology Co., Ltd: cost of revenue

Cost of revenue for Viomi Technology Co., Ltd. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All Viomi Technology Co., Ltd financial histories

What this measure means

Costs attributed to goods produced and sold and services provided during the period. This is not the sum of every operating expense.

Exact concept: us-gaap:CostOfRevenue. Each value covers an annual-duration reporting interval, shown with both start and end dates. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2016-01-01 to 2025-12-31. The SEC response was captured on 2026-09-21.

Coverage by original unit

These are separate reported series. A newer period in one unit does not update another unit’s history or establish a currency conversion.

Selected filing history

Cost of revenue in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-311,813,226,000CNY2026-04-2220-F · 0001493152-26-018435
2024-01-012024-12-311,570,276,000CNY2026-04-2220-F · 0001493152-26-018435
2023-01-012023-12-311,116,720,000CNY2026-04-2220-F · 0001493152-26-018435
2022-01-012022-12-311,195,742,000CNY2025-09-3020-F · 0001104659-25-094717
2021-01-012021-12-314,105,767,000CNY2024-04-2520-F · 0001104659-24-051949
2020-01-012020-12-314,742,668,000CNY2023-04-2520-F · 0001104659-23-049651
2019-01-012019-12-313,565,109,000CNY2022-04-2720-F · 0001564590-22-016090
2018-01-012018-12-311,843,432,000CNY2021-04-2620-F · 0001564590-21-020524
2017-01-012017-12-31598,036,000CNY2020-04-2320-F · 0001564590-20-018219
2016-01-012016-12-31232,544,000CNY2019-04-2320-F · 0001564590-19-012455
2025-01-012025-12-31259,288,000USD2026-04-2220-F · 0001493152-26-018435
2024-01-012024-12-31215,126,000USD2025-09-3020-F · 0001104659-25-094717
2023-01-012023-12-31270,971,000USD2024-04-2520-F · 0001104659-24-051949
2022-01-012022-12-31361,834,000USD2023-04-2520-F · 0001104659-23-049651
2021-01-012021-12-31644,285,000USD2022-04-2720-F · 0001564590-22-016090
2020-01-012020-12-31726,846,000USD2021-04-2620-F · 0001564590-21-020524
2019-01-012019-12-31512,095,000USD2020-04-2320-F · 0001564590-20-018219
2018-01-012018-12-31268,116,000USD2019-04-2320-F · 0001564590-19-012455

Related financial histories

Inspect the source

Entity
Viomi Technology Co., Ltd / CIK 0001742770
Captured
2026-09-21T17:28:35.043Z
SEC response SHA-256
f5c962e5d881ec649c796b29eb370e6682d2a7b419d936ed04963b88a1317b6b

Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON

Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.

Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.

Use this in research

A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.

These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.

Research methodology · Execution and cost assumptions · Check backtest overfitting

Build with the open-source tools

Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.

Read the published dataset with Python
import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0001742770.json") as response:
    record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
    print(concept["tag"], next(iter(concept["observations"])))