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Fangdd Network Group Ltd.: cost of revenue

Cost of revenue for Fangdd Network Group Ltd. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All Fangdd Network Group 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 2017-01-01 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
2025-01-012025-12-31291,232,000CNY2026-05-0620-F · 0001213900-26-052541
2024-01-012024-12-31277,356,000CNY2026-05-0620-F · 0001213900-26-052541
2023-01-012023-12-31243,763,000CNY2026-05-0620-F · 0001213900-26-052541
2022-01-012022-12-31221,213,000CNY2025-04-2320-F · 0001213900-25-034322
2021-01-012021-12-31835,873,000CNY2024-09-1320-F/A · 0001213900-24-078552
2020-01-012020-12-312,036,821,000CNY2023-04-1920-F · 0001104659-23-047180
2019-01-012019-12-312,842,394,000CNY2022-04-2220-F · 0001104659-22-048931
2018-01-012018-12-311,805,588,000CNY2021-03-3120-F · 0001104659-21-044747
2017-01-012017-12-311,416,933,000CNY2020-05-1220-F/A · 0001104659-20-060177
2025-01-012025-12-3141,646,000USD2026-05-0620-F · 0001213900-26-052541
2024-01-012024-12-3137,998,000USD2025-04-2320-F · 0001213900-25-034322
2023-01-012023-12-3134,333,000USD2024-09-1320-F/A · 0001213900-24-078552
2022-01-012022-12-3132,073,000USD2023-04-1920-F · 0001104659-23-047180
2021-01-012021-12-31131,167,000USD2022-04-2220-F · 0001104659-22-048931
2020-01-012020-12-31312,156,000USD2021-03-3120-F · 0001104659-21-044747
2019-01-012019-12-31408,284,000USD2020-05-1220-F/A · 0001104659-20-060177

Related financial histories

Inspect the source

Entity
Fangdd Network Group Ltd. / CIK 0001750593
Captured
2026-09-21T17:29:01.971Z
SEC response SHA-256
eab68ba59160a9cea75274472540fee6d18747735793f1d209d40997cae46a46

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/0001750593.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"])))