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VisionSys AI Inc: total liabilities

Total liabilities for VisionSys AI Inc. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All VisionSys AI Inc financial histories

What this measure means

Recognized obligations at the reporting date. The definition and scope differ from interest-bearing debt.

Exact concept: us-gaap:Liabilities. Each value is a balance at the reporting date, not a flow earned over a year. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2013-12-31 to 2025-12-31. The SEC response was captured on 2026-09-20.

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

Total liabilities in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
At date2025-12-313,820,000CNY2026-05-1120-F · 0001213900-26-054539
At date2024-12-311,904,212,000CNY2026-05-1120-F · 0001213900-26-054539
At date2023-12-312,519,593,000CNY2025-05-1520-F · 0001410578-25-001300
At date2022-12-312,844,181,000CNY2024-04-1920-F · 0001104659-24-049422
At date2021-12-313,234,202,000CNY2023-04-2820-F · 0001410578-23-000835
At date2020-12-313,098,518,000CNY2022-04-2620-F · 0001410578-22-001020
At date2019-12-312,915,084,000CNY2021-04-1320-F · 0001104659-21-049558
At date2018-12-311,306,404,000CNY2020-06-1120-F · 0001104659-20-072468
At date2017-12-31748,918,000CNY2020-04-2420-F · 0001104659-20-050975
At date2016-12-31486,792,000CNY2018-04-3020-F · 0001144204-18-023413
At date2015-12-31315,277,000CNY2017-04-2520-F · 0001144204-17-022025
At date2025-12-31546,000USD2026-05-1120-F · 0001213900-26-054539
At date2024-12-31260,877,000USD2025-05-1520-F · 0001410578-25-001300
At date2023-12-31354,878,000USD2024-04-1920-F · 0001104659-24-049422
At date2022-12-31412,367,000USD2023-04-2820-F · 0001410578-23-000835
At date2021-12-31507,517,000USD2022-04-2620-F · 0001410578-22-001020
At date2020-12-31474,869,000USD2021-04-1320-F · 0001104659-21-049558
At date2019-12-31418,725,000USD2020-06-1120-F · 0001104659-20-072468
At date2015-12-3148,551,933USD2016-04-2020-F · 0001144204-16-095204
At date2014-12-3135,066,942USD2016-04-2020-F · 0001144204-16-095204
At date2013-12-3125,578,223USD2015-04-1520-F · 0001144204-15-022976

Related financial histories

Inspect the source

Entity
VisionSys AI Inc / CIK 0001592560
Captured
2026-09-20T09:24:19.062Z
SEC response SHA-256
5919c855b0988061fb2346196c3c9680aae00f1513514d6cc0ac4fe74232a035

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/0001592560.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"])))