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VisionSys AI Inc: diluted weighted-average shares

Diluted weighted-average shares 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

Weighted-average shares used for diluted earnings per share. Potential shares are included under the applicable dilution rules, not simply added to outstanding shares.

Exact concept: us-gaap:WeightedAverageNumberOfDilutedSharesOutstanding. 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 2012-01-01 to 2025-12-31. The SEC response was captured on 2026-09-20.

Selected filing history

Diluted weighted-average shares in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-31193,836,988shares2026-05-1120-F · 0001213900-26-054539
2024-01-012024-12-3150,029,676shares2026-05-1120-F · 0001213900-26-054539
2023-01-012023-12-3153,873,945shares2026-05-1120-F · 0001213900-26-054539
2022-01-012022-12-3157,730,672shares2025-05-1520-F · 0001410578-25-001300
2021-01-012021-12-3157,630,365shares2024-04-1920-F · 0001104659-24-049422
2020-01-012020-12-3154,341,213shares2023-04-2820-F · 0001410578-23-000835
2019-01-012019-12-3153,386,075shares2021-04-1320-F · 0001104659-21-049558
2018-01-012018-12-3154,929,910shares2021-04-1320-F · 0001104659-21-049558
2017-01-012017-12-3156,849,332shares2020-06-1120-F · 0001104659-20-072468
2016-01-012016-12-3159,005,261shares2020-04-2420-F · 0001104659-20-050975
2015-01-012015-12-3158,750,856shares2018-04-3020-F · 0001144204-18-023413
2014-01-012014-12-3147,770,132shares2017-04-2520-F · 0001144204-17-022025
2013-01-012013-12-3110,930,412shares2016-04-2020-F · 0001144204-16-095204
2012-01-012012-12-3110,851,287shares2015-04-1520-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"])))