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Aurora Mobile Limited: investing cash flow

Investing cash flow for Aurora Mobile Limited. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All Aurora Mobile Limited financial histories

What this measure means

Net cash from investing activities, including asset purchases, disposals and investment transactions. This differs from capital expenditure payments alone.

Exact concept: us-gaap:NetCashProvidedByUsedInInvestingActivities. 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

Investing cash flow in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-31-7,457,000CNY2026-03-2720-F · 0001104659-26-035657
2024-01-012024-12-31-5,375,000CNY2026-03-2720-F · 0001104659-26-035657
2023-01-012023-12-3125,126,000CNY2026-03-2720-F · 0001104659-26-035657
2022-01-012022-12-3126,853,000CNY2025-04-0320-F · 0001410578-25-000628
2021-01-012021-12-3126,442,000CNY2024-04-1220-F · 0001104659-24-046491
2020-01-012020-12-31-144,415,000CNY2023-04-1820-F · 0001193125-23-104207
2019-01-012019-12-31-88,966,000CNY2022-04-1420-F · 0001193125-22-104903
2018-01-012018-12-31-139,206,000CNY2021-04-1220-F · 0001193125-21-112508
2017-01-012017-12-31-28,644,000CNY2020-04-2920-F · 0001193125-20-126226
2016-01-012016-12-31-29,928,000CNY2019-04-0320-F · 0001564590-19-010719
2025-01-012025-12-31-1,065,000USD2026-03-2720-F · 0001104659-26-035657
2024-01-012024-12-31-736,000USD2025-04-0320-F · 0001410578-25-000628
2023-01-012023-12-313,539,000USD2024-04-1220-F · 0001104659-24-046491
2022-01-012022-12-313,893,000USD2023-04-1820-F · 0001193125-23-104207
2021-01-012021-12-314,150,000USD2022-04-1420-F · 0001193125-22-104903
2020-01-012020-12-31-22,133,000USD2021-04-1220-F · 0001193125-21-112508
2019-01-012019-12-31-12,779,000USD2020-04-2920-F · 0001193125-20-126226
2018-01-012018-12-31-20,246,000USD2019-04-0320-F · 0001564590-19-010719

Related financial histories

Inspect the source

Entity
Aurora Mobile Limited / CIK 0001737339
Captured
2026-09-21T17:25:35.881Z
SEC response SHA-256
2ee186cdc48a9ca21ee3e155a1dc02ef308a2531b8882b466c9e0dce61f6c4f5

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/0001737339.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"])))