GENMARK DIAGNOSTICS, INC.: financing cash flow
Financing cash flow for GENMARK DIAGNOSTICS, INC. Inspect selected reporting periods, original units and SEC filing links; download the financial history.
All GENMARK DIAGNOSTICS, INC. financial histories
What this measure means
Net cash from financing activities, including borrowing, repayments and transactions with owners. A positive amount does not establish operating profitability.
Exact concept: us-gaap:NetCashProvidedByUsedInFinancingActivities. 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 2009-01-01 to 2020-12-31. The SEC response was captured on 2026-09-22.
This selected history ends more than two years before capture. Do not treat its final value as a current balance or current annual result. More recent filings may use another accounting tag; inspect the filings before drawing conclusions about the company.
Selected filing history
| Period start | Period end | Value | Unit | Filed | Source filing |
|---|---|---|---|---|---|
| 2020-01-01 | 2020-12-31 | 87,570,000 | USD | 2021-02-25 | 10-K · 0001487371-21-000096 SEC |
| 2019-01-01 | 2019-12-31 | 45,173,000 | USD | 2021-02-25 | 10-K · 0001487371-21-000096 SEC |
| 2018-01-01 | 2018-12-31 | 8,069,000 | USD | 2021-02-25 | 10-K · 0001487371-21-000096 SEC |
| 2017-01-01 | 2017-12-31 | 89,050,000 | USD | 2020-03-02 | 10-K · 0001487371-20-000085 SEC |
| 2016-01-01 | 2016-12-31 | 40,359,000 | USD | 2019-02-25 | 10-K · 0001487371-19-000055 SEC |
| 2015-01-01 | 2015-12-31 | 11,133,000 | USD | 2018-02-27 | 10-K · 0001487371-18-000040 SEC |
| 2014-01-01 | 2014-12-31 | 1,287,000 | USD | 2017-02-28 | 10-K · 0001487371-17-000062 SEC |
| 2013-01-01 | 2013-12-31 | 80,833,000 | USD | 2016-02-23 | 10-K · 0001487371-16-000229 SEC |
| 2012-01-01 | 2012-12-31 | 44,319,000 | USD | 2015-02-24 | 10-K · 0001487371-15-000029 SEC |
| 2011-01-01 | 2011-12-31 | 33,262,000 | USD | 2014-03-11 | 10-K · 0001445305-14-000991 SEC |
| 2010-01-01 | 2010-12-31 | 22,614,000 | USD | 2013-03-14 | 10-K · 0001193125-13-107749 SEC |
| 2009-01-01 | 2009-12-31 | 24,133,000 | USD | 2012-03-21 | 10-K · 0001193125-12-125048 SEC |
Related GENMARK DIAGNOSTICS, INC. histories
Inspect the source
- Entity
- GENMARK DIAGNOSTICS, INC. / CIK 0001487371
- Captured
- SEC response SHA-256
cc54bc3a72a22d11a76ebe7d5d23087481f9011276a47164e9e27acfccef420e
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.
- Get an API key and run your first validation
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- Read the MCP server source and integration examples
Read the published dataset with Python
import json
from urllib.request import urlopen
with urlopen("https://canlicapital.com/company-data/0001487371.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"])))