Hawkeye Digital, Inc.: net current accounts receivable
Net current accounts receivable for Hawkeye Digital, Inc. Inspect selected reporting periods, original units and SEC filing links; download the financial history.
All Hawkeye Digital, Inc. financial histories
What this measure means
Current customer receivables after the allowance for credit loss. The balance is not cash collected or a guarantee of collection.
Exact concept: us-gaap:AccountsReceivableNetCurrent. 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 2019-06-30 to 2025-06-30. The SEC response was captured on 2026-09-21.
Selected filing history
| Period start | Period end | Value | Unit | Filed | Source filing |
|---|---|---|---|---|---|
| At date | 2025-06-30 | 0 | USD | 2025-10-15 | 10-K · 0001477932-25-007562 |
| At date | 2021-06-30 | 0 | USD | 2021-10-13 | 10-K · 0001477932-21-007236 |
| At date | 2020-06-30 | 47,656 | USD | 2021-10-13 | 10-K · 0001477932-21-007236 |
| At date | 2019-06-30 | 0 | USD | 2021-07-01 | 10-K/A · 0001477932-21-004400 |
Related financial histories
- Hawkeye Digital, Inc.: total assets
- Hawkeye Digital, Inc.: total liabilities
- Hawkeye Digital, Inc.: stockholders equity
- Hawkeye Digital, Inc.: cash and cash equivalents
- Hawkeye Digital, Inc.: net income or loss
- Hawkeye Digital, Inc.: operating cash flow
- Hawkeye Digital, Inc.: revenue
- Hawkeye Digital, Inc.: financing cash flow
- Hawkeye Digital, Inc.: investing cash flow
- Hawkeye Digital, Inc.: retained earnings or deficit
- Hawkeye Digital, Inc.: diluted weighted-average shares
- Hawkeye Digital, Inc.: diluted earnings per share
- Hawkeye Digital, Inc.: net property, plant and equipment
- Hawkeye Digital, Inc.: share-based compensation expense
- Hawkeye Digital, Inc.: operating income or loss
- Hawkeye Digital, Inc.: current assets
- Hawkeye Digital, Inc.: interest expense
- Hawkeye Digital, Inc.: current liabilities
- Hawkeye Digital, Inc.: current accounts payable
- Hawkeye Digital, Inc.: operating expenses
- Hawkeye Digital, Inc.: net inventory
- Hawkeye Digital, Inc.: gross profit
- Hawkeye Digital, Inc.: cost of revenue
Inspect the source
- Entity
- Hawkeye Digital, Inc. / CIK 0001750777
- Captured
- 2026-09-21T17:29:03.469Z
- SEC response SHA-256
0624035ab7cb66008429c675c54722a3c9e40427c33dd76729af5bd355497a62
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
- Connect the MCP server to your coding assistant
- Inspect the ALPHAC engine on GitHub
- 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/0001750777.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"])))