Stock OHLCV — Daily Candles (JSON, 252 records)
The daily OHLCV candles as a JSON array — the format twin of the CSV, for charting and time-series testing.
[
{
"date": "2025-01-01",
"open": 100.0,
"high": 100.35,
"low": 99.73,
"close": 100.05,
"volume": 4251429
},
{
"date": "2025-01-02",
"open": 100.05,
"high": 101.04,
"low": 99.13,
"close": 99.19,
"volume": 1513432
},
{
"date": "2025-01-03",
"open": 99.19,
"high": 102.4,
"low": 98.57,
"close": 101.9,
"volume": 4174093
},
{
"date": "2025-01-06",
"open": 101.9,
"high": 102.79,
"low": 100.95,
"close": 102.68,
"volume": 1863645
},
{
"date": "2025-01-07",
"open": 102.68,
"high": 103.39,
"low": 101.29,
"close": 102.67,
"volume": 4134486
},
{
"date": "2025-01-08",
"open": 102.67,
"high": 103.99,
"low": 97.0,
"close": 98.82,
"volume": 4066978
},
{Specifications
- Records
- 252
- Schema
- date, open, high, low, close, volume
- Ticker
- NOVS
- Domain
- finance
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“Stock OHLCV — Daily Candles (JSON, 252 records)” is a deterministic Novus Examples fixture for Data import, Time-series data. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 252 records · schema: date, open, high, low, close, volume. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
Data fixtures document their exact quirks — delimiters, encodings, null handling, schema, and row counts — in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.
Code examples
import json
with open("stock-ohlcv.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
- csvIoT Sensor Readings (CSV, 1440 rows)A day of IoT sensor readings (temperature, humidity, pressure) at one-minute intervals from three sensors, with a realistic daily cycle plus noise — for testing time-series ingestion and downsampling. JSON twin included.
- jsonIoT Sensor Readings (JSON, 1440 records)The IoT sensor readings as a JSON array — the format twin of the CSV, for time-series testing.
- csvWeather Forecast — 30 days (CSV, 30 rows)A 30-day daily weather forecast for one city — high/low temperature, condition, precipitation, wind, and humidity. Synthetic (seeded) data for testing weather widgets, chart tools, and time-series import.
- jsonWeather Forecast — 30 days (JSON, 30 records)The daily forecast as a JSON array — the format twin of the CSV, for import and charting tests.
- nmeaNMEA 0183 — GGA Sentences OnlyThe same twelve fixes reduced to GGA alone. GGA carries time-of-day but no date and no speed, so a parser that only reads GGA cannot place the track on a calendar — a real limitation that this file makes obvious rather than leaving to be discovered later.
- nmeaNMEA 0183 — Mixed GGA/RMC/GSA/GSV/VTG LogA twelve-fix receiver log with the five sentence types a GPS unit normally interleaves, including three-part GSV satellite-in-view sets. Every checksum is correct, so a parser that verifies them should accept the whole file.
Generated by generation/data_realworld.py. Free for any use, no attribution required — license.