Stock OHLCV — Daily Candles (CSV, 252 rows)
A year of daily OHLCV stock candles (open/high/low/close/volume) as a seeded random walk — a realistic finance time-series for testing charting, indicators, and importers. JSON twin included.
date,open,high,low,close,volume
2025-01-01,100.0,100.35,99.73,100.05,4251429
2025-01-02,100.05,101.04,99.13,99.19,1513432
2025-01-03,99.19,102.4,98.57,101.9,4174093
2025-01-06,101.9,102.79,100.95,102.68,1863645
2025-01-07,102.68,103.39,101.29,102.67,4134486
2025-01-08,102.67,103.99,97.0,98.82,4066978
2025-01-09,98.82,100.07,98.13,98.4,2501413
2025-01-10,98.4,100.88,97.55,98.08,660561
2025-01-13,98.08,98.19,96.53,98.03,2207193
2025-01-14,98.03,99.33,97.24,98.37,1613817
2025-01-15,98.37,101.32,98.34,100.51,3278938
2025-01-16,100.51,100.62,99.28,99.39,4235214
2025-01-17,99.39,100.79,99.31,99.57,689513
2025-01-20,99.57,100.43,96.42,96.54,3837969
2025-01-21,96.54,98.47,94.62,95.35,987022
2025-01-22,95.35,96.09,95.17,95.54,766632
2025-01-23,95.54,96.95,94.9,96.89,2265093
2025-01-24,96.89,97.09,95.19,95.63,3154962
2025-01-27,95.63,97.06,95.08,95.92,735171
2025-01-28,95.92,98.81,94.65,97.69,2311242
2025-01-29,97.69,98.32,94.27,96.19,917855
2025-01-30,96.19,97.4,95.39,96.05,3479966
2025-01-31,96.05,96.4,95.23,95.47,3032817
2025-02-03,95.47,95.76,94.37,94.7,1366085
2025-02-04,94.7,94.89,93.47,94.52,772764
2025-02-05,94.52,95.62,93.12,93.73,2193295
2025-02-06,93.73,94.36,93.41,93.73,2647327
2025-02-07,93.73,94.32,92.52,93.77,1949734
2025-02-10,93.77,96.06,91.86,94.47,988271
2025-02-11,94.47,94.62,90.74,92.82,3459923
2025-02-12,92.82,93.4,91.13,91.32,4387193
2025-02-13,91.32,91.51,90.4,91.04,2099361
2025-02-14,91.04,92.98,90.97,92.03,3092341
2025-02-17,92.03,92.27,89.37,90.14,3888398
2025-02-18,90.14,92.12,90.06,91.94,2684040
2025-02-19,91.94,93.78,90.73,91.77,4068606
2025-02-20,91.77,94.45,90.99,92.48,2276556
2025-02-21,92.48,94.72,91.76,93.93,2057276
2025-02-24,93.93,95.67,92.76,94.22,1409896
2025-02-25,94.22,94.48,93.99,94.14,4911776
2025-02-26,94.14,95.17,91.85,92.35,4755922
2025-02-27,92.35,92.93,89.77,90.93,4451845
2025-02-28,90.93,93.4,90.05,93.26,3855981
2025-03-03,93.26,93.56,91.49,92.01,1629496
2025-03-04,92.01,92.42,91.73,92.07,3789985
2025-03-05,92.07,93.59,90.02,90.63,1413102
2025-03-06,90.63,90.94,87.51,88.76,1279184
2025-03-07,88.76,89.32,86.87,88.43,3176081
2025-03-10,88.43,89.79,88.37,89.77,906351Specifications
- Rows
- 252
- Schema
- date, open, high, low, close, volume
- Ticker
- NOVS
- Period
- 1 trading year
- Domain
- finance
What is a .csv file?
CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.
How to use this file
Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.
How to use this file for testing
“Stock OHLCV — Daily Candles (CSV, 252 rows)” is a deterministic Novus Examples fixture for Data import, Time-series data, Conversion testing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 252 rows · 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 pandas as pd
df = pd.read_csv("stock-ohlcv.csv")
print(df.head())
print(df.dtypes)Related files
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- jsonColumnar Nulls Schema (JSON)JSON description of nullable columns in the null-heavy columnar fixtures.
Generated by generation/data_realworld.py. Free for any use, no attribution required — license.