metadata
dataset_info:
features:
- name: timestamp
dtype: timestamp[ns, tz=UTC]
- name: open
dtype: float64
- name: high
dtype: float64
- name: low
dtype: float64
- name: close
dtype: float64
- name: volume
dtype: float64
- name: ticker
dtype: string
splits:
- name: train
num_bytes: 195863846
num_examples: 3538138
download_size: 52097595
dataset_size: 195863846
configs:
- config_name: default
data_files:
- split: train
path: data/ohlcv_*.parquet
📈 OHLCV-1m: US Stock Market Minute-Level Candlestick Data (1992–2025)
This dataset provides minute-level OHLCV (Open, High, Low, Close, Volume) candlestick data for thousands of U.S. stocks across multiple decades (1992 to 2025). The data was originally sourced from Finnhub.io, a real-time market data provider.
It has been aggregated and reformatted from monthly .tar archives into clean and unified Parquet files — one per month — and uploaded to the Hugging Face Hub for easy access.
🧾 Dataset Structure
Each row in the dataset represents one minute of trading for a given stock ticker, and includes the following columns:
| Column | Type | Description |
|---|---|---|
timestamp |
datetime64[ns, UTC] |
Start time of the minute |
open |
float64 |
Opening price |
high |
float64 |
Highest price within the minute |
low |
float64 |
Lowest price within the minute |
close |
float64 |
Closing price |
volume |
float64 |
Volume traded within the minute |
ticker |
string |
Stock ticker symbol |
The data is split by month into files like:
data/ohlcv_1992-01.parquet data/ohlcv_1992-02.parquet ... data/ohlcv_2025-05.parquet
📚 Usage
from datasets import load_dataset
# Load the dataset (will stream across all months)
ds = load_dataset("mito0o852/OHLCV-1m", split="train")
# View one row
print(ds[0])
# To convert it into a pandas DataFrame:
import pandas as pd
df = ds.to_pandas()
print(df.head())