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README.md

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----
-license: cc-by-4.0
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+---
+license: cc-by-4.0
+language:
+  - en
+pretty_name: HF Data Library
+tags:
+  - finance
+  - high-frequency
+  - intraday
+  - OHLCV
+  - market-microstructure
+  - financial-econometrics
+  - equity
+  - ETF
+  - realized-volatility
+size_categories:
+  - 1B<n<10B
+task_categories:
+  - time-series-forecasting
+  - tabular-regression
+---
+
+# HF Data Library: High-Frequency U.S. Equity Data
+
+[![Website](https://img.shields.io/badge/Website-hfdatalibrary.com-2563eb)](https://hfdatalibrary.com) [![DOI](https://img.shields.io/badge/DOI-10.5281%2Fzenodo.19501605-blue)](https://doi.org/10.5281/zenodo.19501605) [![License: CC BY 4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
+
+Free, research-grade collection of OHLCV (Open-High-Low-Close-Volume) data for **1,391 U.S. equities and ETFs**, covering December 2002 through the present (45 tickers extending to January 1991). Data is available in multiple timeframes from 1-minute up to monthly. Updated weekly via automated pipeline.
+
+**Maintainer:** Ahmed Elkassabgi, University of Central Arkansas
+**ORCID:** [0000-0002-5926-7493](https://orcid.org/0000-0002-5926-7493)
+**Permanent DOI:** [10.5281/zenodo.19501605](https://doi.org/10.5281/zenodo.19501605)
+
+## Where to download
+
+**This Hugging Face repository contains documentation only.** The actual data is hosted at:
+
+➡️ **https://hfdatalibrary.com**
+
+Free registration required (email, ORCID, or Google). Data is available as direct downloads (Parquet or CSV) or via REST API at `https://api.hfdatalibrary.com`.
+
+## What's in the dataset
+
+- **1,391 tickers** of U.S. equities and ETFs
+- **1.53 billion** 1-minute bars (clean version)
+- **December 2002 – present** (with 45 tickers extending to January 1991)
+- **Weekly automated updates**
+
+### Cleaning versions
+
+Two cleaning versions are provided:
+
+- **Raw:** as received from the source, no modifications
+- **Clean:** nine-step cleaning pipeline applied (outside-hours removal, OHLC violations, duplicates, Brownlees-Gallo outlier filter, splice-boundary adjustment)
+
+A gap-filled version is intentionally **not** distributed — see the accompanying paper for documented biases introduced by LOCF gap-filling. Researchers who need a regular grid can apply LOCF to the Clean version themselves.
+
+### Available timeframes
+
+All cleaning versions are aggregated into multiple timeframes:
+
+| Timeframe | Description |
+|---|---|
+| 1-minute | Base data (highest resolution) |
+| 5-minute | Aggregated from 1-minute |
+| 15-minute | Aggregated from 1-minute |
+| 30-minute | Aggregated from 1-minute |
+| Hourly | Aggregated from 1-minute |
+| Daily | Open-to-close per trading day |
+| Weekly | Aggregated to trading weeks |
+| Monthly | Aggregated to calendar months |
+
+### Pre-computed academic variables
+
+25 variables computed daily for each ticker in each cleaning version:
+
+**Volatility (5):** Realized variance (1-min and 5-min sampling), bipower variation (BNS 2004), Parkinson (1980), Yang-Zhang (2000)
+
+**Spreads (2):** Roll (1984) implied spread, Corwin-Schultz (2012) high-low spread
+
+**Autocorrelation (3):** First-order return autocorrelation, variance ratio (5-min), variance ratio (10-min)
+
+**Jump detection (3):** BNS z-statistic, BNS jump indicators at 1% and 5% levels
+
+**Liquidity (4):** Amihud (2002) illiquidity ratio, daily dollar volume, share volume, observed trade count
+
+**Data quality (4):** Gap rate, observed bars per day, longest gap, max bars since last trade
+
+**Returns (4):** Open-to-close return, overnight return, daily high-low range, intraday return standard deviation
+
+## Data sources
+
+- **Pre-March 2022:** PiTrading, derived from the consolidated tape (CTA/UTP)
+- **Post-March 2022:** IEX Exchange HIST
+
+## Quick start (Python)
+
+```python
+import requests
+import pandas as pd
+from io import BytesIO
+
+# Register at https://hfdatalibrary.com to get an API key
+API_KEY = "your-key-here"
+
+# Get a download token (links expire after 10 minutes)
+r = requests.get(
+    "https://api.hfdatalibrary.com/v1/download-token/AAPL",
+    params={"version": "clean", "format": "parquet", "timeframe": "1min"},
+    headers={"X-API-Key": API_KEY}
+)
+url = r.json()["url"]
+
+# Download the file
+data = requests.get(url).content
+df = pd.read_parquet(BytesIO(data))
+print(df.head())
+```
+
+## File schema
+
+Each ticker is a single Parquet (or CSV) file. For 1-minute data:
+
+| Column | Type | Description |
+|---|---|---|
+| datetime | datetime64 | Bar timestamp (Eastern Time) |
+| Open | float64 | Opening price (split/dividend adjusted) |
+| High | float64 | Highest price during the bar |
+| Low | float64 | Lowest price during the bar |
+| Close | float64 | Closing price |
+| Volume | int64 | Shares traded |
+| source | string | "pitrading" (pre-2022) or "iex" (post-2022) |
+
+Higher timeframes (5-min, 15-min, daily, etc.) follow the same schema but with the `datetime` column resampled to the chosen interval.
+
+## License
+
+This dataset is licensed under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).
+
+You are free to share and adapt the material for any purpose, including commercially, provided you give appropriate credit.
+
+## How to cite
+
+```bibtex
+@dataset{elkassabgi2026hfdatalibrary,
+  author    = {Elkassabgi, Ahmed},
+  title     = {{HF Data Library: High-Frequency U.S. Equity Data (1-Minute OHLCV)}},
+  year      = {2026},
+  version   = {1.0},
+  publisher = {Zenodo},
+  doi       = {10.5281/zenodo.19501605},
+  url       = {https://hfdatalibrary.com}
+}
+```
+
+## Links
+
+- **Website:** https://hfdatalibrary.com
+- **API:** https://api.hfdatalibrary.com
+- **Documentation:** https://hfdatalibrary.com/pages/docs.html
+- **Data dictionary:** https://hfdatalibrary.com/pages/dictionary.html
+- **Code samples:** https://hfdatalibrary.com/pages/code.html
+- **GitHub:** https://github.com/elkassabgi/hfdatalibrary
+- **Zenodo:** https://zenodo.org/records/19501605
+- **Contact:** aelkassabgi@uca.edu