---
license: mit
pretty_name: Open30 30-Minute Open Equity Features Dataset
task_categories:
  - tabular-classification
  - tabular-regression
tags:
  - finance
  - trading
  - equities
  - intraday
  - tabular
  - time-series
  - xgboost
  - walk-forward-validation
size_categories:
  - 100K<n<1M
---


# Open30 30-Minute Open Equity Features Dataset

Assembled modeling table for Open30, a research project studying short-horizon equity behavior after the U.S. market open.

The rows are candidate trade instances keyed by `(date, ticker, side)`. Features include prior daily price context, volatility and liquidity proxies, opening-minute behavior, market alignment features, calendar features, mean-reversion regime features, and Alpha Vantage news sentiment aggregates. The table also includes supervised outcome labels for multiple reward/risk targets.

Author GitHub: [mospira](https://github.com/mospira)  
Project repo: [mospira/ml-open30](https://github.com/mospira/ml-open30)  
Project site: [Open30 Research](https://mospira.github.io/ml-open30/)  
Research report: [Open30 Research Report](https://mospira.github.io/ml-open30/research_report/)

## Files

- `dataset_open30m.parquet`

## Dataset Details

- Rows: `195,166`
- Columns: `77`
- Date range: `2010-04-28` through `2026-02-27`
- Universe size: `25` tickers
- Candidate sides: `long`, `short`
- Entry assumption: `09:31 ET`
- Label scan window: `09:31` through `09:59 ET`
- Reward/risk multiples: `0.5`, `1.0`, `1.5`, `2.0`

## Column Groups

Identifier columns:

- `date`
- `ticker`
- `side`

Feature groups include:

- prior daily price context
- volatility regime
- liquidity proxies
- first-minute open-window features
- SPY/QQQ market-alignment features
- calendar features
- mean-reversion regime features
- news sentiment aggregates and interactions

Label columns follow this pattern:

- `y_type_m_<multiple>`
- `y_R_m_<multiple>`
- `y_hit_minute_m_<multiple>`
- `y_ambig_m_<multiple>`

Outcome encoding:

- `0`: stop loss
- `1`: take profit
- `2`: time exit
- `3`: ambiguous same-bar stop/target touch

## Usage

```python
from datasets import load_dataset

ds = load_dataset("mospira/open30-equity-features", split="train")
df = ds.to_pandas()
print(df.head())