| --- |
| 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()) |
