Update readme.md

README.md

@@ -1,3 +1,87 @@
 ---
 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("YOUR_HF_USERNAME/open30-equity-features", split="train")
+df = ds.to_pandas()
+print(df.head())