import qrt as q
q.data.datasets.AVAILABLE('aapl',
'btcusd',
'spy',
'spy_breakout',
'spy_ema_cross',
'spy_random',
'spy_rsi2')
q.data.datasets.load ships daily OHLCV parquet files for a handful of symbols so tests, demos, and tutorials (like this one, and the Feature Engineering and Return Statistics pages) don’t need network access:
Alongside OHLCV prices, it bundles four demo strategy trade logs (see Data schemas) used throughout the Trade logs page:
('aapl',
'btcusd',
'spy',
'spy_breakout',
'spy_ema_cross',
'spy_random',
'spy_rsi2')
| open | high | low | close | volume | |
|---|---|---|---|---|---|
| datetime | |||||
| 2026-07-13 | 317.019989 | 323.450012 | 315.779999 | 317.309998 | 43257800 |
| 2026-07-14 | 313.760010 | 316.190002 | 311.910004 | 314.859985 | 36336800 |
| 2026-07-15 | 317.619995 | 328.730011 | 317.320007 | 327.500000 | 60957600 |
| 2026-07-16 | 328.010010 | 334.679993 | 326.790009 | 333.260010 | 62970600 |
| 2026-07-17 | 331.980011 | 334.989990 | 329.000000 | 333.739990 | 63365300 |
These bundled files are refreshed via make datasets before a release, so they’re reasonably current but not guaranteed to have today’s bar — fetch live data through q.data.sources if you need that.