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# Query AAPL minute bars, all 90 fields
import algoseek
client = algoseek.Client()
bars = client.get_bars(
symbol=‘AAPL’,
bar_type=‘minute’,
date=‘2025-03-14’)
✓ 390 rows · 90 fields · 0.41s
bars[[‘time’, ‘open’, ‘high’, ‘low’, ‘close’, ‘volume’, ‘vwap’]].head(5)
| time | open | high | low | close | volume | vwap |
|---|---|---|---|---|---|---|
| 09:30 | 213.41 | 213.89 | 213.12 | 213.67 | 1,241,830 | 213.54 |
| 09:31 | 213.68 | 214.02 | 213.41 | 213.93 | 892,104 | 213.71 |
| 09:32 | 213.94 | 214.17 | 213.66 | 214.08 | 743,219 | 213.92 |
| 09:33 | 214.09 | 214.31 | 213.88 | 214.22 | 651,087 | 214.10 |
| 09:34 | 214.23 | 214.44 | 214.01 | 214.38 | 587,331 | 214.28 |
SELECT
date, ticker,
SUM (volume)
AS total_vol,
AVG (vwap)
AS avg_vwap,
COUNT (*)
AS bars
FROM eq_mb_taq
WHERE ticker =
‘AAPL’
AND date
BETWEEN
‘2025-01-02’
AND
‘2025-03-14’
GROUP BY date, ticker
ORDER BY date
✓ 49 rows · 0.28s
| date | ticker | total_vol | avg_vwap | bars |
|---|---|---|---|---|
| 2025-01-02 | AAPL | 58,241,092 | 243.18 | 390 |
| 2025-01-03 | AAPL | 47,103,881 | 242.67 | 390 |
| 2025-01-06 | AAPL | 63,892,410 | 241.93 | 390 |
Query first, then download exactly the rows you need.
Dataset
eq_mb_taq
Filter
AAPL · 2025-03-14
Rows
390 rows × 90 fields
Format
CSV · 2.4 MB
You only download the specific rows your query selected. The compute and filtering happens inside ArdaDB, so you pull exactly what you need, not the entire dataset.
Request
GET /v1/bars/eq_mb_taq
?symbol=
AAPL
&date=
2025-03-14
&fields=
time,open,high,low,close,volume
&limit=5
Response 200 OK · 0.31s
{
“rows”:
390,
“data”:
[
{
“time”:
“09:30”,
“open”:
213.41,
“high”:
213.89,
“close”:
213.67
},
{
“time”:
“09:31”, …
},
{
“time”:
“09:32”, …
}
]
}
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