CBXA stock price history
STOCKListed1, 5, 15, 30-minute, hourly & daily bars
| Symbol | CBXA |
|---|---|
| History | 2025-04-07 to 2026-10-27 (about 1 years) |
| Bar sizes | 1-minute, 5-minute, 15-minute, 30-minute, hourly and daily |
| Adjustments | Unadjusted, split-adjusted or split-and-dividend-adjusted, chosen on read |
| Survivorship | Included in the survivorship-free universe |
| Free tier | Last 12 months of daily bars |
Price history at a glance
CBXA last closed at $23.60 on 2026-10-27, -21.0% over the past year. Its deepest fall from a prior high in this history was -31.0%, reached on 2026-06-30.
| Last close | $23.60 (2026-10-27) |
|---|---|
| 1-year change | -21.0% |
| Annualized since 2025-04-07 | -3.7% |
| 52-week range | $20.79 – $29.87 |
| Deepest drawdown | -31.0% (2026-06-30) |
| Avg. daily dollar volume (3 months) | $26,692 |
| Daily bars in history | 282 |
Split- and dividend-adjusted closes. Computed by FinzData from its own daily bars.
Institutional ownership (13F, 2026Q2)
| Managers holding | 8 |
|---|---|
| Shares held | 226,637 ($4.7m reported value) |
| Quarter flows | 3 new, 3 added, 1 trimmed, 3 exited; +102,689 shares vs the prior quarter |
From SEC Form 13F filings. Every holder, line by line, and the full history since 2013: yf.Client().holders("CBXA") on the look-back bundles. Top holders of CBXA · All 13F managers
Short interest (2026-10-15)
| Shares short | 208 (-0.5% vs prior report) |
|---|---|
| Days to cover | 2.67 |
CBXA short interest history · Unclassified (ETFs, funds, other) sector
CBXA FAQ
How far back does CBXA price history go?
2025-04-07 to 2026-10-27
Is CBXA still trading?
Yes. CBXA is listed and updated every trading day.
What was CBXA's last close?
$23.60 on 2026-10-27, -21.0% over one year
How do I download CBXA historical data in Python?
pip install finzdata, then yf.Ticker("CBXA").history(start="2025-04-07")
Get it in Python
import finzdata as yf
df = yf.Ticker("CBXA").history(start="2025-04-07", interval="1d")
m15 = yf.Ticker("CBXA").history(period="5d", interval="15m") # also 1m, 5m, 30m, 1h
df.tail()