Historical Prices
Historical Stats
| Low | High | Average | |
|---|---|---|---|
| Close | 167.32 2025-04-21 | 508 days ago |
284.02 2026-08-03 | 39 days ago |
223.56 |
| Open | 162.00 2025-04-07 | 522 days ago |
281.59 2026-08-05 | 37 days ago |
223.62 |
| High | 162.00 2025-04-07 | 522 days ago |
281.59 2026-08-05 | 37 days ago |
223.62 |
| Low | 161.38 2025-04-07 | 522 days ago |
278.00 2026-08-03 | 39 days ago |
220.88 |
| Volume | 11,420,543 2025-12-24 | 261 days ago |
241,155,673 2026-06-26 | 77 days ago |
44,392,881 |
| Price Volume | 2,653,905,782 2025-12-24 | 261 days ago |
56,114,513,550 2026-06-26 | 77 days ago |
9,888,478,509 |
| Change | -19.36 2025-08-01 | 406 days ago |
36.08 2026-07-31 | 42 days ago |
0.14 |
| Change % | -8.98 2025-04-03 | 526 days ago |
15.32 2026-07-31 | 42 days ago |
0.09 |
Historical Prices
Ask the market a question. Get a calculated answer.
The AI is not a chatbot bolted onto a document store. It calls the same analytics engine that powers every screen on this platform — so what comes back is a number it computed from raw history, with the command that produced it.
86,000+ instruments
Global equities, ETFs, funds, options, FX, commodities, crypto, economics, filings, transcripts and news — one normalised symbol universe with adjusted history.
A real analytics engine
Screening, backtesting, technicals, options analytics, correlations, seasonality and factor models — computed on demand from raw prices, never a stale cache.
It shows its working
Answers arrive with the charts, tables and tool calls behind them, so you can check the number instead of trusting a paraphrase.
Your own documents
Upload filings, decks and research. Ask across them and the answer cites the page it came from.
Agents and workflows
Multi-step research that runs the platform's tools for you — screen, pull the history, compute, compare, then write it up.
MCP, CLI and API
The same command catalogue from Claude, your own agent, a shell or your pipeline. The answer on screen is the answer your job gets at 4am.
You ask
“How does NVDA usually trade through earnings?”
It calls
→ ka.options_expected_move(NVDA)
It answers
NVDA has averaged a 9.2% absolute move on the day after earnings and closed higher 67% of the time. Two in three reactions land between −4.2% and +16.3% — the distribution is skewed right, not symmetric.
Every figure computed live from our own history — not scraped, not summarised.
Or start with