Sectors Performance
Sector Price Performance Distribution
For Date: 2026-09-16
Performance Heatmap (%)
| Sector | 1 Day | 1 Week | 1 Month | 3 Months | 6 Months | YTD | 1 Year |
| Health Care | 0.15 | 0.65 | 0.37 | 10.14 | 11.97 | 8.74 | 24.57 |
| Utilities | 0.04 | -3.77 | -6.47 | -7.06 | -11.41 | -3.03 | -0.89 |
| Technology | -0.01 | -2.20 | -3.46 | -4.08 | 32.72 | 27.64 | 35.15 |
| Industrials | -0.06 | -1.71 | -9.38 | -5.27 | 2.21 | 7.44 | 12.30 |
| Consumer Staples | -0.27 | 0.82 | -1.12 | -1.37 | -0.23 | 9.12 | 8.37 |
| Real Estate | -0.58 | -0.78 | -3.93 | -3.43 | 2.72 | 8.32 | 5.33 |
| Consumer Discretionary | -0.61 | -1.40 | -5.03 | -6.30 | -0.78 | -5.94 | -7.15 |
| Materials | -0.69 | -1.28 | -2.89 | -3.00 | 3.53 | 10.89 | 13.59 |
| Communication Services | -0.92 | 2.89 | 2.90 | 1.90 | -0.56 | -1.89 | -2.98 |
| Financials | -1.62 | -0.37 | -1.27 | 6.52 | 16.31 | 4.39 | 7.55 |
| Energy | -2.77 | 0.95 | 5.35 | 19.55 | 15.42 | 46.41 | 53.97 |
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.
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