Sectors Performance
Sector Price Performance Distribution
For Date: 2026-09-01
Performance Heatmap (%)
| Sector | 1 Day | 1 Week | 1 Month | 3 Months | 6 Months | YTD | 1 Year |
| Energy | 1.23 | 3.06 | 7.41 | 12.43 | 13.67 | 42.04 | 45.69 |
| Utilities | 0.65 | -2.49 | -4.78 | -1.40 | -9.65 | -0.89 | 3.30 |
| Health Care | 0.52 | -2.71 | 4.92 | 15.86 | 8.49 | 10.60 | 26.14 |
| Consumer Staples | 0.22 | -1.78 | -0.08 | 4.32 | -3.00 | 10.75 | 8.28 |
| Real Estate | -0.41 | -2.76 | -2.13 | 2.84 | 1.99 | 10.94 | 9.73 |
| Communication Services | -0.59 | -1.52 | 2.97 | -3.34 | -4.90 | -4.10 | 1.49 |
| Financials | -0.94 | -1.03 | 1.35 | 12.60 | 13.47 | 5.97 | 9.36 |
| Materials | -1.21 | -1.66 | 4.48 | 3.86 | -0.25 | 15.17 | 17.14 |
| Industrials | -1.44 | -1.83 | -2.62 | 1.83 | -1.60 | 11.43 | 17.81 |
| Technology | -1.60 | 2.62 | 6.36 | -4.62 | 33.98 | 29.56 | 44.33 |
| Consumer Discretionary | -1.79 | -1.15 | 0.43 | -1.15 | 1.42 | -1.09 | 2.16 |
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