Sectors Performance
Sector Price Performance Distribution
For Date: 2026-09-04
Performance Heatmap (%)
| Sector | 1 Day | 1 Week | 1 Month | 3 Months | 6 Months | YTD | 1 Year |
| Technology | 0.75 | 0.15 | 4.45 | -5.12 | 35.59 | 29.19 | 43.05 |
| Industrials | 0.33 | -1.46 | -4.70 | 0.54 | 0.02 | 11.07 | 18.03 |
| Utilities | -0.12 | 0.70 | -3.00 | -0.92 | -7.36 | 0.99 | 5.49 |
| Materials | -0.25 | -1.05 | 3.16 | 2.29 | 2.14 | 15.02 | 17.77 |
| Real Estate | -0.73 | -0.52 | -2.06 | 2.60 | 2.84 | 11.29 | 10.38 |
| Consumer Staples | -0.83 | -0.22 | 0.47 | 4.50 | -1.60 | 11.12 | 8.72 |
| Financials | -0.84 | 0.79 | 2.06 | 15.53 | 15.34 | 7.53 | 11.16 |
| Energy | -0.84 | 3.10 | 9.92 | 10.86 | 15.89 | 43.50 | 50.56 |
| Health Care | -1.11 | 1.23 | 6.79 | 17.94 | 11.49 | 12.37 | 28.75 |
| Communication Services | -1.20 | 0.35 | 1.83 | 1.42 | -3.33 | -2.45 | 1.54 |
| Consumer Discretionary | -1.31 | -0.64 | -1.48 | -0.03 | 2.25 | -1.20 | 1.64 |
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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