Sectors Performance
Sector Price Performance Distribution
For Date: 2026-08-28
Performance Heatmap (%)
| Sector | 1 Day | 1 Week | 1 Month | 3 Months | 6 Months | YTD | 1 Year |
| Communication Services | 1.42 | 0.60 | 3.03 | -2.90 | -3.59 | -2.79 | 2.50 |
| Consumer Discretionary | 1.15 | -0.92 | 4.21 | -3.78 | 1.96 | -0.57 | 0.89 |
| Energy | 0.63 | -0.68 | 8.88 | 10.86 | 11.39 | 39.20 | 43.76 |
| Consumer Staples | 0.43 | -2.29 | -1.85 | 1.91 | -2.47 | 11.36 | 9.41 |
| Financials | 0.38 | -0.21 | 0.87 | 13.72 | 14.24 | 6.68 | 9.56 |
| Materials | -0.09 | -0.75 | 1.60 | 3.93 | 0.68 | 16.24 | 17.50 |
| Health Care | -0.24 | -2.03 | 2.33 | 13.94 | 8.89 | 11.01 | 27.65 |
| Real Estate | -0.40 | -1.88 | -3.33 | 1.04 | 2.84 | 11.87 | 9.37 |
| Industrials | -0.93 | -1.04 | -2.93 | 2.17 | -0.47 | 12.71 | 16.94 |
| Utilities | -1.04 | -1.13 | -6.13 | -3.65 | -8.58 | 0.28 | 3.79 |
| Technology | -1.55 | 3.13 | 8.53 | -0.50 | 33.40 | 29.00 | 40.07 |
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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