Sectors Performance
Sector Price Performance Distribution
For Date: 2026-08-26
Performance Heatmap (%)
| Sector | 1 Day | 1 Week | 1 Month | 3 Months | 6 Months | YTD | 1 Year |
| Industrials | 1.08 | -1.95 | -2.62 | 2.61 | 2.12 | 13.51 | 19.18 |
| Technology | 0.71 | -1.03 | 4.27 | -1.72 | 27.39 | 26.25 | 39.54 |
| Energy | 0.49 | -2.39 | 6.34 | 8.04 | 14.65 | 37.82 | 44.80 |
| Utilities | 0.29 | -1.61 | -5.19 | -3.84 | -7.32 | 1.64 | 4.64 |
| Materials | 0.17 | 2.02 | 4.26 | 5.47 | 1.81 | 17.11 | 19.01 |
| Financials | -0.04 | 1.44 | 2.51 | 12.85 | 13.38 | 7.07 | 11.17 |
| Consumer Staples | -0.18 | -0.02 | 1.36 | 4.18 | -1.57 | 12.76 | 9.70 |
| Communication Services | -0.28 | 1.67 | 5.13 | -1.80 | -2.30 | -2.62 | 2.79 |
| Real Estate | -0.61 | 0.82 | -0.87 | 2.35 | 6.03 | 14.08 | 11.39 |
| Consumer Discretionary | -0.66 | -0.54 | 6.41 | -1.06 | 1.14 | 0.06 | 2.19 |
| Health Care | -0.86 | -0.22 | 7.28 | 18.55 | 12.01 | 13.68 | 30.94 |
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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