Sectors Performance
Sector Price Performance Distribution
For Date: 2026-08-20
Performance Heatmap (%)
| Sector | 1 Day | 1 Week | 1 Month | 3 Months | 6 Months | YTD | 1 Year |
| Energy | 0.67 | 4.13 | 9.73 | 4.47 | 16.79 | 41.19 | 54.10 |
| Real Estate | 0.40 | -0.29 | -0.53 | 3.28 | 5.76 | 13.15 | 11.69 |
| Materials | 0.35 | 0.40 | 4.98 | 7.49 | 0.23 | 14.80 | 18.74 |
| Technology | 0.04 | -3.74 | 4.51 | 6.13 | 31.29 | 27.57 | 41.12 |
| Financials | -0.24 | -1.34 | 2.57 | 12.88 | 11.18 | 5.55 | 10.97 |
| Utilities | -0.37 | -0.05 | -2.05 | -0.09 | -3.25 | 3.31 | 5.13 |
| Communication Services | -0.65 | -1.09 | 0.47 | -3.66 | -2.76 | -4.22 | 2.31 |
| Health Care | -0.75 | 4.34 | 10.32 | 19.78 | 12.67 | 13.94 | 30.69 |
| Consumer Staples | -0.75 | 0.63 | 1.98 | 1.22 | -0.05 | 12.79 | 7.48 |
| Industrials | -0.80 | -2.07 | 2.15 | 8.09 | 3.72 | 15.77 | 21.75 |
| Consumer Discretionary | -1.35 | 0.12 | 3.47 | 3.30 | 2.43 | 0.60 | 3.99 |
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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