Correlations
See what actually moves together.
Build a correlation matrix across any basket, then watch how a pair drifted apart over time.
Free accounts keep two saved sets and two years of history.
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Clustered heatmap
Up to 30 symbols, ordered so the names that behave alike sit together.
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Your basis, your window
Pearson or Spearman, on prices, daily or weekly returns, over the dates you choose.
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Diversification insights
The least and most correlated pairs, and how tightly the basket moves as a whole.
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One factor, or many
Principal components show how much of the basket is really a single bet, and which names carry it.
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Rolling through time
Follow one pair across regimes, with confidence bands, lead-lag and the spread between them.
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Save a basket
Keep the sets you watch and share them with your team.
Map your first basket.
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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