Scoring Models
Rank the market by what you care about.
Pick the measures that matter to you, weight them, and every grid on the site can rank by your score.
Free accounts keep two scoring models.
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Your factors
Choose from the same field catalog the screener uses — valuation, growth, quality, technicals.
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Your weights
Say how much each factor counts, and change your mind whenever the market does.
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A rank, not a black box
Every name gets a position you can trace back to the factors you chose.
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A score column anywhere
Switch a model on in the screener or a stock list and sort the whole universe by it.
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More than one way to look
Keep a value model and a momentum model side by side and compare what each one likes.
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Always on current data
The ranking is computed when you run it, so it moves with the market.
Build your first scoring model.
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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