Run the strategy before you put clients in it.
Set target weights, choose how often it rebalances, and see what the model would have returned against its benchmark.
Model Portfolios is part of the Advisor plan.
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Target weights
Hold the model by weight, by value or by share count, with a starting value and an annual fee.
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Allocations that change over time
Give the model a new set of weights from a date, and the history is rebuilt around it.
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Rebalancing on a cadence
Pick how often it returns to target, and every allocation change rebalances too.
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Measured against the benchmark
Return, CAGR, Sharpe, drawdown, beta, capture and a monthly heatmap next to the index you chose.
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Start from a template
Copy a ready-made model into your own list and change the holdings.
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A report you can hand over
Print the whole model report or save it as a PDF for the client meeting.
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.
Or start with