Seasonality Analysis
Historical Prices Year-by-Year
Historical Returns (%) By Years/Months
| Year | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Min | Max | avg |
| 2026 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ||||||||||
| 2025 | 0.00 | -66.67 | 0.00 | 0.00 | 0.00 | -66.67 | 0.00 | -13.33 | |||||||
| Summary | |||||||||||||||
| Avg Returns (%) | nan | 0.00 | -66.67 | 0.00 | 0.00 | nan | 0.00 | 0.00 | nan | 0.00 | nan | nan | nan | nan | nan |
| Max Pos Return (%) | 0.00 | 0.00 | -66.67 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | -5.56 |
| Max Neg Return (%) | 0.00 | 0.00 | -66.67 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | -5.56 |
| Pos Occurances (%) | nan | 0 | 0 | 0 | 0 | nan | 0 | 0 | nan | 0 | nan | nan | nan | nan | nan |
| Neg Occurance (%) | nan | 100 | 100 | 100 | 100 | nan | 100 | 100 | nan | 100 | nan | nan | nan | nan | nan |
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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