What's New
Product updates, improvements and fixes across the KlickAnalytics platform.
Historical Time Series Aggregation
Now you can analyze thousands of data points for multiple instruments, companies, ETFs, commodities and any global instrument via our new app called "aggregate". The aggregate app allows you to;
Create your own list of instruments
Chose your field to aggregate e.g Close Price, % Change, Volume etc.
Select historical aggregate via
Sum
Average
Maximum
Minimum
Count
On top of it, calculate results for
Percentage Change
Cumulative Change
Difference
Ratio
Define your own time series range
The system will calculate all the time series for each instrument and then apply specified aggregation, and summary calculations to give you historical trend analysis based on your own list of instruments.
To access: From the top menu, click on Tools > Aggregate
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