What's New
Product updates, improvements and fixes across the KlickAnalytics platform.
Seasonal Trades Analysis
We’re excited to introduce app called Historical Trades Patterns Analysis tool, offering enhanced flexibility and deeper insights into your trading analysis.
Our users can select custom start and end days across different months and years, allowing you to explore trade patterns over any specific time period that interests you. Additionally, we’ve added the ability to filter and customize key metrics such as profit percentage, max rise/drop, Sharpe ratio, and volatility, making it easier for you to focus on the data that matters most.
Interactive charts now update in real-time as you adjust your date range or metrics, giving you a visually engaging way to spot trends and patterns instantly. A new performance summary section has also been added to quickly highlight key takeaways, providing a snapshot of your selected time frame's most critical metrics.
To view: search a symbol say TSLA, and then from the top menu, select Analysis > Seasonality > Trades
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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