Tenki — Kalshi Research Before the Market Moves

What

Tenki is an AI-powered research tool for people who trade on Kalshi prediction markets. It scans 10,000+ active Kalshi markets overnight, filters out low-liquidity or otherwise unattractive markets, and delivers 3–7 researched trade ideas by 7:00 a.m. ET.

The product appears positioned as a lightweight daily decision-support layer rather than an execution platform or advisory service. Its workflow centers on identifying potentially mispriced markets, summarizing bull and bear cases with linked sources, estimating probabilities, and sending users directly to the relevant Kalshi markets so they can decide whether to trade.

Features

• Nightly market-wide scanning — Reviews every active Kalshi market each night and removes dead markets, sports combinations, and low-volume opportunities before deeper analysis.
• Mispricing-focused filtering — Scores markets for potential pricing gaps so users can spend time on a smaller set of higher-priority ideas instead of browsing the full exchange.
• Deep research packets for each pick — Provides probability estimates, historical context, live-news inputs, and sourced arguments for both the bullish and bearish cases.
• “What kills the trade” framing — Highlights the key condition or evidence that would invalidate the thesis, which helps users evaluate risk and disconfirming signals.
• Source verification — States that every claim links to evidence users can check themselves, adding transparency to the research output.
• Outcome tracking and live comparison — Maintains a public track record of past picks and says its dashboard tracks internal estimates against market prices in real time.

Helpful Tips

• Evaluate research quality by category — Since Tenki covers politics, economics, sports, crypto, weather, and culture, buyers should check whether the product is consistently useful in the specific market types they trade most.
• Use it as a triage layer, not a substitute for judgment — The product explicitly describes itself as research, not financial advice, so it is best treated as a way to narrow attention and structure due diligence.
• Review the public track record carefully — A transparent history that includes losses is useful, but it should be assessed for consistency, market regime sensitivity, and whether the documented reasoning was actionable at the time.
• Check how often liquidity constraints matter to your workflow — Tenki filters for volume and liquidity, which is practical for tradeability, but users should confirm that its thresholds match their own position-sizing needs.
• Test whether morning delivery fits your process — The value is tied to overnight scanning and pre-market delivery, so adoption will depend on whether 7:00 a.m. ET output aligns with how and when you place trades.

OpenClaw Skills

Tenki is a strong candidate for OpenClaw workflows built around market research intake, trade thesis review, and daily signal routing. A likely use case would be an OpenClaw agent that ingests Tenki emails or dashboard outputs, classifies picks by market type, extracts bull/bear arguments and “trade killer” conditions, and turns them into structured briefings, watchlists, or internal risk memos. The site does not mention a native OpenClaw integration, so this should be treated as a workflow inference rather than a confirmed capability.

In a broader OpenClaw ecosystem, Tenki could support specialized agents for prediction-market monitoring, thesis drift detection, and post-resolution learning loops. For example, an analyst team or individual trader could use OpenClaw to compare Tenki’s probability estimates with evolving news signals, maintain a journal of accepted and rejected picks, and generate after-action reviews once markets resolve. That combination could make event-contract research more systematic for traders, researchers, or small speculative teams by turning daily picks into repeatable decision workflows rather than one-off inbox alerts.