The Scenario
Most trade-relevant information arrives in formats that are hostile to traders: a 98-minute keynote, a 40-tweet thread, a podcast. Watching all of it is expensive. Skipping it means missing the one paragraph that matters.
And a summary alone isn’t enough. Knowing what someone said doesn’t tell you whether it’s tradeable — that depends on whether the market has already priced it in. Your bot does both halves: extract the signal, then cross-check it against flows, order books, and equities.
Try It
What You Get Back
A long video, reduced to what’s tradeable
You: 6/1 Jensen Huang COMPUTEX 2026 keynote livestream runs over an hour — pull out the trade-relevant parts (how this AI narrative hits crypto sectors), and check whether the market has already priced it in.
Bot: 📹 “Jensen Huang COMPUTEX 2026 Keynote” — NVIDIA Keynote (98min)
Key announcements
- Unveiled next-gen AI acceleration architecture, big jump in inference compute
- AI data-center capex + sovereign-AI demand keep expanding
- Heavy focus on Physical AI / robotics and AI-agent deployment
Trade-relevant extraction
- AI narrative bullish → crypto AI sector (RENDER / TAO / FET etc.)
- Also watch NVDA and AI-compute-chain equities
Market cross-check
- ✅ Crypto AI sector net inflows warming up over the last 7d, narrative not fully played out
- ⚠️ NVDA firm after hours — part of the equities side may be priced in
- ⚠️ On-chain AI tokens highly concentrated; chasing highs is risky
Conclusion: AI narrative leans bullish medium-term; equities have partly reacted while the crypto AI sector lags — a catch-up candidate. Light position, avoid chasing.
The cross-check is the part that makes this useful. A summary would have stopped at “bullish AI narrative.” The reply tells you where that’s already in the price and where it isn’t.
A KOL call, with their track record attached
You: This KOL is calling a trade again. Show me what he said this time, and whether his past calls were accurate.
Bot: 🐦 @CryptoHayes (Arthur Hayes · ~731K followers)
This signal
- Asset: $HYPE
- Direction: Long
- Catalyst: “Hyperliquid fee-buyback flywheel + HIP-3 perp expansion; $HYPE’s value capture is underpriced”
- Timeframe: short-to-mid (1–3 weeks)
Hit-rate backtest (last 90 days)
- Similar calls: 11
- Direction correct: 7/11 (63.6%)
- Average return: +8.2%
- Max loss: −22% (two high-beta alt calls drew down hard)
Assessment: Above-average hit rate overall, but high-beta names (like $HYPE) are volatile. Small position with a 10% stop.
[Set as monitor] [Copy trade]
Note what the max-loss line does. A 63.6% hit rate sounds good until you see that the misses ran −22%. Both numbers get reported, because only one of them tells you how to size.
Hit rates, average returns, and backtest windows in these examples are sample output from a specific moment, shown to illustrate the format — not a performance claim, and not a prediction. A KOL’s past accuracy says nothing certain about their next call.
When to Use
- A keynote or earnings call you can’t sit through. Get the tradeable minutes without the other 90.
- A KOL you’re unsure about. Before you act on a call, see how the last ten went.
- A thread that’s already moving price. Ask whether the move has run its course.
- Cross-asset narratives. When something in equities or macro spills into crypto sectors, this is the fastest way to find which tokens it touches.
Tips
- Paste the link and the question together.
"<link> — is this priced in?" gets you further than the link alone.
- Name the sector you care about. “How does this hit AI tokens” gives a sharper answer than “summarize this.”
- Ask for the counter-argument. Follow up with
"what would make this wrong?" before you size a position on it.
- Turn a call into a monitor. If a signal looks worth watching but not worth taking, ask the bot to watch it — see Scheduled Automations.
