Update: An onchain market intelligence layer (Bitquery + OpenClaw)
You're watching five different screens. Polymarket is printing a large fill. A meme coin on PumpFun is spiking. A cross-chain arb window just opened on wrapped BTC. By the time you've processed the first signal, the other two are gone.
This is the core problem with monitoring onchain markets as a human. The setup below is one attempt to solve it. It's a continuously running AI layer that watches streams in parallel, applies consistent rules, and brings you only what crossed the line. It replaces manual tab-switching with a single approval queue.
The compounding problem
Onchain traders hit two constraints at once:
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Latency during active hours: Bots react in milliseconds. Humans monitoring and context-switching move slower, so edges during peak flow are easy to miss.
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Availability across 24 hours: Polymarket, meme venues, and cross-chain arb rarely line up with sleep or a single screen. Off-hours, signals keep printing and often go unseen when nobody is at the keyboard.
What the layer is doing
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Streaming in parallel: Keeping one clock across prediction markets, high-velocity meme ticks, and wrapped BTC on multiple networks so detection is not waiting on a human serial scan.
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Applying consistent rules: In experiments: large Polymarket prints above a notional threshold trigger mirroring at half size; PumpFun tick jumps above X% trigger queueing a small exploratory buy. Criteria stay repeatable and written down.
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Queueing then approving: The agent batches what crossed the line: “here are N signals that hit your rules; approve or reject.” The trader spends more time gating and refining and less time rebuilding the tape by hand.
Illustrative captures from that setup (terminal UI and logs; simulation-style IDs and books, for illustration only):
Stack (how the pieces map)
| Layer | Role |
|---|---|
| Bitquery | Supplies real-time structured streams: Polymarket fills, PumpFun USD OHLC, multi-network BTC, Trading.Tokens-style charts. The agent consumes APIs, not scraped HTML. |
| OpenClaw skills | Detection layer: small behaviors that load when the user asks for X and run subscription Y (BTC feed, multi-token USD chart, Polymarket trades, PumpFun, stablecoin flows). |
| OpenClaw agent | Decision and orchestration: keeps sessions alive, applies your rules, batches signals, and drives approve or reject (including the simulated books in the screenshots above). |
Where to install and read the code
All Bitquery-backed OpenClaw skills referenced in this note are open source and documented here:
github.com/bitquery/openclaw-skills-master
That repo documents installing via ClawHub (clawhub install …), setting BITQUERY_API_KEY, and running the Python streaming scripts for bitcoin-price-feed, crypto-chart-usd, pumpfun-usd-price-stream, polymarket-real-time-trades, and stablecoin-payments. For registry context, see ClawHub and OpenClaw.
On ClawHub I publish the onchain data streams I actually run—packaged as installable skills with the fields and filters:
- Reliable Bitcoin price feed (
bitquery-crypto-price-stream) - Crypto charting with USD pricing (1s) (
crypto-chart-usd) - Polymarket real-time trades stream (
polymarket-real-time-trades) - PumpFun USD price stream (
pumpfun-usd-price-stream) - Solana stablecoin payments tracking (USDC / USDT;
stablecoin-paymentsin the skills repo)