Original title: How Perplexity + Claude Replace an Entire Analyst Team on Polymarket
Original author: @0xwhrrari
Compilation: Peggy, BlockBeats

Editor’s note: This article introduces a way to identify arbitrage opportunities on Polymarket and execute them in a systematic manner: use Perplexity to conduct research and pinpoint discrepancies between data and market pricing; use Claude to build trading logic, control risk, and automatically execute trades; and finally, complete the trades and cash them out on Polymarket.

The author’s core judgment is that profits come from “structured information gaps.” Market prices reflect the crowd’s intuition more than anything, while data (such as weather forecasts) provides a probability distribution. When the two are out of sync—and the system continuously catches that mismatch—it can be turned into a stable trading opportunity. Claude is the brain, Polymarket is the wallet, and Perplexity is the eyes. Together, the three form a complete arbitrage loop.

On one hand, this pattern lowers the barrier to entry so that individuals can achieve capabilities close to “team-level.” On the other hand, it raises the competitive bar. Once research, analysis, and execution are compressed into a single continuous chain, it becomes increasingly difficult to compete with systematic strategies by relying purely on experience or manual operation.

For ordinary participants, a more realistic path is to first find certainty through research, then amplify returns with a system. Whoever can get this method running earlier is more likely to keep generating consistent returns in these markets that look simple on the surface.

The following is the original article:

Among the top 20 traders on Polymarket, 14 are actually bots. A Claude-based agent turned $1,000 into $14,216 within 48 hours; while another OpenClaw-based agent, in the same time and on the same platform, got liquidated and ended up at zero.

The difference isn’t code quality—it’s preparation.

One agent was simply fed a general prompt and told to “trade on Polymarket.” Behind the other, however, is an entire complete research framework: which specific sub-category to trade, who is already profitable, where the data comes from, and how the underlying mathematical logic holds up.

Perplexity AI handles the research, Claude handles coding, and Polymarket handles the payouts.

This is the complete breakdown—save it.

You can try:

· Perplexity: perplexity.ai

· Strategy lookup: polymarket.com

· Copy-trading bot: t.me/PolyGunSniperBot

· Telegram channel: rari lr

Research layer: from zero to a strategy in 10 minutes

On Polymarket, there are dozens of trading categories: politics, crypto, sports, weather. Most people choose based on instinct—which is exactly where losing starts.

With just one deep research query, Perplexity can scan 47+ information sources in under 3 minutes: including Polymarket’s API documentation, posts on Reddit where traders share screenshots of profits and losses, and Twitter analyses that break down wallet behavior.

More importantly, every conclusion comes with citations and source links—not raw text without proof, but “verifiable data” that you can click and verify.

The breakdown is nearly instant:

BTC 5-minute market: the arbitrage window is only 2.7 seconds—this is the realm of high-frequency trading (HFT). You need co-located data center servers and at least a six-figure budget.

Sports arbitrage: profit margins are usually between 1–3%, and you need at least $5,000 in principal to justify taking execution risk.

Weather markets: profit margins are 3–4 times higher; you can enter with $100. Most participants are retail traders pricing based on intuition.

After the first response, Perplexity AI will also proactively suggest follow-up research questions:

“Should we compare NOAA with other weather forecast providers?”—Yes

“Should we look at Polymarket’s fee structure?”—Yes

“What is the historical accuracy of weather forecasts across different time horizons?”—Yes

It further dug out multiple trading wallet profiles. The system even automatically extracted data that isn’t present in the API: entry-timing patterns, average position size, and the distribution of trading frequency. If you were to track each wallet manually, a junior analyst might need an entire day.

And the common traits across these wallets are very clear: fully automated, running 24/7 around the clock, and zero emotion-based decision-making. No one is sitting in front of a computer clicking a mouse—these bots trade based on mathematics.

The third query further narrows it down: what is the best data source for U.S. weather markets?

Perplexity compared NOAA, OpenWeatherMap, and AccuWeather, evaluating them systematically across multiple dimensions such as accuracy, cost, update frequency, and API availability.

NOAA wins on every truly critical metric. Free, 24–48 hour forecast accuracy at 94%, modeling based on decades of satellite data and supercomputer simulations, hourly updates, an open API, and within reasonable usage limits, it’s almost free of rate restrictions.

After only three queries and about ten minutes, it produced a complete strategy map: which sub-market to do, which players are already profitable, and where the data source comes from.

Without Perplexity, the same research often takes 4 to 5 hours—going back and forth searching across Twitter, Reddit, various documentation pages, and academic papers—and you still can’t guarantee you’ll find the correct sources.

The mathematical logic behind the advantage

Polymarket’s temperature markets are binary: “Will the temperature in New York this Saturday be higher than 72°F?” The answer only has two options: yes or no. Final settlement is either $1 or $0.

But who is pricing these markets? Retail traders. They check the weather apps on their phones and might glance at a 7-day forecast while they’re at it. They won’t go pull NOAA’s probability distribution data.

The result is: NOAA provides a 94% probability confidence for a certain temperature range, but the market prices it at only 11 cents.

That’s what the data shows—and it reflects a structural mismatch between market price and the crowd’s perception.

For example, NOAA says the probability that New York falls in the 74–76°F range is 94%, while on Polymarket that range’s price is only 11 cents. So the bot buys at 11 cents. As more information gradually gets digested by the market over the next few hours, the price rises to 45–60 cents. The bot sells at 47 cents. Profit per share: +36 cents.

If you operate with a $2 position, the return is +$6.50. Run 10 trades like this per day, and that’s $65.

A single trade doesn’t look that impressive. What’s truly exciting is what happens after scaling up.

That’s also why Perplexity’s model council is important. The query about the “optimal position size” isn’t handled by a single model—it’s run in parallel through Claude, GPT, and Gemini.

The final answer isn’t the “opinion” of any one model, but the result of three large language models converging together.

When Claude, GPT, and Gemini independently compute and reach consistent conclusions on the same Kelly position ratio, this is no longer a “hallucination output,” but a cross-validated result.

In real execution, if the principal is only $100, each position should be no more than $2.

Conservative? Of course. But NOAA still has roughly a 6% chance of being wrong. Without proper position control, a single wrong trade can wipe out all the day’s profits. With 6 cities and more than 10 temperature ranges per city, that means there are over 60 markets to scan every day.

Perplexity’s multi-source analysis further consolidates three independent meteorological studies, confirming that NOAA’s 94% forecast accuracy within 24 hours is already a somewhat conservative estimate—accuracy is often even higher for core urban areas covered by more dense weather stations.

And this bot scans the market every 2 minutes. At that pace, it completes 720 scans across more than 60 markets per day. This level of coverage is something humans simply can’t sustain.

Claude as the “brain”

The entire system is divided into three modules: the scanner, the parser, and the executor.

NOAA scanner:

Polymarket parser (Parser):

Decision logic (Decision Logic):

Telegram report module (Reports):

A normal script would only execute if/then logic: if the condition is met → buy. That’s it. Simple. But a Claude-based agent reads “context.”

For example, is a hurricane approaching? NOAA data that originally updated every hour becomes updated every 30 minutes. The agent recognizes that the forecast instability is increasing and automatically reduces position size. It also reads the news feed, monitors sentiment changes on Twitter, and cross-validates multiple data sources—then dynamically adjusts its confidence before placing any actual orders.

That’s the difference between a calculator and an analyst.

With a 15-cent entry and NOAA confidence above 85%, it means there is at least a 5.6x mispricing between the true probability and the market pricing.
With a 45-cent exit, you can lock in 3x profit on every successful trade.

Set the daily loss limit to $50, meaning the worst day can lose at most half the principal—after which the bot will automatically shut down and resume running the next day.

System stack (The Stack)

Perplexity AI addresses the gap in the research layer: selecting sub-markets, locating data sources, mathematical validation, and risk assessment—everything is based on verifiable citations and sources.

Claude addresses the gap in the execution layer: code generation, logic implementation, and real-time adaptive decision-making.

Polymarket is the monetization layer.

Why Perplexity is an asymmetric advantage

Most people underestimate the “research” step. They jump straight to writing code and directly executing a strategy—then wonder why the bot starts losing money on day one.

Perplexity is not a search engine wrapped in a chat interface. Fundamentally, it’s a research infrastructure.

Multi-model consensus mechanism
Your query isn’t sent to just one model—it runs simultaneously on Claude, GPT, and Gemini. When all three models independently produce consistent answers, what you face is no longer “possible hallucinations,” but a cross-validated signal.

All conclusions come with citations
Every judgment can be traced back to sources. It’s not “I think NOAA’s accuracy is 94%,” but: there are research papers, API documentation, and Reddit discussions where traders validated with real profit and loss. You can click and verify item by item.

Deep Research depth
In under 3 minutes, it parses 47+ information sources: academic papers, API documentation, trading forums, and Twitter data analyses. What comes out isn’t just a bunch of links, but strategies you can execute directly.

Automatic generation of follow-up questions
It doesn’t just answer questions—it also tells you what to ask next: “Should we compare different forecast sources?” “Should we break down the fee structure?” It builds the full research path for you.

Compounding effect from speed
Research in 10 minutes replaces 4–5 hours of manual searching. This isn’t just convenience—it’s a structural advantage. While others are still browsing Reddit, your bot is already running and generating returns.

Claude is the brain; Polymarket is the wallet; and Perplexity is the eyes.

Without it, you’re trading blindly. With it, before you place your bet, you’ve already seen the entire chessboard.

Research layer → strategy layer → execution layer → returns. Perplexity is the first step. And that first step is exactly where 90% of traders fail.

Don’t skip it.

Most people read through this, nod, and continue trading manually. But the ones who truly act—right now—have opened Perplexity in another tab and run the first Deep Research query: sub-markets, profitable wallets, data sources, Kelly position size……

The distance from “knowing” to “doing” is just one prompt.

After you’ve earned your first $6.50 in a weather market, come back and read this—your understanding will be completely different.

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