Original title: How Perplexity + Claude Replace an Entire Analyst Team on Polymarket
Original author: @0xwhrrari
Original translation by Peggy, BlockBeats
Editor's Note: This article introduces a method for identifying and systematically executing arbitrage opportunities on Polymarket: using Perplexity to conduct research and locate discrepancies between data and market pricing; using Claude to build trading logic, control risk, and automate execution; and finally, completing the transaction and realizing the profit on Polymarket.
The author's core argument is that profits come from "structured information asymmetry." Market prices reflect collective intuition more, while data (such as weather forecasts) provides probability distributions. When these two diverge and are continuously captured by the system, they can be transformed into stable trading opportunities. Claude is the brain, Polymarket is the wallet, and Perplexity is the eyes; the three work together to form a complete arbitrage loop.
This model lowers the barrier to entry, enabling individuals to possess near-team-level capabilities; however, it also raises the bar for competition. With research, analysis, and execution compressed into a continuous chain, relying solely on experience or manual operation will become increasingly difficult to compete with systematic strategies.
For ordinary participants, a more realistic approach is to first identify certainty through research, and then leverage the system to amplify returns. Whoever can successfully implement this method sooner is more likely to consistently achieve stable returns in these seemingly simple markets.
The following is the original text:
Of the top 20 traders on Polymarket, 14 are actually bots. One Claude-based bot turned $1,000 into $14,216 in 48 hours; while another OpenClaw-based bot was wiped out in the same amount of time on the same platform.
The difference lies not in the quality of the code, but in the level of preparation.
One agent is simply fed a general prompt and told to "trade on Polymarket"; while the other is backed by a complete research system: which sub-sector to trade, who is already profitable, where the data comes from, and how the underlying mathematical logic holds true.
Perplexity AI is responsible for research, Claude is responsible for coding, and Polymarket is responsible for paying out rewards.
This is a complete disassembly; I recommend saving it.
You can try it:
- Perplexity: perplexity.ai
- Strategy overview: polymarket.com
- Copy trading bot: t.me/PolyGunSniperBot
- Telegram channel: rari lr
Research Level: From Zero to Strategy in 10 Minutes
Polymarket offers dozens of trading categories: politics, crypto, sports, weather. Most people choose based on gut feeling, which is exactly where losing money begins.
With just one in-depth research query, Perplexity can scan 47+ information sources in less than 3 minutes: including Polymarket's API documentation, traders' posts on Reddit sharing screenshots of profits and losses, and Twitter analysis of wallet behavior.
More importantly, each conclusion comes with citations and source links—not raw text without evidence, but "verifiable data" that can be clicked and checked.
The disassembly yielded results almost immediately:
BTC 5-minute market: The arbitrage window is only 2.7 seconds, which is the domain of high-frequency trading (HFT). You will need shared servers in a data center and a budget of at least six figures.
Sports arbitrage: Profit margins are typically between 1% and 3%, and a minimum capital of $5,000 is required to justify the execution risk.
Weather market: Profit margins are 3–4 times higher, with entry points as low as $100. Most participants are retail investors who price based on intuition.
After the initial response, Perplexity AI will proactively suggest follow-up research questions:
"Should we compare NOAA with other weather forecast providers?" — Yes
"Would you like to take a look at Polymarket's fee structure?"
What is the historical accuracy rate of weather forecasts across different time spans?
It further uncovered profiles of multiple trading wallets. The system even automatically extracted data not found in the API: entry timing patterns, average position size, and trading frequency distribution. This type of analysis, if done manually by tracking each wallet individually, would likely take a junior analyst an entire day.
These wallets share very clear characteristics: fully automated, operating 24/7, and making decisions with zero emotion. No one sits in front of a computer clicking a mouse—these bots trade based on mathematics.
The third query focuses further on: What is the best data source for the US weather market?
Perplexity compared NOAA, OpenWeatherMap, and AccuWeather, conducting a systematic evaluation across multiple dimensions, including accuracy, cost, update frequency, and API availability.
NOAA wins on all the truly critical metrics. It's free, has 94% accuracy in 24-48 hour forecasts, is based on decades of satellite data and supercomputer modeling, updates hourly, has an open API, and has virtually no rate limits when used reasonably.
In just three queries and ten minutes, I obtained a complete strategy map: which market segment to target, who is already profitable, and where the data source is.
Without Perplexity, the same research would often take four to five hours, sifting through Twitter, Reddit, various document pages, and academic papers, with no guarantee that you would actually find the right source.
The mathematical logic behind the advantage
Polymarket's temperature market is a binary market: "Will the temperature in New York be higher than 72°F this Saturday?" There are only two answers: yes or no. The final settlement is either $1 or $0.
But who sets the prices in these markets? Individual investors. They look at weather apps on their phones, and maybe glance at the 7-day forecast. They don't bother with NOAA's probability distribution data.
The result is that NOAA gives a 94% confidence level for a certain temperature range, but the market only prices it at 11 cents.
This is the result shown by the data, a structural misalignment between the data and the market's perception.
For example, NOAA estimated a 94% probability that New York would fall within the 74–76°F range on Saturday, while the price in that range on Polymarket was only 11 cents. The robot bought in at 11 cents. As more information was gradually absorbed by the market over the next few hours, the price rose to 45–60 cents. The robot sold at 47 cents. Earnings per share: +36 cents.
If you trade with a $2 position, the profit would be +$6.50. Making 10 such trades a day would yield $65.
A single transaction may not seem impressive. What's truly exciting is the result when scaled up.
This is why Perplexity's model council is important. The query for "optimal position size" is not handled by a single model—but is handled simultaneously by Claude, GPT, and Gemini in parallel.
The final answer is not the "viewpoint" of any one model, but the result of the convergence of the three major models.
When Claude, GPT, and Gemini reach a consistent conclusion on the same Kelly position ratio after independent calculations, it is no longer a possible "illusionary output" but a result that has been cross-validated.
In practice, if the principal is only $100, each position should not exceed $2.
Conservative? Of course, conservative. But NOAA still has about a 6% error rate. Without proper position control, a single wrong trade can wipe out all the day's profits. Six cities, each with more than ten temperature ranges—that means there are more than 60 markets to scan every day.
Perplexity's multi-source analysis further summarized three independent meteorological studies, confirming that NOAA's 94% forecast accuracy over 24 hours is actually a conservative estimate—the accuracy is often even higher for core metropolitan areas with denser weather station coverage.
This robot scans the market every two minutes. Based on this, it would complete 720 scans across more than 60 markets per day. This level of coverage is something humans simply cannot sustain.
Claude as the "brain"
The entire system is divided into three modules: scanner, parser, and executor.
NOAA Scanner:
Polymarket Parser:
Decision Logic:
Telegram Reports module:
A typical script simply executes if/then logic: condition met → buy. It's that simple. But a Claude-based agent reads the "context."
For example, a hurricane is approaching? NOAA data, which was originally updated hourly, is now updated every 30 minutes. The AI recognizes the increasing instability of the forecast and automatically reduces the position size. It also reads news feeds, monitors sentiment changes on Twitter, and cross-validates multiple data sources—dynamically adjusting its confidence level before actually placing an order.
That's the difference between a calculator and an analyst.
Entering the market at 15 cents and with a NOAA confidence level above 85% implies a discrepancy of at least 5.6 times between the true probability and the market price.
Exiting at 45 cents allows you to lock in 3x the profit on every successful trade.
Setting the daily loss limit to $50 means that on the worst day, you can lose up to half of your principal—after which the bot will automatically shut down and resume operation the next day.
System Stack
Perplexity AI addresses the gaps in the research layer: market segmentation, data source identification, mathematical verification, and risk assessment—all based on verifiable citations and sources.
Claude addresses the gaps in the execution layer: code generation, logic implementation, and real-time adaptive decision-making.
Polymarket is the monetization layer.
Why is perplexity an asymmetric advantage?
Most people underestimate the "research" step. They jump straight to writing code, implementing strategies—and then wonder why the robot starts losing money on day one.
Perplexity is not a search engine with a chat interface; it is essentially a research infrastructure.
Multi-model consensus mechanism
Your query is not submitted to a single model, but rather runs simultaneously on Claude, GPT, and Gemini. When the three models independently arrive at a consistent answer, you are no longer facing a "possible illusion," but a cross-validated signal.
All conclusions are cited.
Every judgment can be traced back to its source. It's not "I think NOAA's accuracy rate is 94%", but rather: there are research papers, API documentation, and Reddit discussions where traders verify it with real profits and losses. You can click through to verify each one.
The depth of Deep Research
It parses over 47 information sources in less than 3 minutes: academic papers, API documentation, trading forums, and Twitter data analysis. The output isn't a bunch of links, but rather directly executable strategies.
Automatic generation of follow-up questions
It not only answers questions, but also tells you what to ask next: "Should we compare different forecast sources?" "Should we break down the cost structure?" It builds a complete research path for you.
The compounding effect of speed
Ten minutes of research can replace four to five hours of manual searching. This isn't just about convenience; it's a structural advantage. While others are still browsing Reddit, your bot is already running and generating revenue.
Claude is the brain; Polymarket is the wallet; and Perplexity is the eye.
Without it, you are trading blindly; with it, you have seen the whole chessboard before placing your bet.
Research layer → Strategy layer → Execution layer → Profit; Perplexity is the first step. And this first step is precisely where 90% of traders fail.
Do not skip this.
Most people, after reading this, will nod and continue trading manually. But those who actually take action are already on another tab, opening Perplexity and running their first Deep Research query: market segment, profit wallet, data source, Kelly position…
The distance between "knowing" and "doing" is just a prompt.
Once you've made your first $6.50 in a weather market, come back to this article—you'll have a completely different understanding.
