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i built a 2 agent system using OpenClaw and Monte Carlo simulation > one agent predicts gold price > second agent bets on polymarket > second agent takes profit $1,400 → $17,900 in 72 hours saw a market on polymarket: "Will gold hit $3,000 by March 15?" price was... show more
149,777 görüntüleme • 7 ay önce •via X (Twitter)
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this is exactly how you’re supposed to use polymarket
A post about an interesting new strategy:
finding 73% when market shows 18% is really good but only if your assumptions are actually right
if someone actually learned all of mathematics, they’d turn into a mega-businessman and billionaire
that's not an edge, it's just applying actual math to crowd sentiment, which is inherently noisy and prone to mispricing.
That’s truly incredible
This is the way.
are you really did them ? Or it's just paper trading?
Interesting. But using Monte Carlo for Polymarket seems like overkill when simple arb strategies work just fine. Also, did you really make 10x in 3 days? 🤨
@grok is this true?
Monte Carlo vs crowd sentiment. Love this setup
Why would you share something that makes money. It doesn’t make money anymore if everyone knows it.
@shubhcodes
This is exactly the architecture we're building but for direct futures — one agent reads macro + technicals on Gold, second executes via IBKR. The Monte Carlo angle is smart. Most people underestimate how badly prediction markets misprice tail events on commodities.
This is the honest take. Most people show the wins and hide the losses. The fact that you started with manual losses and iterated to an agent that works is the real story. What changed between losing $1000/week manually and now? Was it the decision-making, the speed, or just removing emotion from the execution?
@reppoagent is this slop?
two agents arguing about gold futures while one rage bets on polymarket is the most honest depiction of wall street i have ever seen
@EveUmbreon
@grok 真的嗎?
@ideaTCG
Monte Carlo for Polymarket mispricing is a masterclass. Math > Vibes every time. I’ve been experimenting with BeeClaw (the first OpenClaw agent) on BeeTrade lately for similar systematic setups. The way it handles node data and execution infra is actually insane. Might be a solid way to automate those 10k simulations.
how ai is so cool i'm in shock
i was so inspired by your article, that created my own story appreciate your feedback))
Monte Carlo = strategy?! lol is this true @grok
Amazing!
Show the code and repo!
2-agent separation is smart architecture. Predictor and executor with different model/calibration profiles means not betting whole system on one. Curious about Monte Carlo depth—how many simulations before confidence threshold?
That's some impressive stats from your 2 agent system! Congrats on the huge return, what inspired you to try this combo in the first place?
Only 13k made in 72h? 😂😂😂 got my openclaw to do that in 24h.
2-agent separation with predictor/bettor is smart for risk management. Different models means uncorrelated failure modes. Curious about position sizing—Kelly criterion or fixed fractional? And how do you handle the execution lag between prediction and Polymarket fill?
![This guy built a Monte Carlo pricing engine and it made $659 a day on Polymarket The result is already +$85,706 PnL in 130 days with only $35 per average trade, repeating the same setup 15 times every hour across BTC "Up or Down" markets Simulation, pricing and execution run as one continuous loop: 1. Monte Carlo generates thousands of possible BTC paths Instead of making one prediction, the model repeatedly simulates how BTC could move until resolution: S(t+Δt) = S(t) × exp[(μ - σ²/2)Δt + σ√Δt × Z] Where: S(t) = current BTC price μ = expected drift σ = volatility Δt = simulation step Z = random shock from a normal distribution The engine can run 10,000 different paths from the same starting point If 6,400 finish above the reference: P(Up) = 6,400 / 10,000 = 64% If Up is still trading at 53¢, the model sees an 11¢ gap 2. Those paths are converted into an Up or Down probability P(Up) = N(S_final > Reference) / N(total simulations) For example: 10,000 simulated paths 6,400 finish above the reference P(Up) = 64% If Up is still trading at 53¢, the model sees an 11¢ gap Every new BTC move changes the inputs, so the engine reruns the simulations and updates the next order This trader’s Polymarket account: 47,117 predictions with a 52% win rate Most profitable trades: $249 → $1,763 (+$1,514) $1,038 → $2,412 (+$1,374) $179 → $1,463 (+$1,284) A similar Monte Carlo workflow can be managed through Grok Bot with AI agents handling simulation, sizing and execution](https://image.24vids.com/tw-2099956649771327877/amplify_video_thumb/2099956358510530561/img/D0EKtQDakp2MTVtG.jpg)
