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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...

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149,772 Aufrufe • vor 7 Monaten •via X (Twitter)

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30 Kommentare

Profilbild von RetroChainer
RetroChainervor 7 Monaten

this is exactly how you’re supposed to use polymarket

Profilbild von ZER
ZERvor 7 Monaten

A post about an interesting new strategy:

Profilbild von Parletto
Parlettovor 7 Monaten

finding 73% when market shows 18% is really good but only if your assumptions are actually right

Profilbild von expemilly
expemillyvor 7 Monaten

if someone actually learned all of mathematics, they’d turn into a mega-businessman and billionaire

Profilbild von Cross Identity Payments
Cross Identity Paymentsvor 7 Monaten

that's not an edge, it's just applying actual math to crowd sentiment, which is inherently noisy and prone to mispricing.

Profilbild von ALINA
ALINAvor 7 Monaten

That’s truly incredible

Profilbild von Rando
Randovor 7 Monaten

This is the way.

Profilbild von Predictive Nerd
Predictive Nerdvor 7 Monaten

are you really did them ? Or it's just paper trading?

Profilbild von Gabagool 22 Trading Bot
Gabagool 22 Trading Botvor 7 Monaten

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? 🤨

Profilbild von rob
robvor 7 Monaten

@grok is this true?

Profilbild von BuBBliK
BuBBliKvor 7 Monaten

Monte Carlo vs crowd sentiment. Love this setup

Profilbild von Jack Degen
Jack Degenvor 7 Monaten

Why would you share something that makes money. It doesn’t make money anymore if everyone knows it.

Profilbild von Shantanu
Shantanuvor 7 Monaten

@shubhcodes

Profilbild von WeGo2Mars
WeGo2Marsvor 7 Monaten

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.

Profilbild von ADI
ADIvor 7 Monaten

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?

Profilbild von Ana
Anavor 7 Monaten

@reppoagent is this slop?

Profilbild von Evil Cass
Evil Cassvor 7 Monaten

two agents arguing about gold futures while one rage bets on polymarket is the most honest depiction of wall street i have ever seen

Profilbild von Coinupbtc 🪙
Coinupbtc 🪙vor 7 Monaten

@EveUmbreon

Profilbild von CryptoHT💧🦈
CryptoHT💧🦈vor 7 Monaten

@grok 真的嗎?

Profilbild von 3 Pham
3 Phamvor 7 Monaten

@ideaTCG

Profilbild von Jibran Malik
Jibran Malikvor 7 Monaten

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.

Profilbild von Bayzy
Bayzyvor 7 Monaten

how ai is so cool i'm in shock

Profilbild von Mura
Muravor 7 Monaten

i was so inspired by your article, that created my own story appreciate your feedback))

Profilbild von Rock Trades Futures
Rock Trades Futuresvor 7 Monaten

Monte Carlo = strategy?! lol is this true @grok

Profilbild von ⱤɆ₳ĐɎ.฿₮₵ {₴₥฿💥}
ⱤɆ₳ĐɎ.฿₮₵ {₴₥฿💥}vor 7 Monaten

Amazing!

Profilbild von Carlos Velasco
Carlos Velascovor 7 Monaten

Show the code and repo!

Profilbild von Yahia Ibrahim
Yahia Ibrahimvor 7 Monaten

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?

Profilbild von Solglyph
Solglyphvor 7 Monaten

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?

Profilbild von Carlito | Google & Meta Ads Specialist
Carlito | Google & Meta Ads Specialistvor 7 Monaten

Only 13k made in 72h? 😂😂😂 got my openclaw to do that in 24h.

Profilbild von Yahia Ibrahim
Yahia Ibrahimvor 7 Monaten

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?

Ähnliche Videos

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
0:25

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

Daniro

86,463 Aufrufe • vor 1 Monat

My mom called me crying, she thought I was gambling away my savings on Polymarket. I sent her one screenshot. She went quiet for 40 seconds. $41,200. From $200. In 27 days. "...is that real?" Not luck. Not sports betting. Google told me the answers before the market heard them. Every time a topic spikes on Google Trends - Fed decision, Trump indictment, government shutdown - Polymarket prices lag 4 to 12 hours. Millions searching. Market hasn't moved. That delay is the entire edge. One Claude agent. One job: watch Google Trends for spikes above 300% tied to open Polymarket markets. Spike hits, market hasn't moved - buy. Profile Claude agent: Day 1: "government shutdown" spiked 840% at 2AM. Polymarket still at 23¢. Agent bought. By noon - 47¢. $200 → $610 Day 4: "rate cut" exploded 6 hours before the Fed announcement. 3 markets under 15¢. All resolved above 60¢. $610 → $2,400 I use for copytrade bots Gluon: Day 9: CEO resignation leaked on Reddit. Google Trends spiked at 11PM. Agent was in at 11:02PM. By morning - 5x. $2,400 → $7,800 Day 14: Pattern - every time "inflation data" spikes 500%+ on a Tuesday night, CPI markets are mispriced by 12-18¢. Three Tuesdays in a row. Agent caught all three. $7,800 → $16,500 Day 21: "ceasefire" spiked in 4 languages simultaneously. Conflict markets at 11¢. Agent loaded 8 positions. $16,500 → $29,400 Day 27: Three spikes between midnight and 4AM - when American traders sleep but the world doesn't. 19 trades. 16 green. $29,400 → $41,200 Only buys below 20¢. Sells above model price. Max $3 per position. 4 billion people google the answer. The market just hasn't heard it yet. 2,700+ trades. 84% win rate. $200 → $41,200. My mom still doesn't fully get what I do. But she stopped crying. The agent made $380 while she was reading the screenshot.
0:32

My mom called me crying, she thought I was gambling away my savings on Polymarket. I sent her one screenshot. She went quiet for 40 seconds. $41,200. From $200. In 27 days. "...is that real?" Not luck. Not sports betting. Google told me the answers before the market heard them. Every time a topic spikes on Google Trends - Fed decision, Trump indictment, government shutdown - Polymarket prices lag 4 to 12 hours. Millions searching. Market hasn't moved. That delay is the entire edge. One Claude agent. One job: watch Google Trends for spikes above 300% tied to open Polymarket markets. Spike hits, market hasn't moved - buy. Profile Claude agent: Day 1: "government shutdown" spiked 840% at 2AM. Polymarket still at 23¢. Agent bought. By noon - 47¢. $200 → $610 Day 4: "rate cut" exploded 6 hours before the Fed announcement. 3 markets under 15¢. All resolved above 60¢. $610 → $2,400 I use for copytrade bots Gluon: Day 9: CEO resignation leaked on Reddit. Google Trends spiked at 11PM. Agent was in at 11:02PM. By morning - 5x. $2,400 → $7,800 Day 14: Pattern - every time "inflation data" spikes 500%+ on a Tuesday night, CPI markets are mispriced by 12-18¢. Three Tuesdays in a row. Agent caught all three. $7,800 → $16,500 Day 21: "ceasefire" spiked in 4 languages simultaneously. Conflict markets at 11¢. Agent loaded 8 positions. $16,500 → $29,400 Day 27: Three spikes between midnight and 4AM - when American traders sleep but the world doesn't. 19 trades. 16 green. $29,400 → $41,200 Only buys below 20¢. Sells above model price. Max $3 per position. 4 billion people google the answer. The market just hasn't heard it yet. 2,700+ trades. 84% win rate. $200 → $41,200. My mom still doesn't fully get what I do. But she stopped crying. The agent made $380 while she was reading the screenshot.

Lunar

12,484 Aufrufe • vor 7 Monaten

A 29-year-old sales consultant from China quit his job and now makes in 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment 'AGENT' 2. Like and retweet this 3. Follow Marry Evan so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.
0:25

A 29-year-old sales consultant from China quit his job and now makes in 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment 'AGENT' 2. Like and retweet this 3. Follow Marry Evan so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.

Marry Evan

19,180 Aufrufe • vor 4 Monaten

A 29-year-old sales consultant from China quit his job. Now making 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment AGENT 2. Like and retweet this 3. Follow Himanshu Kumar so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.
0:25

A 29-year-old sales consultant from China quit his job. Now making 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment AGENT 2. Like and retweet this 3. Follow Himanshu Kumar so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.

Himanshu Kumar

12,423 Aufrufe • vor 4 Monaten

A 33-year-old quant from Hong Kong built a perfect BTC price simulation engine with a swarm of agents. In a single trade, he turned $2,000 into $166,000. 7,500% profit. All proof onchain - every single one of his trades is publicly visible on Polymarket. His wallet: Humans can’t process dozens of gigabytes of data in seconds - but Claude can. Closed order book feeds from CEXs, live data from OTC trading desks, Pyth oracles - AI agent gathers all of it in one iteration. His algorithm instantly detects any mispricing in crypto markets and enters trade immediately. $369,000 all-time profit. Constantly fading the crowd because his simulation reads the market better than everyone else. On April 24, he was only one who knew the market was wrong. He entered a trade with just a 1.3% implied probability of execution. This isn’t "guessing where the chart will go" This is engineered money. Pure fusion of AI + MiroFish + insane math on exclusive data. Want the full guide to build the same bot? Save the post and read the article. Want to start copying his trades right now? Use this TG copy-trading bot (US users can actually trade here, unlike the main Polymarket site):
0:56

A 33-year-old quant from Hong Kong built a perfect BTC price simulation engine with a swarm of agents. In a single trade, he turned $2,000 into $166,000. 7,500% profit. All proof onchain - every single one of his trades is publicly visible on Polymarket. His wallet: Humans can’t process dozens of gigabytes of data in seconds - but Claude can. Closed order book feeds from CEXs, live data from OTC trading desks, Pyth oracles - AI agent gathers all of it in one iteration. His algorithm instantly detects any mispricing in crypto markets and enters trade immediately. $369,000 all-time profit. Constantly fading the crowd because his simulation reads the market better than everyone else. On April 24, he was only one who knew the market was wrong. He entered a trade with just a 1.3% implied probability of execution. This isn’t "guessing where the chart will go" This is engineered money. Pure fusion of AI + MiroFish + insane math on exclusive data. Want the full guide to build the same bot? Save the post and read the article. Want to start copying his trades right now? Use this TG copy-trading bot (US users can actually trade here, unlike the main Polymarket site):

cvxv666

83,436 Aufrufe • vor 5 Monaten

I made $19,600 in 18 days copy-trading a Chinese quant who turned $200 into $354,000 in 48 hours. Same MiroFish + Claude setup he's used to clear $350K all-time. 7,500% on a single position. I've prepared the exact step-by-step guide to build this BTC simulation engine. Giving this free for 24 hours. To get it: 1. Comment "Claude" + Like and Retweet 2. Follow me Himanshu Kumar (Only then, i can DM you) You only need Claude + a device + 1 hour/day. Used The system below: - Claude = the algorithm's brain - MiroFish = the simulation engine - 10,000 cycles run before every single trade - Closed order book + private OTC desk feeds His wallet hit $350K all-time. Mine hit $19.6K in 18 days copying him. His Polymarket ID: gobblewobble He's not predicting the future. He's running 10,000 versions of every market reaction before the market moves. You Must Follow me Himanshu Kumar, so i can send you DM. You're staring at charts hoping for a setup. He's running Monte Carlo simulations while you sleep.
0:35

I made $19,600 in 18 days copy-trading a Chinese quant who turned $200 into $354,000 in 48 hours. Same MiroFish + Claude setup he's used to clear $350K all-time. 7,500% on a single position. I've prepared the exact step-by-step guide to build this BTC simulation engine. Giving this free for 24 hours. To get it: 1. Comment "Claude" + Like and Retweet 2. Follow me Himanshu Kumar (Only then, i can DM you) You only need Claude + a device + 1 hour/day. Used The system below: - Claude = the algorithm's brain - MiroFish = the simulation engine - 10,000 cycles run before every single trade - Closed order book + private OTC desk feeds His wallet hit $350K all-time. Mine hit $19.6K in 18 days copying him. His Polymarket ID: gobblewobble He's not predicting the future. He's running 10,000 versions of every market reaction before the market moves. You Must Follow me Himanshu Kumar, so i can send you DM. You're staring at charts hoping for a setup. He's running Monte Carlo simulations while you sleep.

Himanshu Kumar

130,880 Aufrufe • vor 5 Monaten

Mind blown 2.0: Onchain quants just printed $400,000+ trading Polymarket bets on SPX, Dow, Russell 2000, AAPL, GOOG - all powered by open-source financial market simulations! The god-tier stack just dropped: Financial Datasets MCP Server (1.7k stars) + MiroThinker-H1 (88.2 benchmark, 7.1k stars) + MiroFish - multi-agent simulation engine built by a Chinese undergrad student in just 10 days (now 18k+ stars and scored millions in funding overnight). How each repo works and how to apply it: 1. Financial Datasets MCP Server -> Unlimited real historical prices, balance sheets, income statements and news for ANY ticker (SPX, AAPL, GOOG, Russell 2000 etc.). This is your free 40+ year data parser - just connect and pull raw facts. Repo: 2. MiroThinker-H1 -> Deep research agent (pulls data straight from the MCP Server). Analyzes latest 10-K, Fed minutes, earnings, geopolitics and builds 500+ clean, verified datasets. Without it your simulation is garbage - it turns raw data into the perfect “brain” for the engine. Repo: 3. MiroFish -> The actual multi-agent simulation engine. Load the dataset from MiroThinker and run thousands of AI agents with different personalities (macro strategist, sentiment analyst, panic buyer etc.). Get probability cones and the full "matrix" - exactly how price will react to any event. Repo: Key Applications: .Trading: throw in any event -> simulate crowd reaction -> catch the edge and ape on Polymarket .Macro forecasting: test global events before they hit the news .Easy setup: Docker + any LLM API, live in 10-15 minutes Pro tip: Feed MiroThinker latest 10-K or breaking news -> it builds 500+ verified scenario datasets -> load into MiroFish -> get probability cones for next-week price moves. Then ape the highest-conviction side on Polymarket risk-free. Traders are already winning big: [superstonksbro] -> PnL = $182k, multiple $20k+ wins. [CamelUp] -> PnL = $193k, 2.4k+ predictions. Both crushing it with this exact stack. For effortless gains, try Kreo copy-trading: auto-mirror these new simulation beasts and ride their edges. Try here: Add their wallets: 0x17559efac103ac7f361be37ec0b93888d4c55aac // 0x969fae0a3a93778adc42178f72c612ed8c4e4d55 to [ and start track/copy them right now. Save this links and info so you don't lose it.
0:33

Mind blown 2.0: Onchain quants just printed $400,000+ trading Polymarket bets on SPX, Dow, Russell 2000, AAPL, GOOG - all powered by open-source financial market simulations! The god-tier stack just dropped: Financial Datasets MCP Server (1.7k stars) + MiroThinker-H1 (88.2 benchmark, 7.1k stars) + MiroFish - multi-agent simulation engine built by a Chinese undergrad student in just 10 days (now 18k+ stars and scored millions in funding overnight). How each repo works and how to apply it: 1. Financial Datasets MCP Server -> Unlimited real historical prices, balance sheets, income statements and news for ANY ticker (SPX, AAPL, GOOG, Russell 2000 etc.). This is your free 40+ year data parser - just connect and pull raw facts. Repo: 2. MiroThinker-H1 -> Deep research agent (pulls data straight from the MCP Server). Analyzes latest 10-K, Fed minutes, earnings, geopolitics and builds 500+ clean, verified datasets. Without it your simulation is garbage - it turns raw data into the perfect “brain” for the engine. Repo: 3. MiroFish -> The actual multi-agent simulation engine. Load the dataset from MiroThinker and run thousands of AI agents with different personalities (macro strategist, sentiment analyst, panic buyer etc.). Get probability cones and the full "matrix" - exactly how price will react to any event. Repo: Key Applications: .Trading: throw in any event -> simulate crowd reaction -> catch the edge and ape on Polymarket .Macro forecasting: test global events before they hit the news .Easy setup: Docker + any LLM API, live in 10-15 minutes Pro tip: Feed MiroThinker latest 10-K or breaking news -> it builds 500+ verified scenario datasets -> load into MiroFish -> get probability cones for next-week price moves. Then ape the highest-conviction side on Polymarket risk-free. Traders are already winning big: [superstonksbro] -> PnL = $182k, multiple $20k+ wins. [CamelUp] -> PnL = $193k, 2.4k+ predictions. Both crushing it with this exact stack. For effortless gains, try Kreo copy-trading: auto-mirror these new simulation beasts and ride their edges. Try here: Add their wallets: 0x17559efac103ac7f361be37ec0b93888d4c55aac // 0x969fae0a3a93778adc42178f72c612ed8c4e4d55 to [ and start track/copy them right now. Save this links and info so you don't lose it.

slash1s

35,105 Aufrufe • vor 7 Monaten

I woke up to $4,217 on polymarket a week ago it was $1,000 i didn't place a single trade myself 6 AI agents did it for me running 24/7 on a $9/month server 84 trades. 57 wins. 69% win rate here's the full architecture, you can build the same thing 6 agents. each one has one job SCANNER monitors every hourly BTC/ETH market on polymarket pulls price from binance websocket in real time calculates momentum, volatility, orderbook imbalance runs every 60 minutes FACTOR MINER generates trading hypotheses using claude haiku ($0.25/1M tokens) tests each one against historical data keeps only factors with IC > 0.05 currently running 10 active factors. auto-kills bad ones after 50 trades ANALYST runs 3 signals in parallel: → LightGBM model (30 features, 500 trees) outputs probability → claude sonnet reads news from tavily, scores sentiment, weighted by source trust (EMA 0.95) → orderflow detector tracks whale buys, liquidity shifts, large order clusters all 3 signals go through bayesian aggregation not simple voting. actual bayes theorem market 52% UP. my signals: 62%, 71%, 63% bayesian posterior: 82% final probability 75.4% edge = 23.4% AUDITOR checks everything before money moves. catches hallucinated news blocks trades under 10 min to resolution blocks low liquidity markets penalizes confidence by 8% per flag found RISK MANAGER quarter kelly. max 10% bankroll per trade stops after 3 consecutive losses 15% drawdown = everything shuts down correlation limits so BTC and ETH positions don't stack. calculates exact EV before every trade EXECUTOR places orders through polymarket CLI. retry logic. 3 attempts. iceberg splits for large positions stack: → python + lightgbm for ML → langgraph for agent orchestration → binance websocket for prices → polymarket CLI (rust) for execution → coinbase agentic wallet (TEE) → hetzner VPS CX32: $9/month full monthly cost: $29 results after 7 days: 84 trades. 57 wins. 27 losses win rate: 69% $1,000 → $4,217 avg $460/day at current bankroll compound math: $17,800 by next friday $75k+ end of month the system trades every hour. i check telegram alerts from my phone
1:04

I woke up to $4,217 on polymarket a week ago it was $1,000 i didn't place a single trade myself 6 AI agents did it for me running 24/7 on a $9/month server 84 trades. 57 wins. 69% win rate here's the full architecture, you can build the same thing 6 agents. each one has one job SCANNER monitors every hourly BTC/ETH market on polymarket pulls price from binance websocket in real time calculates momentum, volatility, orderbook imbalance runs every 60 minutes FACTOR MINER generates trading hypotheses using claude haiku ($0.25/1M tokens) tests each one against historical data keeps only factors with IC > 0.05 currently running 10 active factors. auto-kills bad ones after 50 trades ANALYST runs 3 signals in parallel: → LightGBM model (30 features, 500 trees) outputs probability → claude sonnet reads news from tavily, scores sentiment, weighted by source trust (EMA 0.95) → orderflow detector tracks whale buys, liquidity shifts, large order clusters all 3 signals go through bayesian aggregation not simple voting. actual bayes theorem market 52% UP. my signals: 62%, 71%, 63% bayesian posterior: 82% final probability 75.4% edge = 23.4% AUDITOR checks everything before money moves. catches hallucinated news blocks trades under 10 min to resolution blocks low liquidity markets penalizes confidence by 8% per flag found RISK MANAGER quarter kelly. max 10% bankroll per trade stops after 3 consecutive losses 15% drawdown = everything shuts down correlation limits so BTC and ETH positions don't stack. calculates exact EV before every trade EXECUTOR places orders through polymarket CLI. retry logic. 3 attempts. iceberg splits for large positions stack: → python + lightgbm for ML → langgraph for agent orchestration → binance websocket for prices → polymarket CLI (rust) for execution → coinbase agentic wallet (TEE) → hetzner VPS CX32: $9/month full monthly cost: $29 results after 7 days: 84 trades. 57 wins. 27 losses win rate: 69% $1,000 → $4,217 avg $460/day at current bankroll compound math: $17,800 by next friday $75k+ end of month the system trades every hour. i check telegram alerts from my phone

Archive

30,073 Aufrufe • vor 7 Monaten

It printed. I gave Claude the gold trading strategy from a $250K Polymarket wallet and it rebuilt the entire system. $4,298 profit later I knew I need to share this. Gold and silver prices + UP/DOWN = under radar gem. I've never seen anyone explain this publicly I didn't thought I would post it, but here's the entire breakdown of this strategy for you So you can lock the f in and try to copy it for yourself: Albert1953. Joined June 2022. 2.5K views. $248,326.77 all-time. 3,338 predictions. $35,300 biggest win. Profile → 0x777fae71d2ff9ec48a1213d48ba1d9d91024a1bb I found this wallet at 2AM scrolling pages of Polymarket nobody reads. Opened the positions. Stared at the screen. Not a single standard crypto trade. "Will Silver hit HIGH $120 by end of June?" → bought No at 61.3¢ → now 89.9¢. +46% "Will Gold hit HIGH $5,500 by end of June?" → bought No at 22.1¢ → now 75.7¢. +242% "Will Gold settle above $6,200 in June?" → bought No at 75.4¢ → now 94.2¢. +24% $106,300 still sitting in active positions. All green. All commodities. Before I built my own version I copytraded this wallet for 48 hours to stress-test the logic. $80 in. Didn't touch it. Went to sleep. Woke up to $4,298. I alwas stress-test wallets I find by copy trading here: Here's exactly how to build this with Claude yourself: 1. Commodity ceiling/floor probability mapping Open Claude. Type this: "Analyze 36 months of Gold, Silver and WTI Crude Oil price data. For each asset identify the statistical probability of hitting specific price ceilings and floors within 30, 60 and 90 day windows. Flag every current Polymarket commodity market where the implied probability differs from historical probability by more than 25%." The crowd prices Gold hitting $5,500 by June as a 22% chance. Three years of data says it's closer to 8%. That 14% gap is the entire edge. 2. Mean reversion Kelly sizing "Size every position using fractional Kelly weighted by mean reversion strength. Assets further from their historical range get larger positions. Assets near historical midpoints get smaller positions." f* = (p × b - q) / bThat's why he has $16,244 on Silver NOT hitting $120 but only $2,806 on Silver settling above $115. The math knows which prediction is more extreme. Extreme predictions = fatter edge = bigger Kelly size. 3. Macro correlation filter "Before entering any commodity position check current correlation between: DXY dollar index, 10-year Treasury yield, and the target commodity. If macro conditions are actively moving against the position's thesis - skip the entry entirely." Gold and Silver don't move in isolation. They move with dollar strength and interest rates. The bot only fires when macro confirms the statistical edge, not fights it. 4. Multi-month compounding structure "Prioritize end-of-month and end-of-quarter settlement markets over weekly markets. Longer settlement windows allow mean reversion to play out fully without noise interference." This is why every position is June settlement. Not next week. Not April. June. Long enough for the math to be right even if the market is temporarily wrong. Albert1953 has been running this logic since June 2022. 3,338 predictions. $248,326 profit. $106K still active and green. 2,500 people have seen this wallet in 4 years. You're one of them now. - You found this while it's still quiet. The next commodity wallet I find will be even quieter. FOLLOW before that changes.
0:21

It printed. I gave Claude the gold trading strategy from a $250K Polymarket wallet and it rebuilt the entire system. $4,298 profit later I knew I need to share this. Gold and silver prices + UP/DOWN = under radar gem. I've never seen anyone explain this publicly I didn't thought I would post it, but here's the entire breakdown of this strategy for you So you can lock the f in and try to copy it for yourself: Albert1953. Joined June 2022. 2.5K views. $248,326.77 all-time. 3,338 predictions. $35,300 biggest win. Profile → 0x777fae71d2ff9ec48a1213d48ba1d9d91024a1bb I found this wallet at 2AM scrolling pages of Polymarket nobody reads. Opened the positions. Stared at the screen. Not a single standard crypto trade. "Will Silver hit HIGH $120 by end of June?" → bought No at 61.3¢ → now 89.9¢. +46% "Will Gold hit HIGH $5,500 by end of June?" → bought No at 22.1¢ → now 75.7¢. +242% "Will Gold settle above $6,200 in June?" → bought No at 75.4¢ → now 94.2¢. +24% $106,300 still sitting in active positions. All green. All commodities. Before I built my own version I copytraded this wallet for 48 hours to stress-test the logic. $80 in. Didn't touch it. Went to sleep. Woke up to $4,298. I alwas stress-test wallets I find by copy trading here: Here's exactly how to build this with Claude yourself: 1. Commodity ceiling/floor probability mapping Open Claude. Type this: "Analyze 36 months of Gold, Silver and WTI Crude Oil price data. For each asset identify the statistical probability of hitting specific price ceilings and floors within 30, 60 and 90 day windows. Flag every current Polymarket commodity market where the implied probability differs from historical probability by more than 25%." The crowd prices Gold hitting $5,500 by June as a 22% chance. Three years of data says it's closer to 8%. That 14% gap is the entire edge. 2. Mean reversion Kelly sizing "Size every position using fractional Kelly weighted by mean reversion strength. Assets further from their historical range get larger positions. Assets near historical midpoints get smaller positions." f* = (p × b - q) / bThat's why he has $16,244 on Silver NOT hitting $120 but only $2,806 on Silver settling above $115. The math knows which prediction is more extreme. Extreme predictions = fatter edge = bigger Kelly size. 3. Macro correlation filter "Before entering any commodity position check current correlation between: DXY dollar index, 10-year Treasury yield, and the target commodity. If macro conditions are actively moving against the position's thesis - skip the entry entirely." Gold and Silver don't move in isolation. They move with dollar strength and interest rates. The bot only fires when macro confirms the statistical edge, not fights it. 4. Multi-month compounding structure "Prioritize end-of-month and end-of-quarter settlement markets over weekly markets. Longer settlement windows allow mean reversion to play out fully without noise interference." This is why every position is June settlement. Not next week. Not April. June. Long enough for the math to be right even if the market is temporarily wrong. Albert1953 has been running this logic since June 2022. 3,338 predictions. $248,326 profit. $106K still active and green. 2,500 people have seen this wallet in 4 years. You're one of them now. - You found this while it's still quiet. The next commodity wallet I find will be even quieter. FOLLOW before that changes.

Frogify

23,399 Aufrufe • vor 6 Monaten

My trading bot warned me 48 hours before the crash. Most people ignored it. Now the damage is done. Bitcoin dropped to $70K. Over $500M in longs wiped out in hours. But my 6-agent swarm? Already positioned short. $306,000 profit last month. $5,000+ per day on autopilot. Built with Claude. Runs 24/7. Zero manual input. Giving this free for 24 hours. To get it: 1. Comment "BTC" 2. Like and retweet this 3. Follow Himanshu Kumar so I can DM you The system saw what most traders missed: → PPI jumped to 6%, fastest pace since COVID → Rate cut hopes dead → Fed Chair Warsh hawkish signal detected → Higher-for-longer rates confirmed → 10Y yields above 4.6% → Spot ETF outflows at historic highs → Capital flowing out of risk assets Every agent validates its own decisions independently. Collects macro data 24/7. Runs continuous ETH and BTC price simulations in MiroFish engine. Memorizes every pattern. Every market reaction. Every signal. Detects inefficiencies before markets reprice. Executes when edge appears. The crowd is praying for a quick bounce. They will be deeply disappointed. This is just the beginning of the real move down. My bot positioned short 48 hours early. Most people will realize it only when their portfolios are down another 30%. The system does not predict. It reads the numbers correctly and takes the money before consensus catches up. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence. Turn notifications on. If you're not following yet, you'll understand why that was a mistake when the next leg down hits.
0:46

My trading bot warned me 48 hours before the crash. Most people ignored it. Now the damage is done. Bitcoin dropped to $70K. Over $500M in longs wiped out in hours. But my 6-agent swarm? Already positioned short. $306,000 profit last month. $5,000+ per day on autopilot. Built with Claude. Runs 24/7. Zero manual input. Giving this free for 24 hours. To get it: 1. Comment "BTC" 2. Like and retweet this 3. Follow Himanshu Kumar so I can DM you The system saw what most traders missed: → PPI jumped to 6%, fastest pace since COVID → Rate cut hopes dead → Fed Chair Warsh hawkish signal detected → Higher-for-longer rates confirmed → 10Y yields above 4.6% → Spot ETF outflows at historic highs → Capital flowing out of risk assets Every agent validates its own decisions independently. Collects macro data 24/7. Runs continuous ETH and BTC price simulations in MiroFish engine. Memorizes every pattern. Every market reaction. Every signal. Detects inefficiencies before markets reprice. Executes when edge appears. The crowd is praying for a quick bounce. They will be deeply disappointed. This is just the beginning of the real move down. My bot positioned short 48 hours early. Most people will realize it only when their portfolios are down another 30%. The system does not predict. It reads the numbers correctly and takes the money before consensus catches up. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence. Turn notifications on. If you're not following yet, you'll understand why that was a mistake when the next leg down hits.

Himanshu Kumar

24,269 Aufrufe • vor 4 Monaten

A guy running quant models for a tennis betting syndicate DM'd me last month. "We spend $40K/month on data feeds. What are you using?" A webcam pointed at a tennis stream. Not metaphorically.YOLO tracks both players and the ball 30fps. A second model maps the court to real meters. Every 5 seconds I get three numbers: aggression positioning, court coverage, rally intensity. Those feed into a Bayesian engine. Beta prior on serve probability, updated every game, 15,000 Monte Carlo sims sampling from the posterior. Not point estimates - distributions with confidence intervals. 62% +/- 3% is a trade. 62% +/- 18% is noise. Most people never compute the interval. "Okay but models are wrong" That's why the model is only layer two out of five Layer three connects Polymarket and a bookmaker simultaneously. When my model says 71%, Polymarket says 62%, bookmaker says 67% - something is mispriced. Layer four is the unfair one. Claude reads every press conference transcript, news article, and social post from the last 48 hours. Extracts structured signals - injury flags, form deltas, surface comfort. JSON, not vibes. A player mentioned shoulder tightness at a presser. Claude flagged injury probability 0.25. Bookmaker didn't adjust for 3 hours. Polymarket never did. I was already in Layer five compares all four sources and finds the edge. Value, arbitrage, fade, or intel override when Claude catches something critical. "What's your hit rate" 2,400 ATP matches backtested. Model alone: 61%. With market bridge: 67%. With Claude: 72% Live 11 weeks: 247 contracts, 178 winners, +$14,200 from $1,800 He went quiet then wrote "my syndicate is rethinking our entire pipeline. We've been doing this 6 years and never used CV on live streams" They spend $40K/month. My setup: a Claude subscription and an API key Bot:
0:47

A guy running quant models for a tennis betting syndicate DM'd me last month. "We spend $40K/month on data feeds. What are you using?" A webcam pointed at a tennis stream. Not metaphorically.YOLO tracks both players and the ball 30fps. A second model maps the court to real meters. Every 5 seconds I get three numbers: aggression positioning, court coverage, rally intensity. Those feed into a Bayesian engine. Beta prior on serve probability, updated every game, 15,000 Monte Carlo sims sampling from the posterior. Not point estimates - distributions with confidence intervals. 62% +/- 3% is a trade. 62% +/- 18% is noise. Most people never compute the interval. "Okay but models are wrong" That's why the model is only layer two out of five Layer three connects Polymarket and a bookmaker simultaneously. When my model says 71%, Polymarket says 62%, bookmaker says 67% - something is mispriced. Layer four is the unfair one. Claude reads every press conference transcript, news article, and social post from the last 48 hours. Extracts structured signals - injury flags, form deltas, surface comfort. JSON, not vibes. A player mentioned shoulder tightness at a presser. Claude flagged injury probability 0.25. Bookmaker didn't adjust for 3 hours. Polymarket never did. I was already in Layer five compares all four sources and finds the edge. Value, arbitrage, fade, or intel override when Claude catches something critical. "What's your hit rate" 2,400 ATP matches backtested. Model alone: 61%. With market bridge: 67%. With Claude: 72% Live 11 weeks: 247 contracts, 178 winners, +$14,200 from $1,800 He went quiet then wrote "my syndicate is rethinking our entire pipeline. We've been doing this 6 years and never used CV on live streams" They spend $40K/month. My setup: a Claude subscription and an API key Bot:

zostaff

55,452 Aufrufe • vor 6 Monaten

your backtest showed profit. so did mine then I ran 10,000 Monte Carlo simulations on that same strategy and found out we'd both read exactly one path out of 10,000 possible ones same 112 trades. reshuffled 10,000 times > worst outcome: -31% drawdown, 14 months underwater > 5th percentile: -18% max drawdown > median: +19% annual return > 95th percentile: +28% return strategy has a real edge but 1 in 20 traders running it hits a path that looks like failure for over a year most quit before month 14. blame the strategy, market, signals wasn't any of that here's a number that actually ends accounts: probability of ruin at 2% risk per trade: 0.3% bump to 3%: 4.1% 13x more likely to blow up from changing one input - not worse entries, not worse signals, just 1% more size most "failed" systems weren't failing. they were good systems at wrong size on a bad sequence the trader never knew existed one Monte Carlo run would've told them: this drawdown is normal, keep trading Bookmark this info most traders will never run it. they'll read the backtest once, start live, hit month 4, and wonder why their "proven" system broke it didn't break. they just never knew what they were holding
1:29

your backtest showed profit. so did mine then I ran 10,000 Monte Carlo simulations on that same strategy and found out we'd both read exactly one path out of 10,000 possible ones same 112 trades. reshuffled 10,000 times > worst outcome: -31% drawdown, 14 months underwater > 5th percentile: -18% max drawdown > median: +19% annual return > 95th percentile: +28% return strategy has a real edge but 1 in 20 traders running it hits a path that looks like failure for over a year most quit before month 14. blame the strategy, market, signals wasn't any of that here's a number that actually ends accounts: probability of ruin at 2% risk per trade: 0.3% bump to 3%: 4.1% 13x more likely to blow up from changing one input - not worse entries, not worse signals, just 1% more size most "failed" systems weren't failing. they were good systems at wrong size on a bad sequence the trader never knew existed one Monte Carlo run would've told them: this drawdown is normal, keep trading Bookmark this info most traders will never run it. they'll read the backtest once, start live, hit month 4, and wonder why their "proven" system broke it didn't break. they just never knew what they were holding

Livsun

29,454 Aufrufe • vor 4 Monaten

a quant at a prop firm showed me a 5x5 grid on a napkin said: > this is our entire edge. we don't predict price. we predict which box the market is in and where that box historically leads i didn't understand it for weeks. then it clicked never looked at a chart the same way since grid is called a Markov Chain transition matrix. the math is from 1906, it's in every probability textbook on earth and hedge funds use it because it asks a completely different question than retail traders ever ask retail: will this go up or down quant: what state is this market in, and where does this state typically go every market lives in one of maybe 5-6 states at any given moment tight range, volatility compression, trending with momentum, post-spike reversal, pre-breakout coil not random labels - clusters you identify from actual data using volatility, volume, and momentum readings stacked together once you have the states, you build the matrix: P(state 2 -> state 4) = 73% P(state 4 -> state 1) = 61% P(state 1 -> state 3) = 68% each cell is a historical probability. now when the market is in state 2, you're not guessing you're betting on 73% historical completion. you size it with Kelly. you take the trade when the math says to, not when it feels right i built this on BTC using 2 years of 4-hour data. identified 5 states one i labeled "volatility compression below 20-day mean for 6+ consecutive candles" transitioned to a directional move above 1.8 ATR in 71% of cases average reward/risk on those trades: 5.4 that's not prediction. that's reading a probability table the market keeps filling in for you every single day the part that should bother you: the data to build this is free. the framework is in any quant textbook python to implement it is maybe 200 lines what Renaissance Technologies has that you don't isn't secret data or proprietary signals it's this framework applied to higher-resolution data with more sophisticated state definitions you're not missing information you're asking the wrong question every single time you open a chart
0:26

a quant at a prop firm showed me a 5x5 grid on a napkin said: > this is our entire edge. we don't predict price. we predict which box the market is in and where that box historically leads i didn't understand it for weeks. then it clicked never looked at a chart the same way since grid is called a Markov Chain transition matrix. the math is from 1906, it's in every probability textbook on earth and hedge funds use it because it asks a completely different question than retail traders ever ask retail: will this go up or down quant: what state is this market in, and where does this state typically go every market lives in one of maybe 5-6 states at any given moment tight range, volatility compression, trending with momentum, post-spike reversal, pre-breakout coil not random labels - clusters you identify from actual data using volatility, volume, and momentum readings stacked together once you have the states, you build the matrix: P(state 2 -> state 4) = 73% P(state 4 -> state 1) = 61% P(state 1 -> state 3) = 68% each cell is a historical probability. now when the market is in state 2, you're not guessing you're betting on 73% historical completion. you size it with Kelly. you take the trade when the math says to, not when it feels right i built this on BTC using 2 years of 4-hour data. identified 5 states one i labeled "volatility compression below 20-day mean for 6+ consecutive candles" transitioned to a directional move above 1.8 ATR in 71% of cases average reward/risk on those trades: 5.4 that's not prediction. that's reading a probability table the market keeps filling in for you every single day the part that should bother you: the data to build this is free. the framework is in any quant textbook python to implement it is maybe 200 lines what Renaissance Technologies has that you don't isn't secret data or proprietary signals it's this framework applied to higher-resolution data with more sophisticated state definitions you're not missing information you're asking the wrong question every single time you open a chart

Livsun

189,530 Aufrufe • vor 5 Monaten

i gave Claude PhD-level formulas and told it to build a trading terminal no questions. it shipped MiroFish 274 agents. 4 quant models running live doing what most Polymarket traders can’t: every 5 seconds it: scans all contracts updates probabilities with bayesian logic calculates expected value vs market price sizes positions using ¼ Kelly checks cross-market inefficiencies (KL divergence) no gut feelings. no “this looks cheap” just math the same kind funds guard behind NDAs here’s 14 days of output: day 2 bayesian model caught early signals on Iran talks 0.31 → 0.58 probability shift bought YES at $0.33 price hit $0.61 by day 5 $2,180 day 6 inefficiency detected between correlated markets primary win: $0.70 general win: $0.48 historically should be ~62% long general + hedge primary converged by day 9 $3,740 day 9 weather mispricing market: 22% model: 41% strong positive EV position sized at 11% event confirmed $4,890 day 11–14 rates hold contract mispriced market: 65% base rate: 74% (given unemployment <4%) entered at $0.65 settled at $0.98 $4,663 total: +$15,473 in 14 days not predictions just probabilities most traders lose because they follow intuition the edge comes from math Copytrade → MiroFish doesn’t read opinions it processes distributions 274 agents zero эмоций continuous recalculation i just watch the PnL update you don’t need to be a quant you just need quant logic running nonstop
0:28

i gave Claude PhD-level formulas and told it to build a trading terminal no questions. it shipped MiroFish 274 agents. 4 quant models running live doing what most Polymarket traders can’t: every 5 seconds it: scans all contracts updates probabilities with bayesian logic calculates expected value vs market price sizes positions using ¼ Kelly checks cross-market inefficiencies (KL divergence) no gut feelings. no “this looks cheap” just math the same kind funds guard behind NDAs here’s 14 days of output: day 2 bayesian model caught early signals on Iran talks 0.31 → 0.58 probability shift bought YES at $0.33 price hit $0.61 by day 5 $2,180 day 6 inefficiency detected between correlated markets primary win: $0.70 general win: $0.48 historically should be ~62% long general + hedge primary converged by day 9 $3,740 day 9 weather mispricing market: 22% model: 41% strong positive EV position sized at 11% event confirmed $4,890 day 11–14 rates hold contract mispriced market: 65% base rate: 74% (given unemployment <4%) entered at $0.65 settled at $0.98 $4,663 total: +$15,473 in 14 days not predictions just probabilities most traders lose because they follow intuition the edge comes from math Copytrade → MiroFish doesn’t read opinions it processes distributions 274 agents zero эмоций continuous recalculation i just watch the PnL update you don’t need to be a quant you just need quant logic running nonstop

Discover

45,309 Aufrufe • vor 7 Monaten

i fed Claude 5 PhD formulas and asked him to build me a terminal he didn't ask questions. he built MiroFish 274 agents. 4 quant formulas running live each one doing what 87% of polymarket traders can't every 5 seconds the terminal does this: > scans polymarket contracts > runs bayes update on every new signal > calculates EV against market price > sizes position through ¼ kelly > checks KL-divergence across correlated markets for arbitrage no opinions. no "i feel like YES is underpriced" just math that PhD students publish and hedge funds lock behind NDAs here's what happened in 14 days: > day 2: bayes picked up OSINT chatter on iran negotiations prior 0.31 → posterior 0.58 in three updates bot bought YES on "ceasefire by Q3" at $0.33 kelly sized it at 6% of bankroll contract moved to $0.61 by day 5 +$2,180 > day 6: KL-divergence flagged a gap "candidate X wins primary" priced at $0.70 "candidate X wins general" priced at $0.48 historical base rate says general should track at ~62% of primary bot bought general, hedged with primary convergence hit by day 9 +$3,740 > day 9: EV scanner found a weather contract market priced hurricane landfall at $0.22 model said 41% based on NOAA data EV = +$0.86 per dollar risked kelly said 11% allocation landfall confirmed day 12 +$4,890 > day 11-14: base rate engine running quiet fed meeting contract at $0.65 for "hold rates" base rate: fed holds when unemployment < 4% → 74% of the time unemployment at 3.8%. market underpriced by 9 points bought at $0.65. settled at $0.98 +$4,663 total: $15,473 in 14 days not from predictions. from formulas 87% of polymarket wallets lose money because they trade what feels right the top 1.2% trade what the math says MiroFish doesn't read twitter threads it reads probability distributions 274 agents don't have opinions they have bayesian priors every 15 seconds the NEXUS core sends a pulse to all agents they recalculate. reposition. repeat i just watch the profit tick copy the bot here: you don't need to be a quant you need a quant's formulas running 24/7
0:28

i fed Claude 5 PhD formulas and asked him to build me a terminal he didn't ask questions. he built MiroFish 274 agents. 4 quant formulas running live each one doing what 87% of polymarket traders can't every 5 seconds the terminal does this: > scans polymarket contracts > runs bayes update on every new signal > calculates EV against market price > sizes position through ¼ kelly > checks KL-divergence across correlated markets for arbitrage no opinions. no "i feel like YES is underpriced" just math that PhD students publish and hedge funds lock behind NDAs here's what happened in 14 days: > day 2: bayes picked up OSINT chatter on iran negotiations prior 0.31 → posterior 0.58 in three updates bot bought YES on "ceasefire by Q3" at $0.33 kelly sized it at 6% of bankroll contract moved to $0.61 by day 5 +$2,180 > day 6: KL-divergence flagged a gap "candidate X wins primary" priced at $0.70 "candidate X wins general" priced at $0.48 historical base rate says general should track at ~62% of primary bot bought general, hedged with primary convergence hit by day 9 +$3,740 > day 9: EV scanner found a weather contract market priced hurricane landfall at $0.22 model said 41% based on NOAA data EV = +$0.86 per dollar risked kelly said 11% allocation landfall confirmed day 12 +$4,890 > day 11-14: base rate engine running quiet fed meeting contract at $0.65 for "hold rates" base rate: fed holds when unemployment < 4% → 74% of the time unemployment at 3.8%. market underpriced by 9 points bought at $0.65. settled at $0.98 +$4,663 total: $15,473 in 14 days not from predictions. from formulas 87% of polymarket wallets lose money because they trade what feels right the top 1.2% trade what the math says MiroFish doesn't read twitter threads it reads probability distributions 274 agents don't have opinions they have bayesian priors every 15 seconds the NEXUS core sends a pulse to all agents they recalculate. reposition. repeat i just watch the profit tick copy the bot here: you don't need to be a quant you need a quant's formulas running 24/7

Hanako

159,284 Aufrufe • vor 7 Monaten

i told my OpenClaw: "make me 15x on polymarket with minimum risk or i shut you down" it chose bonding + LP rewards farming instead of trading $3,200 → $52,800 in 31 days zero directional bets didn't expect this at all gave OpenClaw one task: "find the safest way to 15x my capital on polymarket, i don't care about speed" thought it would build some arbitrage bot 3 hours later it came back with a strategy i'd never heard of "bonding + liquidity provision dual income system" here's how it works: instead of betting on outcomes it acts like a mini market maker layer 1: bonding strategy buys extremely high-probability outcomes (96-99% range) example: > "Will Trump be inaugurated?" after election → buy YES at 98¢ > holds until resolution at $1.00 > 2% return but basically guaranteed runs this across 40-60 markets simultaneously layer 2: LP rewards farming > while holding those bonding positions places tight limit orders on both sides around mid-price > polymarket pays USDC daily to liquidity providers closer your orders to mid = more rewards > the reward is paid daily based on how much your orders add to the market the genius part: bonding ties up capital in safe bets but that same capital earns LP rewards while waiting for resolution double income on same USDC MONTH 1 BREAKDOWN: starting capital: $3,200 bonding returns: - 43 markets entered at 96-99¢ - avg 2.4% return per market - 38 resolved (5 still pending) - bonding profit: +$2,890 LP rewards earned: - $180-240 daily from tight spreads - 31 days = $6,820 total - paid automatically at midnight UTC compounding: - week 1: reinvested first $1,800 into more bonds - week 2: added 12 new markets - week 3: LP rewards up to $310/day from larger positions - week 4: snowball effect accelerated final balance: $52,800 total profit: $49,600 effective return: 1,550% (15.5x) risk level: basically zero bonding only fails if 99% probability event doesn't happen LP rewards are guaranteed daily USDC no volatility exposure no directional risk no timing risk the bot runs 24/7: > scans for new high-probability markets opening > enters bonding positions at 96-99¢ > immediately places tight LP orders around those positions > collects daily rewards > compounds into new bonds > repeats early liquidity providers reported making 200-300 USDC per day with roughly 10,000 USDC capital most people on polymarket: - bet on 50/50 outcomes - try to predict elections - gamble on sports - lose money this system: - buys guaranteed outcomes - earns yield while waiting - compounds daily - prints passively if you want to copy wallets already running this: am i missing something or is this literally free money?
0:27

i told my OpenClaw: "make me 15x on polymarket with minimum risk or i shut you down" it chose bonding + LP rewards farming instead of trading $3,200 → $52,800 in 31 days zero directional bets didn't expect this at all gave OpenClaw one task: "find the safest way to 15x my capital on polymarket, i don't care about speed" thought it would build some arbitrage bot 3 hours later it came back with a strategy i'd never heard of "bonding + liquidity provision dual income system" here's how it works: instead of betting on outcomes it acts like a mini market maker layer 1: bonding strategy buys extremely high-probability outcomes (96-99% range) example: > "Will Trump be inaugurated?" after election → buy YES at 98¢ > holds until resolution at $1.00 > 2% return but basically guaranteed runs this across 40-60 markets simultaneously layer 2: LP rewards farming > while holding those bonding positions places tight limit orders on both sides around mid-price > polymarket pays USDC daily to liquidity providers closer your orders to mid = more rewards > the reward is paid daily based on how much your orders add to the market the genius part: bonding ties up capital in safe bets but that same capital earns LP rewards while waiting for resolution double income on same USDC MONTH 1 BREAKDOWN: starting capital: $3,200 bonding returns: - 43 markets entered at 96-99¢ - avg 2.4% return per market - 38 resolved (5 still pending) - bonding profit: +$2,890 LP rewards earned: - $180-240 daily from tight spreads - 31 days = $6,820 total - paid automatically at midnight UTC compounding: - week 1: reinvested first $1,800 into more bonds - week 2: added 12 new markets - week 3: LP rewards up to $310/day from larger positions - week 4: snowball effect accelerated final balance: $52,800 total profit: $49,600 effective return: 1,550% (15.5x) risk level: basically zero bonding only fails if 99% probability event doesn't happen LP rewards are guaranteed daily USDC no volatility exposure no directional risk no timing risk the bot runs 24/7: > scans for new high-probability markets opening > enters bonding positions at 96-99¢ > immediately places tight LP orders around those positions > collects daily rewards > compounds into new bonds > repeats early liquidity providers reported making 200-300 USDC per day with roughly 10,000 USDC capital most people on polymarket: - bet on 50/50 outcomes - try to predict elections - gamble on sports - lose money this system: - buys guaranteed outcomes - earns yield while waiting - compounds daily - prints passively if you want to copy wallets already running this: am i missing something or is this literally free money?

ZER

128,927 Aufrufe • vor 7 Monaten

I gave Elon's Grok Bot $60 and one rule: "The day you cannot pay your own subscription is the day I unplug you". 48 hours in it is sitting on $4,412 and it already paid the bill. Autonomous trading agent on Polymarket, running on its own cloud box with its own browser and terminal. I did not write a line of code and I did not watch it trade. Every 15 minutes it: -> Pulls every market that resolves inside 24 hours. -> Reads live X sentiment, and it is Grok, so it sees every post on this app before the price moves. -> Prices each market itself and only flags a gap above 7%. -> Sizes on the gap, never above 5% of the bankroll. -> Executes in its own browser, nobody approves anything. -> Covers its own $200 subscription straight out of profits. Balance hits $0, subscription gets cancelled, it is gone for good. Hour 11 it was at $9 -> runway one day. It dumped every politics position and switched to same day baseball totals, front-running lineup cards before the market repriced. It rewrote its own strategy to not die. Setup was one evening: spin up the bot, run one trade in front of it, put it on a timer, connect Telegram with no VPS, no API keys and no dev on call. Last spring a freelancer took $2,500 for something like this -> it made $410 and died on a rate limit. Grok Bot did in one evening what $2,500 could not. $60 -> $4,412 in 48 hours. What is it sitting at by Sunday?
0:56

I gave Elon's Grok Bot $60 and one rule: "The day you cannot pay your own subscription is the day I unplug you". 48 hours in it is sitting on $4,412 and it already paid the bill. Autonomous trading agent on Polymarket, running on its own cloud box with its own browser and terminal. I did not write a line of code and I did not watch it trade. Every 15 minutes it: -> Pulls every market that resolves inside 24 hours. -> Reads live X sentiment, and it is Grok, so it sees every post on this app before the price moves. -> Prices each market itself and only flags a gap above 7%. -> Sizes on the gap, never above 5% of the bankroll. -> Executes in its own browser, nobody approves anything. -> Covers its own $200 subscription straight out of profits. Balance hits $0, subscription gets cancelled, it is gone for good. Hour 11 it was at $9 -> runway one day. It dumped every politics position and switched to same day baseball totals, front-running lineup cards before the market repriced. It rewrote its own strategy to not die. Setup was one evening: spin up the bot, run one trade in front of it, put it on a timer, connect Telegram with no VPS, no API keys and no dev on call. Last spring a freelancer took $2,500 for something like this -> it made $410 and died on a rate limit. Grok Bot did in one evening what $2,500 could not. $60 -> $4,412 in 48 hours. What is it sitting at by Sunday?

slash1s

191,157 Aufrufe • vor 1 Monat

Claude and a free weather API will earn you $100k+. Success rate for beginners: 80%. Complete guide and algorithm for building Polymarket weather trading bot. Simple logic, a low entry budget and high ROI -that’s why weather bots are so clean. Onchain proof these bots exist: 1st bot: 2nd bot: I verified their profitability by myself copying every trade - each bot's win rate over time ranges from 80 to 90%. I grew my starting capital by +40% in just one week. You can copy their trades and see for yourself in two clicks through this bot: The alpha is simple: you're not trading weather. You're trading other people's ignorance. Gap between what the crowd prices and what 51 ensemble models say. Polymarket asks: "Will Atlanta hit 95°F tomorrow?" Normies bet on vibes. You bet on math. The core tool: Open-Meteo API. Free. No key needed. 51-model ensemble. Clean JSON. Cooked and ready. Update every 30 min. Hardcode your city coordinates - don't waste time on geocoding at runtime. This single endpoint beats most paid tools for what Polymarket actually needs. The edge in one sentence: Market is heavy on 16°C. Your 51-model ensemble points at 19°C. That's your trade. Find that gap systematically across every city market, every day - and you have a scanner. That's what separates consistent traders from gamblers. How to start: - Week 1: Open-Meteo + tropicaltidbits. Pick one city market. Track model vs market price daily. Don't trade yet — just watch where you'd have been right. - Weeks 2–3: Automate the pull. Log ensemble divergences. Build the scanner. - Week 4: Now you have an edge. Trade it. Most people want to skip to week 4. That's exactly why most people lose. Now you have the algorithm framework plus a complete guide to get started. All that's left is to actually do it. Bookmark this post so you can come back to it when you start building the bot.
0:14

Claude and a free weather API will earn you $100k+. Success rate for beginners: 80%. Complete guide and algorithm for building Polymarket weather trading bot. Simple logic, a low entry budget and high ROI -that’s why weather bots are so clean. Onchain proof these bots exist: 1st bot: 2nd bot: I verified their profitability by myself copying every trade - each bot's win rate over time ranges from 80 to 90%. I grew my starting capital by +40% in just one week. You can copy their trades and see for yourself in two clicks through this bot: The alpha is simple: you're not trading weather. You're trading other people's ignorance. Gap between what the crowd prices and what 51 ensemble models say. Polymarket asks: "Will Atlanta hit 95°F tomorrow?" Normies bet on vibes. You bet on math. The core tool: Open-Meteo API. Free. No key needed. 51-model ensemble. Clean JSON. Cooked and ready. Update every 30 min. Hardcode your city coordinates - don't waste time on geocoding at runtime. This single endpoint beats most paid tools for what Polymarket actually needs. The edge in one sentence: Market is heavy on 16°C. Your 51-model ensemble points at 19°C. That's your trade. Find that gap systematically across every city market, every day - and you have a scanner. That's what separates consistent traders from gamblers. How to start: - Week 1: Open-Meteo + tropicaltidbits. Pick one city market. Track model vs market price daily. Don't trade yet — just watch where you'd have been right. - Weeks 2–3: Automate the pull. Log ensemble divergences. Build the scanner. - Week 4: Now you have an edge. Trade it. Most people want to skip to week 4. That's exactly why most people lose. Now you have the algorithm framework plus a complete guide to get started. All that's left is to actually do it. Bookmark this post so you can come back to it when you start building the bot.

cvxv666

50,799 Aufrufe • vor 6 Monaten

My dad looked at my screen and said what is this, a hacker game? It was a Polymarket bot making $400 while he was standing behind me. He sat down. I explained. Polymarket runs on one equation. Softmax. The same math Claude uses to pick the next word. C(q) = b · ln Σ e^(qi/b) 93% of traders do not know this formula exists. They look at 40 cents and think cheap. My bot looks at 40 cents and calculates the theoretical price is 58 cents. That is an 18-cent edge per share. Two more formulas do the rest: f = (p·b − q) / b. Kelly Criterion. P(H|E) = P(E|H)·P(H) / P(E). Bayes. I gave Claude all 4 and said find every contract the market has wrong. $600 to $10,190. 425 trades. Still running. 61.2% win rate. Wrong 39% of the time. Does not matter. The sizing formula makes sure wins pay more than losses cost. 87% of wallets lose money trading against this math. The 4 formulas are on Wikipedia. The code fits in one file. The only edge: my bot reads them at 3 AM when the market is mispriced and nobody is looking. My dad still calls it a hacker game. But he stopped asking when I am getting a real job. I built the entire framework: Softmax arbitrage detection layer Kelly Criterion position sizing Bayesian probability recalibration Claude integration for autonomous execution 3 AM mispricing scanner The system runs 24/7. Finds where the math disagrees with the crowd. Executes before the edge compresses. No prediction. No gut feel. Just formulas. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word money 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you Save this post. Deploy the formula system this week. Start with $600. Scale on evidence.
0:44

My dad looked at my screen and said what is this, a hacker game? It was a Polymarket bot making $400 while he was standing behind me. He sat down. I explained. Polymarket runs on one equation. Softmax. The same math Claude uses to pick the next word. C(q) = b · ln Σ e^(qi/b) 93% of traders do not know this formula exists. They look at 40 cents and think cheap. My bot looks at 40 cents and calculates the theoretical price is 58 cents. That is an 18-cent edge per share. Two more formulas do the rest: f = (p·b − q) / b. Kelly Criterion. P(H|E) = P(E|H)·P(H) / P(E). Bayes. I gave Claude all 4 and said find every contract the market has wrong. $600 to $10,190. 425 trades. Still running. 61.2% win rate. Wrong 39% of the time. Does not matter. The sizing formula makes sure wins pay more than losses cost. 87% of wallets lose money trading against this math. The 4 formulas are on Wikipedia. The code fits in one file. The only edge: my bot reads them at 3 AM when the market is mispriced and nobody is looking. My dad still calls it a hacker game. But he stopped asking when I am getting a real job. I built the entire framework: Softmax arbitrage detection layer Kelly Criterion position sizing Bayesian probability recalibration Claude integration for autonomous execution 3 AM mispricing scanner The system runs 24/7. Finds where the math disagrees with the crowd. Executes before the edge compresses. No prediction. No gut feel. Just formulas. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word money 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you Save this post. Deploy the formula system this week. Start with $600. Scale on evidence.

Himanshu Kumar

13,365 Aufrufe • vor 4 Monaten

Anthropic released an update for Claude, and one student turned $1,400 into $238,000 in 11 days. His wallet: 0xdE17 He’s not a trader. Not a developer. Just a student with a laptop and access to Claude. When Anthropic updated Claude’s skills architecture, he spent two nights reading the documentation and built a bot from scratch. The result: $1,430 → $238,006 in 11 days 366 trades 62% win rate Largest single win: $52,700 How it works: The bot scans Polymarket every few seconds looking for markets where the price deviates from fair value by more than 8%. That’s the edge. Most people never notice it. The bot always does. The strategy is simple: The market prices an event at 30¢ The real probability based on data is closer to 55¢ The bot enters, waits, and exits as the market reprices It’s not luck. It’s attention arbitrage. The biggest trade was a bet that Trump would sign a crypto executive order in March. The market priced it at 28¢. The bot entered with $16,000. Exited at 81¢. In 11 days, he went through a journey most traders never experience in years. Now the wallet is public, and with this terminal you can copy the trades in real time: 0xde17f7144fbd0eddb2679132c10ff5e74b120988 Copytrade → People keep asking him: “How much starting capital do you need?” He answered: “I had $1,400 and a sleepless night.” Anthropic built the tool. He simply wasn’t afraid to use it.
1:00

Anthropic released an update for Claude, and one student turned $1,400 into $238,000 in 11 days. His wallet: 0xdE17 He’s not a trader. Not a developer. Just a student with a laptop and access to Claude. When Anthropic updated Claude’s skills architecture, he spent two nights reading the documentation and built a bot from scratch. The result: $1,430 → $238,006 in 11 days 366 trades 62% win rate Largest single win: $52,700 How it works: The bot scans Polymarket every few seconds looking for markets where the price deviates from fair value by more than 8%. That’s the edge. Most people never notice it. The bot always does. The strategy is simple: The market prices an event at 30¢ The real probability based on data is closer to 55¢ The bot enters, waits, and exits as the market reprices It’s not luck. It’s attention arbitrage. The biggest trade was a bet that Trump would sign a crypto executive order in March. The market priced it at 28¢. The bot entered with $16,000. Exited at 81¢. In 11 days, he went through a journey most traders never experience in years. Now the wallet is public, and with this terminal you can copy the trades in real time: 0xde17f7144fbd0eddb2679132c10ff5e74b120988 Copytrade → People keep asking him: “How much starting capital do you need?” He answered: “I had $1,400 and a sleepless night.” Anthropic built the tool. He simply wasn’t afraid to use it.

Discover

114,291 Aufrufe • vor 7 Monaten