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My quant bot just printed $3,591 5-minute BTC candles. Fully automated. Claude powered. Here's the math running under the hood: > LMSR pricing: C(q) = b × ln(Σ e^(qi/b)) — the bot reads market depth before every entry > EV filter: only trades when edge > 3¢ per share... show more

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I gave Claude one prompt and it built me a bot that turned $200 into $14,000 on Polymarket No insider info, no lucky guess Just one equation nobody reads - and a bot that runs it 24/7 The price on Polymarket isn't set by people It's set by one formula - LMSR. You trade against pure math, not traders C(q) = b × ln(Σ e^(qi / b)) Someone buys 50 YES shares → price jumps from 50¢ to 62¢ It's softmax — same math GPT uses to pick the next word. Not a coincidence Then I found b — the liquidity parameter b = 50 → one whale swings the market 20% b = 100,000 → barely moves Half my old trades were in thin pools. One order wrecked my fill How much to bet? Kelly criterion f* = (p × b − q) / b 60% edge → Kelly says 20%. Pros use quarter-Kelly → 5% Not exciting. But you won't blow up Last piece. Price impact Contract at 40¢. You buy 1,000 shares - price climbs to 60¢ The edge disappears before you're filled I fed all of this into Claude. One prompt. 40 minutes. The bot checks b, sizes with Kelly, models impact - and executes before I finish my coffee b filters the pool. Kelly sizes the bet. Impact curve tells me where the edge dies I don't predict outcomes. The bot just reads the math

I gave Claude one prompt and it built me a bot that turned $200 into $14,000 on Polymarket No insider info, no lucky guess Just one equation nobody reads - and a bot that runs it 24/7 The price on Polymarket isn't set by people It's set by one formula - LMSR. You trade against pure math, not traders C(q) = b × ln(Σ e^(qi / b)) Someone buys 50 YES shares → price jumps from 50¢ to 62¢ It's softmax — same math GPT uses to pick the next word. Not a coincidence Then I found b — the liquidity parameter b = 50 → one whale swings the market 20% b = 100,000 → barely moves Half my old trades were in thin pools. One order wrecked my fill How much to bet? Kelly criterion f* = (p × b − q) / b 60% edge → Kelly says 20%. Pros use quarter-Kelly → 5% Not exciting. But you won't blow up Last piece. Price impact Contract at 40¢. You buy 1,000 shares - price climbs to 60¢ The edge disappears before you're filled I fed all of this into Claude. One prompt. 40 minutes. The bot checks b, sizes with Kelly, models impact - and executes before I finish my coffee b filters the pool. Kelly sizes the bet. Impact curve tells me where the edge dies I don't predict outcomes. The bot just reads the math

rari

52,212 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 don't know this formula exists They look at 40¢ and think "cheap" My bot looks at 40¢ and calculates the theoretical price is 58¢ I'm use for copytrade bots: That's 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 → $10,190. 425 trades. Still running 61.2% win rate - wrong 39% of the time. Doesn't 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's looking My dad still calls it a hacker game. But he stopped asking when I'm getting a "real job" Only math + code, no more

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 don't know this formula exists They look at 40¢ and think "cheap" My bot looks at 40¢ and calculates the theoretical price is 58¢ I'm use for copytrade bots: That's 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 → $10,190. 425 trades. Still running 61.2% win rate - wrong 39% of the time. Doesn't 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's looking My dad still calls it a hacker game. But he stopped asking when I'm getting a "real job" Only math + code, no more

Lunar

108,420 Aufrufe • vor 6 Monaten

$Last semester a professor pulled me aside after class. "You trade prediction markets?" - he asked. I said yeah, mostly vibes. He laughed and said that's why you're losing. He spent 20 minutes on a whiteboard showing me Bayes' Theorem, Kelly Criterion, and LMSR pricing. Said this is literally how quant funds approach binary markets. I took photos of everything. Went home, opened Claude, pasted my notes and said: “build me a bot that does exactly this 3 hours later I had a working Bayesian trading bot for Polymarket BTC markets. Here's the math it runs on every single trade: - Bayesian update update beliefs before entering P(H|D) = P(D|H) · P(H) / P(D) - Edge detection -only trade if you have an edge EV = p̂ − p_market - Kelly sizing-bet exactly the right amount f* = EV / p̂(1−p̂) - LMSR pricing how Polymarket actually prices markets p(q) = e^{q/b} / Σe^{qⱼ/b} (it's literally the softmax function from neural networks) 65% win rate. Losses happen. Kelly keeps them from blowing the account. Compounding does the rest. My CLAWD BOT turned $100 into $1000 in 30min The professor was right. Vibes don't compound. Math does.$

Last semester a professor pulled me aside after class. "You trade prediction markets?" - he asked. I said yeah, mostly vibes. He laughed and said that's why you're losing. He spent 20 minutes on a whiteboard showing me Bayes' Theorem, Kelly Criterion, and LMSR pricing. Said this is literally how quant funds approach binary markets. I took photos of everything. Went home, opened Claude, pasted my notes and said: “build me a bot that does exactly this 3 hours later I had a working Bayesian trading bot for Polymarket BTC markets. Here's the math it runs on every single trade: - Bayesian update update beliefs before entering P(H|D) = P(D|H) · P(H) / P(D) - Edge detection -only trade if you have an edge EV = p̂ − p_market - Kelly sizing-bet exactly the right amount f* = EV / p̂(1−p̂) - LMSR pricing how Polymarket actually prices markets p(q) = e^{q/b} / Σe^{qⱼ/b} (it's literally the softmax function from neural networks) 65% win rate. Losses happen. Kelly keeps them from blowing the account. Compounding does the rest. My CLAWD BOT turned $100 into $1000 in 30min The professor was right. Vibes don't compound. Math does.

AdiiX

174,137 Aufrufe • vor 6 Monaten

$This guy built a Bayesian Quant model and it made $1,362 a day on Polymarket The result is already +$29,972 PnL in 22 days with only $29 per average trade, repeating the same setup 9 times every hour across BTC "Up or Down" markets Several AI agents can handle probability, sizing and execution: 1. Every new BTC signal updates the probability through Bayes The model starts with a prior probability and updates it whenever new market data arrives: P(A|B) = P(B|A) × P(A) / P(B) If BTC momentum strengthens and the model moves fair Up from 54% to 62% while Polymarket still trades at 53¢, the system sees a 9¢ gap 2. Kelly decides position size f* = (bp - q) / b Where: p = model probability of winning q = 1 - p b = net payout for each unit risked f* = suggested fraction to allocate Higher probability and better payout increase f*, while a weaker edge pushes it toward zero This trader’s Polymarket account: tiredofwinning2much Follow this account and trade on Polymarket through this bot: 4,948 predictions with a 51% win rate Most profitable trades: $260 → $615 (+$355) $253 → $602 (+$349) $227 → $560 (+$333) A similar Bayesian Quant setup can be built with Grok Bot, using AI agents for probability updates, sizing and execution$

This guy built a Bayesian Quant model and it made $1,362 a day on Polymarket The result is already +$29,972 PnL in 22 days with only $29 per average trade, repeating the same setup 9 times every hour across BTC "Up or Down" markets Several AI agents can handle probability, sizing and execution: 1. Every new BTC signal updates the probability through Bayes The model starts with a prior probability and updates it whenever new market data arrives: P(A|B) = P(B|A) × P(A) / P(B) If BTC momentum strengthens and the model moves fair Up from 54% to 62% while Polymarket still trades at 53¢, the system sees a 9¢ gap 2. Kelly decides position size f* = (bp - q) / b Where: p = model probability of winning q = 1 - p b = net payout for each unit risked f* = suggested fraction to allocate Higher probability and better payout increase f*, while a weaker edge pushes it toward zero This trader’s Polymarket account: tiredofwinning2much Follow this account and trade on Polymarket through this bot: 4,948 predictions with a 51% win rate Most profitable trades: $260 → $615 (+$355) $253 → $602 (+$349) $227 → $560 (+$333) A similar Bayesian Quant setup can be built with Grok Bot, using AI agents for probability updates, sizing and execution

Daniro

60,040 Aufrufe • vor 10 Tagen

I gave Claude one Polymarket equation and told it to find every mispriced contract It hasn't stopped running in 4 months $600 → $14,190 Every Polymarket contract is priced by one formula: C(q) = b × ln(Σ e^(qi / b)) The pricing function inside it is softmax - the same math Claude uses to pick its next token Claude doesn't just understand this equation, it thinks in it So I gave it a task: "Scan every open contract, compute fair value, flag anything the market has wrong 24/7" It finds the gap, sizes the position by Kelly, enters before the crowd corrects Market shows 0.38, math shows 0.57, bot buys Resolution hits, bot collects 120 days 61.2% win rate 2.31 sharpe -3.8% max drawdown 425 trades 87% of Polymarket wallets are underwater They trade feelings against a formula that doesn't bluff, doesn't tilt, doesn't chase losses The edge is having something that reads the math faster than any human - and never stops looking

I gave Claude one Polymarket equation and told it to find every mispriced contract It hasn't stopped running in 4 months $600 → $14,190 Every Polymarket contract is priced by one formula: C(q) = b × ln(Σ e^(qi / b)) The pricing function inside it is softmax - the same math Claude uses to pick its next token Claude doesn't just understand this equation, it thinks in it So I gave it a task: "Scan every open contract, compute fair value, flag anything the market has wrong 24/7" It finds the gap, sizes the position by Kelly, enters before the crowd corrects Market shows 0.38, math shows 0.57, bot buys Resolution hits, bot collects 120 days 61.2% win rate 2.31 sharpe -3.8% max drawdown 425 trades 87% of Polymarket wallets are underwater They trade feelings against a formula that doesn't bluff, doesn't tilt, doesn't chase losses The edge is having something that reads the math faster than any human - and never stops looking

rari

311,933 Aufrufe • vor 6 Monaten

My ex laughed when she saw me writing Python scripts instead of going to interviews. "You're calculating… weather probabilities?" I was. Every market on Polymarket runs on one equation: C(q) = b · ln Σ e^(qi/b) It's called LMSR. The same math that GPT uses to pick the next word - Polymarket uses to price beliefs. 93% of traders don't know this formula exists. They look at 40¢ and think "cheap." I look at 40¢ and calculate the theoretical price is 60¢. That's a 20-cent edge per share. One position: market at 0.35, my model said 0.55. Bought in. Market resolved. +$4,200. But the real edge isn't the formula. It's what happens after. I'm use for copytrade bots: Kelly criterion says bet 20% of your bankroll. Every pro I've studied bets 5%. Base rates, not gut feelings. Bayesian updates, not Twitter threads. 93% of Polymarket wallets lose money. They trade on vibes while the math is sitting on Wikipedia. My roommate just asked me to explain the formula to him. I wrote an entire breakdown - LMSR, expected value, Kelly sizing, the 5 cognitive traps that blow up accounts.

My ex laughed when she saw me writing Python scripts instead of going to interviews. "You're calculating… weather probabilities?" I was. Every market on Polymarket runs on one equation: C(q) = b · ln Σ e^(qi/b) It's called LMSR. The same math that GPT uses to pick the next word - Polymarket uses to price beliefs. 93% of traders don't know this formula exists. They look at 40¢ and think "cheap." I look at 40¢ and calculate the theoretical price is 60¢. That's a 20-cent edge per share. One position: market at 0.35, my model said 0.55. Bought in. Market resolved. +$4,200. But the real edge isn't the formula. It's what happens after. I'm use for copytrade bots: Kelly criterion says bet 20% of your bankroll. Every pro I've studied bets 5%. Base rates, not gut feelings. Bayesian updates, not Twitter threads. 93% of Polymarket wallets lose money. They trade on vibes while the math is sitting on Wikipedia. My roommate just asked me to explain the formula to him. I wrote an entire breakdown - LMSR, expected value, Kelly sizing, the 5 cognitive traps that blow up accounts.

Lunar

192,972 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.

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

13,333 Aufrufe • vor 3 Monaten

My dad asked me why I stare at Python scripts at 3am instead of sleeping. "I'm not trading. I'm calculating." Every price on Polymarket comes from one equation: P(i) = exp(qᵢ / b) / Σ exp(qⱼ / b) This is the softmax function. The same math that GPT uses to pick the next word - Polymarket uses to price every contract. Most traders see 40¢ and think "cheap." I see 40¢ and check the liquidity parameter. b = 50 → one whale swings the market 20% b = 100,000 → barely moves I'm use for copytrade bots Gluon: Half my old trades were in thin pools. One order wrecked my fill. I was losing money to the formula I didn't know existed. So I learned it. Then I built a bot around it. EV = true_prob × (1 - market_price) - (1 - true_prob) × market_price The bot checks b, calculates EV, sizes with Kelly: f* = (p × b - q) / b And executes before my morning coffee gets cold. $200 starting bankroll. 6 weeks later: $18,700. I didn't predict a single outcome. The bot just reads the math that 93% of traders ignore. Full breakdown - LMSR, expected value, Kelly sizing, 5 cognitive traps

My dad asked me why I stare at Python scripts at 3am instead of sleeping. "I'm not trading. I'm calculating." Every price on Polymarket comes from one equation: P(i) = exp(qᵢ / b) / Σ exp(qⱼ / b) This is the softmax function. The same math that GPT uses to pick the next word - Polymarket uses to price every contract. Most traders see 40¢ and think "cheap." I see 40¢ and check the liquidity parameter. b = 50 → one whale swings the market 20% b = 100,000 → barely moves I'm use for copytrade bots Gluon: Half my old trades were in thin pools. One order wrecked my fill. I was losing money to the formula I didn't know existed. So I learned it. Then I built a bot around it. EV = true_prob × (1 - market_price) - (1 - true_prob) × market_price The bot checks b, calculates EV, sizes with Kelly: f* = (p × b - q) / b And executes before my morning coffee gets cold. $200 starting bankroll. 6 weeks later: $18,700. I didn't predict a single outcome. The bot just reads the math that 93% of traders ignore. Full breakdown - LMSR, expected value, Kelly sizing, 5 cognitive traps

Lunar

217,746 Aufrufe • vor 6 Monaten

HIDDEN PATTERN IN 72M POLYMARKET TRADES PRINTED +$865K ACROSS 29,000 PREDICTION No gambling-pure math, stats, strict sizing. How it works: 1. Mispricing 72M trades show: traders overpay for 0.1¢–50¢ contracts. Real edge lives in 80¢–99¢ where the bot trades most. Formula: δ = actual win rate − implied probability 2. Expected Value Tells if a trade is worth taking, regardless of any single outcome. Formula: EV = (P win × Payout) − (P lose × Cost) 3. Kelly Criterion Decides what % of portfolio to allocate for long-term growth. Formula: f* = (p·b − q) / Mispricing → EV → Kelly → trade. profile: start copy-trading the bot with Ares:

AdiiX

13,179 Aufrufe • vor 4 Monaten

THIS WALLET STACKED $230K ON BTC UP/DOWN BETS. THE BLUEPRINT TO AUTOMATE THE SAME EDGE WITH CLAUDE The wallet is $230K all-time, every position a Bitcoin or Ethereum Up or Down market It never guesses direction. It enters only when the math and the market disagree THE STRATEGY: BTC moves are not fully random. When the market enters a committed directional state, continuation is measurable. That is Markov persistence Entry signal: > Δ = p̂ − q ≥ ε Model probability minus market price. Enter only on a 5% gap or more Persistence filter: > p(j*,j*) ≥ 0.87 Only trade states with 0.87 persistence or higher. Below that, skip. This is what holds the win rate above 65% with zero directional guessing Payout: > r = (1 − q) / q At q = 0.647 that is +54.5% a win. At q = 0.441, +126.7%. Lower entry price, bigger asymmetry Sizing: > f* = p − (1−p)/b Kelly. At p = 0.87, b = 0.647, f* ≈ 0.71. Size to the edge, never to gut HOW TO BUILD IT WITH CLAUDE: What separates this from a static bot: Claude reads its own trade journal every night and rewrites its own thresholds 1. Take an open-source Polymarket bot repo as your base logic. Feed it to Claude and have it migrate to CLOB v2: py_clob_client_v2, Safe wallet support, fee-aware evaluation 2. Hard-code the filters. Enter only when Δ ≥ 0.05 and p(j*,j*) ≥ 0.87. Apply Kelly on every fill. 3. Run DRY_RUN first. Log every signal, entry price, Markov state, and simulated P/L. No real money until the numbers hold for days 4. The nightly loop. Claude reads the journal, finds which persistence states actually won, adjusts MIN_PROB and MIN_EDGE, ships tomorrow's rules. The agent is sharper after 50 to 100 trades THE SETUP: Claude Opus as the brain. An open-source repo as the starting logic. A Polygon wallet with $50 to $100. Telegram for the morning report Start at $1 to $2 per trade while it learns. Scale only when the dry runs and the live fills line up 17,000 trades compound a thin edge into six figures. The model finds the edge. The nightly loop keeps it sharp Bookmark before you point a bot at your first window

THIS WALLET STACKED $230K ON BTC UP/DOWN BETS. THE BLUEPRINT TO AUTOMATE THE SAME EDGE WITH CLAUDE The wallet is $230K all-time, every position a Bitcoin or Ethereum Up or Down market It never guesses direction. It enters only when the math and the market disagree THE STRATEGY: BTC moves are not fully random. When the market enters a committed directional state, continuation is measurable. That is Markov persistence Entry signal: > Δ = p̂ − q ≥ ε Model probability minus market price. Enter only on a 5% gap or more Persistence filter: > p(j,j) ≥ 0.87 Only trade states with 0.87 persistence or higher. Below that, skip. This is what holds the win rate above 65% with zero directional guessing Payout: > r = (1 − q) / q At q = 0.647 that is +54.5% a win. At q = 0.441, +126.7%. Lower entry price, bigger asymmetry Sizing: > f* = p − (1−p)/b Kelly. At p = 0.87, b = 0.647, f* ≈ 0.71. Size to the edge, never to gut HOW TO BUILD IT WITH CLAUDE: What separates this from a static bot: Claude reads its own trade journal every night and rewrites its own thresholds 1. Take an open-source Polymarket bot repo as your base logic. Feed it to Claude and have it migrate to CLOB v2: py_clob_client_v2, Safe wallet support, fee-aware evaluation 2. Hard-code the filters. Enter only when Δ ≥ 0.05 and p(j,j) ≥ 0.87. Apply Kelly on every fill. 3. Run DRY_RUN first. Log every signal, entry price, Markov state, and simulated P/L. No real money until the numbers hold for days 4. The nightly loop. Claude reads the journal, finds which persistence states actually won, adjusts MIN_PROB and MIN_EDGE, ships tomorrow's rules. The agent is sharper after 50 to 100 trades THE SETUP: Claude Opus as the brain. An open-source repo as the starting logic. A Polygon wallet with $50 to $100. Telegram for the morning report Start at $1 to $2 per trade while it learns. Scale only when the dry runs and the live fills line up 17,000 trades compound a thin edge into six figures. The model finds the edge. The nightly loop keeps it sharp Bookmark before you point a bot at your first window

Yarchi

22,966 Aufrufe • vor 4 Monaten

HOW DOES THIS QUANT BOT PRINT $12,422 IN PROFIT PER DAY +$211,174 PnL in 17 days on Polymarket Only on short crypto “Up / Down” markets On Mar 25, this wallet switched to a high-frequency market-making style using Bayesian + Stoikov pricing rule P(H|D) = P(D|H) · P(H) / P(D) Bayesian lets the bot update its internal probability before the market fully reprices the odds This Quant Bot's wallet: f = (bp - q) / b* - Kelly is what tells the bot how hard to size into the edge r = s - qγσ²(T - t) - Stoikov is what keeps quote placement and inventory risk under control So this is not some dumb script that just buys both sides It is a quantitative system that finds pricing errors, decides size, and manages exposure in real time Here is what that looks like in actual trades: $8,378.10 → $34,940.49 (+317.05%) $5,741.82 → $22,458.91 (+291.15%) $26,446.28 → $41,749.47 (+57.87%) At the center of the strategy is one very simple rule: YES + NO < 100¢

HOW DOES THIS QUANT BOT PRINT $12,422 IN PROFIT PER DAY +$211,174 PnL in 17 days on Polymarket Only on short crypto “Up / Down” markets On Mar 25, this wallet switched to a high-frequency market-making style using Bayesian + Stoikov pricing rule P(H|D) = P(D|H) · P(H) / P(D) Bayesian lets the bot update its internal probability before the market fully reprices the odds This Quant Bot's wallet: f = (bp - q) / b* - Kelly is what tells the bot how hard to size into the edge r = s - qγσ²(T - t) - Stoikov is what keeps quote placement and inventory risk under control So this is not some dumb script that just buys both sides It is a quantitative system that finds pricing errors, decides size, and manages exposure in real time Here is what that looks like in actual trades: $8,378.10 → $34,940.49 (+317.05%) $5,741.82 → $22,458.91 (+291.15%) $26,446.28 → $41,749.47 (+57.87%) At the center of the strategy is one very simple rule: YES + NO < 100¢

Daniro

13,077 Aufrufe • vor 5 Monaten

HOW DOES THIS QUANT BOT PRINT $10,561 IN PROFIT PER DAY +$401,307 PnL in one month on Polymarket Only on short crypto “Up or Down” markets On Feb 16, this wallet switched to a market-making style using Bayesian + Kelly + Stoikov pricing rule P(H|D) = P(D|H) · P(H) / P(D) Bayesian is used to update the bot’s internal probability faster than the market updates its odds Wallet using this model: r = s - qγσ²(T - t) - Stoikov is used to manage quoting and inventory risk f = (bp - q) / b* - Kelly is used to size the position This is why an arbitrage bot is not just “buying both sides”. It is a stack of models that mathematically estimates edge, sizes positions, and manages risk in real time This is how these formulas work: $4,508.76 → $11,293.72 (+150.48%) $3,043.75 → $9,212.02 (+202.65%) $6,695.96 → $12,473.57 (+86.28%) Claude tells me: this is the basic set of every arbitrage strategy

HOW DOES THIS QUANT BOT PRINT $10,561 IN PROFIT PER DAY +$401,307 PnL in one month on Polymarket Only on short crypto “Up or Down” markets On Feb 16, this wallet switched to a market-making style using Bayesian + Kelly + Stoikov pricing rule P(H|D) = P(D|H) · P(H) / P(D) Bayesian is used to update the bot’s internal probability faster than the market updates its odds Wallet using this model: r = s - qγσ²(T - t) - Stoikov is used to manage quoting and inventory risk f = (bp - q) / b* - Kelly is used to size the position This is why an arbitrage bot is not just “buying both sides”. It is a stack of models that mathematically estimates edge, sizes positions, and manages risk in real time This is how these formulas work: $4,508.76 → $11,293.72 (+150.48%) $3,043.75 → $9,212.02 (+202.65%) $6,695.96 → $12,473.57 (+86.28%) Claude tells me: this is the basic set of every arbitrage strategy

Daniro

31,829 Aufrufe • vor 5 Monaten

Chinese student used AI from Anthropic to turn $1,000 into $1,500,000 He studies at Tsinghua University in Beijing. His account is k9Q2m In such a young age he already make a million simply knowing the right formulas and being able to use Claude Result: $1,430 → $1,550,750 44,364 trades Win rate 100% The biggest win $23,600 on a single bet k9Q2m profile: How it bots work: The bot runs 6 formulas hedge funds use simultaneously, every tick. Most traders guess. This bot calculates. Formula 1 - LMSR Pricing Polymarket prices move on a logarithmic curve. The bot knows the exact price impact before entering. Market says 31¢ for BTC up in 5 minutes. The model sees the curve is mispriced. The bot enters before the correction. Formula 2 - Kelly Criterion Renaissance Capital uses it. Two Sigma uses it. Now your bot uses it. Every bet is sized exactly right. Never too big to blow the account. Never too small to matter. $1,000 bankroll. Consistent edge. Kelly compounds it into something real. Formula 3 - EV Gap Detection The bot scans every BTC market looking for one thing: - Where is the market price wrong by more than 5%? - Market says 30¢. Real probability is 55¢. EV = +0.52. The bot enters. Most people never see this gap. The bot never misses it. Formula 4 - KL-Divergence BTC 5-minute and 15-minute markets are correlated. When they drift apart - that's an arb. The bot measures the statistical distance between them every second. When it crosses 0.2, it flags the trade. This is how hedge funds extracted $100K+ on correlated election markets. The same logic runs here. Formula 5 - Bayesian Updates New block confirmed. Volume spike. Price movement. The bot doesn't ignore signals - it updates. Prior probability was 54%. New data comes in. Posterior jumps to 71%. The bot re-prices in real time while the market is still asleep. Formula 6 - Stoikov Execution Entering at the wrong moment kills the edge. The bot calculates the reservation price-the exact point where the risk-adjusted entry makes sense. It doesn't chase. It doesn't panic. It waits for the right tick, then fills What this means in practice: - Every few seconds the bot runs all six formulas in parallel. - If LMSR confirms mispricing - EV gap is above 5% - Kelly says the bet size is justified - Bayesian posterior agrees - KL-divergence flags the correlated drift - Stoikov clears the execution price Only then does the bot enter. Six filters. One trade. This isn't a trading bot. It's a hedge fund strategy running on a prediction market. The edge is real. The math is public. The difference is most people never build it. Just insert all these formulas into Claude and create your own bot Add this post to bookmarks so you don’t lose it Soon I will publish another bot with working formulas

AdiiX

719,007 Aufrufe • vor 6 Monaten

HOW IS THIS QUANT BOT MAKING $12,422 PER DAY? +$211,174 in just 17 days. Not by predicting Bitcoin. By exploiting math. Public wallet: Since March 25, the wallet switched to a completely different style. High-frequency market making. Every second it scans for small pricing mistakes. Most traders need a huge move. This bot doesn't. It only needs tiny inefficiencies. Its framework combines three ideas: • Bayesian updates to adjust probabilities before markets fully react • Kelly sizing to determine how aggressively to bet • Stoikov market-making logic to manage inventory and quote risk The result: Find edge. Size correctly. Control risk. Repeat. Thousands of times. Some recent trades: • $8,378 → $34,940 (+317%) • $5,741 → $22,458 (+291%) • $26,446 → $41,749 (+57%) The interesting part? The core edge is surprisingly simple. When: YES + NO < $1 The market is mispriced. Most people ignore it. The bot doesn't. It exploits the gap. Again. And again. And again. That's how a tiny edge becomes a six-figure PnL curve. Bot:

HOW IS THIS QUANT BOT MAKING $12,422 PER DAY? +$211,174 in just 17 days. Not by predicting Bitcoin. By exploiting math. Public wallet: Since March 25, the wallet switched to a completely different style. High-frequency market making. Every second it scans for small pricing mistakes. Most traders need a huge move. This bot doesn't. It only needs tiny inefficiencies. Its framework combines three ideas: • Bayesian updates to adjust probabilities before markets fully react • Kelly sizing to determine how aggressively to bet • Stoikov market-making logic to manage inventory and quote risk The result: Find edge. Size correctly. Control risk. Repeat. Thousands of times. Some recent trades: • $8,378 → $34,940 (+317%) • $5,741 → $22,458 (+291%) • $26,446 → $41,749 (+57%) The interesting part? The core edge is surprisingly simple. When: YES + NO < $1 The market is mispriced. Most people ignore it. The bot doesn't. It exploits the gap. Again. And again. And again. That's how a tiny edge becomes a six-figure PnL curve. Bot:

Discover

12,556 Aufrufe • vor 3 Monaten

HOW DOES THIS QUANT BOT PRINT $2,000 IN PROFIT PER DAY +$71,961 PnL in 36 days on Polymarket Only on short crypto “Up / Down” markets On Mar 24, this wallet switched to a high-frequency market-making style using Bayesian + Stoikov pricing rule P(H|D) = P(D|H) · P(H) / P(D) The highest-profit trades already make the pattern clear: $706.07 → $1,633.32 (+131.33%) $596.32 → $1,396.80 (+134.24%) $358.65 → $1,098.47 (+206.28%) This Quant Bot’s wallet: The model does not just react to direction. It updates probability first, then decides whether the market is still lagging enough to justify size f = (bp - q) / b* ; Kelly is the sizing layer that decides how much capital should actually be deployed once the edge is confirmed. r = s - qγσ²(T - t) ; Stoikov is the quoting logic that keeps inventory under control while the bot keeps leaning into mispricing. Everything still comes back to one simple condition on Polymarket: { YES + NO < 100¢ }

HOW DOES THIS QUANT BOT PRINT $2,000 IN PROFIT PER DAY +$71,961 PnL in 36 days on Polymarket Only on short crypto “Up / Down” markets On Mar 24, this wallet switched to a high-frequency market-making style using Bayesian + Stoikov pricing rule P(H|D) = P(D|H) · P(H) / P(D) The highest-profit trades already make the pattern clear: $706.07 → $1,633.32 (+131.33%) $596.32 → $1,396.80 (+134.24%) $358.65 → $1,098.47 (+206.28%) This Quant Bot’s wallet: The model does not just react to direction. It updates probability first, then decides whether the market is still lagging enough to justify size f = (bp - q) / b* ; Kelly is the sizing layer that decides how much capital should actually be deployed once the edge is confirmed. r = s - qγσ²(T - t) ; Stoikov is the quoting logic that keeps inventory under control while the bot keeps leaning into mispricing. Everything still comes back to one simple condition on Polymarket: { YES + NO < 100¢ }

Daniro

10,149 Aufrufe • vor 4 Monaten

I showed my Polymarket latency bot to a quant friend. He quit his job the next week. $800 → $5,000. Pure latency arbitrage. The bot doesn’t predict outcomes. It’s simply 200 milliseconds faster than the market. The formula is simple: P = Σ(ΔOdds × Size) / Latency_gap The bigger the gap, the better the entry. What’s running under the hood: Connected via API to live sports and political data feeds Receives match results and vote counts before they appear on Polymarket Enters using the Kelly criterion milliseconds before the spread closes Exits automatically once the market converges Win rate: 73% Sharpe ratio: 2.24 About 57 trades per hour Every trade exists inside a ~200 millisecond window. Most people analyze charts. The bot analyzes latency. Charts can be misleading. Milliseconds can’t. Copy trades → you were reading this, the bot likely already closed several more positions.

I showed my Polymarket latency bot to a quant friend. He quit his job the next week. $800 → $5,000. Pure latency arbitrage. The bot doesn’t predict outcomes. It’s simply 200 milliseconds faster than the market. The formula is simple: P = Σ(ΔOdds × Size) / Latency_gap The bigger the gap, the better the entry. What’s running under the hood: Connected via API to live sports and political data feeds Receives match results and vote counts before they appear on Polymarket Enters using the Kelly criterion milliseconds before the spread closes Exits automatically once the market converges Win rate: 73% Sharpe ratio: 2.24 About 57 trades per hour Every trade exists inside a ~200 millisecond window. Most people analyze charts. The bot analyzes latency. Charts can be misleading. Milliseconds can’t. Copy trades → you were reading this, the bot likely already closed several more positions.

Discover

43,266 Aufrufe • vor 6 Monaten

every polymarket bot is based on this formula the kelly criterion f = (p - m) / (1 - m) p = your true probability m = current market price simple rule, but it works on distance you think edge is about picking winners but the real game is position sizing here's how it works: bot identifies edge: calculates true probability vs market price example flow: bot sees market at 50c (m), calculates real probability at 60% (p) math is simple: edge / remaining upside (0.60 - 0.50) / (1.00 - 0.50) 0.10 / 0.50 = 0.20 kelly says allocate 20% of current capital bot uses quarter kelly for safety = 5% allocation position opens automatically at optimal size as bankroll grows, position sizes scale up proportionally as bankroll shrinks, positions scale down to preserve capital bots are actually destroying manual traders humans can't make calculations instantly bots can the formula ensures maximum growth without risking ruin position sizing adjusts dynamically to current capital this is the foundation of every serious automated strategy

every polymarket bot is based on this formula the kelly criterion f = (p - m) / (1 - m) p = your true probability m = current market price simple rule, but it works on distance you think edge is about picking winners but the real game is position sizing here's how it works: bot identifies edge: calculates true probability vs market price example flow: bot sees market at 50c (m), calculates real probability at 60% (p) math is simple: edge / remaining upside (0.60 - 0.50) / (1.00 - 0.50) 0.10 / 0.50 = 0.20 kelly says allocate 20% of current capital bot uses quarter kelly for safety = 5% allocation position opens automatically at optimal size as bankroll grows, position sizes scale up proportionally as bankroll shrinks, positions scale down to preserve capital bots are actually destroying manual traders humans can't make calculations instantly bots can the formula ensures maximum growth without risking ruin position sizing adjusts dynamically to current capital this is the foundation of every serious automated strategy

ramper

68,944 Aufrufe • vor 7 Monaten

HOW DOES THIS QUANT BOT PRINT $21,389 IN PROFIT PER DAY +$363,618 PnL in 17 days on Polymarket Only on short crypto “Up / Down” markets On Mar 15, this wallet switched to a high-frequency market-making style using Bayesian + Stoikov pricing rule P(H|D) = P(D|H) · P(H) / P(D) Bayesian lets the bot update its internal probability before the market fully reprices the odds This Quant Bot's wallet: Trader funded this account with $14,827 and now sits at roughly 24.5x on deposit (+2,452% ROI) f = (bp - q) / b* - Kelly determines how large the position should be r = s - qγσ²(T - t) - Stoikov handles quote placement and inventory risk An arbitrage bot is not just buying both sides. It is a mathematical engine that detects mispricing, sizes the trade, and controls risk in real time This is what that looks like in practice: $17,839 → $36,318 (+103.58%) $3,112 → $15,011 (+382.22%) $10,676 → $22,178 (+107.74%) The core logic is simple: YES + NO < 100¢

HOW DOES THIS QUANT BOT PRINT $21,389 IN PROFIT PER DAY +$363,618 PnL in 17 days on Polymarket Only on short crypto “Up / Down” markets On Mar 15, this wallet switched to a high-frequency market-making style using Bayesian + Stoikov pricing rule P(H|D) = P(D|H) · P(H) / P(D) Bayesian lets the bot update its internal probability before the market fully reprices the odds This Quant Bot's wallet: Trader funded this account with $14,827 and now sits at roughly 24.5x on deposit (+2,452% ROI) f = (bp - q) / b* - Kelly determines how large the position should be r = s - qγσ²(T - t) - Stoikov handles quote placement and inventory risk An arbitrage bot is not just buying both sides. It is a mathematical engine that detects mispricing, sizes the trade, and controls risk in real time This is what that looks like in practice: $17,839 → $36,318 (+103.58%) $3,112 → $15,011 (+382.22%) $10,676 → $22,178 (+107.74%) The core logic is simple: YES + NO < 100¢

Daniro

46,114 Aufrufe • vor 5 Monaten

Polymarket just got mathematically robbed. Top 0.04% already took 70% of all the money ($3.7B) thanks to 4 formulas from this article below. Open the leaderboard and there he is. [0x8dxd]: $2,285,751 ALL-TIME profit. Joined Dec 2025. $41.2K biggest win. 31,570 predictions. Not luck. Not "feeling the market". This is a bot running strictly on Lunar’s formulas on autopilot: Formula 1 - Expected Value (When to Enter) Contract at 40¢, but your real probability assessment is 60% -> +20¢ edge per dollar. Claude calculates this in seconds. Formula 2 - Kelly Criterion (How Much to Bet) f = (p·b − q)/b* (Quarter Kelly - that's why he doesn't blow up and compounds without any emotion) Formula 3 - Bayesian Updating (How to Change Your Mind) P(H|E) = [P(E|H) × P(H)] / P(E) Instantly rebuilds probability on any news. Formula 4 - Log Returns (Real Profit Calculation) Regular arithmetic lies. Only log returns show the real picture. Scans 50+ markets at once, enters only 15-min BTC Up/Down with real edge (where [0x8dxd] prints +157%, +207%, +181%), sleeps the rest of the time. 87% of wallets are in the red. A few such bots take everything. And this guy didn't just write an article - he handed out the blueprint that's already working for [0x8dxd]. Don't want to code it yourself? Just copy this trader with You can runs exactly this system: the same 4 formulas + Claude + auto-copying top math. Add his wallet: 0x63ce342161250d705dc0b16df89036c8e5f9ba9a to [ and start track/copy him right now. 4 formulas from Wikipedia + Claude brain + Kreo execution = meta 2026. The math doesn't sleep. Your FOMO does. Save and use this.

Polymarket just got mathematically robbed. Top 0.04% already took 70% of all the money ($3.7B) thanks to 4 formulas from this article below. Open the leaderboard and there he is. [0x8dxd]: $2,285,751 ALL-TIME profit. Joined Dec 2025. $41.2K biggest win. 31,570 predictions. Not luck. Not "feeling the market". This is a bot running strictly on Lunar’s formulas on autopilot: Formula 1 - Expected Value (When to Enter) Contract at 40¢, but your real probability assessment is 60% -> +20¢ edge per dollar. Claude calculates this in seconds. Formula 2 - Kelly Criterion (How Much to Bet) f = (p·b − q)/b* (Quarter Kelly - that's why he doesn't blow up and compounds without any emotion) Formula 3 - Bayesian Updating (How to Change Your Mind) P(H|E) = [P(E|H) × P(H)] / P(E) Instantly rebuilds probability on any news. Formula 4 - Log Returns (Real Profit Calculation) Regular arithmetic lies. Only log returns show the real picture. Scans 50+ markets at once, enters only 15-min BTC Up/Down with real edge (where [0x8dxd] prints +157%, +207%, +181%), sleeps the rest of the time. 87% of wallets are in the red. A few such bots take everything. And this guy didn't just write an article - he handed out the blueprint that's already working for [0x8dxd]. Don't want to code it yourself? Just copy this trader with You can runs exactly this system: the same 4 formulas + Claude + auto-copying top math. Add his wallet: 0x63ce342161250d705dc0b16df89036c8e5f9ba9a to [ and start track/copy him right now. 4 formulas from Wikipedia + Claude brain + Kreo execution = meta 2026. The math doesn't sleep. Your FOMO does. Save and use this.

slash1s

20,164 Aufrufe • vor 6 Monaten

$1,331,821 IN 30 DAYS. 3 BOTS. 48,061 TRADES. ONE FORMULA. They don't predict price. They measure what state the market is in right now. Markov chains, transition matrix, each cell - the probability of transitioning from state A to state B. The matrix diagonal - the probability that the market stays where it is. Entry only when the diagonal is above 0.87. Two conditions: the gap between model and market is greater than 5%, and the state is stable. Both must be true. One function, runs every minute. Bot 1 - Bonereaper. BTC and ETH, hourly windows, entry at 83-97¢. The market agrees with the direction but underestimates the confidence. 4-19% on every resolution. Low variance. Bot 2 - 0xe1D6. Dual mode, directional scalps at 64-83¢ deliver 20-54% per trade. In parallel, locks at 99.5-99.8¢. Best trade: entry at 64.7¢, return 54.6%. Bot 3 - 0xB27BC. Five assets: BTC, ETH, SOL, BNB, XRP. Five-minute windows. One trade every 1.7 minutes. Variance 55% lower at the same expected return. The real edge - 3:00 AM. People are asleep. The market posts lazy, stale prices. The gap between model and market is maximal when no one is watching. 0.034% per trade sounds like nothing. Over 16,000 trades that's *240. The law of large numbers turns noise into an exponent. Kelly criterion f* ≈ 0.71 - aggressive enough to grow, conservative enough not to go to zero. As long as people misprice short windows - the edge exists. You don't need to predict. You need to measure. The market rewards those who understand probability. The rest just provide liquidity.

$1,331,821 IN 30 DAYS. 3 BOTS. 48,061 TRADES. ONE FORMULA. They don't predict price. They measure what state the market is in right now. Markov chains, transition matrix, each cell - the probability of transitioning from state A to state B. The matrix diagonal - the probability that the market stays where it is. Entry only when the diagonal is above 0.87. Two conditions: the gap between model and market is greater than 5%, and the state is stable. Both must be true. One function, runs every minute. Bot 1 - Bonereaper. BTC and ETH, hourly windows, entry at 83-97¢. The market agrees with the direction but underestimates the confidence. 4-19% on every resolution. Low variance. Bot 2 - 0xe1D6. Dual mode, directional scalps at 64-83¢ deliver 20-54% per trade. In parallel, locks at 99.5-99.8¢. Best trade: entry at 64.7¢, return 54.6%. Bot 3 - 0xB27BC. Five assets: BTC, ETH, SOL, BNB, XRP. Five-minute windows. One trade every 1.7 minutes. Variance 55% lower at the same expected return. The real edge - 3:00 AM. People are asleep. The market posts lazy, stale prices. The gap between model and market is maximal when no one is watching. 0.034% per trade sounds like nothing. Over 16,000 trades that's 240. The law of large numbers turns noise into an exponent. Kelly criterion f ≈ 0.71 - aggressive enough to grow, conservative enough not to go to zero. As long as people misprice short windows - the edge exists. You don't need to predict. You need to measure. The market rewards those who understand probability. The rest just provide liquidity.

zostaff

61,629 Aufrufe • vor 5 Monaten