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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 executionshow more

Daniro

5,610 subscribers

86,463 Aufrufe • vor 17 Tagen •via X (Twitter)

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10 Kommentare

Profilbild von Jamie Torres

Jamie Torresvor 16 Tagen

Forget selling a $9 Notion template with "3 spots left" 😭 This is what actual leverage looks like. Building a system that runs in a continuous loop, executes 15 times an hour, and prints $659 a day. The market rewards execution, not fluff.

Profilbild von Akbar Shaik

Akbar Shaikvor 16 Tagen

The architecture is what makes this interesting as an AI system. The Monte Carlo engine handles quantitative simulation, the agent layer can manage parameter selection and execution logic, and the feedback layer can continuously compare expected probability vs realized outcome.

Profilbild von VALIX

VALIXvor 17 Tagen

Thanks for sharing. This guy has achieved an incredible return over 130 days

Profilbild von 0xSLEEPY

0xSLEEPYvor 17 Tagen

有力气

Profilbild von Korens

Korensvor 17 Tagen

Monte Carlo pricing on Polymarket is fascinating

Profilbild von Daniro

Danirovor 17 Tagen

Traders have to choose from a wide variety of mathematical models, but only a few are most suitable for building software

Profilbild von HeckyeaWin

HeckyeaWinvor 16 Tagen

first step: be able to run next to their servers

Profilbild von ChilliJesus

ChilliJesusvor 16 Tagen

890

Profilbild von Peter Roebel

Peter Roebelvor 16 Tagen

@grok real or fake?

Profilbild von magsimich

magsimichvor 16 Tagen

659 a day is pretty wild

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VALIX

369,056 Aufrufe • vor 26 Tagen

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

This guy built a trading bot on Polymarket with Claude Result: +$228,209 in 4 months His bot trades the microstructure of crypto Up/Down markets using a strategy with 5 core elements: 1. Compares the current asset price from external sources with the price on Polymarket 2. Enters a position when his model sees one side is undervalued 3. If the asset’s direction changes, it starts buying the opposite outcome or fully flips the position 4. Constantly rebalances positions based on new information 5. Buying both sides works either as a hedge or as an arbitrage opportunity His Polymarket username: nj23adsknml3 Using high-frequency directional trading with constant position rebalancing, he turned $9.5k into $228k

This guy built a trading bot on Polymarket with Claude Result: +$228,209 in 4 months His bot trades the microstructure of crypto Up/Down markets using a strategy with 5 core elements: 1. Compares the current asset price from external sources with the price on Polymarket 2. Enters a position when his model sees one side is undervalued 3. If the asset’s direction changes, it starts buying the opposite outcome or fully flips the position 4. Constantly rebalances positions based on new information 5. Buying both sides works either as a hedge or as an arbitrage opportunity His Polymarket username: nj23adsknml3 Using high-frequency directional trading with constant position rebalancing, he turned $9.5k into $228k

VALIX

19,104 Aufrufe • vor 2 Monaten

This guy built an HFT bot on Polymarket with an average trade size of $8 Result: +$243,619 His bot trades 5- and 15-minute crypto Up/Down markets using a directional latency strategy with fast position switching: 1. It looks for latency between external crypto markets and Polymarket It gets the underlying price from external sources and tracks momentum, acceleration, order-flow imbalance, and distance from the starting price of the current window Based on this data, it constantly recalculates its own fair probability for Up and Down 2. It trades mispricing before Polymarket fully reprices If the external market has already moved, but the outcome price is still lagging, the algorithm sees an edge and starts building a position 3. It quickly changes direction and dynamically hedges the old position If the signal reverses, it can sell the old side or start accumulating the opposite outcome, constantly rebuilding its net exposure His Polymarket nickname: nj23adsknml3 You can build your own trading bot here: (A trial period will be available after registration) This guy’s edge is built on speed. The bot tries to detect changes in external market prices before they are fully reflected on Polymarket, then quickly rebuilds its position after every new signal It repeats this micro edge thousands of times and keeps growing its capital

This guy built an HFT bot on Polymarket with an average trade size of $8 Result: +$243,619 His bot trades 5- and 15-minute crypto Up/Down markets using a directional latency strategy with fast position switching: 1. It looks for latency between external crypto markets and Polymarket It gets the underlying price from external sources and tracks momentum, acceleration, order-flow imbalance, and distance from the starting price of the current window Based on this data, it constantly recalculates its own fair probability for Up and Down 2. It trades mispricing before Polymarket fully reprices If the external market has already moved, but the outcome price is still lagging, the algorithm sees an edge and starts building a position 3. It quickly changes direction and dynamically hedges the old position If the signal reverses, it can sell the old side or start accumulating the opposite outcome, constantly rebuilding its net exposure His Polymarket nickname: nj23adsknml3 You can build your own trading bot here: (A trial period will be available after registration) This guy’s edge is built on speed. The bot tries to detect changes in external market prices before they are fully reflected on Polymarket, then quickly rebuilds its position after every new signal It repeats this micro edge thousands of times and keeps growing its capital

VALIX

84,178 Aufrufe • vor 1 Monat

This guy built an HFT algorithm on Polymarket with an average trade size of $10 Result: +$262,402 His bot trades 15-minute BTC/ETH Up/Down markets using a hybrid of two-sided market making, volatility harvesting, and asynchronous complete-set accumulation: 1. It builds positions where Up + Down Buys Up at 42¢, then later buys Down at 53¢ > Complete set = 95¢ -> 5¢ gross edge 2. It leaves a directional residual on top of the paired inventory For example: > Buys 100 Up + 150 Down > 100 Up + 100 Down form complete sets, while the remaining 50 Down maintain directional exposure Around 86% of all purchased shares are part of the paired position, while the remaining inventory acts as the directional layer of the strategy His Polymarket nickname: aswfadq1555 You can build your own trading bot here: (Trial period available after registration) This guy’s stats: > Trades / active hour: 324 > Average trade: $10.51 > Win Rate: 50% The main edge of his HFT algorithm comes from using volatility within 15-minute markets to asynchronously accumulate Up and Down for less than $1. The unmatched remainder is kept as directional exposure Using this strategy and his algorithm, this guy steadily grows his capital

This guy built an HFT algorithm on Polymarket with an average trade size of $10 Result: +$262,402 His bot trades 15-minute BTC/ETH Up/Down markets using a hybrid of two-sided market making, volatility harvesting, and asynchronous complete-set accumulation: 1. It builds positions where Up + Down Buys Up at 42¢, then later buys Down at 53¢ > Complete set = 95¢ -> 5¢ gross edge 2. It leaves a directional residual on top of the paired inventory For example: > Buys 100 Up + 150 Down > 100 Up + 100 Down form complete sets, while the remaining 50 Down maintain directional exposure Around 86% of all purchased shares are part of the paired position, while the remaining inventory acts as the directional layer of the strategy His Polymarket nickname: aswfadq1555 You can build your own trading bot here: (Trial period available after registration) This guy’s stats: > Trades / active hour: 324 > Average trade: $10.51 > Win Rate: 50% The main edge of his HFT algorithm comes from using volatility within 15-minute markets to asynchronously accumulate Up and Down for less than $1. The unmatched remainder is kept as directional exposure Using this strategy and his algorithm, this guy steadily grows his capital

VALIX

134,541 Aufrufe • vor 1 Monat

This guy built an HFT bot on Polymarket with a $50 average trade size Result: +$220,792 His bot trades 5-minute BTC/ETH Up/Down markets using a hybrid of inventory rebalancing, dynamic hedging, and accumulating cheap complete sets: 1. It builds complete sets so that Up + Down costs less than $1 It uses limit orders to buy different sides during different phases of the underlying asset’s movement Average cost of a matched complete set: $0.9387. Edge = 6.13¢ 2. It leaves directional residual on top of the hedged inventory Part of the capital gets a structural edge through pairing, while the rest keeps exposure on the side the algorithm sees as stronger For example: 200 Up + 150 Down 150 Up + 150 Down form complete sets, while the remaining 50 Up stay as a directional position You can build your own trading bot here: (Free trial available after registration) Stats: > Trades / active hour: 284 > Average trade: $49.8 > Win Rate: 50% His Polymarket nickname: hot-garbage This guy’s edge is built on two main layers: he turns most of his inventory into Up + Down < $1, while using the unmatched remainder as a directional position Using this strategy, he captures a consistent edge, repeats it thousands of times, and steadily grows his PnL

This guy built an HFT bot on Polymarket with a $50 average trade size Result: +$220,792 His bot trades 5-minute BTC/ETH Up/Down markets using a hybrid of inventory rebalancing, dynamic hedging, and accumulating cheap complete sets: 1. It builds complete sets so that Up + Down costs less than $1 It uses limit orders to buy different sides during different phases of the underlying asset’s movement Average cost of a matched complete set: $0.9387. Edge = 6.13¢ 2. It leaves directional residual on top of the hedged inventory Part of the capital gets a structural edge through pairing, while the rest keeps exposure on the side the algorithm sees as stronger For example: 200 Up + 150 Down 150 Up + 150 Down form complete sets, while the remaining 50 Up stay as a directional position You can build your own trading bot here: (Free trial available after registration) Stats: > Trades / active hour: 284 > Average trade: $49.8 > Win Rate: 50% His Polymarket nickname: hot-garbage This guy’s edge is built on two main layers: he turns most of his inventory into Up + Down < $1, while using the unmatched remainder as a directional position Using this strategy, he captures a consistent edge, repeats it thousands of times, and steadily grows his PnL

VALIX

87,974 Aufrufe • vor 1 Monat

This trader made $132,356 on Polymarket using trading bot, buying markets for $11 His bot trades 5-minute crypto Up/Down markets and combines high-frequency directional trading with dynamic hedging Its strategy is simple: > Finds mispricing with its probability model and buys the outcome where it sees an edge > Increases the position if the signal is confirmed, and partially reduces exposure when the signal weakens > When the underlying asset reverses, it buys the opposite side as a hedge His Polymarket nickname: trinity42 You can build your own trading bot here: (Free trial available after registration) Using its strategy and high-quality execution, this guy turned $2,000 into $132,356 in 4 months of trading

This trader made $132,356 on Polymarket using trading bot, buying markets for $11 His bot trades 5-minute crypto Up/Down markets and combines high-frequency directional trading with dynamic hedging Its strategy is simple: > Finds mispricing with its probability model and buys the outcome where it sees an edge > Increases the position if the signal is confirmed, and partially reduces exposure when the signal weakens > When the underlying asset reverses, it buys the opposite side as a hedge His Polymarket nickname: trinity42 You can build your own trading bot here: (Free trial available after registration) Using its strategy and high-quality execution, this guy turned $2,000 into $132,356 in 4 months of trading

VALIX

50,580 Aufrufe • vor 1 Monat

$6 formulas is not a playbook. It's half a playbook But here's what's missing Four models any serious quant stack should also account for: 1. Random Walk Paths dS_t = μS_tdt + σS_tdW_t Geometric Brownian Motion - the backbone of price simulation Without modeling hundreds of possible scenarios building up over time, you're flying blind on tail risk 2. Monte Carlo Simulation P(A) ≈ N_hit / N_total Thousands of random samples to solve problems that closed-form math can't This is how you stress-test your Kelly sizing before real capital hits the market 3. Probability Distribution f(x) = (1/σ√2π) · e^(-½((x-μ)/σ)²) Results converge into a clear distribution of potential risk If you're not mapping your P&L outcomes to a distribution, you're guessing not trading 4. Quantitative Analysis / VaR VaR_α = inf{l ∈ ℝ : P(L > l) ≤ 1 − α} Predicting the future by simulating uncertainty and volatility Value at Risk shows you the worst-case loss at a given confidence level, the one number risk desks actually care about The original playbook gives you the edge. These four give you the guardrails Without guardrails, edges kill you faster than no edge at all Auto-copy trades from pros like this and ride their edge:$

6 formulas is not a playbook. It's half a playbook But here's what's missing Four models any serious quant stack should also account for: 1. Random Walk Paths dS_t = μS_tdt + σS_tdW_t Geometric Brownian Motion - the backbone of price simulation Without modeling hundreds of possible scenarios building up over time, you're flying blind on tail risk 2. Monte Carlo Simulation P(A) ≈ N_hit / N_total Thousands of random samples to solve problems that closed-form math can't This is how you stress-test your Kelly sizing before real capital hits the market 3. Probability Distribution f(x) = (1/σ√2π) · e^(-½((x-μ)/σ)²) Results converge into a clear distribution of potential risk If you're not mapping your P&L outcomes to a distribution, you're guessing not trading 4. Quantitative Analysis / VaR VaR_α = inf{l ∈ ℝ : P(L > l) ≤ 1 − α} Predicting the future by simulating uncertainty and volatility Value at Risk shows you the worst-case loss at a given confidence level, the one number risk desks actually care about The original playbook gives you the edge. These four give you the guardrails Without guardrails, edges kill you faster than no edge at all Auto-copy trades from pros like this and ride their edge:

sopersone

15,585 Aufrufe • vor 6 Monaten