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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 views • 17 days ago •via X (Twitter)

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10 Comments

Jamie Torres16 days ago

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.

Akbar Shaik16 days ago

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.

VALIX17 days ago

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

0xSLEEPY16 days ago

有力气

Korens17 days ago

Monte Carlo pricing on Polymarket is fascinating

Daniro17 days ago

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

HeckyeaWin16 days ago

first step: be able to run next to their servers

ChilliJesus16 days ago

890

Peter Roebel16 days ago

@grok real or fake?

magsimich16 days ago

659 a day is pretty wild

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28,577 views • 1 month ago

There’s a guy running a high-frequency trading algorithm on Polymarket, averaging just $53 per trade. In the end, he made $129,923. His bot specializes in short-term BTC and ETH up/down markets, using a mixed strategy of two-sided market making plus complete-pair accumulation, combined with inventory rebalancing. The specific logic works like this: First, it accumulates positions on both the “up” and “down” sides at different times. On both sides it hangs buy orders, taking advantage of market volatility to get filled at different prices. For example, on the “up” side it buys at 47 cents, and a while later on the “down” side it buys at 52 cents, adding up to 99 cents combined, which locks in a guaranteed 1 cent gross profit. Second, it keeps a bit of directional exposure. After putting together complete pairs, if its fair-probability model sees an edge, it holds onto part of the directional position. The model is based on external data on the underlying assets, order book depth, and things like momentum, acceleration, and volatility. His main Polymarket page: The core edge of his high-frequency algorithm is constantly repeating the same cycle, steadily eating up the price difference to keep compounding his capital into a bigger and bigger pile.

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draven

60,523 views • 1 month ago

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VALIX

27,231 views • 1 month ago

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Daniro

22,807 views • 4 months ago

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VALIX

369,056 views • 26 days ago

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 views • 4 months ago

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 views • 2 months ago

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 views • 1 month ago

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 views • 1 month ago

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 views • 1 month ago

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 views • 1 month ago

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

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 views • 6 months ago