VALIX

This guy used Grok Bot to build a Two-Sided Market Making system on Polymarket Result: +$204,743 It makes 112.5 trades per hour in BTC and ETH Up/Down markets, combining asynchronous arbitrage with inventory rebalancing Several Grok Bot AI agents work together as one algorithm, making $1,188 per day: 1. Continually buys both sides with limit orders It places BUY orders on Up and Down, building positions as the underlying asset moves This lets it build two-sided inventory without buying both outcomes at the same time 2. Uses repricing to build low-cost complete sets It pairs Up and Down shares bought at different times For example: > Up = 37¢ > Down = 61¢ > Combined cost = 98¢ → edge = 2¢ 3. Continuously rebalances inventory and reduces directional exposure When one side accumulates faster, it adjusts its orders to buy more shares of the opposite outcome It tries to match nearly all of its Up and Down shares while keeping a small directional residual Its Polymarket account: Polkadot-Frog The system’s main edge comes from combining passive execution with inventory rebalancing: > It uses price fluctuations to build low-cost complete sets > It continually balances its positions and repeats the cycle

This guy used Grok Bot to build a two-sided trading bot on Polymarket Result: +$701,615 It makes 249 trades per hour in crypto Up/Down markets, combining asynchronous arbitrage, inventory rebalancing, and directional trading Several Grok Bot AI agents work as one system, making $3,874 per day: 1. Buys both sides through a grid of limit orders It places BUY limit orders at different price levels for Up and Down As the underlying asset moves, the orders fill, forming complete sets with a structural edge For example: Up = 43¢ Down = 54¢ Combined cost = 97¢ → edge = 3¢ 2. Keeps a directional residual alongside the paired position If one side looks undervalued relative to its fair value estimate, it keeps extra inventory on that side Matched inventory reduces net exposure, while the directional residual provides an additional edge This trader’s Polymarket account: polymarket.com/@0xce25e214d5… The algorithm’s main edge comes from accumulating Up and Down at different times: > It uses repricing within each market to build complete sets for less than $1 > It also keeps a directional residual on the undervalued side

This guy used Grok Bot to build an asynchronous arbitrage system on Polymarket Result: +$1,340,486 It makes 340 trades per active hour in crypto Up/Down markets, combining market making, pairing, and a directional residual Several Grok Bot AI agents work together as one algorithm, making $7,406 per day: 1. Buys Up and Down at different times It places BUY limit orders on both sides of the market in advance and waits for temporary price dislocations The goal is: Avg Up price + Avg Down price < $1 2. Keeps a directional residual alongside the matched position when it sees an edge Its valuation model uses external spot/perp data, the distance from the opening price, time remaining, and CLOB liquidity It builds a directional residual when: p_model(Up) > Up price + fees → BUY Up This trader’s Polymarket account: polymarket.com/@0xb55fa1296e… The system’s main edge comes from combining asynchronous pairing with short-term repricing: > It buys each side at different points as the market moves > Locks in an edge on low-cost Up + Down pairs > Keeps additional directional exposure where its model identifies mispricing

This guy used Grok Bot to build a repricing system on Polymarket Result: +$207,170 It trades 5 and 15 minute crypto Up/Down markets, combining a fair value model with dynamic hedging Several Grok Bot AI agents work together as one algorithm: 1. Finds undervalued outcomes At the start of each market, it records the reference price S₀ and monitors spot/perp feeds for the underlying asset The model then updates fair value based on how far the price is from S₀, time to resolution, momentum, acceleration, volatility, and order flow Model Edge = p_model(outcome) - market price If the edge is large enough to cover the spread and fees, it starts buying that outcome 2. Adds the opposite outcome to its existing position when the signal reverses As its fair value estimates shift toward the other side, the trading sequence can look like this: Up → Down → Up → Down Buying the opposite outcome reduces net exposure and acts as a dynamic hedge Its Polymarket account: std0 The system’s main edge comes from how fast it updates fair value: > Tries to detect changes in fair value before market prices adjust > Increases exposure as the signal strengthens > Uses the opposite outcome to adjust its position

This guy used Grok Bot to build a Two-Sided Accumulation bot on Polymarket Result: +$130,904 It trades 5 minute BTC Up/Down markets, combining complete-set accumulation with a directional residual Several Grok Bot AI agents work together as one algorithm: 1. Builds positions in Up and Down at different times It first accumulates one outcome, then starts buying the opposite side after the market moves Once it has matching shares on both sides, it merges them For example: 80 Up + 80 Down -> MERGE -> $80 If a pair’s total cost, including fees, is below $1, the difference becomes a structural edge 2. Keeps unpaired inventory directional If it buys: > 100 Up > 80 Down It can form 80 complete sets, while the remaining 20 Up shares stay directional Its Polymarket account: flippingsharks The system’s main edge comes from its inventory cycle: > Accumulates both sides > Builds complete sets for less than $1 > Keeps the unpaired remainder directional until resolution

This guy used Grok Bot to build an asynchronous complete-set trading system on Polymarket Result: +$319,430 The system makes 131 trades an hour in short-term crypto Up/Down markets, with an average trade size of $30.58 Several Grok Bot AI agents work together as one algorithm: 1. Builds positions on both sides It bought both Up and Down in 88.76% of markets The median gap between its first trades on each side is 35 seconds It then keeps switching between them as the market reprices, turning directional inventory into matched Up/Down pairs Average cost per pair: > Up + Down before fees = 96.35¢ > Up + Down after taker fees = 98.69¢ > Structural edge = 1.31¢ 2. Builds directional positions Only 58.66% of two-sided markets ended up with an average cost per pair below $1 after fees Some shares remain directional and provide an additional edge Its Polymarket account: norm1e69 The system’s main edge comes from asynchronous pairing: > It uses repricing to turn part of its Up and Down inventory into complete sets < $1 > It holds the unpaired remainder as a directional position until resolution

This guy used Grok Bot to build a directional repricing system on Polymarket Result: +$149,920 It trades 5 minute BTC, ETH, and SOL Up/Down markets using fair value estimates and dynamic hedging Several Grok Bot AI agents work together as one algorithm: 1. Looks for an undervalued outcome It uses data from external sources to continuously update P(Up) and P(Down) The model factors in the distance between the spot price and the opening level, time remaining, momentum, volatility, order flow, and a latency edge For example: > Down price = 67¢ > Model’s estimate for Down = 74% It enters a position when it identifies an edge After entry, the model keeps updating every few seconds. If confidence increases, the algorithm buys more of the same outcome 2. Hedges when the signal changes If the edge starts to disappear, it may sell part of its original position If the signal completely reverses, it sometimes buys the opposite outcome Its Polymarket account: trinity42 The algorithm’s main edge comes from the gap between its own fair value estimate and the current market price: > Buys undervalued outcomes quickly > Increases position size as confidence grows > Reduces exposure when the original signal starts to change

This guy used Grok Bot to build a Quant Signature Map system and made $431,498 on Polymarket His system trades BTC Up/Down markets with an average trade size of $22.31 and 109 trades per active hour, averaging $3,038 a day Several AI agents work together as one algorithm: 1. Path Signature captures the sequence of BTC price and volume changes The first agent calculates a numerical measure from BTC trades on external markets: A = ½ × (∫ X dV - ∫ V dX) Both series are measured from the start of the market. This measure captures information about the order in which price and volume change 2. Calibration Map translates the movement pattern into Up and Down values The second agent combines the signature, distance from the reference price, volatility, and time remaining into a Score: pUp = 1 / (1 + e^(-Score)) pDown = 1 - pUp Entry condition: Up Entry Price + Costs < pUp × $1 When the condition is met, the system places an order. As external order flow changes, it updates its valuation and position parameters This trader’s Polymarket account: bosona If you want to trade BTC/ETH price movements directly, you can use KCEX: kcex.com/register?inviteCode… This exchange currently offers 0% fees on some spot/perp pairs, as well as 0% fees on the first 100 withdrawals each month across all assets At $1M in trading volume, even a 0.1% fee adds up to $1,000 in costs. That’s why zero fees can help preserve more of a trading strategy’s edge

This guy used Grok Bot to build a Quant Flow Memory system and it made $1,700 a day on Polymarket The result is already +$357,000 PnL over 210 days. It makes 48.8 trades per active hour across BTC Up/Down markets Several AI agents work together as one algorithm: 1. Multilevel Flow tracks order changes across several order book levels The first agent records new orders, cancellations, and fills separately for Up and Down For each side, it calculates: Flowₜ = Σ wₖ × OFIₖ,ₜ Where: > OFIₖ,ₜ = order flow imbalance at level k > wₖ = the weight assigned to that level The calculation tracks changes in buy and sell queues This helps the system distinguish a single change at the best price from a consistent shift in liquidity across multiple levels 2. Flow Memory connects past order flow with subsequent repricing The second agent combines several recent readings: Responseₕ = Σ gⱼ × Flowₜ₋ⱼ Where: > gⱼ = the learned weight assigned to a flow reading from j intervals earlier > h = the selected holding period The weights are calibrated using historical data: each flow pattern is matched with the change in the midpoint of the spread h seconds later For example: > Calculated price move = 3¢ > Entry and exit costs = 1¢ > Edge = 2¢ per share It keeps BUY limit orders in place while the calculated price response still leaves an edge after costs The position is closed at an executable sell price when the chosen price level or time limit is reached This trader’s Polymarket account: BoneOhio You can build your own trading bot here: join.horizon.trade/retrovali… (A trial is available after signing up) Top trades: $225.32 → $1,709.44 (+$1,484.12) $2,237.72 → $3,562.38 (+$1,324.66) $995.78 → $2,306.53 (+$1,310.75) The system looks for an edge in subsequent repricing: Multilevel Flow measures order flow across price levels, while Flow Memory identifies the price response associated with that sequence