Daniro
@Dan1ro0 • 7,123 subscribers
Researching AI-driven trading systems, models & frameworks | 100+ Quant strategies analyzed • 1M+ transactions processed
Videos
I used Claude Fable 5.1 and GPT-6 Astra to build a self-learning trading algorithm The result: +$1,840.82 in two days It makes about 17 trades a day or 521 a month, with an average trade size of $53.2 Six AI agents track new coins on Solana and learn to make trading decisions as one system I called it QuantumOrb GitHub repo: Here’s how it works: SCOUT tracks new launches, bonding curve activity and PumpSwap swaps, then records the trading data CARTOGRAPHER detects shared wallet funding sources, same-bundle activity and connections to the token creator JUDGE analyzes traders from their transaction history and estimates their performance and edge after fees, entry delay and copybot activity QUANT calculates execution on the curve or inside the pool, accounting for fees, market impact, priority fees and Jito tips GUARD checks holder concentration, connected wallet groups and launch risks EXECUTOR models opening and closing positions through take-profit, stop-loss or time expiry At the center of the system is the Orb Every launch is classified into three probability states: +60% is reached first −30% is reached first Neither threshold is reached within 60 minutes Most profitable trades: $45 → $158 (+$113) $37 → $146 (+$109) $31 → $125 (+$94) Organic buyer flow shifts probability toward one scenario Connected-wallet activity, sells and holder concentration adjust the estimate in the opposite direction With every new signal, the state updates. After that, QUANT determines whether the setup still makes sense after all execution costs, while GUARD decides whether the risk is acceptable Every launch that passes GUARD is used for learning, including the ones the system decides to skip One hour later, the actual outcome is recorded and fed back into the model That feedback becomes part of the next decision.
Daniro292,703 次观看 • 2 天前
This guy used Grok Bot to build a Quant Temporal Arbitrage system and it made $874 a day on Polymarket The result is already +$50,666 PnL in 58 days with about $464 per market traded, repeating the same setup roughly 11 times every hour across BTC "Up or Down" markets Several AI agents work together as one algorithm: 1. The bot looks for short delays between BTC movement and market quotes BTC can move first while the corresponding Up or Down level takes a moment to react Latency Edge = Reference Probability - Market Quote If BTC implies 61% Up while Up is still at 56¢: Latency Edge = 5¢ The bot enters before that gap disappears 2. The position is closed once the market catches up The same BTC move that created the entry eventually gets reflected in Up or Down PnL = (Exit Quote - Entry Quote) × Shares - Fees For example: Entry = 56¢ Exit = 60¢ Edge captured = 4¢ per share So the bot enters during the short delay and exits once the repricing is complete This trader’s Polymarket account: Most profitable trades: $1,307 → $2,593 (+$1,286) $1,023 → $2,232 (+$1,209) $675 → $1,773 (+$1,098) The bot exploits short delays between BTC movement and Up or Down repricing
Daniro68,112 次观看 • 3 天前
This guy used Grok Bot to build a Quant Hedge Limit system and it made $731 a day on Polymarket The result is already +$130,885 PnL in 179 days with about $525 per market traded, repeating the same setup across roughly 7 markets every hour in BTC "Up or Down" Several AI agents work together as one algorithm: 1. The bot tracks both sides of the same market instead of predicting one direction Every BTC market has two outcomes: Up and Down One of them always settles at 100¢, so the bot looks for moments when both sides together leave an edge Gross Edge = 100¢ - (Up Entry + Down Entry) For example: Up = 47¢ Down = 50¢ Gross Edge = 3¢ Instead of predicting BTC, the bot is trying to capture the difference between the two entries and their final 100¢ settlement value 2. Bayes continuously updates the probability behind each outcome As new BTC and order book data arrive: P(A|B) = P(B|A) × P(A) / P(B) Where: P(A) = previous probability B = new market information P(A|B) = updated probability The bot can compare that updated probability with the current Up and Down quotes The larger the mismatch, the stronger the potential edge This trader’s Polymarket account: Most profitable trades: $274 → $967 (+$693) $42 → $668 (+$626) $30 → $640 (+$609) The bot finds a two-sided edge first, then uses Kelly to determine the optimal size
Daniro31,657 次观看 • 4 天前
This guy used OpenAI Dots to build a Quant Execution Arbitrage system and it made $4,004 a day on Polymarket The result is already +$232,207 PnL in 58 days with about $390 per average trade, repeating the same setup 32 times every hour across BTC "Up or Down" markets Several specialized Dots agents powered by GPT-6 Astra work together as one system: 1. Bid-Ask Spread measures the immediate cost of entering Spread = Best Ask - Best Bid A wider spread makes execution more expensive The bot only enters when the available edge is large enough to survive that spread 2. All-in Execution Cost determines whether the arbitrage is actually worth taking The displayed edge is not the real edge after execution: Net Edge = Raw Edge - Spread - Slippage - Fees Slippage appears when the order consumes several price levels Fees add another cost to every completed trade If those costs consume the edge, the bot skips or cancels the remaining order This trader’s Polymarket account: Most profitable trades: $1,591 → $4,044 (+$2,453) $1,584 → $3,517 (+$1,932) $1,674 → $3,424 (+$1,750) The bot trades only the edge that remains after spread, slippage and fees
Daniro37,610 次观看 • 8 天前
This guy used Grok Bot to build a Quant EMA system and it made $4,497 a day on Polymarket The result is already +$692,556 PnL in 154 days with about $518 per average trade, repeating the same setup 34 times every hour across BTC "Up or Down" markets Several AI agents work together as one algorithm: 1. EMA establishes the current short term direction EMA gives more weight to the latest prices than a normal Moving Average Price above EMA → Up bias Price below EMA → Down bias This keeps the system focused on the most recent direction instead of reacting to every individual tick 2. RSI measures the strength behind that direction RSI = 100 - 100 / (1 + RS) Where: RS = average gains / average losses Instead of using RSI alone, the system combines it with the EMA direction Above EMA + RSI strengthening → stronger Up signal Below EMA + RSI weakening → stronger Down signal This trader’s Polymarket account: Most profitable trades: $242 → $15,531 (+$15,289) $330 → $5,792 (+$5,462) $2,948 → $7,332 (+$4,383) The system uses EMA for direction and RSI to measure whether enough momentum exists behind that move
Daniro50,493 次观看 • 12 天前
![This guy built a Monte Carlo pricing engine and it made $659 a day on Polymarket The result is already +$85,706 PnL in 130 days with only $35 per average trade, repeating the same setup 15 times every hour across BTC "Up or Down" markets Simulation, pricing and execution run as one continuous loop: 1. Monte Carlo generates thousands of possible BTC paths Instead of making one prediction, the model repeatedly simulates how BTC could move until resolution: S(t+Δt) = S(t) × exp[(μ - σ²/2)Δt + σ√Δt × Z] Where: S(t) = current BTC price μ = expected drift σ = volatility Δt = simulation step Z = random shock from a normal distribution The engine can run 10,000 different paths from the same starting point If 6,400 finish above the reference: P(Up) = 6,400 / 10,000 = 64% If Up is still trading at 53¢, the model sees an 11¢ gap 2. Those paths are converted into an Up or Down probability P(Up) = N(S_final > Reference) / N(total simulations) For example: 10,000 simulated paths 6,400 finish above the reference P(Up) = 64% If Up is still trading at 53¢, the model sees an 11¢ gap Every new BTC move changes the inputs, so the engine reruns the simulations and updates the next order This trader’s Polymarket account: 47,117 predictions with a 52% win rate Most profitable trades: $249 → $1,763 (+$1,514) $1,038 → $2,412 (+$1,374) $179 → $1,463 (+$1,284) A similar Monte Carlo workflow can be managed through Grok Bot with AI agents handling simulation, sizing and execution](https://image.24vids.com/tw-2099956649771327877/amplify_video_thumb/2099956358510530561/img/D0EKtQDakp2MTVtG.jpg)
This guy built a Monte Carlo pricing engine and it made $659 a day on Polymarket The result is already +$85,706 PnL in 130 days with only $35 per average trade, repeating the same setup 15 times every hour across BTC "Up or Down" markets Simulation, pricing and execution run as one continuous loop: 1. Monte Carlo generates thousands of possible BTC paths Instead of making one prediction, the model repeatedly simulates how BTC could move until resolution: S(t+Δt) = S(t) × exp[(μ - σ²/2)Δt + σ√Δt × Z] Where: S(t) = current BTC price μ = expected drift σ = volatility Δt = simulation step Z = random shock from a normal distribution The engine can run 10,000 different paths from the same starting point If 6,400 finish above the reference: P(Up) = 6,400 / 10,000 = 64% If Up is still trading at 53¢, the model sees an 11¢ gap 2. Those paths are converted into an Up or Down probability P(Up) = N(S_final > Reference) / N(total simulations) For example: 10,000 simulated paths 6,400 finish above the reference P(Up) = 64% If Up is still trading at 53¢, the model sees an 11¢ gap Every new BTC move changes the inputs, so the engine reruns the simulations and updates the next order This trader’s Polymarket account: 47,117 predictions with a 52% win rate Most profitable trades: $249 → $1,763 (+$1,514) $1,038 → $2,412 (+$1,374) $179 → $1,463 (+$1,284) A similar Monte Carlo workflow can be managed through Grok Bot with AI agents handling simulation, sizing and execution
Daniro86,463 次观看 • 29 天前
This guy made Grok Bot harvest BTC volatility and it made $2,701 a day The result is already +$40,522 PnL after 4,127 predictions while repeating the same setup 11 times every hour across BTC “Up or Down” markets Six AI agents work together as one algorithm: 1. They use BTC volatility to build Up + Down below 100¢ The bot does not need both outcomes at a good price simultaneously It can accumulate one side first and wait for BTC to move before buying the opposite side For example: Up at 34¢ + Down later at 58¢ = 92¢ total cost (8¢ locked on every complete set) 2. Inventory Rotation decides how much gets paired The system continuously compares both inventories: Paired = min(Qup, Qdown) Everything that can be matched becomes a complete set Any remaining imbalance stays directional only while the agents still see an edge This trader’s Polymarket account: AdanaKebab 4,127 predictions with a 52% win rate Most profitable trades: $759 → $2,880 (+$2,121) $1,121 → $3,120 (+$1,999) $1,441 → $3,420 (+$1,979) The system keeps rotating between directional exposure and paired inventory as BTC moves
Daniro99,592 次观看 • 1 个月前
This guy used Grok Bot to build a Quant position scaling system and it made $1,375 a day The result is already +$235,199 PnL in 171 days with only $46 per average trade, repeating the same setup 25 times every hour across BTC "Up or Down" markets Several agents can split the workflow between pricing, sizing and execution: 1. Every new BTC move changes the next order > A position can begin small: First fill: $18 at 37¢ > If the signal strengthens: Second fill: $34 at 40¢ > If probability continues moving in the same direction: Third fill: $61 at 44¢ The system scales only while the estimated edge survives 2. Maker-Taker logic controls how aggressively it gets filled With enough time, the system leaves passive orders and waits for fills As the edge starts disappearing, it can switch to aggressive execution Expected Edge > Spread + Slippage + Execution Cost If that condition disappears, the next order is cancelled This trader’s Polymarket account: 101,608 predictions with a 50% win rate Most profitable trades: $2,796 → $6,360 (+$3,564) $1,630 → $3,989 (+$2,359) $1,300 → $3,602 (+$2,302) The edge comes from scaling into the position only while the signal survives and switching execution style as market conditions change
Daniro75,588 次观看 • 1 个月前
This guy used OpenAI Dots to build a Quant Binary Arbitrage system and it made $1,135 a day on Polymarket The result is already +$95,340 PnL in 84 days with about $289 per market traded, repeating the same setup 9 times every hour across BTC "Up or Down" markets Several AI agents powered by GPT-6 Astra work together as one system: 1. Parity Arbitrage checks whether both outcomes can be bought below 100¢ For a binary market: Up + Down = 100¢ at settlement The bot continuously checks the executable prices of both sides: Up Ask + Down Ask 100¢ → no locked edge It only matters what prices can actually be filled, not the last price shown on screen 2. Fees determine whether that apparent arbitrage survives execution The real condition becomes: Net Edge = 100¢ - Up Cost - Down Cost - Fees A visible pair below 100¢ can still be ignored if fees consume the remaining difference The bot only sends orders when the complete pair still leaves a positive edge after costs This trader’s Polymarket account: Most profitable trades: $1,007 → $2,205 (+$1,198) $1,007 → $2,057 (+$1,050) $589 → $1,606 (+$1,017) The bot is constantly testing whether the two executable sides still add up to less than their 100¢ settlement value
Daniro14,637 次观看 • 5 天前
I turned Grok Bot into a full BTC trading desk and it makes $418 a day The result is already +$2,090 PnL in 5 days from just $200, with only $32 per average trade All six AI agents work on the same 5 minute BTC “Up or Down” markets, repeating the same setup 17 times every hour SPOTTER → PRIOR → EDGE → KELLY → TAKER → CLOSER 1. SPOTTER finds the entry BTC moves higher, but Polymarket has not fully repriced yet: BTC spot moves first / Up is still 41¢ SPOTTER detects the lag and passes the setup forward 2. PRIOR calculates fair probability P(new) = P(old) + ΔBTC + ΔVol + ΔTime If fair Up becomes 47¢ while the market is still 41¢, EDGE checks whether the 6¢ gap survives spread, fees and slippage 3. KELLY sizes the position f* = (bp - q) / b Stronger edge gets more size, weaker edge gets less 4. TAKER executes and builds the second side For example: Up = 41¢ / Down later = 52¢ / Combined = 93¢ -> Complete set = $1 The two entries can happen at completely different moments 5. CLOSER manages the remaining inventory If the bot holds more Up than Down, the paired part is locked while the residual stays directional only while the model still sees an edge Most profitable trades: $47 → $168 (+$121) $36 → $141 (+$105) $29 → $126 (+$97) In my experiment, Grok Bot is only one part of the setup. It works together with other AI tools, external data sources and automation layers inside my AI trading office
Daniro76,118 次观看 • 1 个月前
This guy used Grok Bot to build a Quant Arbitrage system that made $8,045 a day on Polymarket The result is already +$1,262,999 PnL in 157 days with about $873 per average trade, repeating the same setup 41 times every hour across crypto "Up or Down" markets Several AI agents work together as one system: 1. The bot continuously tracks the average cost of both outcomes Instead of looking at one fill, it recalculates the weighted average after every order: Average Up = Total Up Cost / Up Shares Average Down = Total Down Cost / Down Shares So dozens of small fills gradually change the real cost of the position 2. Arbitrage appears only when the matched inventory costs less than 100¢ For equal amounts of Up and Down: Arb Edge = 100¢ - (Average Up + Average Down) If: Average Up + Average Down 100¢ → no locked edge The bot keeps repricing and adding orders until part of its Up and Down inventory can be paired below the 100¢ settlement value This trader’s Polymarket account: Most profitable trades: $20,877 → $30,781 (+$9,904) $1,767 → $10,655 (+$8,888) $5,058 → $12,800 (+$7,742) The strategy is built around the average cost of both sides rather than the result of any single trade
Daniro20,672 次观看 • 9 天前
This guy used Grok Bot to build a Quant Z-Score system and it made $4,654 a day on Polymarket The result is already +$525,853 PnL in 113 days with about $1,142 per average trade, repeating the same setup 11 times every hour across BTC "Up or Down" markets Multiple AI agents work together as one algorithm: 1. Z-Score measures how unusual the current move is The bot tracks short term BTC returns and compares the latest move with recent price behavior Z = (r - μ) / σ Where: r = latest BTC return μ = average return over the recent window σ = standard deviation of those returns A large positive or negative Z tells the bot that the latest move is unusually strong relative to recent volatility It uses this as an entry filter and ignores moves that stay inside the normal range 2. Expected Value determines whether the setup is worth taking EV = P(win) × Payout - Entry Cost Positive EV means the estimated payout still exceeds the cost of taking the position As the price changes, the agents recalculate EV and stop adding once the advantage disappears Most profitable trades: $4,588 → $7,905 (+$3,316) $1,578 → $4,811 (+$3,233) $2,216 → $5,305 (+$3,090) The system uses Z-Score to filter the signal and Kelly Criterion to control how much exposure each setup receives
Daniro28,116 次观看 • 13 天前
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
Daniro60,040 次观看 • 1 个月前
This guy used Grok Bot to build a Quant Probability Repricing system and it made $2,934 a day on Polymarket The result is already +$217,100 PnL in 74 days with about $635 per average trade, repeating the same setup 11 times every hour across BTC "Up or Down" markets Six AI agents work together as one algorithm: 1. They calculate fair probability after every BTC move For example: BTC moves Down >Fair Down = 64% >Market Down = 54¢ >Edge = 10¢ If Polymarket is still pricing Down at 54¢ while the model estimates 64%, the system starts accumulating that side 2. Bayesian updating recalculates the probability after every new signal P(A|B) = P(B|A) × P(A) / P(B) Where: P(A) = probability before new information P(B|A) = how likely the new signal is if the prediction is correct P(A|B) = updated probability after that signal Every new signal changes the estimate instead of forcing the bot to make a completely new trade This trader’s Polymarket account: 20,300 predictions with a 50% win rate Most profitable trades: $2,305 → $4,139 (+$1,834) $886 → $2,697 (+$1,811) $1,132 → $2,888 (+$1,756) The whole strategy is built around repeatedly catching the gap between BTC movement and Polymarket repricing
Daniro29,476 次观看 • 14 天前
This guy used Grok Bot to build a Quant MACD system and it made $1,891 a day on Polymarket The result is already +$304,411 PnL in 161 days with about $105 per average trade, repeating the same setup 39 times every hour across BTC "Up or Down" markets Several AI agents work together as one algorithm: 1. MACD identifies changes in short term momentum MACD = EMA(12) - EMA(26) When the faster EMA moves above the slower EMA, momentum is shifting Up When it moves below, momentum is shifting Down The system uses the crossover to identify when direction is beginning to change rather than reacting to every price tick 2. Signal Line confirms whether that momentum shift is strong enough The Signal Line is a moving average of MACD itself: Signal Line = 9-period average of MACD The bot compares the two: MACD > Signal Line → Up confirmation MACD < Signal Line → Down confirmation The wider the gap between them becomes, the stronger the current momentum signal This trader’s Polymarket account: Most profitable trades: $535 → $2,860 (+$2,325) $632 → $2,896 (+$2,264) $685 → $2,602 (+$1,916) The system uses MACD to detect direction and volume to confirm whether that move is strong enough to trade
Daniro18,125 次观看 • 11 天前
This trader used Claude to build a Quant Bot and made +$115,615 on Polymarket Since Apr 2, this wallet has been bringing in about $1,501 per day and running at a little over 22 trades per hour 41,972 predictions with a 39% win rate in 80 days This trader’s Polymarket account: This trader started with $219.7K in deposits and is now sitting at roughly +52.6% ROI The bot strategy is simple: > It hunts for short crypto markets where pricing is still out of line > Gets in before the move is fully reflected in the odds > Then keeps firing the same setup across a very large number of entries Most profitable trades: $62.4 → $1,745.5 (+$1,683, +2,693.8%) $27.9 → $1,365.0 (+$1,337, +4,784%) $79.6 → $1,350.0 (+$1,270, +1,595.7%) The result does not come from one outsized win, but from repeating the same edge over and over
Daniro129,079 次观看 • 3 个月前
This guy used Grok Bot to build a Quant Directional Movement system and it made $3,784 a day on Polymarket The result is already +$809,704 PnL in 214 days with about $2,832 per average trade, repeating the same setup 20 times every hour across BTC "Up or Down" markets Several AI agents work together as one algorithm: 1. Directional Movement Index determines which side currently controls price The model compares upward and downward price pressure: +DI > -DI → Up pressure dominates -DI > +DI → Down pressure dominates The larger the gap between the two, the clearer the directional imbalance becomes 2. ADX measures whether that direction is actually strong enough to trade ADX ignores direction and measures only trend strength: ADX 25 → stronger trend ADX rising → trend gaining strength So the system can see both which direction dominates and whether that move is strong enough to act on This trader’s Polymarket account: Most profitable trades: $17,838 → $36,270 (+$18,432) $10,675 → $21,950 (+$11,275) $10,698 → $21,647 (+$10,949) The system uses directional movement to choose the side and ADX to filter out weak trends
Daniro14,074 次观看 • 10 天前
This trader used Claude to build a Quant Bot and made +$130,992 on Polymarket 29,721 predictions in 132 days with a 50% win rate How is this account bringing in about $992 per day? The logic is simple: 1. It focuses on short crypto “Up / Down” markets, averaging roughly 9 trades per hour 2. Instead of relying on one entry, the bot appears to build its position gradually as the probabilities change throughout each window 3. It can take exposure on one outcome first, then wait for a favorable move before adding the opposite side and locking part of the position into a hedge This trader’s Polymarket account: nagi777 Most profitable trades: $54 → $1,165 (+$1,111 +2,077.2%) $28 → $1,127 (+$1,100 +3,961.7%) $234 → $1,311 (+$1,077 +459.8%) A 50% win rate is enough for this system because the result depends on how each position is constructed and modified over time, not simply on finishing more markets on the winning side
Daniro53,229 次观看 • 1 个月前
This trader used Claude to build a Quant Bot and made +$1,252,572 on Polymarket 110,140 predictions in 136 days with a 55% win rate How is this account bringing in about $9,210 per day? The logic is simple: 1. It runs through short crypto “Up / Down” markets at roughly 34 trades per hour 2. The framework appears to mix temporal arbitrage, hedged directional exposure, and active inventory rotation 3. The bot can start with one outcome, wait for the probabilities to move, and bring in the opposite side later when that improves the hedge This trader’s Polymarket account: Most profitable trades: $2,520 → $8,070 (+$5,549 +220.2%) $1,136 → $6,568 (+$5,432 +478.1%) $677 → $6,082 (+$5,405 +798.6%) The result comes from running the same adaptive framework across more than 110,000 entries and letting thousands of smaller edges accumulate over time
Daniro65,329 次观看 • 2 个月前
This guy gave Grok Bot a BTC trading strategy and it made $1,907 a day The account is already at +$57,220 PnL with an average trade of just $36, repeating its setup roughly 25 times every hour across BTC “Up or Down” markets AI agents combine their signals into one algorithm: 1. Bayesian repricing decides when the position should change Every new BTC signal updates the model odds: O(new) = O(old) × LR If Up remains underpriced, the agents can continue accumulating it If the edge shifts toward Down, the system can start building the opposite side and convert part of the inventory into complete sets 2. KELLY and TAKER control how the opportunity gets executed The sizing agent converts the estimated probability edge into an allocation: f* = (bp - q) / b TAKER then receives the side, maximum price and allowed size It can wait for a better fill, execute the first leg, or begin acquiring the opposite outcome when the model sees a better entry This trader’s Polymarket account: norm1e69 17,702 predictions with a 50% win rate Most profitable trades: $882 → $2,088 (+$1,206) $1,102 → $2,295 (+$1,194) $1,529 → $2,604 (+$1,075) The system combines probability, sizing and execution to keep rebuilding the position as BTC moves instead of treating every entry as an isolated market
Daniro36,569 次观看 • 1 个月前