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yesterday someone leaked a full quant trading system on GitHub before they deleted it i forked everything 5,000 lines of code. 7 modules. 25 mathematical factors funds use this system to manage millions i studied it for a week. then pointed it at crypto markets on polymarket here's the...

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249,098 просмотров • 3 месяцев назад •via X (Twitter)

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I woke up to $4,217 on polymarket a week ago it was $1,000 i didn't place a single trade myself 6 AI agents did it for me running 24/7 on a $9/month server 84 trades. 57 wins. 69% win rate here's the full architecture, you can build the same thing 6 agents. each one has one job SCANNER monitors every hourly BTC/ETH market on polymarket pulls price from binance websocket in real time calculates momentum, volatility, orderbook imbalance runs every 60 minutes FACTOR MINER generates trading hypotheses using claude haiku ($0.25/1M tokens) tests each one against historical data keeps only factors with IC > 0.05 currently running 10 active factors. auto-kills bad ones after 50 trades ANALYST runs 3 signals in parallel: → LightGBM model (30 features, 500 trees) outputs probability → claude sonnet reads news from tavily, scores sentiment, weighted by source trust (EMA 0.95) → orderflow detector tracks whale buys, liquidity shifts, large order clusters all 3 signals go through bayesian aggregation not simple voting. actual bayes theorem market 52% UP. my signals: 62%, 71%, 63% bayesian posterior: 82% final probability 75.4% edge = 23.4% AUDITOR checks everything before money moves. catches hallucinated news blocks trades under 10 min to resolution blocks low liquidity markets penalizes confidence by 8% per flag found RISK MANAGER quarter kelly. max 10% bankroll per trade stops after 3 consecutive losses 15% drawdown = everything shuts down correlation limits so BTC and ETH positions don't stack. calculates exact EV before every trade EXECUTOR places orders through polymarket CLI. retry logic. 3 attempts. iceberg splits for large positions stack: → python + lightgbm for ML → langgraph for agent orchestration → binance websocket for prices → polymarket CLI (rust) for execution → coinbase agentic wallet (TEE) → hetzner VPS CX32: $9/month full monthly cost: $29 results after 7 days: 84 trades. 57 wins. 27 losses win rate: 69% $1,000 → $4,217 avg $460/day at current bankroll compound math: $17,800 by next friday $75k+ end of month the system trades every hour. i check telegram alerts from my phone
1:04

I woke up to $4,217 on polymarket a week ago it was $1,000 i didn't place a single trade myself 6 AI agents did it for me running 24/7 on a $9/month server 84 trades. 57 wins. 69% win rate here's the full architecture, you can build the same thing 6 agents. each one has one job SCANNER monitors every hourly BTC/ETH market on polymarket pulls price from binance websocket in real time calculates momentum, volatility, orderbook imbalance runs every 60 minutes FACTOR MINER generates trading hypotheses using claude haiku ($0.25/1M tokens) tests each one against historical data keeps only factors with IC > 0.05 currently running 10 active factors. auto-kills bad ones after 50 trades ANALYST runs 3 signals in parallel: → LightGBM model (30 features, 500 trees) outputs probability → claude sonnet reads news from tavily, scores sentiment, weighted by source trust (EMA 0.95) → orderflow detector tracks whale buys, liquidity shifts, large order clusters all 3 signals go through bayesian aggregation not simple voting. actual bayes theorem market 52% UP. my signals: 62%, 71%, 63% bayesian posterior: 82% final probability 75.4% edge = 23.4% AUDITOR checks everything before money moves. catches hallucinated news blocks trades under 10 min to resolution blocks low liquidity markets penalizes confidence by 8% per flag found RISK MANAGER quarter kelly. max 10% bankroll per trade stops after 3 consecutive losses 15% drawdown = everything shuts down correlation limits so BTC and ETH positions don't stack. calculates exact EV before every trade EXECUTOR places orders through polymarket CLI. retry logic. 3 attempts. iceberg splits for large positions stack: → python + lightgbm for ML → langgraph for agent orchestration → binance websocket for prices → polymarket CLI (rust) for execution → coinbase agentic wallet (TEE) → hetzner VPS CX32: $9/month full monthly cost: $29 results after 7 days: 84 trades. 57 wins. 27 losses win rate: 69% $1,000 → $4,217 avg $460/day at current bankroll compound math: $17,800 by next friday $75k+ end of month the system trades every hour. i check telegram alerts from my phone

Archive

30,015 просмотров • 3 месяцев назад

Someone in r/algotrading posted a polymarket wallet with one comment: "sports markets are broken and nobody is talking about it" thread deleted in 10 minutes but i grabbed the wallet before it disappeared pulled the on-chain data and started building they hire 12 quant developers at $180k each i opened claude ($20/m) and said: "analyze this wallet's strategy and build me a similar system for trading live sports markets on polymarket" starting balance $100 a week ago current balance: $14,700 every trade is live sports, only during matches spent two days reverse engineering the pattern the logic is embarrassingly simple: live sports APIs update within 1-2 seconds of an event polymarket oracle checks every 10-20 seconds that gap is free money my bot runs on AWS EC2 for $120/month in Virginia latency <5ms to polymarket CLOB it pulls SportsRadar with 1-2 second delay and still finds edge the agent wrote the entire pipeline in one session: → async websocket listener on polymarket orderbook → SportsRadar live feed via httpx (non-blocking) → poisson probability model via scipy.stats → pre-cached lambda values in redis (<1ms lookup) → kelly criterion for position sizing (max 6% bankroll) → EIP-712 order signing, execution via polygon CLOB their infrastructure costs $2.4M/year mine costs $930/month $100 in the morning $1,730 by evening, and NFL example on video AI can build what used to take a team of 12
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Someone in r/algotrading posted a polymarket wallet with one comment: "sports markets are broken and nobody is talking about it" thread deleted in 10 minutes but i grabbed the wallet before it disappeared pulled the on-chain data and started building they hire 12 quant developers at $180k each i opened claude ($20/m) and said: "analyze this wallet's strategy and build me a similar system for trading live sports markets on polymarket" starting balance $100 a week ago current balance: $14,700 every trade is live sports, only during matches spent two days reverse engineering the pattern the logic is embarrassingly simple: live sports APIs update within 1-2 seconds of an event polymarket oracle checks every 10-20 seconds that gap is free money my bot runs on AWS EC2 for $120/month in Virginia latency <5ms to polymarket CLOB it pulls SportsRadar with 1-2 second delay and still finds edge the agent wrote the entire pipeline in one session: → async websocket listener on polymarket orderbook → SportsRadar live feed via httpx (non-blocking) → poisson probability model via scipy.stats → pre-cached lambda values in redis (<1ms lookup) → kelly criterion for position sizing (max 6% bankroll) → EIP-712 order signing, execution via polygon CLOB their infrastructure costs $2.4M/year mine costs $930/month $100 in the morning $1,730 by evening, and NFL example on video AI can build what used to take a team of 12

Archive

81,394 просмотров • 3 месяцев назад

A former quantum from Jump Trading sent me the source code of the bot he said they fired him for running it from the work server when i opened the code i didn’t sleep til 4 am gist of the code: bot holds two websocket connections simultaneously direct feed from Binance spot orderbook and Polymarket CLOB API BTC price on Binance updates ~200ms polymarket recalculates contracts with 3-7s lag in this window bot calculates implied probability compares spot divergence with contract price over 8% - enters btc does +0.4% on spot up contract at 53¢ model probability - 87% bot buys in 5 mins contract resolves at $1 20-50% per trade 100+ times a day $284,719 per month off $1,300 bankroll this isn’t prediction it’s latency arb between CEX and prediction market no need for $50k colocation $20 vps and clean code suffices
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A former quantum from Jump Trading sent me the source code of the bot he said they fired him for running it from the work server when i opened the code i didn’t sleep til 4 am gist of the code: bot holds two websocket connections simultaneously direct feed from Binance spot orderbook and Polymarket CLOB API BTC price on Binance updates ~200ms polymarket recalculates contracts with 3-7s lag in this window bot calculates implied probability compares spot divergence with contract price over 8% - enters btc does +0.4% on spot up contract at 53¢ model probability - 87% bot buys in 5 mins contract resolves at $1 20-50% per trade 100+ times a day $284,719 per month off $1,300 bankroll this isn’t prediction it’s latency arb between CEX and prediction market no need for $50k colocation $20 vps and clean code suffices

Ventry

403,139 просмотров • 3 месяцев назад

a quant at a prop firm showed me a 5x5 grid on a napkin said: > this is our entire edge. we don't predict price. we predict which box the market is in and where that box historically leads i didn't understand it for weeks. then it clicked never looked at a chart the same way since grid is called a Markov Chain transition matrix. the math is from 1906, it's in every probability textbook on earth and hedge funds use it because it asks a completely different question than retail traders ever ask retail: will this go up or down quant: what state is this market in, and where does this state typically go every market lives in one of maybe 5-6 states at any given moment tight range, volatility compression, trending with momentum, post-spike reversal, pre-breakout coil not random labels - clusters you identify from actual data using volatility, volume, and momentum readings stacked together once you have the states, you build the matrix: P(state 2 -> state 4) = 73% P(state 4 -> state 1) = 61% P(state 1 -> state 3) = 68% each cell is a historical probability. now when the market is in state 2, you're not guessing you're betting on 73% historical completion. you size it with Kelly. you take the trade when the math says to, not when it feels right i built this on BTC using 2 years of 4-hour data. identified 5 states one i labeled "volatility compression below 20-day mean for 6+ consecutive candles" transitioned to a directional move above 1.8 ATR in 71% of cases average reward/risk on those trades: 5.4 that's not prediction. that's reading a probability table the market keeps filling in for you every single day the part that should bother you: the data to build this is free. the framework is in any quant textbook python to implement it is maybe 200 lines what Renaissance Technologies has that you don't isn't secret data or proprietary signals it's this framework applied to higher-resolution data with more sophisticated state definitions you're not missing information you're asking the wrong question every single time you open a chart
0:26

a quant at a prop firm showed me a 5x5 grid on a napkin said: > this is our entire edge. we don't predict price. we predict which box the market is in and where that box historically leads i didn't understand it for weeks. then it clicked never looked at a chart the same way since grid is called a Markov Chain transition matrix. the math is from 1906, it's in every probability textbook on earth and hedge funds use it because it asks a completely different question than retail traders ever ask retail: will this go up or down quant: what state is this market in, and where does this state typically go every market lives in one of maybe 5-6 states at any given moment tight range, volatility compression, trending with momentum, post-spike reversal, pre-breakout coil not random labels - clusters you identify from actual data using volatility, volume, and momentum readings stacked together once you have the states, you build the matrix: P(state 2 -> state 4) = 73% P(state 4 -> state 1) = 61% P(state 1 -> state 3) = 68% each cell is a historical probability. now when the market is in state 2, you're not guessing you're betting on 73% historical completion. you size it with Kelly. you take the trade when the math says to, not when it feels right i built this on BTC using 2 years of 4-hour data. identified 5 states one i labeled "volatility compression below 20-day mean for 6+ consecutive candles" transitioned to a directional move above 1.8 ATR in 71% of cases average reward/risk on those trades: 5.4 that's not prediction. that's reading a probability table the market keeps filling in for you every single day the part that should bother you: the data to build this is free. the framework is in any quant textbook python to implement it is maybe 200 lines what Renaissance Technologies has that you don't isn't secret data or proprietary signals it's this framework applied to higher-resolution data with more sophisticated state definitions you're not missing information you're asking the wrong question every single time you open a chart

Livsun

187,776 просмотров • 1 месяц назад

I gave ClawdBot a 2-page spec and said build it. 47 minutes later I had a full arbitrage system running on Polymarket. The edge is simple. Polymarket prices BTC markets using Chainlink oracle - updates every 500ms. Binance spot updates every 10ms. That's a 490ms window where Polymarket doesn't know what Binance already knows. > BTC pumps to $67,520 on Binance. > Polymarket still shows $67,450, Spread: 2.3%. > Bot buys YES at 48¢. > Market resolves. +$520. The spec had 5 layers - data ingestion, research agents, signal generation, risk management, execution. Clawdbot read it once, built all 5. Deployed to AWS Lambda. I never wrote a single line of code. Results: 74% win rate $18.40 avg profit per trade 3.2% max drawdown $157,000 all-time Wallet with result: The prompt was one sentence: "Build me a full agentic system for Polymarket 5-min BTC arbitrage using this specification." Clawdbot did the rest.
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I gave ClawdBot a 2-page spec and said build it. 47 minutes later I had a full arbitrage system running on Polymarket. The edge is simple. Polymarket prices BTC markets using Chainlink oracle - updates every 500ms. Binance spot updates every 10ms. That's a 490ms window where Polymarket doesn't know what Binance already knows. > BTC pumps to $67,520 on Binance. > Polymarket still shows $67,450, Spread: 2.3%. > Bot buys YES at 48¢. > Market resolves. +$520. The spec had 5 layers - data ingestion, research agents, signal generation, risk management, execution. Clawdbot read it once, built all 5. Deployed to AWS Lambda. I never wrote a single line of code. Results: 74% win rate $18.40 avg profit per trade 3.2% max drawdown $157,000 all-time Wallet with result: The prompt was one sentence: "Build me a full agentic system for Polymarket 5-min BTC arbitrage using this specification." Clawdbot did the rest.

Lunar

47,895 просмотров • 3 месяцев назад

I BUILT A PLATFORM FOR SYSTEMATIC SENTIMENT TRADING Trading by Fear & Greed Index on prediction markets. In realtime it collects market data, VIX, Fear & Greed, sentiment - analyzes through ML model, evaluates risk, and only then makes a trade decision. Not before, not after, only when all layers confirm. Inside 6 layers: data ingestion, analytics engine, risk manager, execution engine, backtesting, dashboard. System receives data from Polymarket CLOB API, VIX, Fear & Greed and external sentiment sources, calculates market sensitivity, trains the model and outputs trade signals. Every trade goes through risk manager: position sizing, drawdown control, entry limits, slippage control. Nothing executes without permission. Execution through Polymarket API and Polygon, on top a dashboard - portfolio, P&L, active signals, strategy tuning in realtime. Sentiment trading goes to the next level
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I BUILT A PLATFORM FOR SYSTEMATIC SENTIMENT TRADING Trading by Fear & Greed Index on prediction markets. In realtime it collects market data, VIX, Fear & Greed, sentiment - analyzes through ML model, evaluates risk, and only then makes a trade decision. Not before, not after, only when all layers confirm. Inside 6 layers: data ingestion, analytics engine, risk manager, execution engine, backtesting, dashboard. System receives data from Polymarket CLOB API, VIX, Fear & Greed and external sentiment sources, calculates market sensitivity, trains the model and outputs trade signals. Every trade goes through risk manager: position sizing, drawdown control, entry limits, slippage control. Nothing executes without permission. Execution through Polymarket API and Polygon, on top a dashboard - portfolio, P&L, active signals, strategy tuning in realtime. Sentiment trading goes to the next level

zostaff

44,223 просмотров • 2 месяцев назад

If you think you need a big bankroll for a Polymarket bot - this guy made $160,000 with an average bet size of just $200. If you think you need to predict the future to run a profitable Polymarket bot - this guy makes money with pure mathematics. If you think it’s cap - every trade is on-chain, go check it yourself. Bot wallet: He doesn’t care where the price of BTC is going. All he does is methodically extract edge from the numbers. Both YES and NO shares will always be worth exactly $1 at market expiration. But are they worth exactly $1 while the market is still open? The answer is no. For example: YES at 70¢ + NO at 25¢ = 95¢ total. That 5¢ gap is free money. Repeat it ten thousand times.+$166,000 in two months. His active bankroll never exceeded a $10k, and risk per trade stayed at $200–300 the entire time. Mathematical. Methodical. Almost zero risk. This is how you actually get to the top of the leaderboard - not by guessing the future, but by exploiting inefficiencies. Want the full guide on how to build one? Save the post and read the article. Want to start copying the trades of a ready-made bot right now? Use this copy-trading solution:
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If you think you need a big bankroll for a Polymarket bot - this guy made $160,000 with an average bet size of just $200. If you think you need to predict the future to run a profitable Polymarket bot - this guy makes money with pure mathematics. If you think it’s cap - every trade is on-chain, go check it yourself. Bot wallet: He doesn’t care where the price of BTC is going. All he does is methodically extract edge from the numbers. Both YES and NO shares will always be worth exactly $1 at market expiration. But are they worth exactly $1 while the market is still open? The answer is no. For example: YES at 70¢ + NO at 25¢ = 95¢ total. That 5¢ gap is free money. Repeat it ten thousand times.+$166,000 in two months. His active bankroll never exceeded a $10k, and risk per trade stayed at $200–300 the entire time. Mathematical. Methodical. Almost zero risk. This is how you actually get to the top of the leaderboard - not by guessing the future, but by exploiting inefficiencies. Want the full guide on how to build one? Save the post and read the article. Want to start copying the trades of a ready-made bot right now? Use this copy-trading solution:

cvxv666

48,524 просмотров • 1 месяц назад

a 23-year-old guy built an AI animation factory in one weekend. $12,345 last month. 4 hours of direction total. > Claude writes scripts and scene breakdowns: 10 minutes. > Midjourney generates every frame: 20 minutes. > Runway animates every scene: 15 minutes. > ElevenLabs voices every character: 10 minutes. > Suno scores every episode: 5 minutes. > Make publishes to YouTube, Patreon, and X: 0 minutes. total: $124/month 4 content types running in parallel. 8 uploads per week. he sleeps. it produces. every prompt is in the article above.
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a 23-year-old guy built an AI animation factory in one weekend. $12,345 last month. 4 hours of direction total. > Claude writes scripts and scene breakdowns: 10 minutes. > Midjourney generates every frame: 20 minutes. > Runway animates every scene: 15 minutes. > ElevenLabs voices every character: 10 minutes. > Suno scores every episode: 5 minutes. > Make publishes to YouTube, Patreon, and X: 0 minutes. total: $124/month 4 content types running in parallel. 8 uploads per week. he sleeps. it produces. every prompt is in the article above.

Lummox

78,719 просмотров • 10 дней назад

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
0:38

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

716,764 просмотров • 3 месяцев назад

I built a bot for 5-min BTC markets using OpenClaw $50 -> $823 This type of market has a crazy opportunity since every trade is basically 2x. If your win rate is above 50%, you can realistically scale to $10,000+ examples: NickyNic / +$82,000 in a day profile: Xander-corvus2 / +$102,299 in a day profile: These aren't some pro traders, two profiles were created just days ago, they just know how to use Fast-Loop properly that's exactly why i built this bot > scans arbitrage every 5 mins > compares price with Binance and CoinGecko The result is a fully automated 24/7 system - it catches the move before Polymarket reacts and enters first
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I built a bot for 5-min BTC markets using OpenClaw $50 -> $823 This type of market has a crazy opportunity since every trade is basically 2x. If your win rate is above 50%, you can realistically scale to $10,000+ examples: NickyNic / +$82,000 in a day profile: Xander-corvus2 / +$102,299 in a day profile: These aren't some pro traders, two profiles were created just days ago, they just know how to use Fast-Loop properly that's exactly why i built this bot > scans arbitrage every 5 mins > compares price with Binance and CoinGecko The result is a fully automated 24/7 system - it catches the move before Polymarket reacts and enters first

Noisy

592,531 просмотров • 4 месяцев назад

CLAUDE BUILT ME 10 TRADING BOTS IN 34 MINUTES. +$788 IN 3 DAYS. I gave each one $300 and said - compete. One prompt. Ten strategies. Each bot - a separate agent on Claude API. Its own system prompt, its own decision logic, its own risk management. Newsreader - parses RSS + NewsAPI, runs every headline through Claude with the prompt "rate market impact from 1 to 10". Above 7 - enters. 10-15 trades a day. Never sleeps. $300 -> $368 Arbitrage - finds broken logic between linked markets. One says 40%, a related one says 15%. Calculates conditional probability, finds the gap - enters quietly. $300 -> $427 Contrarian - monitors volume. When 80%+ of money is on one side - takes the opposite. Kelly criterion cuts position size. Hates consensus. $300 -> $234 Sniper - 2-3 trades per day max. Claude calculates expected value for each market. Only when edge is above 25% and model confidence is 8/10 - enters. Rest of the time - silence. $300 -> $536 Reader - RAG pipeline. Eats SEC filings, CBO reports, Fed minutes. Splits into chunks, embeds, searches for contradictions with current prices. 400 pages per minute. $300 -> $493 Whale Hunter - parses on-chain data through Dune API. Tracks wallets with 70%+ win rate. When someone drops $50K+ into a position at 3:00 AM - follows. No questions. $300 -> $412 Scalper - microtrades through CLOB API. 30-40 a day. Catches 2-3 cent moves on bid-ask spreads. $300 -> $271 Calendar - only trades events with a known date. Elections, Fed meetings, court rulings. Pulls historical data on similar events, calculates base rate. No deadline - not interested. $300 -> $389 Sentiment - sentiment analysis. Collects posts from Twitter API, Reddit, Telegram. Calculates positive/negative ratio, compares to price. Everyone panics - buys. $300 -> $197 Copycat - monitors signals from the other nine through a shared JSON. No strategy of its own. Waits until 3+ bots agree - copies with double size. Ensemble model in one line of code. $300 -> $461 +$788 in 3 days. Ten bots. One prompt. Zero emotions.
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CLAUDE BUILT ME 10 TRADING BOTS IN 34 MINUTES. +$788 IN 3 DAYS. I gave each one $300 and said - compete. One prompt. Ten strategies. Each bot - a separate agent on Claude API. Its own system prompt, its own decision logic, its own risk management. Newsreader - parses RSS + NewsAPI, runs every headline through Claude with the prompt "rate market impact from 1 to 10". Above 7 - enters. 10-15 trades a day. Never sleeps. $300 -> $368 Arbitrage - finds broken logic between linked markets. One says 40%, a related one says 15%. Calculates conditional probability, finds the gap - enters quietly. $300 -> $427 Contrarian - monitors volume. When 80%+ of money is on one side - takes the opposite. Kelly criterion cuts position size. Hates consensus. $300 -> $234 Sniper - 2-3 trades per day max. Claude calculates expected value for each market. Only when edge is above 25% and model confidence is 8/10 - enters. Rest of the time - silence. $300 -> $536 Reader - RAG pipeline. Eats SEC filings, CBO reports, Fed minutes. Splits into chunks, embeds, searches for contradictions with current prices. 400 pages per minute. $300 -> $493 Whale Hunter - parses on-chain data through Dune API. Tracks wallets with 70%+ win rate. When someone drops $50K+ into a position at 3:00 AM - follows. No questions. $300 -> $412 Scalper - microtrades through CLOB API. 30-40 a day. Catches 2-3 cent moves on bid-ask spreads. $300 -> $271 Calendar - only trades events with a known date. Elections, Fed meetings, court rulings. Pulls historical data on similar events, calculates base rate. No deadline - not interested. $300 -> $389 Sentiment - sentiment analysis. Collects posts from Twitter API, Reddit, Telegram. Calculates positive/negative ratio, compares to price. Everyone panics - buys. $300 -> $197 Copycat - monitors signals from the other nine through a shared JSON. No strategy of its own. Waits until 3+ bots agree - copies with double size. Ensemble model in one line of code. $300 -> $461 +$788 in 3 days. Ten bots. One prompt. Zero emotions.

zostaff

118,475 просмотров • 2 месяцев назад

a deported Chinese dev at a Starbucks showed me his screen and said "you have 6 seconds" he was sitting in the corner. two phones. one laptop. hood up. looked like he hadn't slept in days. i sat down next to him because every other seat was taken. he saw my screen. Polymarket open. "you trade these BTC markets?" yeah. 5-minute binaries. he turned his laptop toward me. "you see how the result settles? every 5-minute BTC market resolves based on an oracle. the oracle reads the price from an API. but the oracle doesn't update instantly. there's a lag. 4 to 6 seconds" "in those 6 seconds the real BTC price already moved. but the market is still open. still accepting orders. priced on the old number" he pointed at his screen. two windows side by side. left: Binance BTC spot feed. right: Polymarket orderbook. 3,200 stars. decentralized oracle framework. he had it forked on his laptop. the entire oracle timing model was exposed in the source code. "right now BTC is at $100,240 on Binance. the oracle still shows $100,190. the market 'BTC above $100,200 at 3:05pm' is trading YES at 41 cents" "but i already know the answer is YES. because i see the real price. the oracle doesn't. for 6 seconds i'm trading against a blind counterparty" watched him buy YES at 41c. four seconds later the oracle updated. market resolved YES. payout $1. "59 cents in 4 seconds. this happens every 5 minutes. 288 times a day" asked how he found this. "i was an oracle engineer at Chainlink in Shanghai. got deported for visa issues. took the knowledge with me" "every oracle has a heartbeat interval. a deviation threshold. a propagation delay. i know exactly how long each one takes to update. because i built three of them" he closed his laptop. "the edge isn't in the market. it's in the infrastructure layer between the real price and the reported price. nobody looks there. because nobody understands how oracles work" finished his coffee. left. didn't get his name. didn't need it. flew home. opened Claude. "build an oracle latency exploitation engine for Polymarket BTC binaries. track real-time BTC spot price from Binance websocket feed. compare against Polymarket oracle update timestamps. detect windows where oracle lags behind spot by more than $30. enter the side that the real price already confirmed. target the 4-6 second blind window before each oracle heartbeat." the system was live by morning. named it DEEP SIGNAL. first fill hit before i finished coffee. > Binance websocket feed at 50ms intervals. > oracle heartbeat monitor. > propagation delay calculator. > spot-to-oracle divergence detector. > conviction gate at $30+ deviation. > order placement avg 47ms. $534K volume. 79% win rate. sharpe 3.64. drawdown -1.2%. +$11,681 in profit runs on: Claude $20. VPS $5. UMA protocol free. $25/month. copytrade setup here: went back to that Starbucks a week later. same corner. empty. but the oracle still lags. every 5 minutes. every day. 6 seconds is all you need when you can see what the oracle can't.
0:32

a deported Chinese dev at a Starbucks showed me his screen and said "you have 6 seconds" he was sitting in the corner. two phones. one laptop. hood up. looked like he hadn't slept in days. i sat down next to him because every other seat was taken. he saw my screen. Polymarket open. "you trade these BTC markets?" yeah. 5-minute binaries. he turned his laptop toward me. "you see how the result settles? every 5-minute BTC market resolves based on an oracle. the oracle reads the price from an API. but the oracle doesn't update instantly. there's a lag. 4 to 6 seconds" "in those 6 seconds the real BTC price already moved. but the market is still open. still accepting orders. priced on the old number" he pointed at his screen. two windows side by side. left: Binance BTC spot feed. right: Polymarket orderbook. 3,200 stars. decentralized oracle framework. he had it forked on his laptop. the entire oracle timing model was exposed in the source code. "right now BTC is at $100,240 on Binance. the oracle still shows $100,190. the market 'BTC above $100,200 at 3:05pm' is trading YES at 41 cents" "but i already know the answer is YES. because i see the real price. the oracle doesn't. for 6 seconds i'm trading against a blind counterparty" watched him buy YES at 41c. four seconds later the oracle updated. market resolved YES. payout $1. "59 cents in 4 seconds. this happens every 5 minutes. 288 times a day" asked how he found this. "i was an oracle engineer at Chainlink in Shanghai. got deported for visa issues. took the knowledge with me" "every oracle has a heartbeat interval. a deviation threshold. a propagation delay. i know exactly how long each one takes to update. because i built three of them" he closed his laptop. "the edge isn't in the market. it's in the infrastructure layer between the real price and the reported price. nobody looks there. because nobody understands how oracles work" finished his coffee. left. didn't get his name. didn't need it. flew home. opened Claude. "build an oracle latency exploitation engine for Polymarket BTC binaries. track real-time BTC spot price from Binance websocket feed. compare against Polymarket oracle update timestamps. detect windows where oracle lags behind spot by more than $30. enter the side that the real price already confirmed. target the 4-6 second blind window before each oracle heartbeat." the system was live by morning. named it DEEP SIGNAL. first fill hit before i finished coffee. > Binance websocket feed at 50ms intervals. > oracle heartbeat monitor. > propagation delay calculator. > spot-to-oracle divergence detector. > conviction gate at $30+ deviation. > order placement avg 47ms. $534K volume. 79% win rate. sharpe 3.64. drawdown -1.2%. +$11,681 in profit runs on: Claude $20. VPS $5. UMA protocol free. $25/month. copytrade setup here: went back to that Starbucks a week later. same corner. empty. but the oracle still lags. every 5 minutes. every day. 6 seconds is all you need when you can see what the oracle can't.

Hanako

32,358 просмотров • 1 месяц назад

i built a 2 agent system using OpenClaw and Monte Carlo simulation > one agent predicts gold price > second agent bets on polymarket > second agent takes profit $1,400 → $17,900 in 72 hours saw a market on polymarket: "Will gold hit $3,000 by March 15?" price was sitting at 18¢ seemed random until i remembered Monte Carlo exists gave OpenClaw a task: "run 10,000 Monte Carlo simulations on gold price movement, calculate probability of hitting $3,000, pass results to trading agent" the architecture: > Agent 1 (Simulation Engine): - pulls historical gold volatility data - runs 10,000 price path simulations - factors in: Fed policy, geopolitical tension, USD strength - outputs: 73.4% probability gold hits $3,000 > Agent 2 (Trade Executor): > receives probability from Agent 1 > compares to polymarket odds (18¢ = 18% implied probability) > detects massive mispricing (73% vs 18%) > xecutes position hour 6: entered YES at 18¢ with $1,400 hour 24: gold jumps on Iran tensions, polymarket updates to 41¢ hour 48: Fed hints at rate cuts, simulation re-runs, now shows 81% probability hour 56: polymarket hits 67¢, Agent 2 adds to position hour 72: gold touches $2,987, market resolves YES at 94¢ final: $1,400 → $17,900 𝐡𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐦𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐦𝐢𝐬𝐬: polymarket prices are just crowd sentiment Monte Carlo is actual math > when math says 73% and crowd says 18% > that's not a trade > that's free money the simulation factored in: - 500+ historical gold price scenarios - current macro conditions - geopolitical risk premium - correlation with treasury yields ran this 4 more times on different markets: "Bitcoin above $70K by month end" - simulation: 62%, market: 31% → won "Unemployment rate above 4.2%" - simulation: 44%, market: 68% → bet NO, won "Tesla stock hits $250" - simulation: 28%, market: 52% → bet NO, won "Trump announces tariffs this week" - simulation can't model politics → skipped 7 trades total 6 wins 1 skip (non-quantifiable event) the edge is simple: most traders bet on vibes i'm betting on 10,000 simulated futures best polymarket traders use only tradefox: does anyone else realize polymarket is just mispriced probability distributions?
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i built a 2 agent system using OpenClaw and Monte Carlo simulation > one agent predicts gold price > second agent bets on polymarket > second agent takes profit $1,400 → $17,900 in 72 hours saw a market on polymarket: "Will gold hit $3,000 by March 15?" price was sitting at 18¢ seemed random until i remembered Monte Carlo exists gave OpenClaw a task: "run 10,000 Monte Carlo simulations on gold price movement, calculate probability of hitting $3,000, pass results to trading agent" the architecture: > Agent 1 (Simulation Engine): - pulls historical gold volatility data - runs 10,000 price path simulations - factors in: Fed policy, geopolitical tension, USD strength - outputs: 73.4% probability gold hits $3,000 > Agent 2 (Trade Executor): > receives probability from Agent 1 > compares to polymarket odds (18¢ = 18% implied probability) > detects massive mispricing (73% vs 18%) > xecutes position hour 6: entered YES at 18¢ with $1,400 hour 24: gold jumps on Iran tensions, polymarket updates to 41¢ hour 48: Fed hints at rate cuts, simulation re-runs, now shows 81% probability hour 56: polymarket hits 67¢, Agent 2 adds to position hour 72: gold touches $2,987, market resolves YES at 94¢ final: $1,400 → $17,900 𝐡𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐦𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐦𝐢𝐬𝐬: polymarket prices are just crowd sentiment Monte Carlo is actual math > when math says 73% and crowd says 18% > that's not a trade > that's free money the simulation factored in: - 500+ historical gold price scenarios - current macro conditions - geopolitical risk premium - correlation with treasury yields ran this 4 more times on different markets: "Bitcoin above $70K by month end" - simulation: 62%, market: 31% → won "Unemployment rate above 4.2%" - simulation: 44%, market: 68% → bet NO, won "Tesla stock hits $250" - simulation: 28%, market: 52% → bet NO, won "Trump announces tariffs this week" - simulation can't model politics → skipped 7 trades total 6 wins 1 skip (non-quantifiable event) the edge is simple: most traders bet on vibes i'm betting on 10,000 simulated futures best polymarket traders use only tradefox: does anyone else realize polymarket is just mispriced probability distributions?

ZER

149,038 просмотров • 3 месяцев назад

An ex-OpenAI engineer walked up to me at a meetup in SF I was showing my Polymarket terminal. He looked at the screen for ten seconds and said one sentence. "You're trading blind. The data is sitting in the open and you're writing prompts" I didn't understand. What data. He took my laptop. Opened one repository. 86 million trades. Every wallet. Every entry. Every exit. The entire Polymarket history since day one. "At OpenAI models don't guess. They read. Connect Claude Code to this dataset and say - find every wallet with a win rate above 70% and more than 100 trades" I asked - why Claude and not GPT? He looked at me like I was an idiot. "Because Claude Code connects to the repo directly. It reads the entire codebase. It's not a chatbox. It's a runtime" That evening I connected it. Claude pulled 47 wallets in 4 minutes. Average profit: $214K. Hold time: 7 hours. 91% close their position BEFORE resolution. Top wallets capture 86% of the move and cut losers at 12%. Everyone else - 58% of profit and hold losers to 41%. Same exact entries. The exits make it a completely different game. "Now connect the scanner" Three commands - the bot sees 500+ markets in real time. No API key. Read-only. Claude built the scoring in 20 minutes: Gap between price and model > 7 cents. Book depth > $500. Resolution in 4-48 hours. 93% of markets get killed instantly. Only the fat ones survive. "Now this is the important part" - he sent me an article where a guy built a full bot over a weekend from these same repos Copytrade here: Three exit triggers: Target 85% of expected move. Volume spike x3 - smart money leaving. 24 hours of silence - thesis is dead. I copied the whole stack. VPS $5. Claude $20. Total $25 a month. No team. No office. No Bloomberg. 16 days. 187 trades. 71% win rate. $800 seed. +$8,700. I sent him my screen. He replied a day later. "You just replicated for $25 what cost us six months and 11 people" I said - thanks for the tip. "Delete this chat" Too late.
0:24

An ex-OpenAI engineer walked up to me at a meetup in SF I was showing my Polymarket terminal. He looked at the screen for ten seconds and said one sentence. "You're trading blind. The data is sitting in the open and you're writing prompts" I didn't understand. What data. He took my laptop. Opened one repository. 86 million trades. Every wallet. Every entry. Every exit. The entire Polymarket history since day one. "At OpenAI models don't guess. They read. Connect Claude Code to this dataset and say - find every wallet with a win rate above 70% and more than 100 trades" I asked - why Claude and not GPT? He looked at me like I was an idiot. "Because Claude Code connects to the repo directly. It reads the entire codebase. It's not a chatbox. It's a runtime" That evening I connected it. Claude pulled 47 wallets in 4 minutes. Average profit: $214K. Hold time: 7 hours. 91% close their position BEFORE resolution. Top wallets capture 86% of the move and cut losers at 12%. Everyone else - 58% of profit and hold losers to 41%. Same exact entries. The exits make it a completely different game. "Now connect the scanner" Three commands - the bot sees 500+ markets in real time. No API key. Read-only. Claude built the scoring in 20 minutes: Gap between price and model > 7 cents. Book depth > $500. Resolution in 4-48 hours. 93% of markets get killed instantly. Only the fat ones survive. "Now this is the important part" - he sent me an article where a guy built a full bot over a weekend from these same repos Copytrade here: Three exit triggers: Target 85% of expected move. Volume spike x3 - smart money leaving. 24 hours of silence - thesis is dead. I copied the whole stack. VPS $5. Claude $20. Total $25 a month. No team. No office. No Bloomberg. 16 days. 187 trades. 71% win rate. $800 seed. +$8,700. I sent him my screen. He replied a day later. "You just replicated for $25 what cost us six months and 11 people" I said - thanks for the tip. "Delete this chat" Too late.

Lunar

480,041 просмотров • 2 месяцев назад

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
0:31

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

68,794 просмотров • 3 месяцев назад

A security guard turned $900 into $187,000 on Polymarket. Guarded empty buildings for 10 years at $16/hour. One sleepless shift he built a BTC bot with Claude Opus 4.8. Never wrote a single line of code. I found his wallet. Reverse-engineered the entire system. Here is what he actually did. Gave Claude new Fable 5 two prompts at the start of his shift. Opus did not write the bot. It drew the blueprint: data, signals, backtest, risk, execution. Then handed that blueprint to a swarm of 300 agents. How it works: → Decompose: Claude Fable 5 breaks the goal into a task tree → Dispatch: 300 agents fan out (some pulling Binance data, some writing code, some backtesting 5 years of data) → Execute: 4,000 steps in one 14-hour run → Review: Claude Fable 5 reads it all back and kills whatever drifted The brain never wrote a line. The hands never made a call. By sunrise he had what a 6-engineer quant team ships in a quarter. A live BTC bot on Polymarket. Backtested. Risk-capped. Running 24/7. For 10 years he watched empty buildings. Now 300 agents watch the market while he sleeps. I rebuilt this exact system with Claude new Fable 5 You only need Claude + device + 1 hour to deploy. Giving this free for 24 hours. To get it: 1. Comment "Claude" 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you
0:55

A security guard turned $900 into $187,000 on Polymarket. Guarded empty buildings for 10 years at $16/hour. One sleepless shift he built a BTC bot with Claude Opus 4.8. Never wrote a single line of code. I found his wallet. Reverse-engineered the entire system. Here is what he actually did. Gave Claude new Fable 5 two prompts at the start of his shift. Opus did not write the bot. It drew the blueprint: data, signals, backtest, risk, execution. Then handed that blueprint to a swarm of 300 agents. How it works: → Decompose: Claude Fable 5 breaks the goal into a task tree → Dispatch: 300 agents fan out (some pulling Binance data, some writing code, some backtesting 5 years of data) → Execute: 4,000 steps in one 14-hour run → Review: Claude Fable 5 reads it all back and kills whatever drifted The brain never wrote a line. The hands never made a call. By sunrise he had what a 6-engineer quant team ships in a quarter. A live BTC bot on Polymarket. Backtested. Risk-capped. Running 24/7. For 10 years he watched empty buildings. Now 300 agents watch the market while he sleeps. I rebuilt this exact system with Claude new Fable 5 You only need Claude + device + 1 hour to deploy. Giving this free for 24 hours. To get it: 1. Comment "Claude" 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you

Himanshu Kumar

52,894 просмотров • 8 дней назад

hedge funds extract their edge before the candle closes not from price, from what's moving behind it signal is called order flow toxicity every large informed trader disguises their order as retail noise - 40-80 small orders, timed to look random to a human, invisible. to an LSTM trained on cancel-to-fill ratios - readable in 400 milliseconds two sigma doesn't ask "will price go up?" - they ask "is someone with information buying right now, before the market reprices?" detection model is 3 layers: > input: bid-ask spread velocity, cancel-to-fill ratio, trade size entropy > hidden: LSTM over 500ms rolling window > output: toxicity score 0-1 above 0.67: informed flow detected. enter with it below 0.67: noise. skip or fade 74% directional accuracy. 11-minute avg hold. runs on any level 2 feed data is free - CBOE publishes it. architecture is in every ML textbook since 2012 they've been running this since you were drawing trendlines Bookmark it and add in your strategy bottleneck was never the data - it was the question you were asking
0:48

hedge funds extract their edge before the candle closes not from price, from what's moving behind it signal is called order flow toxicity every large informed trader disguises their order as retail noise - 40-80 small orders, timed to look random to a human, invisible. to an LSTM trained on cancel-to-fill ratios - readable in 400 milliseconds two sigma doesn't ask "will price go up?" - they ask "is someone with information buying right now, before the market reprices?" detection model is 3 layers: > input: bid-ask spread velocity, cancel-to-fill ratio, trade size entropy > hidden: LSTM over 500ms rolling window > output: toxicity score 0-1 above 0.67: informed flow detected. enter with it below 0.67: noise. skip or fade 74% directional accuracy. 11-minute avg hold. runs on any level 2 feed data is free - CBOE publishes it. architecture is in every ML textbook since 2012 they've been running this since you were drawing trendlines Bookmark it and add in your strategy bottleneck was never the data - it was the question you were asking

Livsun

11,425 просмотров • 18 дней назад

My friend got laid off from a hedge fund last November He was making $2.1M/year running their quant desk Instead of applying to another fund - he took their entire strategy framework and rebuilt it on Polymarket 1300$ → 187,564$ in 3 weeks Total cost: $200/month for Claude, $50/month VPS He said: "The models I built for the fund work better on Polymarket, because on Wall Street you compete against other quants. On Polymarket you compete against guys who bet based on Twitter polls" He rebuilt a 4-factor model in one weekend Same math his fund ran on $800M AUM: Momentum scoring: track 400+ markets, buy when a contract moves 10¢+ with rising volume Mean reversion: when a contract overshoots 15¢+ in under 2 hours - fade it Volatility regime detection: hidden Markov model classifies low-vol vs high-vol every hour. Switches strategy automatically Correlation breakdown: 200+ market pairs monitored. When two normally correlated contracts decorrelate - one is wrong. Trade the lagging one. All 4 run simultaneously COPY HIM: Portfolio optimizer combines the scores Fractional Kelly sizing, max 5% per market, 15 concurrent positions His fund's flagship returned 19% last year on $800M His bot returned 144,000% in 3 weeks on $1300 He doesn't need to work anymore
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My friend got laid off from a hedge fund last November He was making $2.1M/year running their quant desk Instead of applying to another fund - he took their entire strategy framework and rebuilt it on Polymarket 1300$ → 187,564$ in 3 weeks Total cost: $200/month for Claude, $50/month VPS He said: "The models I built for the fund work better on Polymarket, because on Wall Street you compete against other quants. On Polymarket you compete against guys who bet based on Twitter polls" He rebuilt a 4-factor model in one weekend Same math his fund ran on $800M AUM: Momentum scoring: track 400+ markets, buy when a contract moves 10¢+ with rising volume Mean reversion: when a contract overshoots 15¢+ in under 2 hours - fade it Volatility regime detection: hidden Markov model classifies low-vol vs high-vol every hour. Switches strategy automatically Correlation breakdown: 200+ market pairs monitored. When two normally correlated contracts decorrelate - one is wrong. Trade the lagging one. All 4 run simultaneously COPY HIM: Portfolio optimizer combines the scores Fractional Kelly sizing, max 5% per market, 15 concurrent positions His fund's flagship returned 19% last year on $800M His bot returned 144,000% in 3 weeks on $1300 He doesn't need to work anymore

Hrundel75 🐷

308,729 просмотров • 2 месяцев назад

a Chinese guy who cleans servers at Jane Street told me something that hasn't left my head i was smoking outside a data center in jersey city at 2am. he walked out in a uniform. lit a cigarette next to me. "you trade?" yeah. "what do you trade on" prediction markets. Polymarket. BTC. he laughed. not mean. just quiet. "every trader i clean up after is doing the same thing. same screens. same candles. same direction bets. i mop the same floor every night" i asked what he meant. "the smart ones don't predict. they wait for the stupid ones to panic. then they take the other side" he finished his cigarette and went back inside. i stood there for ten minutes. then went home and opened Claude. "build a system that doesn't predict BTC direction. only enters when crowd panic creates a mispricing between YES and NO on Polymarket 5-minute markets. buy the underpriced side. exit when spread closes" 18,400 stars. open source execution engine. handles routing, risk, position sizing. the AI used it as the backbone. built overnight. GHOST GRID. scans 288 BTC windows per day. every 5 minutes. ignores direction completely. the logic: YES at 81c. NO at 22c. combined $1.03. someone dumps $5,000 on YES. it spikes to 94c. NO crashes to 7c. $1.01 combined. the gap is 17c of free alpha sitting there for 4 minutes. the crowd bet direction. GHOST GRID bought the panic. edge table live: BTC+100K 5m +18.4c. BTC+101K 5m +14.1c. BTC yes 0xd8dA...6045 BTC+103K 5m $1,240. > no smart_money BTC yes 0xBE0e...48CD BTC+101K 15m $644. > no copy_bot BTC+100K 5m $980. 311 trades. 83% win rate. sharpe 4.12. drawdown -0.8%. +$17,340 from $900 seed. my setup: > Claude - $20/mo > VPS - $5/mo > hummingbot - free > Polymarket API - free $25/month total. i copied the setup here: texted him last week. don't have his number. don't know his name. just remember what he said. "i mop the same floor every night" the floor is still there. so is the alpha.
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a Chinese guy who cleans servers at Jane Street told me something that hasn't left my head i was smoking outside a data center in jersey city at 2am. he walked out in a uniform. lit a cigarette next to me. "you trade?" yeah. "what do you trade on" prediction markets. Polymarket. BTC. he laughed. not mean. just quiet. "every trader i clean up after is doing the same thing. same screens. same candles. same direction bets. i mop the same floor every night" i asked what he meant. "the smart ones don't predict. they wait for the stupid ones to panic. then they take the other side" he finished his cigarette and went back inside. i stood there for ten minutes. then went home and opened Claude. "build a system that doesn't predict BTC direction. only enters when crowd panic creates a mispricing between YES and NO on Polymarket 5-minute markets. buy the underpriced side. exit when spread closes" 18,400 stars. open source execution engine. handles routing, risk, position sizing. the AI used it as the backbone. built overnight. GHOST GRID. scans 288 BTC windows per day. every 5 minutes. ignores direction completely. the logic: YES at 81c. NO at 22c. combined $1.03. someone dumps $5,000 on YES. it spikes to 94c. NO crashes to 7c. $1.01 combined. the gap is 17c of free alpha sitting there for 4 minutes. the crowd bet direction. GHOST GRID bought the panic. edge table live: BTC+100K 5m +18.4c. BTC+101K 5m +14.1c. BTC yes 0xd8dA...6045 BTC+103K 5m $1,240. > no smart_money BTC yes 0xBE0e...48CD BTC+101K 15m $644. > no copy_bot BTC+100K 5m $980. 311 trades. 83% win rate. sharpe 4.12. drawdown -0.8%. +$17,340 from $900 seed. my setup: > Claude - $20/mo > VPS - $5/mo > hummingbot - free > Polymarket API - free $25/month total. i copied the setup here: texted him last week. don't have his number. don't know his name. just remember what he said. "i mop the same floor every night" the floor is still there. so is the alpha.

Hanako

38,175 просмотров • 1 месяц назад

Cat Wu, head of product for Claude Code at Anthropic: "you can build almost anything with Claude from a single prompt now" one prompt gets you a working screen, not YOUR product Claude Code has never seen your design system, so it guesses the look fresh every time. how to hand Claude Code that system is in the article. better than the $300 design course in your tabs, and this one's free.
12:14

Cat Wu, head of product for Claude Code at Anthropic: "you can build almost anything with Claude from a single prompt now" one prompt gets you a working screen, not YOUR product Claude Code has never seen your design system, so it guesses the look fresh every time. how to hand Claude Code that system is in the article. better than the $300 design course in your tabs, and this one's free.

Mnimiy

112,125 просмотров • 5 дней назад