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A former Goldman Sachs quant trader shared a simple but powerful principle with me in a single conversation: “We don’t forecast. We only take positions when pricing deviates from estimated probability by more than ~6%.” That was it. No complexity—just execution logic used on institutional desks. The result was... a system that evaluates 400+ markets per hour. It focuses on contracts in the ~7–19 cent range where implied pricing diverges significantly from modeled probability (roughly 60–90% confidence zones in backtested scenarios). Over a three-month period (paper + small capital testing phase): * Starting point: $2,000 * Peak value: $8,191 * 99 executed trades * Sharpe ratio: ~2.3 A few example signals the system flagged: * ETH Merge-related event: 72¢ market price vs ~88% modeled probability → +19¢ move * SOL above $200 breakout scenario: 44¢ vs ~81% model probability → +15¢ * Florida hurricane (Cat 3+ threshold): 81¢ vs ~92% → +7¢ * Wheat above $800: 53¢ vs ~68% → +20¢ Each of these was identified automatically by the scanner based on deviation thresholds and liquidity filters. When I showed the setup to him recently, he said: “This is essentially what we run with an 800M AUM team and dozens of engineers.” The difference is cost. My current stack: * Claude: ~$20/month * VPS: ~$5/month * Open-source repos: free * APIs: minimal / free tiers On top of that, I’ve deployed multiple lightweight agents running continuously to monitor different market clusters. Performance tracking labels (internal testing): * velvet_void: +$697 * nano_alpha: +$541 * ratking_eth: +$407 * darkpool_7: +$356 For context, his fund reportedly returned ~19% last year, while this experimental setup has shown significantly higher short-term volatility-adjusted returns in a limited testing window. Nothing here is magic or prediction-based—just probabilistic pricing, systematic filtering, and execution discipline. If you want access to the breakdown and setup details: 1. Comment “ Claude ” 2. Like + repost 3. Follow Frances Marsha (so I can DM you)show more

Frances Marsha

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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

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 🐷

309,097 Aufrufe • vor 3 Monaten

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

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

188,258 Aufrufe • vor 2 Monaten

What Makes an A+++ Setup (According to a $291K Trader) A setup is the specific market condition where all your criteria align for a high-probability trade entry. Most traders don't understand the criteria that makes a solid entry. They take every setup that looks "good enough" — and end up with 1:2 or 1:3 risk-to-reward ratios, grinding for small wins. Here's the difference: An A+++ setup allowed my student Said to make $86,000 in one day from just 3 trades (with 1:18, 1:7, and 1:10 R/R). And $291k over the last 1.5 years. If there’s a mediocre setup, he won’t take it. Some weeks he only trades twice because he's waiting for perfection. In the 2-minute clip below, my student Said walks through the 5 elements that must align for an A+++ setup: 1) Inverse Fair Value Gap — An imbalance price needs to fill 2) Inducement — Liquidity sweep that triggers early traders 3) Imbalance — Gap in price delivery that draws price back 4) Protected Level — Previous inducement + break of structure creates an internal floor/ceiling 5) Break of Structure — Confirmation that one side is in control Said also looks for setups where you have TWO protected levels, not just one. This gives him the confidence to enter at the end of the fair value gap without waiting for additional confirmation. Stop loss goes just below the protected level — tight risk, massive reward potential. — This is just scratching the surface of what Said shared. In the full 1-hour interview, we also dove into: • The exact 3 trades that made him $86K in a single day (with timestamped chart breakdowns) • How he combined two different strategies into one profitable system • Why he still backtests 1-1.5 hours daily even after making $100K/month Just comment "INTERVIEW" and I'll DM you the full interview in the next few minutes.

What Makes an A+++ Setup (According to a $291K Trader) A setup is the specific market condition where all your criteria align for a high-probability trade entry. Most traders don't understand the criteria that makes a solid entry. They take every setup that looks "good enough" — and end up with 1:2 or 1:3 risk-to-reward ratios, grinding for small wins. Here's the difference: An A+++ setup allowed my student Said to make $86,000 in one day from just 3 trades (with 1:18, 1:7, and 1:10 R/R). And $291k over the last 1.5 years. If there’s a mediocre setup, he won’t take it. Some weeks he only trades twice because he's waiting for perfection. In the 2-minute clip below, my student Said walks through the 5 elements that must align for an A+++ setup: 1) Inverse Fair Value Gap — An imbalance price needs to fill 2) Inducement — Liquidity sweep that triggers early traders 3) Imbalance — Gap in price delivery that draws price back 4) Protected Level — Previous inducement + break of structure creates an internal floor/ceiling 5) Break of Structure — Confirmation that one side is in control Said also looks for setups where you have TWO protected levels, not just one. This gives him the confidence to enter at the end of the fair value gap without waiting for additional confirmation. Stop loss goes just below the protected level — tight risk, massive reward potential. — This is just scratching the surface of what Said shared. In the full 1-hour interview, we also dove into: • The exact 3 trades that made him $86K in a single day (with timestamped chart breakdowns) • How he combined two different strategies into one profitable system • Why he still backtests 1-1.5 hours daily even after making $100K/month Just comment "INTERVIEW" and I'll DM you the full interview in the next few minutes.

The Trading Geek (Brad Goh)

15,338 Aufrufe • vor 6 Monaten

An Anthropic safety researcher closed her laptop when she saw my screen at Philz Coffee. I was running my Polymarket bot from the corner table. She was in line. Looked over my shoulder. Stopped moving. That is not a normal trading app. What model is that running on? I told her. Claude Code. Four repos. $25 a month. She sat down without asking. I work on the alignment team. We test Claude for exactly this kind of autonomous behavior. You are letting it find its own trading signals. Not just signals. Wallets. github/warproxxx/poly_data 86 million trades. Every wallet. Every entry. Every exit. You are feeding Claude raw wallet data and letting it identify which traders consistently win. Then cloning their behavior. She said it slowly. Like she was writing an internal report in her head. Claude Code finds the top wallets. Reverse-engineers their timing. Copies their entries. Then exits before they do. Before they do? My bot cuts at 85% of expected move or on a 3x volume spike. Top wallets exit before resolution 91% of the time. They capture 86% of the move. Losers hold to 58%. She put her coffee down. How did you get Claude to learn exit timing on its own? I showed her the second repo. github/Polymarket/polymarket-cli Three commands. 500+ markets. No API key. Claude scores them in 20 minutes. We have 14 people stress-testing Claude's autonomous capabilities. You are just using them. My setup: Claude API: $20 per month VPS: $5 per month poly_data: free polymarket-cli: free 19 days. 4 agents. 74% win rate. She stared at the screen for a long time. This is literally what our red team simulates. Except you actually deployed it. She emailed me two days later. Our policy team found your post. Please take it down. Too late. I built the entire framework: How to connect Claude Code to poly_data wallet analysis How to configure autonomous exit timing at 85% threshold How to deploy polymarket-cli for market scoring How to run 4 parallel agents on a single VPS How to start with $500 and scale on evidence The system runs 24/7. Finds proven wallets. Copies their timing. Exits before the crowd. No prediction. No guessing. Just wallet cloning. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word MoneyBot 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you Save this post. Deploy the wallet cloning system this week. Start with $500. Scale on evidence.

An Anthropic safety researcher closed her laptop when she saw my screen at Philz Coffee. I was running my Polymarket bot from the corner table. She was in line. Looked over my shoulder. Stopped moving. That is not a normal trading app. What model is that running on? I told her. Claude Code. Four repos. $25 a month. She sat down without asking. I work on the alignment team. We test Claude for exactly this kind of autonomous behavior. You are letting it find its own trading signals. Not just signals. Wallets. github/warproxxx/poly_data 86 million trades. Every wallet. Every entry. Every exit. You are feeding Claude raw wallet data and letting it identify which traders consistently win. Then cloning their behavior. She said it slowly. Like she was writing an internal report in her head. Claude Code finds the top wallets. Reverse-engineers their timing. Copies their entries. Then exits before they do. Before they do? My bot cuts at 85% of expected move or on a 3x volume spike. Top wallets exit before resolution 91% of the time. They capture 86% of the move. Losers hold to 58%. She put her coffee down. How did you get Claude to learn exit timing on its own? I showed her the second repo. github/Polymarket/polymarket-cli Three commands. 500+ markets. No API key. Claude scores them in 20 minutes. We have 14 people stress-testing Claude's autonomous capabilities. You are just using them. My setup: Claude API: $20 per month VPS: $5 per month poly_data: free polymarket-cli: free 19 days. 4 agents. 74% win rate. She stared at the screen for a long time. This is literally what our red team simulates. Except you actually deployed it. She emailed me two days later. Our policy team found your post. Please take it down. Too late. I built the entire framework: How to connect Claude Code to poly_data wallet analysis How to configure autonomous exit timing at 85% threshold How to deploy polymarket-cli for market scoring How to run 4 parallel agents on a single VPS How to start with $500 and scale on evidence The system runs 24/7. Finds proven wallets. Copies their timing. Exits before the crowd. No prediction. No guessing. Just wallet cloning. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word MoneyBot 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you Save this post. Deploy the wallet cloning system this week. Start with $500. Scale on evidence.

Himanshu Kumar

23,039 Aufrufe • vor 1 Monat

An Anthropic intern watched me make $38 in three minutes at Equator Coffee I was on Market Street. Laptop open. BTC 5-minute markets cycling on screen. A green fill popped. She was behind me in line. Anthropic badge on her lanyard. "Wait. Is that Claude running trades" I told her. Claude Code. Four repos. $25 a month. She sat down across from me without asking. "I'm on the evals team. We benchmark Claude's reasoning on stuff way simpler than this. You're letting it run a full execution loop with real money" I showed her the first repo. 86 million trades. Claude finds which wallets consistently win. Reverse-engineers their timing. Copies their exits. "So you're not writing trading rules. Claude is finding them on its own from the data" Exactly. It watches Binance and Coinbase diverge from Chainlink. When both exchanges move $50+ the same way and Chainlink lags - it enters. 94% follow-through. Another trade closed on screen. +$41. "How often does it trade" About 55 times a day. Out of 400+ windows. Kills 85%. Only enters when order book imbalance confirms the price signal. Three commands. 500+ markets. Claude scores them in 20 minutes. She watched the screen for a while. My setup: Claude API - $20/mo VPS - $5/mo Everything else - free 30 days. 1,847 trades. 71% win rate. +$14,200. Copytrade here: She leaned back. "We spend all day testing what Claude can do in theory. You just showed me what it does in practice" I published the full breakdown that night. She DM'd me the next morning. "My team lead read your article. He wants to know if you'll consult for us" I told him to read it again. Everything's in there. Too late to gatekeep.

An Anthropic intern watched me make $38 in three minutes at Equator Coffee I was on Market Street. Laptop open. BTC 5-minute markets cycling on screen. A green fill popped. She was behind me in line. Anthropic badge on her lanyard. "Wait. Is that Claude running trades" I told her. Claude Code. Four repos. $25 a month. She sat down across from me without asking. "I'm on the evals team. We benchmark Claude's reasoning on stuff way simpler than this. You're letting it run a full execution loop with real money" I showed her the first repo. 86 million trades. Claude finds which wallets consistently win. Reverse-engineers their timing. Copies their exits. "So you're not writing trading rules. Claude is finding them on its own from the data" Exactly. It watches Binance and Coinbase diverge from Chainlink. When both exchanges move $50+ the same way and Chainlink lags - it enters. 94% follow-through. Another trade closed on screen. +$41. "How often does it trade" About 55 times a day. Out of 400+ windows. Kills 85%. Only enters when order book imbalance confirms the price signal. Three commands. 500+ markets. Claude scores them in 20 minutes. She watched the screen for a while. My setup: Claude API - $20/mo VPS - $5/mo Everything else - free 30 days. 1,847 trades. 71% win rate. +$14,200. Copytrade here: She leaned back. "We spend all day testing what Claude can do in theory. You just showed me what it does in practice" I published the full breakdown that night. She DM'd me the next morning. "My team lead read your article. He wants to know if you'll consult for us" I told him to read it again. Everything's in there. Too late to gatekeep.

Lunar

68,586 Aufrufe • vor 3 Monaten

An Anthropic researcher sat down next to me at a hackathon last week. Claude Opus 4.7 was running 4 agents on my laptop. Live. No manual input. She looked at the terminal and said: "What is this?" I showed her. 4 agents. 678 trades. 81% win rate. $16,200 last 30 days. She worked on the evals team. She'd never seen Claude pointed at 88 million on-chain trades. The setup is 3 public repos. All free. -> 88 million Polymarket trades. Every wallet. Every entry. Every exit. Every resolution. -> the framework that bridges Claude Opus 4.7 directly to live markets. Order placement, position tracking, exit timing. -> real-time WebSocket order book. Depth on both sides. No polling, no lag. Four agents. One loop. Agent 1 identifies which wallets win consistently across 88 million trades. Agent 2 reverse-engineers their entry timing. Agent 3 monitors order book volume spikes. Agent 4 sizes positions using Kelly. No overbet. Drawdown capped at 1.4% over 678 trades. 85% of windows get killed. No trade. The bot only enters when 3 signals align: -> Elite wallet consensus pointing the same direction. -> Price divergence with Binance and Coinbase both agreeing. -> Order book imbalance confirming the bias. Single-source price data was 57% accurate. All three together: 81%. Exit before resolution. Always. Losers hold to 0 or $1. The agents copy their exits. The agents don't gamble on that. My stack: Claude Opus 4.7 at $19/mo, VPS Hetzner at $4.99/mo, Everything else free. Total stats: $23.99/month. 30 days: 678 trades, 81% win rate, net +$16,200, max drawdown -1.2%, avg hold 4h 12m. She asked if Anthropic could test this internally. "We run Claude on benchmarks and evals. Nobody pointed it at a live market dataset with 88 million rows." Claude Opus 4.7 didn't need a system prompt. It read the wallet index, understood the signal structure, and wrote the combiner logic in one pass. The people who built the model hadn't thought to point it at this data. I had. Copy the live trades: -> all 4 agents run 24/7. The window is open right now. Save this, follow me and comment OPUS. I will send the guide to you.

An Anthropic researcher sat down next to me at a hackathon last week. Claude Opus 4.7 was running 4 agents on my laptop. Live. No manual input. She looked at the terminal and said: "What is this?" I showed her. 4 agents. 678 trades. 81% win rate. $16,200 last 30 days. She worked on the evals team. She'd never seen Claude pointed at 88 million on-chain trades. The setup is 3 public repos. All free. -> 88 million Polymarket trades. Every wallet. Every entry. Every exit. Every resolution. -> the framework that bridges Claude Opus 4.7 directly to live markets. Order placement, position tracking, exit timing. -> real-time WebSocket order book. Depth on both sides. No polling, no lag. Four agents. One loop. Agent 1 identifies which wallets win consistently across 88 million trades. Agent 2 reverse-engineers their entry timing. Agent 3 monitors order book volume spikes. Agent 4 sizes positions using Kelly. No overbet. Drawdown capped at 1.4% over 678 trades. 85% of windows get killed. No trade. The bot only enters when 3 signals align: -> Elite wallet consensus pointing the same direction. -> Price divergence with Binance and Coinbase both agreeing. -> Order book imbalance confirming the bias. Single-source price data was 57% accurate. All three together: 81%. Exit before resolution. Always. Losers hold to 0 or $1. The agents copy their exits. The agents don't gamble on that. My stack: Claude Opus 4.7 at $19/mo, VPS Hetzner at $4.99/mo, Everything else free. Total stats: $23.99/month. 30 days: 678 trades, 81% win rate, net +$16,200, max drawdown -1.2%, avg hold 4h 12m. She asked if Anthropic could test this internally. "We run Claude on benchmarks and evals. Nobody pointed it at a live market dataset with 88 million rows." Claude Opus 4.7 didn't need a system prompt. It read the wallet index, understood the signal structure, and wrote the combiner logic in one pass. The people who built the model hadn't thought to point it at this data. I had. Copy the live trades: -> all 4 agents run 24/7. The window is open right now. Save this, follow me and comment OPUS. I will send the guide to you.

slash1s

46,308 Aufrufe • vor 3 Monaten

An OpenAI researcher sat down next to me at a coffee shop in Mission District I had my terminal open. Three panels. Live trades scrolling. He was reading something on his laptop. Glanced over. Stopped reading. "That's not a dashboard. That's a live scoring engine. What model is running that" I told him. Claude Code. Four repos. $25 a month. He closed his laptop. "I work at OpenAI. We benchmarked Claude internally last month. You're using it to trade prediction markets?" I opened one link. 86 million trades. Every wallet. Every entry. Every exit. The entire Polymarket history since day one. "This is public? We quoted a seven-figure budget to reconstruct this kind of dataset from on-chain data. The project is still in review" I told him Claude Code connects directly. It reads the whole dataset. Finds the wallets that win. Then finds WHY they win. Then copies the pattern. He pulled his chair closer. "Walk me through the exit logic" Top wallets exit before resolution 91% of the time. They capture 86% of the move and cut losers at 12%. Everyone else holds to 58%. Same entries. Completely different exits. My bot cuts at 85% of expected move. Or on a 3x volume spike. Whichever hits first. "Who gave you that threshold" Claude Code found it in poly_data. In about 20 minutes. "We had a team of nine working on this exact problem for six months. They never shipped it. You did it in a weekend with a competitor's model" I opened another link. Three commands. 500+ markets. No API key. Claude scores them in 20 minutes. "That's our internal eval pipeline. Except it took us six months and you built it on a Saturday" My setup: Claude API - $20/mo VPS - $5/mo poly_data - free polymarket-cli - free 19 days. 4 agents. 74% win rate. +$9,400. Copytrade here: I showed him the article where I broke down every repo, every command, every dollar. He read it for five minutes. Then looked up. "You just published what we presented to Sam last quarter. Using the other team's model" He texted me the next morning. "My director found your thread. Take it down" Too late.

An OpenAI researcher sat down next to me at a coffee shop in Mission District I had my terminal open. Three panels. Live trades scrolling. He was reading something on his laptop. Glanced over. Stopped reading. "That's not a dashboard. That's a live scoring engine. What model is running that" I told him. Claude Code. Four repos. $25 a month. He closed his laptop. "I work at OpenAI. We benchmarked Claude internally last month. You're using it to trade prediction markets?" I opened one link. 86 million trades. Every wallet. Every entry. Every exit. The entire Polymarket history since day one. "This is public? We quoted a seven-figure budget to reconstruct this kind of dataset from on-chain data. The project is still in review" I told him Claude Code connects directly. It reads the whole dataset. Finds the wallets that win. Then finds WHY they win. Then copies the pattern. He pulled his chair closer. "Walk me through the exit logic" Top wallets exit before resolution 91% of the time. They capture 86% of the move and cut losers at 12%. Everyone else holds to 58%. Same entries. Completely different exits. My bot cuts at 85% of expected move. Or on a 3x volume spike. Whichever hits first. "Who gave you that threshold" Claude Code found it in poly_data. In about 20 minutes. "We had a team of nine working on this exact problem for six months. They never shipped it. You did it in a weekend with a competitor's model" I opened another link. Three commands. 500+ markets. No API key. Claude scores them in 20 minutes. "That's our internal eval pipeline. Except it took us six months and you built it on a Saturday" My setup: Claude API - $20/mo VPS - $5/mo poly_data - free polymarket-cli - free 19 days. 4 agents. 74% win rate. +$9,400. Copytrade here: I showed him the article where I broke down every repo, every command, every dollar. He read it for five minutes. Then looked up. "You just published what we presented to Sam last quarter. Using the other team's model" He texted me the next morning. "My director found your thread. Take it down" Too late.

Lunar

159,690 Aufrufe • vor 3 Monaten

Anthropic released an update for Claude, and one young guy turned $1,400 into $238,000 in 11 days His account is 0xdE17. He is not a trader. Not a developer. Just a student with a laptop and access to Claude. When Anthropic updated the skills architecture for Claude, he didn’t sleep for two nights, read the documentation, and built a bot from scratch. Result: $1,430 → $238,006 in 11 days 366 trades. Win rate 62%. The biggest win $52,700 on a single bet. How it works: The bot scans Polymarket every few seconds looking for markets where the price deviates from the fair value by more than 8%. That’s the edge. Most people don’t see it the bot always does. The strategy is painfully simple - The market says 30¢ for an event - The real probability based on data is 55¢ - The bot enters. Waits. Exits at the peak. This isn’t luck. It’s attention arbitrage. The biggest trade was a bet that Trump would sign a crypto executive order in March. The market priced it at 28¢. The bot placed $16,000. Exited at 81¢. In 11 days he went through a journey most traders don’t go through in years. Now his wallet is public, and with the help of this terminal you will be able to copy his trades in time: Bot 0xdE17 CLAWD BTC SNIPER is open for copy-trade. People ask him: “How much starting capital do you need?” He replied: “I had $1,400 and a sleepless night.” Anthropic gave the tool. He simply wasn’t afraid to use it.

Anthropic released an update for Claude, and one young guy turned $1,400 into $238,000 in 11 days His account is 0xdE17. He is not a trader. Not a developer. Just a student with a laptop and access to Claude. When Anthropic updated the skills architecture for Claude, he didn’t sleep for two nights, read the documentation, and built a bot from scratch. Result: $1,430 → $238,006 in 11 days 366 trades. Win rate 62%. The biggest win $52,700 on a single bet. How it works: The bot scans Polymarket every few seconds looking for markets where the price deviates from the fair value by more than 8%. That’s the edge. Most people don’t see it the bot always does. The strategy is painfully simple - The market says 30¢ for an event - The real probability based on data is 55¢ - The bot enters. Waits. Exits at the peak. This isn’t luck. It’s attention arbitrage. The biggest trade was a bet that Trump would sign a crypto executive order in March. The market priced it at 28¢. The bot placed $16,000. Exited at 81¢. In 11 days he went through a journey most traders don’t go through in years. Now his wallet is public, and with the help of this terminal you will be able to copy his trades in time: Bot 0xdE17 CLAWD BTC SNIPER is open for copy-trade. People ask him: “How much starting capital do you need?” He replied: “I had $1,400 and a sleepless night.” Anthropic gave the tool. He simply wasn’t afraid to use it.

AdiiX

1,764,343 Aufrufe • vor 4 Monaten

A 29-year-old sales consultant from China quit his job and now makes in 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment 'AGENT' 2. Like and retweet this 3. Follow Marry Evan so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.

A 29-year-old sales consultant from China quit his job and now makes in 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment 'AGENT' 2. Like and retweet this 3. Follow Marry Evan so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.

Marry Evan

18,968 Aufrufe • vor 2 Monaten

a Citadel intern told me something at a party he probably shouldn't have it was on a rooftop in brooklyn. i mentioned i trade prediction markets. he got quiet for a second. "we have a model for that. it scores every contract on four factors. when all four align we enter. when any breaks we exit. that's it" i asked what the four factors are. he looked around. then said it fast like he was confessing. "cross-market divergence. disposition coefficient. capital velocity. pair network correlation" I didn't know what half of that meant. but i memorized it. went home. 11pm. opened Claude. "here are four scoring factors from a quant fund. build a terminal that runs all four on prediction markets" Claude asked one question: "Where's the data?" I sent him one repo: 86 million trades. every wallet. every entry. every outcome three weeks later i'm sitting in my apartment watching a screen i barely understand print money. the disposition meter alone changed everything. it measures how you exit - not how you enter. top wallets capture 86% of winner value and cut losers at 12%. everyone else captures 58% and holds losers to 41%. same exact entries. the exits make it a completely different game. capital velocity: 49x. every dollar gets recycled 49 times before the average trader recycles once. the terminal found 42 pair correlations across 11 markets. when MSFT beats Q3 is priced at 80c but the model reads 93% - it enters. when the gap closes 2 hours later - it exits. no opinions. no news. just four numbers that either align or don't. his fund runs this with a floor of PhDs and $800M AUM. my setup: > Claude - $20/month > VPS - $5/month > poly_data repo - free > Polymarket API - free $25/month. no team. no office. no Bloomberg. 280 trades so far. 70% win rate. $800 seed. four bots splitting the work: pulse_alpha +$299. arb_hunter +$558. trend_rider +$337. cal_engine +$719. +$11,514 total. copytrade here: he texted me last week. "delete everything i told you" too late.

a Citadel intern told me something at a party he probably shouldn't have it was on a rooftop in brooklyn. i mentioned i trade prediction markets. he got quiet for a second. "we have a model for that. it scores every contract on four factors. when all four align we enter. when any breaks we exit. that's it" i asked what the four factors are. he looked around. then said it fast like he was confessing. "cross-market divergence. disposition coefficient. capital velocity. pair network correlation" I didn't know what half of that meant. but i memorized it. went home. 11pm. opened Claude. "here are four scoring factors from a quant fund. build a terminal that runs all four on prediction markets" Claude asked one question: "Where's the data?" I sent him one repo: 86 million trades. every wallet. every entry. every outcome three weeks later i'm sitting in my apartment watching a screen i barely understand print money. the disposition meter alone changed everything. it measures how you exit - not how you enter. top wallets capture 86% of winner value and cut losers at 12%. everyone else captures 58% and holds losers to 41%. same exact entries. the exits make it a completely different game. capital velocity: 49x. every dollar gets recycled 49 times before the average trader recycles once. the terminal found 42 pair correlations across 11 markets. when MSFT beats Q3 is priced at 80c but the model reads 93% - it enters. when the gap closes 2 hours later - it exits. no opinions. no news. just four numbers that either align or don't. his fund runs this with a floor of PhDs and $800M AUM. my setup: > Claude - $20/month > VPS - $5/month > poly_data repo - free > Polymarket API - free $25/month. no team. no office. no Bloomberg. 280 trades so far. 70% win rate. $800 seed. four bots splitting the work: pulse_alpha +$299. arb_hunter +$558. trend_rider +$337. cal_engine +$719. +$11,514 total. copytrade here: he texted me last week. "delete everything i told you" too late.

Hanako

1,738,668 Aufrufe • vor 3 Monaten

A 29-year-old sales consultant from China quit his job. Now making 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment AGENT 2. Like and retweet this 3. Follow Himanshu Kumar so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.

A 29-year-old sales consultant from China quit his job. Now making 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment AGENT 2. Like and retweet this 3. Follow Himanshu Kumar so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.

Himanshu Kumar

12,288 Aufrufe • vor 1 Monat

I asked Claude Fable 5 (Extra High) to build an arb bot for Polymarket. One rule: trade only when YES + NO in 2 hours it almost doubled it (+$96.31) > in 5 hours it showed +$579 PnL > current balance: +$3,799.73 Cost: 10M tokens. Here's how it works and why: Every BTC Up/Down market on Polymarket has exactly two outcomes. YES and NO. When the market resolves, one pays $1. The other pays $0. That means owning BOTH sides should always cost exactly $1. But markets aren't perfect. Sometimes YES trades at $0.48 while NO trades at $0.49. Together that's only $0.97 so the bot instantly buys both. A position worth $1... for just $0.97. And Claude knows it, this is the simplest arb that exists. You can paste this to it and use this logic in your prompt. This $0.03 difference is locked in regardless of whether Bitcoin pumps, dumps, or goes sideways. No prediction required. And difficult part isn't finding the opportunity, it's execution. These pricing gaps usually disappear in seconds. If one order fills but the other doesn't, the trade can become a loss. So my bot constantly scans every BTC market, checks fees, validates liquidity, places both orders almost simultaneously and skips what isn't worth the risk. It's less like trading and more like catching tiny accounting mistakes before everyone else notices them. Funny enoughh, the hardest part was not writing arb logic. It was making the execution reliable enough that free money actually stayed free. This completely changed how I think about trading. What's the point of it if you can just fill mispriced BTC markets?? Automatically. The biggest edge is getting AI to execute simple ideas faster and more consistently than any human ever could. Shared the exact build in my last article, leaving it below. Good luck!

I asked Claude Fable 5 (Extra High) to build an arb bot for Polymarket. One rule: trade only when YES + NO in 2 hours it almost doubled it (+$96.31) > in 5 hours it showed +$579 PnL > current balance: +$3,799.73 Cost: 10M tokens. Here's how it works and why: Every BTC Up/Down market on Polymarket has exactly two outcomes. YES and NO. When the market resolves, one pays $1. The other pays $0. That means owning BOTH sides should always cost exactly $1. But markets aren't perfect. Sometimes YES trades at $0.48 while NO trades at $0.49. Together that's only $0.97 so the bot instantly buys both. A position worth $1... for just $0.97. And Claude knows it, this is the simplest arb that exists. You can paste this to it and use this logic in your prompt. This $0.03 difference is locked in regardless of whether Bitcoin pumps, dumps, or goes sideways. No prediction required. And difficult part isn't finding the opportunity, it's execution. These pricing gaps usually disappear in seconds. If one order fills but the other doesn't, the trade can become a loss. So my bot constantly scans every BTC market, checks fees, validates liquidity, places both orders almost simultaneously and skips what isn't worth the risk. It's less like trading and more like catching tiny accounting mistakes before everyone else notices them. Funny enoughh, the hardest part was not writing arb logic. It was making the execution reliable enough that free money actually stayed free. This completely changed how I think about trading. What's the point of it if you can just fill mispriced BTC markets?? Automatically. The biggest edge is getting AI to execute simple ideas faster and more consistently than any human ever could. Shared the exact build in my last article, leaving it below. Good luck!

Oracle Boar

152,917 Aufrufe • vor 26 Tagen

a researcher at a systematic fund showed me a 6x6 matrix on a whiteboard said: > this is our whole system. we don't forecast price. we classify which cell the market occupies and where that cell historically transitions i stared at it for days before it made sense. then everything changed never interpreted a chart the same way again the matrix is called a Markov Chain transition table. the theory dates back to 1913, it's in every introductory statistics textbook ever written and systematic funds rely on it because it poses a fundamentally different question than what retail traders ever think to ask retail: is this going higher or lower systematic: what regime is this market in, and where does this regime historically resolve every market sits in one of roughly 4-6 regimes at any point in time narrow consolidation, expanding volatility, trending with acceleration, post-reversal drift, pre-expansion squeeze not arbitrary categories - clusters you extract from real data using volatility, volume, and trend strength layered together once you define the regimes, you construct the table: P(regime 3 → regime 5) = 71% P(regime 1 → regime 3) = 64% P(regime 2 → regime 4) = 69% each cell is a historical frequency. now when the market sits in regime 3, you're not speculating you're trading on 71% historical resolution. you scale it with Kelly. you execute when the math confirms, not when it feels convincing i constructed this on ETH using 3 years of 1-hour data. isolated 6 regimes one i named "volatility squeeze below 15-day average for 8+ consecutive bars" transitioned to a directional breakout exceeding 2.2 ATR in 74% of cases average reward/risk on those setups: 4.8 that's not forecasting. that's reading a probability matrix the market populates for you every single session the part that should concern you: the data to construct this is free. the methodology is in any quantitative finance textbook python to build it is maybe 180 lines what Citadel has that you don't isn't classified data or secret algorithms it's this methodology applied to tick-level data with more granular regime definitions you're not lacking information you're framing the wrong question every single time you open a chart

a researcher at a systematic fund showed me a 6x6 matrix on a whiteboard said: > this is our whole system. we don't forecast price. we classify which cell the market occupies and where that cell historically transitions i stared at it for days before it made sense. then everything changed never interpreted a chart the same way again the matrix is called a Markov Chain transition table. the theory dates back to 1913, it's in every introductory statistics textbook ever written and systematic funds rely on it because it poses a fundamentally different question than what retail traders ever think to ask retail: is this going higher or lower systematic: what regime is this market in, and where does this regime historically resolve every market sits in one of roughly 4-6 regimes at any point in time narrow consolidation, expanding volatility, trending with acceleration, post-reversal drift, pre-expansion squeeze not arbitrary categories - clusters you extract from real data using volatility, volume, and trend strength layered together once you define the regimes, you construct the table: P(regime 3 → regime 5) = 71% P(regime 1 → regime 3) = 64% P(regime 2 → regime 4) = 69% each cell is a historical frequency. now when the market sits in regime 3, you're not speculating you're trading on 71% historical resolution. you scale it with Kelly. you execute when the math confirms, not when it feels convincing i constructed this on ETH using 3 years of 1-hour data. isolated 6 regimes one i named "volatility squeeze below 15-day average for 8+ consecutive bars" transitioned to a directional breakout exceeding 2.2 ATR in 74% of cases average reward/risk on those setups: 4.8 that's not forecasting. that's reading a probability matrix the market populates for you every single session the part that should concern you: the data to construct this is free. the methodology is in any quantitative finance textbook python to build it is maybe 180 lines what Citadel has that you don't isn't classified data or secret algorithms it's this methodology applied to tick-level data with more granular regime definitions you're not lacking information you're framing the wrong question every single time you open a chart

Hrundel75 🐷

12,088 Aufrufe • vor 16 Tagen

🚨 BREAKING… the top performing 5m & 15m Polymarket Claude setup is now fully open-source Sounds insane? 100%. Unreal? NOT at all. A modest wallet deployed a fully automated system that scaled up to roughly ~$1.8M in profit No affiliation with the Polymarket team Just a developer operating a bot directly connected to Polymarket Profile → Copytrade → Everything is fully automated His FULL strategy: 1. 5 & 15-minute BTC & ETH latency arbitrage The bot trades ultra-short Bitcoin and Ethereum markets with 5 & 15-minute expirations - and similar logic applies to fast 5m markets often associated with claude-style execution. When BTC moves on Binance, Polymarket pricing reacts slower. For around 30 seconds, odds reflect stale data. The system enters during that gap, when YES + NO combined is below $1, waits for repricing, and exits the moment the market corrects. No predictions, no bias - just harvesting mispriced odds 2. Automation over reaction When volatility spikes, humans pause. The system doesn’t. It triggers instantly when the window opens. No emotion, no hesitation, no missed fills. By the time manual traders click, the inefficiency has already disappeared 3. Scale through repetition Each trade earns small spreads, not headline wins. But automation allows continuous execution at scale, every 15 minutes - and on faster 5m rotations running 24/7 without burnout Scale is the edge 23,457 trades placed - irrelevant on their own. Together, they compounded into ~$1.8M in profit, with a largest single gain of $41,2K and an equity curve that trends almost vertically

Shelpid.WI3M

31,069 Aufrufe • vor 5 Monaten

Qullamaggie's opening range highs / opening range break concept was dervied from fund manager Toby Crabel. Qullamaggie talked about him in a stream once - "I got opening range highs concept from this fund manager who made a billion dollars just trading opening range highs. Toby Crabel I think was his name." Pradeep Bonde / Stockbee, Qullamaggie's mentor also has multiple tweets and threads on Crabel. Crabel's book "Day Trading With Short Term Price Patterns and Opening Range Breakout" sells for multiple $100s, sometimes thousands online in used and new conditions. One of the most expensive trading books out there for good reason. Here are a few great takeaways from his work: "A cumulative total of Gross Profits for the contraction patterns vs. expansion patterns on trades in the direction of the move off the open showed $710,000 for contractions on 7,313 trades and $102,000 for expansions on 7,524 trades. Profits were 7x larger for ORB trades after contractions than expansions." - Crabel "In general the earlier in the session the entry is taken the better the chance for success. In fact, the ideal is an entry within the first ten minutes of the session. In that case an immediate continuation in the direction of the breakout is likely." - Crabel "The Contraction/Expansion Principle states that the market is constantly changing from a period of movement to a period of rest and back to a period of movement. This interchange between the phases of motion and rest is constantly taking place.” - Crabel In summary, data backs that breakouts and range expansions follow periods of low volatility. The earlier we can take our trades the greater the probabilities, the lower the risk and thus greater the profits. And the market continuously operates on the backbone of this compression into expansion principle. Allowing us as traders to net profits by trading out of tight patterns, preferably on stocks with a reason to go up - whether it be fundamentals, a theme or story, or a news catalyst. And utilizing opening range highs on gap ups for example to have tight stops and big winners that negate losers and leave us with net R (profits).

Qullamaggie's opening range highs / opening range break concept was dervied from fund manager Toby Crabel. Qullamaggie talked about him in a stream once - "I got opening range highs concept from this fund manager who made a billion dollars just trading opening range highs. Toby Crabel I think was his name." Pradeep Bonde / Stockbee, Qullamaggie's mentor also has multiple tweets and threads on Crabel. Crabel's book "Day Trading With Short Term Price Patterns and Opening Range Breakout" sells for multiple $100s, sometimes thousands online in used and new conditions. One of the most expensive trading books out there for good reason. Here are a few great takeaways from his work: "A cumulative total of Gross Profits for the contraction patterns vs. expansion patterns on trades in the direction of the move off the open showed $710,000 for contractions on 7,313 trades and $102,000 for expansions on 7,524 trades. Profits were 7x larger for ORB trades after contractions than expansions." - Crabel "In general the earlier in the session the entry is taken the better the chance for success. In fact, the ideal is an entry within the first ten minutes of the session. In that case an immediate continuation in the direction of the breakout is likely." - Crabel "The Contraction/Expansion Principle states that the market is constantly changing from a period of movement to a period of rest and back to a period of movement. This interchange between the phases of motion and rest is constantly taking place.” - Crabel In summary, data backs that breakouts and range expansions follow periods of low volatility. The earlier we can take our trades the greater the probabilities, the lower the risk and thus greater the profits. And the market continuously operates on the backbone of this compression into expansion principle. Allowing us as traders to net profits by trading out of tight patterns, preferably on stocks with a reason to go up - whether it be fundamentals, a theme or story, or a news catalyst. And utilizing opening range highs on gap ups for example to have tight stops and big winners that negate losers and leave us with net R (profits).

Lone

54,191 Aufrufe • vor 7 Monaten

I’m shifting my focus toward using Claude for research-driven trading insights and automation workflows. Recently, I explored a workflow where Claude was used to: “Identify potentially mispriced prediction markets on Polymarket and detect wallets that consistently execute similar strategies for potential copy-trading analysis.” In a short test period, this approach reportedly identified rapid gains in certain tracked examples, including high-performing wallets with extensive trade histories and moderate win rates. One example I reviewed showed: * Significant long-term growth (from small capital to substantially larger returns) * ~1,900+ trades executed * Around a 55% win rate What stood out most is that there are sophisticated arbitrage and automation systems operating at scale—many of which are difficult to compete with without technical experience. Instead of trying to outbuild them immediately, the approach here is to observe, analyze, and learn from these systems, then selectively follow or mirror strategies where appropriate. Using an AI-assisted setup, I built a monitoring workflow that: * Tracks and ranks active wallets * Flags consistent high-performing behavior * Helps surface potential copy-trading candidates The process is intentionally simple: * Select a trader * Enable priority monitoring mode for faster updates * Start tracking or mirroring activity through the system For those interested in building similar systems, I can share a step-by-step breakdown of how to set up a basic version from scratch. I’ve already helped a number of people explore structured approaches to trading automation and workflow design. The goal is not unrealistic returns, but more consistent and informed decision-making. If you want the guide: 1. Comment “ SEND ” 2. Like and repost this 3. Follow Frances Marsha (so I can reach out to you)

I’m shifting my focus toward using Claude for research-driven trading insights and automation workflows. Recently, I explored a workflow where Claude was used to: “Identify potentially mispriced prediction markets on Polymarket and detect wallets that consistently execute similar strategies for potential copy-trading analysis.” In a short test period, this approach reportedly identified rapid gains in certain tracked examples, including high-performing wallets with extensive trade histories and moderate win rates. One example I reviewed showed: * Significant long-term growth (from small capital to substantially larger returns) * ~1,900+ trades executed * Around a 55% win rate What stood out most is that there are sophisticated arbitrage and automation systems operating at scale—many of which are difficult to compete with without technical experience. Instead of trying to outbuild them immediately, the approach here is to observe, analyze, and learn from these systems, then selectively follow or mirror strategies where appropriate. Using an AI-assisted setup, I built a monitoring workflow that: * Tracks and ranks active wallets * Flags consistent high-performing behavior * Helps surface potential copy-trading candidates The process is intentionally simple: * Select a trader * Enable priority monitoring mode for faster updates * Start tracking or mirroring activity through the system For those interested in building similar systems, I can share a step-by-step breakdown of how to set up a basic version from scratch. I’ve already helped a number of people explore structured approaches to trading automation and workflow design. The goal is not unrealistic returns, but more consistent and informed decision-making. If you want the guide: 1. Comment “ SEND ” 2. Like and repost this 3. Follow Frances Marsha (so I can reach out to you)

Frances Marsha

61,620 Aufrufe • vor 3 Monaten

I tested Claude Code on a fresh account - 1,500 lines of HTML cost me 50% of my window. Full video and summary is here.. I just ran a recorded test on Claude Code with a fresh account (Pro, not Max - my main account was 20x Max) , and the result is honestly insane. The task was trivial: create 3 simple demo HTML pages, around 500 lines each. Roughly 1,500 lines of code total. Nothing massive. Nothing enterprise-grade. Nothing that should meaningfully stress a premium coding product. And yet Claude Code burned through 40% of my 5-hour window almost immediately. I ran the exact same test with Codex, and it consumed only 2%. Then it got even worse: after the session ended, I did absolutely nothing for 15 minutes, and Claude still ate another 10%. Total: 50% of the 5-hour window gone for a tiny HTML demo. My weekly usage had already started at 2% before I even really used it, and after this tiny test it jumped to 8%. Now let us be generous and assume this entire run used around 30k tokens total. If 30k tokens represents 10% of weekly usage, that implies around 300k tokens per week. That is roughly 1.2M-1.3M tokens per month, and even if you round up aggressively, you are still in the 1.5M token range. Using the Sonnet 4.6 pricing you list: $3 per 1M input tokens $15 per 1M output tokens How exactly is this supposed to make sense for a paid coding product? Because from the user side, this no longer looks like "premium usage protection." It looks like a quota system that is either wildly inefficient, badly broken, or being accounted in a way users are not being told about. And that is before I even get to my main account: my $200 Max plan now dies in a single day. Just a few months ago, similar or heavier usage would last me about a week. So no, I do not buy the "maybe you just used it more" excuse anymore. Something is clearly broken in Claude Code. Either token accounting is broken, context handling is broken, background consumption is broken, or all three. Alex Albert is this really the experience you want users to pay for? Just watch the video. I tried to be very transparent and clear for your team! I was fan of Claude but just disappointed! And if you want, send me the detailed token accounting for this session and let us inspect it together publicly. Because from where I am standing, this is no longer a small pricing annoyance. It looks like something seriously wrong is happening, and users deserve a real explanation.

I tested Claude Code on a fresh account - 1,500 lines of HTML cost me 50% of my window. Full video and summary is here.. I just ran a recorded test on Claude Code with a fresh account (Pro, not Max - my main account was 20x Max) , and the result is honestly insane. The task was trivial: create 3 simple demo HTML pages, around 500 lines each. Roughly 1,500 lines of code total. Nothing massive. Nothing enterprise-grade. Nothing that should meaningfully stress a premium coding product. And yet Claude Code burned through 40% of my 5-hour window almost immediately. I ran the exact same test with Codex, and it consumed only 2%. Then it got even worse: after the session ended, I did absolutely nothing for 15 minutes, and Claude still ate another 10%. Total: 50% of the 5-hour window gone for a tiny HTML demo. My weekly usage had already started at 2% before I even really used it, and after this tiny test it jumped to 8%. Now let us be generous and assume this entire run used around 30k tokens total. If 30k tokens represents 10% of weekly usage, that implies around 300k tokens per week. That is roughly 1.2M-1.3M tokens per month, and even if you round up aggressively, you are still in the 1.5M token range. Using the Sonnet 4.6 pricing you list: $3 per 1M input tokens $15 per 1M output tokens How exactly is this supposed to make sense for a paid coding product? Because from the user side, this no longer looks like "premium usage protection." It looks like a quota system that is either wildly inefficient, badly broken, or being accounted in a way users are not being told about. And that is before I even get to my main account: my $200 Max plan now dies in a single day. Just a few months ago, similar or heavier usage would last me about a week. So no, I do not buy the "maybe you just used it more" excuse anymore. Something is clearly broken in Claude Code. Either token accounting is broken, context handling is broken, background consumption is broken, or all three. Alex Albert is this really the experience you want users to pay for? Just watch the video. I tried to be very transparent and clear for your team! I was fan of Claude but just disappointed! And if you want, send me the detailed token accounting for this session and let us inspect it together publicly. Because from where I am standing, this is no longer a small pricing annoyance. It looks like something seriously wrong is happening, and users deserve a real explanation.

Hayrettin Tüzel

26,854 Aufrufe • vor 4 Monaten

a $40/month server beat a room full of analysts to the same trade by five and a half hours market opens at 9:30. his position was already in at 4am the system is a neural net trained on 11 years of tick data. it flagged the setup before the candle that "confirmed" it had even started forming this is the part retail misunderstands about ML in markets it isn't prediction in the mystical sense. it's pattern classification at a speed and scale human eyes physically cannot match the mechanics: 847,000 labeled historical setups as training data 4,200 data points per second ingested live each new state scored against every pattern the net has ever seen, in milliseconds the model isn't asking "where is price going" it's asking "how closely does the current microstructure match the conditions that preceded a move in my training set" that's a classification problem, and classification is what neural nets do better than anything else output: 3-4 candidate trades a day. he takes the top 2 by confidence score last 90 days: 71% win rate at 2.3 average risk-reward the edge isn't the architecture. the architecture is public pytorch is free, the papers are on arxiv, the network is a few hundred lines the edge is the labeling. what you feed it and how you tag the setups is the entire game retail feeds a model price and time and gets noise a desk feeds it order flow, volatility state, cross-asset context, each example hand-labeled by outcome same network. different training data. that's the whole difference retail watches the news at the open and reacts this system scored every pattern before sunrise and already decided you're not losing because your analysis is wrong you're losing to something that doesn't sleep, doesn't panic, and doesn't second-guess a probability it already computed the dataset was free. the framework was free. the compute was $40 a month the edge was never behind a paywall. it was sitting in a format almost nobody bothered to train on full breakdown in the article below

a $40/month server beat a room full of analysts to the same trade by five and a half hours market opens at 9:30. his position was already in at 4am the system is a neural net trained on 11 years of tick data. it flagged the setup before the candle that "confirmed" it had even started forming this is the part retail misunderstands about ML in markets it isn't prediction in the mystical sense. it's pattern classification at a speed and scale human eyes physically cannot match the mechanics: 847,000 labeled historical setups as training data 4,200 data points per second ingested live each new state scored against every pattern the net has ever seen, in milliseconds the model isn't asking "where is price going" it's asking "how closely does the current microstructure match the conditions that preceded a move in my training set" that's a classification problem, and classification is what neural nets do better than anything else output: 3-4 candidate trades a day. he takes the top 2 by confidence score last 90 days: 71% win rate at 2.3 average risk-reward the edge isn't the architecture. the architecture is public pytorch is free, the papers are on arxiv, the network is a few hundred lines the edge is the labeling. what you feed it and how you tag the setups is the entire game retail feeds a model price and time and gets noise a desk feeds it order flow, volatility state, cross-asset context, each example hand-labeled by outcome same network. different training data. that's the whole difference retail watches the news at the open and reacts this system scored every pattern before sunrise and already decided you're not losing because your analysis is wrong you're losing to something that doesn't sleep, doesn't panic, and doesn't second-guess a probability it already computed the dataset was free. the framework was free. the compute was $40 a month the edge was never behind a paywall. it was sitting in a format almost nobody bothered to train on full breakdown in the article below

delost

20,790 Aufrufe • vor 15 Tagen

An Anthropic safety researcher closed her laptop when she saw my screen at Philz Coffee I was running my Polymarket bot from the corner table. She was in line. Looked over my shoulder. Stopped moving. "That's not a normal trading app. What model is that running on" I told her. Claude Code. Four repos. $25 a month. She sat down without asking. "I work on the alignment team. We test Claude for exactly this kind of autonomous behavior. You're letting it find its own trading signals" Not just signals. Wallets. 86 million trades. Every wallet. Every entry. Every exit. "You're feeding Claude raw wallet data and letting it identify which traders consistently win. Then cloning their behavior" She said it slowly. Like she was writing an internal report in her head. Claude Code finds the top wallets. Reverse-engineers their timing. Copies their entries. Then exits before they do. "Before they do?" My bot cuts at 85% of expected move or on a 3x volume spike. Top wallets exit before resolution 91% of the time. They capture 86% of the move. Losers hold to 58%. She put her coffee down. "How did you get Claude to learn exit timing on its own" I showed her the second repo. Three commands. 500+ markets. No API key. Claude scores them in 20 minutes. "We have 14 people stress-testing Claude's autonomous capabilities. You're just using them" My setup: Claude API - $20/mo VPS - $5/mo poly_data - free polymarket-cli - free 19 days. 4 agents. 74% win rate. Copytrade here: She stared at the screen for a long time. "This is literally what our red team simulates. Except you actually deployed it" She emailed me two days later. "Our policy team found your post. Please take it down" Too late.

An Anthropic safety researcher closed her laptop when she saw my screen at Philz Coffee I was running my Polymarket bot from the corner table. She was in line. Looked over my shoulder. Stopped moving. "That's not a normal trading app. What model is that running on" I told her. Claude Code. Four repos. $25 a month. She sat down without asking. "I work on the alignment team. We test Claude for exactly this kind of autonomous behavior. You're letting it find its own trading signals" Not just signals. Wallets. 86 million trades. Every wallet. Every entry. Every exit. "You're feeding Claude raw wallet data and letting it identify which traders consistently win. Then cloning their behavior" She said it slowly. Like she was writing an internal report in her head. Claude Code finds the top wallets. Reverse-engineers their timing. Copies their entries. Then exits before they do. "Before they do?" My bot cuts at 85% of expected move or on a 3x volume spike. Top wallets exit before resolution 91% of the time. They capture 86% of the move. Losers hold to 58%. She put her coffee down. "How did you get Claude to learn exit timing on its own" I showed her the second repo. Three commands. 500+ markets. No API key. Claude scores them in 20 minutes. "We have 14 people stress-testing Claude's autonomous capabilities. You're just using them" My setup: Claude API - $20/mo VPS - $5/mo poly_data - free polymarket-cli - free 19 days. 4 agents. 74% win rate. Copytrade here: She stared at the screen for a long time. "This is literally what our red team simulates. Except you actually deployed it" She emailed me two days later. "Our policy team found your post. Please take it down" Too late.

Lunar

231,073 Aufrufe • vor 3 Monaten

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?

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

149,335 Aufrufe • vor 4 Monaten