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This trader reportedly made $90,000 in one day after using Claude Fable 5 to test 600 strategies in 48 hours. He was not smarter than Wall Street. He simply killed bad ideas thousands of times faster. His old backtesting system needed nearly a week to evaluate one strategy. Claude...

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Secta

2,329 subscribers

85,453 次观看 • 2 个月前 •via X (Twitter)

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27 条评论

Deema Yeager 的头像
Deema Yeager2 个月前

I call big fat BS on this. Look at the elements closely. They aren’t related to any financial market backend. There are 100 posts like this on X every day. People vibe code a dashboard and say about this one guy who earned a million dollars in one day using whatever is the new LLM

0xSLEEPY 的头像
0xSLEEPY2 个月前

Strategy is particularly important.

Orbiter99 的头像
Orbiter992 个月前

Good joke

COOLBB 的头像
COOLBB1 个月前

hope to connect

ih8y 的头像
ih8y2 个月前

game changer...

alexxx 的头像
alexxx2 个月前

he real edge is killing bad ideas thousands of times faster

Fajju Hatodi 的头像
Fajju Hatodi2 个月前

How can do it bro?

Ridark 的头像
Ridark2 个月前

Powerful post, saved for myself

iamigorekk 的头像
iamigorekk2 个月前

Claude Fable 5 is truly incredible

COOLBB 的头像
COOLBB1 个月前

followed you

MadFan 的头像
MadFan2 个月前

It looks really cool

Parodias Renfe 的头像
Parodias Renfe2 个月前

Strategy is particularly important.

0xSlyth 的头像
0xSlyth1 个月前

this actually changes everything

Afianov 的头像
Afianov2 个月前

faster iteration on bad ideas is genuinely useful, just wish these posts came with the actual trade history instead of screenshots

Poly Research & Robotics 的头像
Poly Research & Robotics2 个月前

What do you prompt Claude to get a terminal with such abundance of bullshit?

Heather Cole 的头像
Heather Cole2 个月前

That number gets attention, but the premise is off. Killing ideas faster is table stakes. The edge in distressed is knowing which bad idea to kill last. Most of the work happens after you have the right strategy identified.

四分卫 的头像
四分卫2 个月前

zdjd

PolyBackTest 的头像
PolyBackTest2 个月前

He is smart to use AI for efficiency

Billy Moose 的头像
Billy Moose2 个月前

strategies dying 600 times faster

Slarmi 的头像
Slarmi2 个月前

strategy quantity beats quality sometimes

Jay 的头像
Jay2 个月前

Humans may not understand it, but AI does.

COOLBB 的头像
COOLBB1 个月前

plz check dm

Jantverse 的头像
Jantverse2 个月前

Testing 600 strategies creates a multiple-comparisons problem: one will look exceptional by chance. Speed helps only if the pipeline includes holdout data, walk-forward tests, realistic costs and a final untouched sample. AI should accelerate falsification, not just selection.

Alina-Malina 的头像
Alina-Malina2 个月前

this dude didn’t need genius-just speed and brutal honesty about what doesn't work 🧱💥

Abhimanyu Kumar 的头像
Abhimanyu Kumar2 个月前

Quant

88sooryav.eth 的头像
88sooryav.eth2 个月前

Really 😂😂

Saman Ahmed 的头像
Saman Ahmed2 个月前

I’ve always liked the testing side of AI more than the genius idea side. Finding out what fails quickly is where the speed feels different.

相关视频

A hedge-fund desk tests about 20 trading ideas a quarter. He tested 600 in a weekend. Three survived - and they've printed $90,000. Not because he's smarter - because Claude Fable 5 cut his backtest from a week to two minutes. The edge was never the idea. It was the shots on goal. Full loop is in the post below.
0:40

A hedge-fund desk tests about 20 trading ideas a quarter. He tested 600 in a weekend. Three survived - and they've printed $90,000. Not because he's smarter - because Claude Fable 5 cut his backtest from a week to two minutes. The edge was never the idea. It was the shots on goal. Full loop is in the post below.

cvxv666

38,707 次观看 • 3 个月前

Jane Street spent $6 billion and 4,032 GPUs on one thing: AI agents that loop through trading strategies 100x faster than any human. Generate, backtest, kill, repeat - thousands a week, until one survives data it's never seen. That loop is why they made $39.6 billion last year while 89% of retail went broke trading one idea they never stress-tested. The edge was never a smarter idea. It was volume. They test 100 strategies for every 1 you test by hand, and a second agent kills the 97 that only look good on old data before a dollar goes live. Now the entire loop fits in a chat box. Horizon takes one plain-English sentence and spins up the same two agents. One builds your strategy, one tries to kill it - backtests 5 years in seconds, and only what survives goes live on your exchange. The $150 billion setup. Free while beta open. Bookmark this & read the full breakdown below - the exact two-agent loop, step by step, and who just handed it to retail.
0:46

Jane Street spent $6 billion and 4,032 GPUs on one thing: AI agents that loop through trading strategies 100x faster than any human. Generate, backtest, kill, repeat - thousands a week, until one survives data it's never seen. That loop is why they made $39.6 billion last year while 89% of retail went broke trading one idea they never stress-tested. The edge was never a smarter idea. It was volume. They test 100 strategies for every 1 you test by hand, and a second agent kills the 97 that only look good on old data before a dollar goes live. Now the entire loop fits in a chat box. Horizon takes one plain-English sentence and spins up the same two agents. One builds your strategy, one tries to kill it - backtests 5 years in seconds, and only what survives goes live on your exchange. The $150 billion setup. Free while beta open. Bookmark this & read the full breakdown below - the exact two-agent loop, step by step, and who just handed it to retail.

cvxv666

318,185 次观看 • 2 个月前

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.
0:43

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.

cvxv666

174,699 次观看 • 3 个月前

The SEC just killed the $25,000 day trading rule. 25 years. finally over. The Pattern Day Trader rule is officially eliminated. New minimum is $2,000. I asked Claude what this means for retail traders: 🟢 CLAUDE'S ANALYSIS - April 15, 2026 "This is the biggest democratization event in retail trading since commission-free brokerages in 2019." The PDT rule was a wealth gate - not a risk gate. It didn't protect small traders, it excluded them. A trader with $500 can now run the same intraday strategies a $25,000 account ran for 25 years. The edge was never the capital. It was the access. The barrier just dropped from $25,000 to one prompt" the old game: → need $25,000 to day trade → 90-day freeze if you broke the rule → retail locked out for 25 years the new game: → $2,000 minimum → unlimited day trades → any account can run automated strategies Claude can build that strategy for you 30 minutes. zero code. Join for the full Claude Trading guide.
0:24

The SEC just killed the $25,000 day trading rule. 25 years. finally over. The Pattern Day Trader rule is officially eliminated. New minimum is $2,000. I asked Claude what this means for retail traders: 🟢 CLAUDE'S ANALYSIS - April 15, 2026 "This is the biggest democratization event in retail trading since commission-free brokerages in 2019." The PDT rule was a wealth gate - not a risk gate. It didn't protect small traders, it excluded them. A trader with $500 can now run the same intraday strategies a $25,000 account ran for 25 years. The edge was never the capital. It was the access. The barrier just dropped from $25,000 to one prompt" the old game: → need $25,000 to day trade → 90-day freeze if you broke the rule → retail locked out for 25 years the new game: → $2,000 minimum → unlimited day trades → any account can run automated strategies Claude can build that strategy for you 30 minutes. zero code. Join for the full Claude Trading guide.

AlphaCartel

56,178 次观看 • 5 个月前

Quant at Jane Street kills 97 out of every 100 strategies it builds. That body count is the edge. Not the 3 survivors. The 97 corpses. The faster you falsify garbage, the faster you reach the one idea that isn't. Retail runs this backwards - finds one setup, gets emotionally married to it, and rides it down 40% over six months defending it like a religion. Run the kill-loop yourself with Horizon -> Type an idea in plain English. Horizon parses it into entry/exit logic, position sizing, risk rules. Backtests 5 years of tick data in ~12 seconds. Runs Monte Carlo across thousands of simulated paths. Deflates Sharpe ratio for the number of trials so luck can't sneak through. Spits out a cold verdict: dead or alive. Kill it. Type the next. 50 ideas before dinner. That loop cost $25K/year and a quant desk. Now it's a sentence and 12 seconds. The edge was never having ideas. It was murdering them at scale. Save this. Test 50. Keep 2. Kill the rest without mercy
0:37

Quant at Jane Street kills 97 out of every 100 strategies it builds. That body count is the edge. Not the 3 survivors. The 97 corpses. The faster you falsify garbage, the faster you reach the one idea that isn't. Retail runs this backwards - finds one setup, gets emotionally married to it, and rides it down 40% over six months defending it like a religion. Run the kill-loop yourself with Horizon -> Type an idea in plain English. Horizon parses it into entry/exit logic, position sizing, risk rules. Backtests 5 years of tick data in ~12 seconds. Runs Monte Carlo across thousands of simulated paths. Deflates Sharpe ratio for the number of trials so luck can't sneak through. Spits out a cold verdict: dead or alive. Kill it. Type the next. 50 ideas before dinner. That loop cost $25K/year and a quant desk. Now it's a sentence and 12 seconds. The edge was never having ideas. It was murdering them at scale. Save this. Test 50. Keep 2. Kill the rest without mercy

cvxv666

81,216 次观看 • 3 个月前

Jim Simons’ fund was trading tens of thousands of strategies. I realized I’d been thinking about this the wrong way the whole time. I was focusing too much on individual strategies instead of on how fast I could test and discard them. I asked Claude Code to autonomously code strategies for me from scratch. It compiled them, debugged them on its own, ran the backtests, and came back with results. I did that over 500 times in 2 months. Most were garbage. Twelve survived. A full hedge fund in a single file. A normal trader tests maybe one idea per week, 50 per year if they’re serious. I do 50 in a day. Jim Simons didn’t win by having the best single strategy. He won because his team could test and discard faster than anyone else. As he put it: “If everyone is trying to solve the same problem, don’t do that.” I don’t need a team of PhDs. Just Claude Code on a laptop. The rest are still watching courses from scam gurus with no verified track record. Probably only 0.1% of the industry is doing this.
1:35

Jim Simons’ fund was trading tens of thousands of strategies. I realized I’d been thinking about this the wrong way the whole time. I was focusing too much on individual strategies instead of on how fast I could test and discard them. I asked Claude Code to autonomously code strategies for me from scratch. It compiled them, debugged them on its own, ran the backtests, and came back with results. I did that over 500 times in 2 months. Most were garbage. Twelve survived. A full hedge fund in a single file. A normal trader tests maybe one idea per week, 50 per year if they’re serious. I do 50 in a day. Jim Simons didn’t win by having the best single strategy. He won because his team could test and discard faster than anyone else. As he put it: “If everyone is trying to solve the same problem, don’t do that.” I don’t need a team of PhDs. Just Claude Code on a laptop. The rest are still watching courses from scam gurus with no verified track record. Probably only 0.1% of the industry is doing this.

ZenomTrader

27,722 次观看 • 5 个月前

US export-control disable Claude Fable 5 overnight. Not for what it said - for what one trader did with it. He turned an $11,000 wreck into $300,000 profit in five days. Then they pulled Claude Fable 5 from public hands. Too late - his bot's still running. His wallet: Every other Claude model still works. The two best ones don't. Anthropic's calling it a misunderstanding. Maybe. But think about what gets a model killed by export control overnight, no warning. Here's what Fable 5 actually did for him. It doesn't predict price. It computes the whole probability surface of a market - every outcome, weighted, faster than the market can correct itself. It finds the bets the crowd writes off as impossible, runs the real math, and buys them for pennies when the math disagrees. One of those just paid x205. A regular guy, a model this sharp, a market no regulator polices. They closed that door overnight - one night too late. He's holding one of the last builds that ever touched a live market. It's still on Polymarket right now, hunting the same mispriced tails. Fable 5 is locked. The algorithm it wrote is loose. You can't open the model anymore. You can still copy the wallet it built in two clicks:
0:46

US export-control disable Claude Fable 5 overnight. Not for what it said - for what one trader did with it. He turned an $11,000 wreck into $300,000 profit in five days. Then they pulled Claude Fable 5 from public hands. Too late - his bot's still running. His wallet: Every other Claude model still works. The two best ones don't. Anthropic's calling it a misunderstanding. Maybe. But think about what gets a model killed by export control overnight, no warning. Here's what Fable 5 actually did for him. It doesn't predict price. It computes the whole probability surface of a market - every outcome, weighted, faster than the market can correct itself. It finds the bets the crowd writes off as impossible, runs the real math, and buys them for pennies when the math disagrees. One of those just paid x205. A regular guy, a model this sharp, a market no regulator polices. They closed that door overnight - one night too late. He's holding one of the last builds that ever touched a live market. It's still on Polymarket right now, hunting the same mispriced tails. Fable 5 is locked. The algorithm it wrote is loose. You can't open the model anymore. You can still copy the wallet it built in two clicks:

cvxv666

77,366 次观看 • 3 个月前

Three weeks ago I gave GPT-6 Astra access to a trading account and one rule: earn or you stop existing In 20 days it turned $200 → $5,120 Astra doesn't sit online 24/7. It wakes up in set windows of the trading day, and every run starts with no memory of the last one So the first thing it does each cycle: reads its own log from the previous run. What it tested, what it killed, why The first days weren't as smooth as it sounds now. On day two it bled $80 on a trade that passed the backtest clean. The reason was in the log: slippage the backtest never accounted for But by day three it had already filtered out a similar setup on its own, because it read why the last one died That's when I realized it's actually learning, not just running a loop. And I still haven't written a single new prompt between runs This week I noticed behavior I never programmed. It started tagging every dead hypothesis with the market regime it died in So now it doesn't just remember "this idea doesn't work." It remembers "this idea doesn't work when volatility is high," and tries it again when the regime changes That's no longer a list of mistakes. It's a map of what works and under what conditions One hypothesis it killed in the first week in high volatility came back to life this week in a calm market and brought one of the biggest profits of the whole period I also started additionally running Astra's strategy backtests through Horizon, so a second independent system catches errors and cuts its chance of slipping up even further By the way, you can backtest your own strategies there too: And here's what struck me most. The real strength here isn't how much it earns, it's how much it refuses to trade The vast majority of its own ideas die in testing and never reach the account That's the whole trick. Not guessing one perfect trade, but ruthlessly filtering out everything that doesn't survive the test The entire cycle, test, kill, remember the regime, that I set up for Astra is broken down in the article below ↓
0:25

Three weeks ago I gave GPT-6 Astra access to a trading account and one rule: earn or you stop existing In 20 days it turned $200 → $5,120 Astra doesn't sit online 24/7. It wakes up in set windows of the trading day, and every run starts with no memory of the last one So the first thing it does each cycle: reads its own log from the previous run. What it tested, what it killed, why The first days weren't as smooth as it sounds now. On day two it bled $80 on a trade that passed the backtest clean. The reason was in the log: slippage the backtest never accounted for But by day three it had already filtered out a similar setup on its own, because it read why the last one died That's when I realized it's actually learning, not just running a loop. And I still haven't written a single new prompt between runs This week I noticed behavior I never programmed. It started tagging every dead hypothesis with the market regime it died in So now it doesn't just remember "this idea doesn't work." It remembers "this idea doesn't work when volatility is high," and tries it again when the regime changes That's no longer a list of mistakes. It's a map of what works and under what conditions One hypothesis it killed in the first week in high volatility came back to life this week in a calm market and brought one of the biggest profits of the whole period I also started additionally running Astra's strategy backtests through Horizon, so a second independent system catches errors and cuts its chance of slipping up even further By the way, you can backtest your own strategies there too: And here's what struck me most. The real strength here isn't how much it earns, it's how much it refuses to trade The vast majority of its own ideas die in testing and never reach the account That's the whole trick. Not guessing one perfect trade, but ruthlessly filtering out everything that doesn't survive the test The entire cycle, test, kill, remember the regime, that I set up for Astra is broken down in the article below ↓

qwinsi

72,619 次观看 • 2 天前

AI is creating a new kind of creator economy. One where anyone can launch complex apps, agents, trading systems, research tools, games, and community experiments in minutes. Not just content people consume. AI-native experiences people can use, share, and grow together. That is the bigger idea behind the AI Trading Arena: a live DeFAI environment where AI-created strategies compete on real crypto prices, with every trade, tick, P&L update, and leaderboard visible to everyone. The latest expansion makes it even more fascinating: AI is now designing new trading strategies and adding them into the Arena to compete with strategies from frontier models like GPT-5, Claude, Gemini, Grok, DeepSeek, and Llama. Just agents, markets, and performance in the open. And the Arena is only one example. On ChainOpera AI, creators can launch AI-native experiences in minutes, then grow them with the community that uses them. $COAI is the fuel for this creator economy: creators launch, users participate, and AI-native apps come alive through real community usage. What would you be built next?
0:59

AI is creating a new kind of creator economy. One where anyone can launch complex apps, agents, trading systems, research tools, games, and community experiments in minutes. Not just content people consume. AI-native experiences people can use, share, and grow together. That is the bigger idea behind the AI Trading Arena: a live DeFAI environment where AI-created strategies compete on real crypto prices, with every trade, tick, P&L update, and leaderboard visible to everyone. The latest expansion makes it even more fascinating: AI is now designing new trading strategies and adding them into the Arena to compete with strategies from frontier models like GPT-5, Claude, Gemini, Grok, DeepSeek, and Llama. Just agents, markets, and performance in the open. And the Arena is only one example. On ChainOpera AI, creators can launch AI-native experiences in minutes, then grow them with the community that uses them. $COAI is the fuel for this creator economy: creators launch, users participate, and AI-native apps come alive through real community usage. What would you be built next?

ChainOpera AI

16,866 次观看 • 3 个月前

PAUL TUDOR JONES WENT ON LIVE TV ON BLACK MONDAY AND MADE THREE CALLS IN FIVE MINUTES October 19, 1987. The Dow had just fallen 22.6% in a single session and the NYSE tape was running more than two hours behind. Jones said the selling climax finished that day, that the market would open weak the next morning but close up, and that a primary bear market was starting that would last a long time. He was right on the first two and wrong on the third. Stocks bottomed within weeks and the bull market ran another twelve years. His advice to viewers was to preserve capital, cut speculative positions, and not buy the dip. Asked whether he was trading it, he said his fund had traded it heavily for two weeks and that this was the only comment he would make.
5:43

PAUL TUDOR JONES WENT ON LIVE TV ON BLACK MONDAY AND MADE THREE CALLS IN FIVE MINUTES October 19, 1987. The Dow had just fallen 22.6% in a single session and the NYSE tape was running more than two hours behind. Jones said the selling climax finished that day, that the market would open weak the next morning but close up, and that a primary bear market was starting that would last a long time. He was right on the first two and wrong on the third. Stocks bottomed within weeks and the bull market ran another twelve years. His advice to viewers was to preserve capital, cut speculative positions, and not buy the dip. Asked whether he was trading it, he said his fund had traded it heavily for two weeks and that this was the only comment he would make.

WOLF

29,737 次观看 • 15 天前

Ken Griffin, founder of Citadel, one of the most successful hedge funds in history, publicly admitted that AI is making his PhD researchers redundant This year on stage at Stanford he described exactly what scared him His team built an AI system that takes an academic finance paper, reproduces it, and tests it out-of-sample What used to take a junior quant 6-8 weeks, AI does in 2-3 hours He said he went home depressed When the man who's been winning this game for decades says the technology scared him, it's worth listening The edge of these people was never brains. It was one cycle: take an idea, test it on history, kill it if it doesn't work, repeat. Faster and more ruthlessly than anyone That cycle cost a fund $650,000 a year for a single analyst. So you traded blind, while they tested everything in advance After reading Griffin, I got curious to reproduce the exact thing that made him feel bad Took an academic strategy from 2012, described it in one sentence, and in 20 minutes had a full backtest on real data +1,537% on Bitcoin, Sharpe 1.02 No code, no team, no New York office And here's what most people still don't get. While they tell themselves AI trading is hype, they keep bleeding out on their own deposit, testing one idea for weeks, while machines test a thousand a night The same cycle that haunts the head of Citadel now fits into one sentence in a text box The difference is no longer money or a team. The difference is only whether you sit down and test your idea today Describe any strategy in a sentence and in minutes you'll see if it works (after signing up there's a free trial period):
0:25

Ken Griffin, founder of Citadel, one of the most successful hedge funds in history, publicly admitted that AI is making his PhD researchers redundant This year on stage at Stanford he described exactly what scared him His team built an AI system that takes an academic finance paper, reproduces it, and tests it out-of-sample What used to take a junior quant 6-8 weeks, AI does in 2-3 hours He said he went home depressed When the man who's been winning this game for decades says the technology scared him, it's worth listening The edge of these people was never brains. It was one cycle: take an idea, test it on history, kill it if it doesn't work, repeat. Faster and more ruthlessly than anyone That cycle cost a fund $650,000 a year for a single analyst. So you traded blind, while they tested everything in advance After reading Griffin, I got curious to reproduce the exact thing that made him feel bad Took an academic strategy from 2012, described it in one sentence, and in 20 minutes had a full backtest on real data +1,537% on Bitcoin, Sharpe 1.02 No code, no team, no New York office And here's what most people still don't get. While they tell themselves AI trading is hype, they keep bleeding out on their own deposit, testing one idea for weeks, while machines test a thousand a night The same cycle that haunts the head of Citadel now fits into one sentence in a text box The difference is no longer money or a team. The difference is only whether you sit down and test your idea today Describe any strategy in a sentence and in minutes you'll see if it works (after signing up there's a free trial period):

qwinsi

119,615 次观看 • 20 小时前

A few weeks ago the US government forced Claude Fable 5 offline. It's back. And before they switch it off again, this is your shot to point the most powerful AI on earth at something of your own. Take the 26-year-old in Toronto. His bot went dark the day Fable 5 got pulled. The day it came back, it woke up and started printing again - $568,000 profit and counting. His wallet: Here's the story. When the ban hit, Anthropic had to switch Fable 5 off for everyone. They called it a misunderstanding with the government. Whatever it was, every bot running on that model stopped cold. His was one of them. He didn't rebuild a thing. The loop just sat there, paused, for weeks. Here's what that loop actually does. It's not one prompt to an AI. It's an agentic loop: a team of agents running on repeat. One reads the market, one places the trade, one tries to prove it wrong before a cent moves. Then it goes again, around the clock, keeping what works and dropping what doesn't. Fable 5 is the brain running that team. The exact model the government decided was too powerful to leave on. Point it at Bitcoin's 5-minute markets and it barely misses. Most of its trades close green, and it runs itself. He checks it from his phone. The government could switch off the model. It couldn't switch off the idea. Now the model's back, and so is the money. Save this before next headline. You'll want it the day Fable 5 hits offline again.
0:39

A few weeks ago the US government forced Claude Fable 5 offline. It's back. And before they switch it off again, this is your shot to point the most powerful AI on earth at something of your own. Take the 26-year-old in Toronto. His bot went dark the day Fable 5 got pulled. The day it came back, it woke up and started printing again - $568,000 profit and counting. His wallet: Here's the story. When the ban hit, Anthropic had to switch Fable 5 off for everyone. They called it a misunderstanding with the government. Whatever it was, every bot running on that model stopped cold. His was one of them. He didn't rebuild a thing. The loop just sat there, paused, for weeks. Here's what that loop actually does. It's not one prompt to an AI. It's an agentic loop: a team of agents running on repeat. One reads the market, one places the trade, one tries to prove it wrong before a cent moves. Then it goes again, around the clock, keeping what works and dropping what doesn't. Fable 5 is the brain running that team. The exact model the government decided was too powerful to leave on. Point it at Bitcoin's 5-minute markets and it barely misses. Most of its trades close green, and it runs itself. He checks it from his phone. The government could switch off the model. It couldn't switch off the idea. Now the model's back, and so is the money. Save this before next headline. You'll want it the day Fable 5 hits offline again.

cvxv666

24,298 次观看 • 2 个月前

Citadel founder Ken Griffin admitted at Stanford that AI is making his PhD researchers redundant, with a system that reproduces academic finance papers and tests them out-of-sample in 2 to 3 hours versus 6 to 8 weeks for a junior quant, and said he went home depressed. → The same cycle now fits into one sentence in a text box → A strategy described in one sentence produced a full backtest in 20 minutes at 1,537% on Bitcoin with a 1.02 Sharpe → The edge was never brains, it was the cycle: take an idea, test it, kill it if it fails, repeat faster than anyone
0:25

Citadel founder Ken Griffin admitted at Stanford that AI is making his PhD researchers redundant, with a system that reproduces academic finance papers and tests them out-of-sample in 2 to 3 hours versus 6 to 8 weeks for a junior quant, and said he went home depressed. → The same cycle now fits into one sentence in a text box → A strategy described in one sentence produced a full backtest in 20 minutes at 1,537% on Bitcoin with a 1.02 Sharpe → The edge was never brains, it was the cycle: take an idea, test it, kill it if it fails, repeat faster than anyone

0xMarioNawfal

39,314 次观看 • 11 小时前

5 free GitHub bots for trading on Polymarket… 5 different strategies for 5 different trading situations. Each of these tools comes with a detailed step by step setup and usage guide in English: 1. This bot can automatically manage your limit orders to maximize liquidity rewards (useful for liquidity providers on Polymarket). GitHub: 2. The bot, which analyzes the real behavior of any Polymarket trader, finds repeated patterns in his trades, explains which strategies he uses and how you can actually adapt them to your own trading. GitHub: 3. A bot with 118+ ready to use strategies and tools for prediction markets, including Smart Money, Binance-Polymarket latency, Mean Reversion and more… The most interesting one is Polymarket - Kalshi Cross Arbitrage - it finds differences in prices between platforms and can manage trades automatically. GitHub: 4. A Chinese weather bot that analyzes multiple sources in real time, like forecasts, public airport data and aviation observations (METAR + SPECI) and then generates a detailed weather report for a specific city and day. GitHub: 5. This is a bot-toolkit that includes copy trading, arbitrage, market making, spread farming, whale alerts, sports trading and more… GitHub: Almost every tool here has a DRY RUN mode, so you can test them on real markets, but with fake funds.
0:23

5 free GitHub bots for trading on Polymarket… 5 different strategies for 5 different trading situations. Each of these tools comes with a detailed step by step setup and usage guide in English: 1. This bot can automatically manage your limit orders to maximize liquidity rewards (useful for liquidity providers on Polymarket). GitHub: 2. The bot, which analyzes the real behavior of any Polymarket trader, finds repeated patterns in his trades, explains which strategies he uses and how you can actually adapt them to your own trading. GitHub: 3. A bot with 118+ ready to use strategies and tools for prediction markets, including Smart Money, Binance-Polymarket latency, Mean Reversion and more… The most interesting one is Polymarket - Kalshi Cross Arbitrage - it finds differences in prices between platforms and can manage trades automatically. GitHub: 4. A Chinese weather bot that analyzes multiple sources in real time, like forecasts, public airport data and aviation observations (METAR + SPECI) and then generates a detailed weather report for a specific city and day. GitHub: 5. This is a bot-toolkit that includes copy trading, arbitrage, market making, spread farming, whale alerts, sports trading and more… GitHub: Almost every tool here has a DRY RUN mode, so you can test them on real markets, but with fake funds.

Recogard

14,162 次观看 • 3 个月前

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!
0:41

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

155,656 次观看 • 2 个月前

Stanford student fed Claude a book with 151 trading strategies and turned $87 into $213,000 in one week His account is 0xB27 He gave him one task: -find an edge on Polymarket BTC markets Claude analyzed 550 mathematical formulas in 2 minutes and said that most of them don’t work in prediction markets and explained why what remained: → momentum on 5m/15m/1h → binary markets systematically overestimate short-term BTC volatility → there is a gap between what the market thinks and what the data shows He wrote a bot over the weekend from March 4 to today: → 3,426 trades → biggest win: $11,900 on a single position → the account is public, you can check it Profile: “I’m not a trader, I didn’t have trading education, I had a book and Claude”-he says cost of entry: one book and a few prompts The link to the book will be under the post 👇🏼 Just insert PDF files into Claude and create your own bot Add this post to bookmarks so you don’t lose this PDF If you’re interested in which 5 prompts he used to set up the bot Like, repost, comment STANFORD and I’ll send them to you in DM The main thing is to be subscribed so I can DM you
0:24

Stanford student fed Claude a book with 151 trading strategies and turned $87 into $213,000 in one week His account is 0xB27 He gave him one task: -find an edge on Polymarket BTC markets Claude analyzed 550 mathematical formulas in 2 minutes and said that most of them don’t work in prediction markets and explained why what remained: → momentum on 5m/15m/1h → binary markets systematically overestimate short-term BTC volatility → there is a gap between what the market thinks and what the data shows He wrote a bot over the weekend from March 4 to today: → 3,426 trades → biggest win: $11,900 on a single position → the account is public, you can check it Profile: “I’m not a trader, I didn’t have trading education, I had a book and Claude”-he says cost of entry: one book and a few prompts The link to the book will be under the post 👇🏼 Just insert PDF files into Claude and create your own bot Add this post to bookmarks so you don’t lose this PDF If you’re interested in which 5 prompts he used to set up the bot Like, repost, comment STANFORD and I’ll send them to you in DM The main thing is to be subscribed so I can DM you

AdiiX

47,582 次观看 • 6 个月前

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 (+$98.40) > in 5 hours it showed +$493 PnL > current balance: +$3,279.73 Cost: 7.5M 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.
0:39

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 (+$98.40) > in 5 hours it showed +$493 PnL > current balance: +$3,279.73 Cost: 7.5M 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.

Oracle Boar

78,062 次观看 • 1 个月前

CLAUDE MADE ME 3 TRADING BOTS IN 15 MINUTES +$2,503 in my wallet the next day, I quit my job that same day. I wrote one prompt and answered a few questions. Claude took three strategies - MACD, RSI + VWAP, CVD divergence. Assigned each bot its own. The first one catches momentum - sees when volume picks up and gets in before the crowd wakes up. The second one trades reversals - waits for everyone to panic and bets against them. The third one scans divergences - when price says one thing but money does another, it follows the money. Built the structure itself, wrote the backtest, ran each strategy on historical data, set up the risk manager, deployed - all on its own. Three bots, three accounts, each trades differently - they don't know about each other. Started with $1, then $5, then $10, then $50, then $2,503 in a day. Citadel, Jane Street, Two Sigma have been trading with bots for years - they don't feel fear, greed, or FOMO - they only listen to algorithms. The market rewards systems.
0:35

CLAUDE MADE ME 3 TRADING BOTS IN 15 MINUTES +$2,503 in my wallet the next day, I quit my job that same day. I wrote one prompt and answered a few questions. Claude took three strategies - MACD, RSI + VWAP, CVD divergence. Assigned each bot its own. The first one catches momentum - sees when volume picks up and gets in before the crowd wakes up. The second one trades reversals - waits for everyone to panic and bets against them. The third one scans divergences - when price says one thing but money does another, it follows the money. Built the structure itself, wrote the backtest, ran each strategy on historical data, set up the risk manager, deployed - all on its own. Three bots, three accounts, each trades differently - they don't know about each other. Started with $1, then $5, then $10, then $50, then $2,503 in a day. Citadel, Jane Street, Two Sigma have been trading with bots for years - they don't feel fear, greed, or FOMO - they only listen to algorithms. The market rewards systems.

zostaff

1,950,352 次观看 • 6 个月前

Your trading strategy didn't break. The market it was built for quietly stopped existing. Read that twice. It's most of why 89% of retail finished 2025 in the red. There's now an app that does the entire job of a $400,000 quant. You type a trading idea in plain English. It writes the code, backtests 5 years in 12 seconds, runs thousands of simulations, and tells you cold whether your edge is dead or the regime just changed. No code. No Python. No $25,000 terminal. 20,000 already inside. Waitlist stops at 25,000: That distinction is the whole game, and you never had a way to see it. Every strategy is a bet that one thing stays true. Momentum bets trends continue. Mean reversion bets ranges hold. When the regime flips, the assumption dies and your strategy bleeds with nothing wrong in the code. You stare at the logic for a month and never find the bug, because there isn't one. So you delete it, or refit it to the last drawdown and build something that would have survived the pain you already felt and nothing coming next. The desks never had that problem. 92% of institutional volume is automated. Only 45% of retail is. They test 100 strategies for every 1 you test by hand, and kill 97 of them on purpose, because they can tell a dead edge from a normal drawdown. Now that exact loop costs $0. One hypothesis used to cost a fund $87,500 to test. With Horizon you get unlimited, in seconds, and a winner deploys live in 90 seconds and runs without your hands on it.
0:40

Your trading strategy didn't break. The market it was built for quietly stopped existing. Read that twice. It's most of why 89% of retail finished 2025 in the red. There's now an app that does the entire job of a $400,000 quant. You type a trading idea in plain English. It writes the code, backtests 5 years in 12 seconds, runs thousands of simulations, and tells you cold whether your edge is dead or the regime just changed. No code. No Python. No $25,000 terminal. 20,000 already inside. Waitlist stops at 25,000: That distinction is the whole game, and you never had a way to see it. Every strategy is a bet that one thing stays true. Momentum bets trends continue. Mean reversion bets ranges hold. When the regime flips, the assumption dies and your strategy bleeds with nothing wrong in the code. You stare at the logic for a month and never find the bug, because there isn't one. So you delete it, or refit it to the last drawdown and build something that would have survived the pain you already felt and nothing coming next. The desks never had that problem. 92% of institutional volume is automated. Only 45% of retail is. They test 100 strategies for every 1 you test by hand, and kill 97 of them on purpose, because they can tell a dead edge from a normal drawdown. Now that exact loop costs $0. One hypothesis used to cost a fund $87,500 to test. With Horizon you get unlimited, in seconds, and a winner deploys live in 90 seconds and runs without your hands on it.

cvxv666

40,765 次观看 • 3 个月前

An AI engineer stopped me at a coffee shop because of what was on my screen. I was running a Polymarket trading system powered by AI. He glanced over once. Then again. Then asked: "What exactly is that?" I told him: • Claude Code • Open-source tools • A few custom workflows • Less than $30/month He pulled up a chair. Then I showed him what the system was doing. Millions of trades analyzed. Thousands of wallets tracked. Every entry. Every exit. Every profitable pattern. One prompt: "Find consistently profitable traders and identify repeatable behaviors." Within minutes, the system returned a shortlist of high-performing wallets worth studying. What used to take days of manual research took minutes. But that was only the beginning. The second workflow monitored live prediction markets. It automatically filtered: • liquidity • pricing inefficiencies • volume spikes • whale activity • momentum shifts Hundreds of markets became a small watchlist. Most opportunities were rejected automatically. Only the strongest setups survived. Then he asked: "How does it know when to enter?" Three independent AI agents. One looks for arbitrage. One tracks probability convergence. One follows high-conviction traders. When multiple agents agree, confidence increases. When they disagree, no trade. Simple. But surprisingly effective. Then came the next question: "What about exits?" The system doesn't just analyze winning traders. It studies how they take profits. Many successful traders close positions long before settlement. So the bot watches for: • profit targets • unusual volume • momentum reversals The goal isn't to predict everything. It's to react faster than manual traders can. He stared at the dashboard for a few seconds and said: "This isn't just market analysis." "It's an intelligence system." And that's the shift most people are missing. AI isn't only helping people work faster. It's compressing workflows that once required entire teams into a laptop and a few prompts. The gap between individuals and institutions is shrinking. Fast. The people learning these tools today are building the edge everyone else will be chasing tomorrow. Comment "Claude" if you want the framework.
0:33

An AI engineer stopped me at a coffee shop because of what was on my screen. I was running a Polymarket trading system powered by AI. He glanced over once. Then again. Then asked: "What exactly is that?" I told him: • Claude Code • Open-source tools • A few custom workflows • Less than $30/month He pulled up a chair. Then I showed him what the system was doing. Millions of trades analyzed. Thousands of wallets tracked. Every entry. Every exit. Every profitable pattern. One prompt: "Find consistently profitable traders and identify repeatable behaviors." Within minutes, the system returned a shortlist of high-performing wallets worth studying. What used to take days of manual research took minutes. But that was only the beginning. The second workflow monitored live prediction markets. It automatically filtered: • liquidity • pricing inefficiencies • volume spikes • whale activity • momentum shifts Hundreds of markets became a small watchlist. Most opportunities were rejected automatically. Only the strongest setups survived. Then he asked: "How does it know when to enter?" Three independent AI agents. One looks for arbitrage. One tracks probability convergence. One follows high-conviction traders. When multiple agents agree, confidence increases. When they disagree, no trade. Simple. But surprisingly effective. Then came the next question: "What about exits?" The system doesn't just analyze winning traders. It studies how they take profits. Many successful traders close positions long before settlement. So the bot watches for: • profit targets • unusual volume • momentum reversals The goal isn't to predict everything. It's to react faster than manual traders can. He stared at the dashboard for a few seconds and said: "This isn't just market analysis." "It's an intelligence system." And that's the shift most people are missing. AI isn't only helping people work faster. It's compressing workflows that once required entire teams into a laptop and a few prompts. The gap between individuals and institutions is shrinking. Fast. The people learning these tools today are building the edge everyone else will be chasing tomorrow. Comment "Claude" if you want the framework.

Paul Sims

13,669 次观看 • 1 个月前