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a kid still at university just tested inverse fair value gaps more seriously than most traders do before funding a $100,000 account he coded five-minute IFVGs on NQ, marked the first one-minute tap after each setup formed, then measured what price did next at first, the setup looked profitable...

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Sammy

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22,054 次观看 • 2 个月前 •via X (Twitter)

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a portfolio manager running $8.4 billion once paid a quant researcher $410,000 a year to answer one question: “can you tell me which breakouts are real before we risk the money?” the researcher started with an idea traders had repeated for decades a breakout is stronger when the tape becomes “twitchy” everyone understood what that looked like nobody could define it precisely enough to code so he measured the speed at which trades forced price to update not raw volume because one million orders hitting the same price can create activity without moving the market the difference was enormous during lunch, price changed around 4 times per second near the closing bell, it jumped to almost 70 a 17.5x increase in the speed of price discovery then he built the test: breakout = 5-minute close beyond the previous 30-minute high or low signal = normalized price-update speed success = profit barrier reached before the equal-distance loss barrier now imagine the fund puts $25 million behind each breakout basket filtering out one false 0.8% move protects roughly $200,000 that is why the manager did not care whether the chart looked convincing he cared whether “twitchy” could be turned into a number most traders protect their intuition from being tested quants turn it into code and make it fight for capital and this is exactly the skill firms pay six figures for: taking a vague market idea, defining it mathematically, then testing whether it deserves real money I broke down how to build that skill from zero in 16 weeks bookmark this lesson then read the full quant roadmap below ↓
6:18

a portfolio manager running $8.4 billion once paid a quant researcher $410,000 a year to answer one question: “can you tell me which breakouts are real before we risk the money?” the researcher started with an idea traders had repeated for decades a breakout is stronger when the tape becomes “twitchy” everyone understood what that looked like nobody could define it precisely enough to code so he measured the speed at which trades forced price to update not raw volume because one million orders hitting the same price can create activity without moving the market the difference was enormous during lunch, price changed around 4 times per second near the closing bell, it jumped to almost 70 a 17.5x increase in the speed of price discovery then he built the test: breakout = 5-minute close beyond the previous 30-minute high or low signal = normalized price-update speed success = profit barrier reached before the equal-distance loss barrier now imagine the fund puts $25 million behind each breakout basket filtering out one false 0.8% move protects roughly $200,000 that is why the manager did not care whether the chart looked convincing he cared whether “twitchy” could be turned into a number most traders protect their intuition from being tested quants turn it into code and make it fight for capital and this is exactly the skill firms pay six figures for: taking a vague market idea, defining it mathematically, then testing whether it deserves real money I broke down how to build that skill from zero in 16 weeks bookmark this lesson then read the full quant roadmap below ↓

Sammy

54,858 次观看 • 2 个月前

A+ NQ setup has exactly three parts. 1. Statistical bias: Probability Map shows 75%+ to one side. Not an opinion. Backtested across 10 years of NQ session data. That number narrows the playing field before price moves an inch. 2. The sweep: Liquidity is resting in the opposite direction. When price grabs it, that is not a fake-out. That is the fuel for the real move. You were expecting it. You were watching for it. 3. The entry model: After the sweep, the structure confirms. iFVG / CISD. A defined ICT concept with clear invalidation. Not a hope, not a gut feel. A model with a precise entry and a reason to be there. Probability tells you where. The sweep tells you when. The entry model tells you how. Remove any one of the three and you do not have an edge. You have a trade. How many of these boxes do you tick before you pull the trigger?
1:01

A+ NQ setup has exactly three parts. 1. Statistical bias: Probability Map shows 75%+ to one side. Not an opinion. Backtested across 10 years of NQ session data. That number narrows the playing field before price moves an inch. 2. The sweep: Liquidity is resting in the opposite direction. When price grabs it, that is not a fake-out. That is the fuel for the real move. You were expecting it. You were watching for it. 3. The entry model: After the sweep, the structure confirms. iFVG / CISD. A defined ICT concept with clear invalidation. Not a hope, not a gut feel. A model with a precise entry and a reason to be there. Probability tells you where. The sweep tells you when. The entry model tells you how. Remove any one of the three and you do not have an edge. You have a trade. How many of these boxes do you tick before you pull the trigger?

Herman Trading

20,079 次观看 • 4 个月前

Alan Oppenheim, MIT professor: "Every trader alive uses a moving average. Almost none of them know it's a filter, and funds pay quants $600K for the version they were never taught." a moving average is a filter. it smooths a jumpy price to reveal the slower signal hiding underneath. that is all it is, and it is the bluntest one there is. the same math gives you filters that lag less and cut more noise, and that gap is a real edge. this is the second half of what Simons was doing. first you accept the market is a signal buried in noise. then you build the filter that reads it, and the moving average on your chart is the crudest possible version. Oppenheim builds the real ones at the board in this lecture. here is the catch. a filter only helps if there is a real signal under the noise. smooth pure randomness and you will see a beautiful trend that means nothing. the filter is free. knowing there is a real signal worth filtering is the edge.
40:58

Alan Oppenheim, MIT professor: "Every trader alive uses a moving average. Almost none of them know it's a filter, and funds pay quants $600K for the version they were never taught." a moving average is a filter. it smooths a jumpy price to reveal the slower signal hiding underneath. that is all it is, and it is the bluntest one there is. the same math gives you filters that lag less and cut more noise, and that gap is a real edge. this is the second half of what Simons was doing. first you accept the market is a signal buried in noise. then you build the filter that reads it, and the moving average on your chart is the crudest possible version. Oppenheim builds the real ones at the board in this lecture. here is the catch. a filter only helps if there is a real signal under the noise. smooth pure randomness and you will see a beautiful trend that means nothing. the filter is free. knowing there is a real signal worth filtering is the edge.

Rossst.03

214,907 次观看 • 3 个月前

Alan Oppenheim, MIT professor: "Jump Trading pays quants $650K to pull one real signal out of market noise. I wrote the textbook on that exact math and taught it for free." this free MIT lecture is the math the whole "quant edge" is built on, and almost nobody selling you a course will name it. the man teaching it wrote the signal-processing textbook every engineer on earth learned from, and he put the lectures online for nothing. here is the idea. a price chart is a signal buried in noise. most of what you see is random static, and hidden inside is a faint, repeating pattern with real mathematics for pulling it out. that is not a metaphor. jim simons was a codebreaker who did exactly this to enemy transmissions, then pointed the same math at the market and made a hundred billion dollars. none of it is new. oppenheim taught this in 1987, the math is older, and every lecture is free. same story i told in the article above. the edge was never a secret formula. it was knowing a market is a noisy signal, and having the tools to read it. here is what the gurus skip. the math to separate signal from noise is free and everyone has it. the edge is cleaner data than the next desk, and the discipline to trust a faint signal across thousands of trades instead of chasing the static. the filter is free. the patience to use it is not.
29:44

Alan Oppenheim, MIT professor: "Jump Trading pays quants $650K to pull one real signal out of market noise. I wrote the textbook on that exact math and taught it for free." this free MIT lecture is the math the whole "quant edge" is built on, and almost nobody selling you a course will name it. the man teaching it wrote the signal-processing textbook every engineer on earth learned from, and he put the lectures online for nothing. here is the idea. a price chart is a signal buried in noise. most of what you see is random static, and hidden inside is a faint, repeating pattern with real mathematics for pulling it out. that is not a metaphor. jim simons was a codebreaker who did exactly this to enemy transmissions, then pointed the same math at the market and made a hundred billion dollars. none of it is new. oppenheim taught this in 1987, the math is older, and every lecture is free. same story i told in the article above. the edge was never a secret formula. it was knowing a market is a noisy signal, and having the tools to read it. here is what the gurus skip. the math to separate signal from noise is free and everyone has it. the edge is cleaner data than the next desk, and the discipline to trust a faint signal across thousands of trades instead of chasing the static. the filter is free. the patience to use it is not.

Rossst.03

281,905 次观看 • 3 个月前

The best signal is sometimes NO signal. That is why I do not trade when Intelligent Balance shows less than 60%. Below 60% means one thing to me: NO CLEAR EDGE. And that is exactly where most traders destroy their account. They see a sweep. They see an iFVG. They see CISD. They convince themselves there is a trade. But without a strong more probable side, the setup has no real filter. For me: 60%+ = I have a side to focus on. Below 60% = I protect capital. Simple. Intelligent Balance is not only there to tell me where to trade. It is there to tell me when NOT to trade. Because forcing trades in a low-edge environment is not trading. It is gambling with extra steps. Would you skip a session if the indicator told you there is no clear edge?
0:41

The best signal is sometimes NO signal. That is why I do not trade when Intelligent Balance shows less than 60%. Below 60% means one thing to me: NO CLEAR EDGE. And that is exactly where most traders destroy their account. They see a sweep. They see an iFVG. They see CISD. They convince themselves there is a trade. But without a strong more probable side, the setup has no real filter. For me: 60%+ = I have a side to focus on. Below 60% = I protect capital. Simple. Intelligent Balance is not only there to tell me where to trade. It is there to tell me when NOT to trade. Because forcing trades in a low-edge environment is not trading. It is gambling with extra steps. Would you skip a session if the indicator told you there is no clear edge?

Herman Trading

19,182 次观看 • 3 个月前

Wall Street burns billions trying to predict Bitcoin. A 28-year-old self-taught coder in Warsaw made $377,000 by not even trying. He'd lost money on three trading bots before this one. Each looked perfect on paper, then started losing money the moment he ran it for real market. So he built bot that doesn't trust itself. His wallet: The truth is simple: you can't predict the next five minutes of Bitcoin. It's a coin flip. Anyone selling you a "prediction" is selling you nothing. So he stopped predicting. The bot hunts the moments the crowd is wrong instead. Here's the part that makes the money, and it's the opposite of what everyone builds. Any strategy can be made to look amazing on past data. On a 5-minute chart, most of them are just lucky, not real - and they stop working fast. So the bot treats every strategy it finds as fake until it proves otherwise. Each one has to pass a hard test: > test it on old data → test it on data it's never seen → try to break it on purpose → cut it down to the one thing that matters → run it forward → keep it only if it still works Last round, 10 of its 12 "winning" strategies turned out to be fake. It kept the 2 that actually worked and dropped the rest. And it never stops - building, testing, and dumping strategies around the clock. What worked yesterday can stop working today, so the second one starts losing, it gets cut before it costs you a thing. The result: $433,000 across 2,955 trades All his old bots tried to be right. This one just tries to catch itself being wrong - and that's why it's still alive. Bookmark this article below - it's the breakdown that explains why your last bot died. It's pruning and trading right now. Copy its wallet and skip to the edges that survived:
0:47

Wall Street burns billions trying to predict Bitcoin. A 28-year-old self-taught coder in Warsaw made $377,000 by not even trying. He'd lost money on three trading bots before this one. Each looked perfect on paper, then started losing money the moment he ran it for real market. So he built bot that doesn't trust itself. His wallet: The truth is simple: you can't predict the next five minutes of Bitcoin. It's a coin flip. Anyone selling you a "prediction" is selling you nothing. So he stopped predicting. The bot hunts the moments the crowd is wrong instead. Here's the part that makes the money, and it's the opposite of what everyone builds. Any strategy can be made to look amazing on past data. On a 5-minute chart, most of them are just lucky, not real - and they stop working fast. So the bot treats every strategy it finds as fake until it proves otherwise. Each one has to pass a hard test: > test it on old data → test it on data it's never seen → try to break it on purpose → cut it down to the one thing that matters → run it forward → keep it only if it still works Last round, 10 of its 12 "winning" strategies turned out to be fake. It kept the 2 that actually worked and dropped the rest. And it never stops - building, testing, and dumping strategies around the clock. What worked yesterday can stop working today, so the second one starts losing, it gets cut before it costs you a thing. The result: $433,000 across 2,955 trades All his old bots tried to be right. This one just tries to catch itself being wrong - and that's why it's still alive. Bookmark this article below - it's the breakdown that explains why your last bot died. It's pruning and trading right now. Copy its wallet and skip to the edges that survived:

cvxv666

27,854 次观看 • 3 个月前

You have 100 dollars and you want 200. Betting red one dollar at a time, your chance is one in 33,171. Betting the whole 100 on a single spin, it is 47.4 percent. The careful version is fifteen thousand times worse. This is also the arithmetic of your trading account, and it means almost everything you have been taught about managing risk is, in the strict mathematical sense, a method for losing more reliably. An MIT professor works this out on a blackboard in a lecture on random walks. The result is called gambler's ruin. Two and a half points of disadvantage is nothing on one spin. But a dollar at a time you are not making one spin, you are making hundreds, and the edge gets a fresh attempt at you on every one of them. It was never the size of the disadvantage. It is the number of times you agree to face it. One hundred on red, once: 47.4 percent. Twenty dollar bets: 37.3 percent. Five dollar bets: 11.1 percent. One dollar bets: 0.003 percent. Mathematicians call the right answer bold play. It was proved optimal for unfavourable games in 1965. It is not a strategy for winning. It is the least ruinous way to play a game you should not be in. If your edge after spread and fees is negative, and for most active retail accounts it is, then your position sizing rule is not protecting you. Risking one percent per trade is the one dollar bet. It arrives at zero with near certainty, just politely, over a longer period, with a spreadsheet. Discipline does not beat a negative edge. It schedules it. All of this reverses if you have a real edge. Then small and frequent is correct and bold play is madness. So the only question that has ever mattered is whether you have one, and the number of people certain they do has never resembled the number who do. Your broker does not need you to be wrong. It needs you to be frequent.
1:16:13

You have 100 dollars and you want 200. Betting red one dollar at a time, your chance is one in 33,171. Betting the whole 100 on a single spin, it is 47.4 percent. The careful version is fifteen thousand times worse. This is also the arithmetic of your trading account, and it means almost everything you have been taught about managing risk is, in the strict mathematical sense, a method for losing more reliably. An MIT professor works this out on a blackboard in a lecture on random walks. The result is called gambler's ruin. Two and a half points of disadvantage is nothing on one spin. But a dollar at a time you are not making one spin, you are making hundreds, and the edge gets a fresh attempt at you on every one of them. It was never the size of the disadvantage. It is the number of times you agree to face it. One hundred on red, once: 47.4 percent. Twenty dollar bets: 37.3 percent. Five dollar bets: 11.1 percent. One dollar bets: 0.003 percent. Mathematicians call the right answer bold play. It was proved optimal for unfavourable games in 1965. It is not a strategy for winning. It is the least ruinous way to play a game you should not be in. If your edge after spread and fees is negative, and for most active retail accounts it is, then your position sizing rule is not protecting you. Risking one percent per trade is the one dollar bet. It arrives at zero with near certainty, just politely, over a longer period, with a spreadsheet. Discipline does not beat a negative edge. It schedules it. All of this reverses if you have a real edge. Then small and frequent is correct and bold play is madness. So the only question that has ever mattered is whether you have one, and the number of people certain they do has never resembled the number who do. Your broker does not need you to be wrong. It needs you to be frequent.

Verax

85,294 次观看 • 1 个月前

Millions of people are asking AI to find them a profitable trading strategy. The problem is AI will find one even where it doesn't exist One quant proved it. He gave an AI agent 4 years of prices with, by design, no pattern in them whatsoever. Pure noise The agent came back with a long/short strategy and a Sharpe of 2.1 on the backtest. It even added a paragraph explaining the economics of an effect that doesn't exist in nature It didn't lie on purpose. It just found a pattern where there was none And that's the scariest part. Because that's exactly how any AI will behave when you hand it your idea. It will always hand you back a beautiful curve, and you'll believe it, because it looks perfect 89% of retail traders lost money last year. Not because they had no ideas. Because not one of their ideas ever went through this kind of test That's why generating strategies is worth nothing. Anyone and anything can draw a profit on history Only one thing matters: testing on data the strategy has never seen. The moment the beautiful curve either survives or falls apart That's the whole difference between a hedge fund and a trader who blows up. A fund doesn't trust any curve until it's been through fire. It kills 97 ideas out of 100 before risking a single cent I ran the most-cited quant strategies in history through exactly this test, with AI. Momentum on BTC returned +1,537%. Carry trade on currencies fell apart and showed its premium is dead. And it was the one that fell apart that would have saved me money AI will draw you a strategy in seconds. The only question is whether you test it before it tests your account You can try it here: How to set up that test for yourself, the same one that separates a hedge fund from a casino, is in the article below
0:25

Millions of people are asking AI to find them a profitable trading strategy. The problem is AI will find one even where it doesn't exist One quant proved it. He gave an AI agent 4 years of prices with, by design, no pattern in them whatsoever. Pure noise The agent came back with a long/short strategy and a Sharpe of 2.1 on the backtest. It even added a paragraph explaining the economics of an effect that doesn't exist in nature It didn't lie on purpose. It just found a pattern where there was none And that's the scariest part. Because that's exactly how any AI will behave when you hand it your idea. It will always hand you back a beautiful curve, and you'll believe it, because it looks perfect 89% of retail traders lost money last year. Not because they had no ideas. Because not one of their ideas ever went through this kind of test That's why generating strategies is worth nothing. Anyone and anything can draw a profit on history Only one thing matters: testing on data the strategy has never seen. The moment the beautiful curve either survives or falls apart That's the whole difference between a hedge fund and a trader who blows up. A fund doesn't trust any curve until it's been through fire. It kills 97 ideas out of 100 before risking a single cent I ran the most-cited quant strategies in history through exactly this test, with AI. Momentum on BTC returned +1,537%. Carry trade on currencies fell apart and showed its premium is dead. And it was the one that fell apart that would have saved me money AI will draw you a strategy in seconds. The only question is whether you test it before it tests your account You can try it here: How to set up that test for yourself, the same one that separates a hedge fund from a casino, is in the article below

qwinsi

47,584 次观看 • 14 天前

This is what a textbook A+ setup on NQ looks like. The direction was already defined before the trade appeared. Today, the London Probability Map showed a 73.5% probability for the more probable LOW. So I was not interested in longs - SHORTS ONLY. I only needed one clean SHORT confirmation: • Sweep of liquidity • Bearish iFVG • Entry aligned with the statistically more probable direction That is it. Historical data and probabilities define the direction. Price action gives the entry. Trade entry + directional edge = A+ setup. The attached video shows the full London session execution step by step. How much simpler would your trading become if you stopped guessing the direction first?
1:12

This is what a textbook A+ setup on NQ looks like. The direction was already defined before the trade appeared. Today, the London Probability Map showed a 73.5% probability for the more probable LOW. So I was not interested in longs - SHORTS ONLY. I only needed one clean SHORT confirmation: • Sweep of liquidity • Bearish iFVG • Entry aligned with the statistically more probable direction That is it. Historical data and probabilities define the direction. Price action gives the entry. Trade entry + directional edge = A+ setup. The attached video shows the full London session execution step by step. How much simpler would your trading become if you stopped guessing the direction first?

Herman Trading

17,352 次观看 • 4 个月前

Every trader thinks the market is a fair fight. Ed Thorp - the man who beat both the casino and Wall Street - explains why some players simply cannot lose, and it is not about being right. Ed Thorp - math professor who broke blackjack, then ran a hedge fund at 20% a year for decades. he points to one firm that went public and had to reveal its record in the prospectus: "for about the last 1300 trading days they had won every day except one - and the one day they didn't win was because they had a computer problem." 1299 winning days out of 1300. that is not prediction - that is access. these players see the order book 30 milliseconds before you do, and 30 milliseconds is the whole game. your edge dies here. you are sizing bets on a coin flip while someone else reads the coin mid-air. the market was never a fair fight - it is tiered, and most traders sit on the tier that pays. he names the firm, the number, and the man who turned Renaissance around - all in one free interview. the edge was never being smart. it is being early.
5:28

Every trader thinks the market is a fair fight. Ed Thorp - the man who beat both the casino and Wall Street - explains why some players simply cannot lose, and it is not about being right. Ed Thorp - math professor who broke blackjack, then ran a hedge fund at 20% a year for decades. he points to one firm that went public and had to reveal its record in the prospectus: "for about the last 1300 trading days they had won every day except one - and the one day they didn't win was because they had a computer problem." 1299 winning days out of 1300. that is not prediction - that is access. these players see the order book 30 milliseconds before you do, and 30 milliseconds is the whole game. your edge dies here. you are sizing bets on a coin flip while someone else reads the coin mid-air. the market was never a fair fight - it is tiered, and most traders sit on the tier that pays. he names the firm, the number, and the man who turned Renaissance around - all in one free interview. the edge was never being smart. it is being early.

Di Krass

33,605 次观看 • 1 个月前

One Chicago trader made $2.5 million in five days. Another explained how a five-tick move could put you up $1 million or wipe out the same amount before lunch. That was the world a young Chicago worker walked into for the first time. Paper was flying. Thousands of traders were screaming prices, throwing hand signals, and risking their own money in a room that looked more like a riot than a workplace. He went back to his job that same day and gave two weeks’ notice He just knew: “This was where I needed to be.” At its peak, Chicago had roughly 9,000 pit traders across its exchanges. Some were making $20,000 to $30,000 a day. But the most revealing line had nothing to do with getting rich: “It’s not what you make. It’s what you don’t lose.” Because the same aggression that made traders fortunes could keep them trapped inside a losing position while a car, then a house, disappeared in real time. Bookmark it and watch this piece of market history Before the next generation forgets what trading looked like when every emotion was visible.
44:35

One Chicago trader made $2.5 million in five days. Another explained how a five-tick move could put you up $1 million or wipe out the same amount before lunch. That was the world a young Chicago worker walked into for the first time. Paper was flying. Thousands of traders were screaming prices, throwing hand signals, and risking their own money in a room that looked more like a riot than a workplace. He went back to his job that same day and gave two weeks’ notice He just knew: “This was where I needed to be.” At its peak, Chicago had roughly 9,000 pit traders across its exchanges. Some were making $20,000 to $30,000 a day. But the most revealing line had nothing to do with getting rich: “It’s not what you make. It’s what you don’t lose.” Because the same aggression that made traders fortunes could keep them trapped inside a losing position while a car, then a house, disappeared in real time. Bookmark it and watch this piece of market history Before the next generation forgets what trading looked like when every emotion was visible.

Sammy

114,859 次观看 • 2 个月前

"At school or university, the thing is to be brilliant and to get the answer right first time. And there are brilliant people who can do that, but for the rest of us, we're not brilliant. And to get there, we have to strive and we have to go through failure. And we realize that you don't get it right first time, you don't get it right second time. In my case, and I counted it, it's 5,127 times. One of the things I always want to say is that sounds like a struggle. It was a struggle, but actually it was a hugely enjoyable struggle. The debt was mounting and I had three children, a wife and a home, and then a mortgage to pay, like everybody else. But I had a real point in life, I had a real aim and I had to get there. And the failures were interesting because I'd learned from every single one of them."
0:51

"At school or university, the thing is to be brilliant and to get the answer right first time. And there are brilliant people who can do that, but for the rest of us, we're not brilliant. And to get there, we have to strive and we have to go through failure. And we realize that you don't get it right first time, you don't get it right second time. In my case, and I counted it, it's 5,127 times. One of the things I always want to say is that sounds like a struggle. It was a struggle, but actually it was a hugely enjoyable struggle. The debt was mounting and I had three children, a wife and a home, and then a mortgage to pay, like everybody else. But I had a real point in life, I had a real aim and I had to get there. And the failures were interesting because I'd learned from every single one of them."

David Senra

137,546 次观看 • 9 个月前

Why Exchanges Banned This Bot: The 142,000% Return Liquidation Strategy Revealed i finally posted the strategy that got me banned and now the exchanges are probably sweating because i am handing you the keys to the liquidation engine. most people think trading is about charts but the real alpha is hidden in the moments when other traders lose everything. if you can understand why market makers hunt these positions you will never look at a candlestick the same way again. it took years of losing money to liquidations and over trading to realize that hand trading is a losing game for almost everyone on the planet. code is the great equalizer because it removes the emotion that usually causes you to hold a losing position until your account hits zero. i spent hundreds of thousands on developers in the past thinking i could not code myself until i realized i just needed to iterate to success. trading by hand is just driving a horse while everyone else is in a ferrari and the fees alone will chop you up before you even realize you were wrong. i watched a guy with a six million dollar short position sitting just two percent away from total liquidation while i was building this. seeing those numbers on the screen gives me ideas that i can automate into a bot so i dont have to spend my life staring at a monitor. the process i follow is called the rbi system which stands for research backtest and implement. most traders skip the first two steps and go straight to implementation which is why they get smoked on their very first bot. research starts with a backlog of ideas from books or papers or even just watching how the market reacts to big moves. once you have that idea you have to see if it worked in the past using a backtest because if it did not work then it certainly won't work in the future. i have been collecting liquidation data for eighteen months because that data is the lifeblood of a winning system. there is a hidden loop in the market where market makers try to liquidate as many people as possible to find liquidity. i wanted to build a strategy that either trades with that momentum or bets on the bounce right after the liquidation happens. the first strategy i tested was a pure liquidation momentum play that looks for a threshold of nine hundred seventy five thousand dollars in liquidations. when longs get liquidated it shorts the market to continue the down move and it tries to take a one percent profit. this strategy showed a return of over four hundred percent in the backtest while the buy and hold was only thirty three percent. it sounds amazing but you have to be careful with optimized results because you can search with math until you find anything. i decided to flip the logic on its head and create an inverse liquidation strategy that acts as a contrarian. instead of following the move it waits for the longs to get liquidated and then buys the dip after a small price spread. this is where i stumbled onto something that felt like a mistake but turned out to be pure alpha. i accidentally typed in a threshold of three hundred thousand dollars instead of three million and the results were unbelievable. the backtest return jumped to over one hundred forty thousand percent because the bot was catching every single micro bounce in the market. even when i doubled the commission fees to account for the high trade volume the strategy still stayed incredibly profitable. most people would have missed this because they are too busy trying to be right instead of just looking at what the data says. i use tools like claude and cursor to build these bots in minutes when it used to take me an entire week to write the code. if you are not using ai to automate your ideas you are essentially choosing to work ten times harder for less money. i built three separate bots during this session including a momentum bot and two different versions of the inverse spread bot. running these together creates a sort of statistical arbitrage where you can hedge your positions across different market conditions. one bot wins when the market cascades and the other wins when it fakes out and reverses. you have to start with tiny ten dollar sizes because a backtest is never a hundred percent guarantee of what will happen today. i always run my p and l close logic first to make sure the bot exits the position if the stop loss or take profit is hit. it is vital to check your position every fifteen seconds and make sure you are not double ordering or getting stuck in a trade. the goal is to have fully automated systems trading for you so you can actually live your life while the bots do the work. i push all of this code to my private github because i believe that wall street will never show you how this actually works. you have to be a doer and not a dabbler if you want to actually make it in this industry. the reason i show everything live on youtube is to prove that anyone can learn to do this if they are willing to iterate. you dont need to be a math genius you just need to follow the rbi system and stay disciplined with your risk. every liquidation you see on the chart is a signal and if you know how to read them you are no longer the one being hunted. i am currently running the third version of the bot to see how it handles the live market volatility. it is a beautiful thing to see a system enter and exit a trade perfectly without you having to click a single button. the fees are the silent killer of hand traders but a bot can be programmed to use limit orders and stay efficient. if you learn to code you can build anything for the rest of your life regardless of where you are in the world. stop trying to guess which way the candle will go and start building systems that can handle both directions. i am going to keep testing these three strategies against each other to find the ultimate ensemble for this current market. once you find a winning edge you just have to scale it up slowly and keep refining the parameters. the exchanges might not like that i am sharing this but code is the great equalizer and it is time for you to use it. i will be back tomorrow to show the results and keep building more systems until everything is fully automated
1:23:35

Why Exchanges Banned This Bot: The 142,000% Return Liquidation Strategy Revealed i finally posted the strategy that got me banned and now the exchanges are probably sweating because i am handing you the keys to the liquidation engine. most people think trading is about charts but the real alpha is hidden in the moments when other traders lose everything. if you can understand why market makers hunt these positions you will never look at a candlestick the same way again. it took years of losing money to liquidations and over trading to realize that hand trading is a losing game for almost everyone on the planet. code is the great equalizer because it removes the emotion that usually causes you to hold a losing position until your account hits zero. i spent hundreds of thousands on developers in the past thinking i could not code myself until i realized i just needed to iterate to success. trading by hand is just driving a horse while everyone else is in a ferrari and the fees alone will chop you up before you even realize you were wrong. i watched a guy with a six million dollar short position sitting just two percent away from total liquidation while i was building this. seeing those numbers on the screen gives me ideas that i can automate into a bot so i dont have to spend my life staring at a monitor. the process i follow is called the rbi system which stands for research backtest and implement. most traders skip the first two steps and go straight to implementation which is why they get smoked on their very first bot. research starts with a backlog of ideas from books or papers or even just watching how the market reacts to big moves. once you have that idea you have to see if it worked in the past using a backtest because if it did not work then it certainly won't work in the future. i have been collecting liquidation data for eighteen months because that data is the lifeblood of a winning system. there is a hidden loop in the market where market makers try to liquidate as many people as possible to find liquidity. i wanted to build a strategy that either trades with that momentum or bets on the bounce right after the liquidation happens. the first strategy i tested was a pure liquidation momentum play that looks for a threshold of nine hundred seventy five thousand dollars in liquidations. when longs get liquidated it shorts the market to continue the down move and it tries to take a one percent profit. this strategy showed a return of over four hundred percent in the backtest while the buy and hold was only thirty three percent. it sounds amazing but you have to be careful with optimized results because you can search with math until you find anything. i decided to flip the logic on its head and create an inverse liquidation strategy that acts as a contrarian. instead of following the move it waits for the longs to get liquidated and then buys the dip after a small price spread. this is where i stumbled onto something that felt like a mistake but turned out to be pure alpha. i accidentally typed in a threshold of three hundred thousand dollars instead of three million and the results were unbelievable. the backtest return jumped to over one hundred forty thousand percent because the bot was catching every single micro bounce in the market. even when i doubled the commission fees to account for the high trade volume the strategy still stayed incredibly profitable. most people would have missed this because they are too busy trying to be right instead of just looking at what the data says. i use tools like claude and cursor to build these bots in minutes when it used to take me an entire week to write the code. if you are not using ai to automate your ideas you are essentially choosing to work ten times harder for less money. i built three separate bots during this session including a momentum bot and two different versions of the inverse spread bot. running these together creates a sort of statistical arbitrage where you can hedge your positions across different market conditions. one bot wins when the market cascades and the other wins when it fakes out and reverses. you have to start with tiny ten dollar sizes because a backtest is never a hundred percent guarantee of what will happen today. i always run my p and l close logic first to make sure the bot exits the position if the stop loss or take profit is hit. it is vital to check your position every fifteen seconds and make sure you are not double ordering or getting stuck in a trade. the goal is to have fully automated systems trading for you so you can actually live your life while the bots do the work. i push all of this code to my private github because i believe that wall street will never show you how this actually works. you have to be a doer and not a dabbler if you want to actually make it in this industry. the reason i show everything live on youtube is to prove that anyone can learn to do this if they are willing to iterate. you dont need to be a math genius you just need to follow the rbi system and stay disciplined with your risk. every liquidation you see on the chart is a signal and if you know how to read them you are no longer the one being hunted. i am currently running the third version of the bot to see how it handles the live market volatility. it is a beautiful thing to see a system enter and exit a trade perfectly without you having to click a single button. the fees are the silent killer of hand traders but a bot can be programmed to use limit orders and stay efficient. if you learn to code you can build anything for the rest of your life regardless of where you are in the world. stop trying to guess which way the candle will go and start building systems that can handle both directions. i am going to keep testing these three strategies against each other to find the ultimate ensemble for this current market. once you find a winning edge you just have to scale it up slowly and keep refining the parameters. the exchanges might not like that i am sharing this but code is the great equalizer and it is time for you to use it. i will be back tomorrow to show the results and keep building more systems until everything is fully automated

Moon Dev

11,948 次观看 • 6 个月前

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.
0:41

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.

cvxv666

104,237 次观看 • 3 个月前

Marc Andreessen on the 3 things he looks for when investing in a startup The first thing Marc Andreesen looks for is a big market: “Is there a big existing market that you think you can go after and displace incumbents? Or do you believe there will be a new market that will be big?” The second thing he looks for is a 10x better product: “Is there a fundamental technology or economic change that justifies a new company? And the way I always think about that is: Is there a 10x change happening in the technology landscape? Is something 10x faster, 10x cheaper, or 10x better? If it’s not 10x, we as both VCs and entrepreneurs have to ask ourselves if it’s really worth doing because it’s really hard to start new companies . . . Existing companies are usually pretty good at what they do. So for a new company to exist, it has to bring a product to market that’s so much better than what exists that it punches through the status quo.” The third is the team: “Is the team outstanding? . . . You want to have a founding team of complementary skillsets. You want to have at least one super strong technologist — quite possibly more than one. Some of the best startups are actually more than one founding technologist. And then it often helps to have someone who is a marketing or salesperson who has a really good understanding of business.” Marc believes that you need all three of these, but if you’re going to compromise on one of those as an investor, it should be the product: “A great market is a lot easier to make up for with iterative product execution. The problem with a poor or small market is that even if you do a good job on the product, there just aren’t that many customers so it’s hard to ever get big and people get demoralized . . . And then we evaluate the team of a startup by its ability to get into a big market with a good product.”
2:43

Marc Andreessen on the 3 things he looks for when investing in a startup The first thing Marc Andreesen looks for is a big market: “Is there a big existing market that you think you can go after and displace incumbents? Or do you believe there will be a new market that will be big?” The second thing he looks for is a 10x better product: “Is there a fundamental technology or economic change that justifies a new company? And the way I always think about that is: Is there a 10x change happening in the technology landscape? Is something 10x faster, 10x cheaper, or 10x better? If it’s not 10x, we as both VCs and entrepreneurs have to ask ourselves if it’s really worth doing because it’s really hard to start new companies . . . Existing companies are usually pretty good at what they do. So for a new company to exist, it has to bring a product to market that’s so much better than what exists that it punches through the status quo.” The third is the team: “Is the team outstanding? . . . You want to have a founding team of complementary skillsets. You want to have at least one super strong technologist — quite possibly more than one. Some of the best startups are actually more than one founding technologist. And then it often helps to have someone who is a marketing or salesperson who has a really good understanding of business.” Marc believes that you need all three of these, but if you’re going to compromise on one of those as an investor, it should be the product: “A great market is a lot easier to make up for with iterative product execution. The problem with a poor or small market is that even if you do a good job on the product, there just aren’t that many customers so it’s hard to ever get big and people get demoralized . . . And then we evaluate the team of a startup by its ability to get into a big market with a good product.”

Startup Archive

17,333 次观看 • 8 个月前

When one young Chicago trader first heard people were making $50,000 to $100,000 in a day He told himself he would quit the moment he ever made that kind of money. Then he had his first five-figure day. He was back in the pit the next morning. “After a while, the money becomes just a way of keeping score.” By the late 1980s, a seat on the exchange could cost more than $300,000, and the trading floors were said to hold more millionaires per square inch than anywhere else on earth. But the footage shows what that money did to people. Traders carried live quote machines on dates, practiced screaming so they could be heard above the crowd, wore the same clothes after winning days, and came back even after they had already made enough to leave. The technology looks ancient now. The psychology does not. This rare documentary captures the exact moment trading stops being about money and turns into identity, competition, and addiction to the game. Bookmark it and watch as a lesson from the past Especially now that markets are once again rewarding speed, hype, and risk-taking.
20:02

When one young Chicago trader first heard people were making $50,000 to $100,000 in a day He told himself he would quit the moment he ever made that kind of money. Then he had his first five-figure day. He was back in the pit the next morning. “After a while, the money becomes just a way of keeping score.” By the late 1980s, a seat on the exchange could cost more than $300,000, and the trading floors were said to hold more millionaires per square inch than anywhere else on earth. But the footage shows what that money did to people. Traders carried live quote machines on dates, practiced screaming so they could be heard above the crowd, wore the same clothes after winning days, and came back even after they had already made enough to leave. The technology looks ancient now. The psychology does not. This rare documentary captures the exact moment trading stops being about money and turns into identity, competition, and addiction to the game. Bookmark it and watch as a lesson from the past Especially now that markets are once again rewarding speed, hype, and risk-taking.

Sammy

940,314 次观看 • 2 个月前

THIS DEV BUILT A POLYMARKET BOT WITH GROK BUILD Now his AI agents print ~$2,700 per day PASSIVELY His wallet: [ Wanna hear the best part? He published the whole build for free. I've been building on Polymarket long enough to know what's real and what's a screenshot. This is real, and the wallet's public so anyone can check it. Here's what he actually did, for those who don't want to read the full thing yet: He took an arb bot, the kind that catches when Up and Down don't add up to a dollar, and rebuilt it around a team of Grok Build agents instead of one giant script. One agent hunts for the gap. One checks both legs can actually fill so you never end up naked on one side. One runs the fee math so a rebate-vs-fee mistake doesn't eat the whole edge. Then the bot merges each pair back into $1 instantly and recycles the capital, over and over. That recycling is the real secret, not the gap itself. A tiny edge run hundreds of times a day is where the $2,700 comes from. Here's what I respect about it. He's honest that the agents don't hand you the edge. They build the machine fast, but the strategy and the discipline are still on you. No "one prompt and you're rich" nonsense. Most people in this space would've locked this behind a paywall or a paid telegram. He put the code, the mechanics, and the wallet out in the open. If you're building bots and you're not following him, fix that. This is the honest version of what everyone else is trying to sell you. Attaching the full guide below.
0:21

THIS DEV BUILT A POLYMARKET BOT WITH GROK BUILD Now his AI agents print ~$2,700 per day PASSIVELY His wallet: [ Wanna hear the best part? He published the whole build for free. I've been building on Polymarket long enough to know what's real and what's a screenshot. This is real, and the wallet's public so anyone can check it. Here's what he actually did, for those who don't want to read the full thing yet: He took an arb bot, the kind that catches when Up and Down don't add up to a dollar, and rebuilt it around a team of Grok Build agents instead of one giant script. One agent hunts for the gap. One checks both legs can actually fill so you never end up naked on one side. One runs the fee math so a rebate-vs-fee mistake doesn't eat the whole edge. Then the bot merges each pair back into $1 instantly and recycles the capital, over and over. That recycling is the real secret, not the gap itself. A tiny edge run hundreds of times a day is where the $2,700 comes from. Here's what I respect about it. He's honest that the agents don't hand you the edge. They build the machine fast, but the strategy and the discipline are still on you. No "one prompt and you're rich" nonsense. Most people in this space would've locked this behind a paywall or a paid telegram. He put the code, the mechanics, and the wallet out in the open. If you're building bots and you're not following him, fix that. This is the honest version of what everyone else is trying to sell you. Attaching the full guide below.

Oracle Boar

30,741 次观看 • 1 个月前

One of the largest trading firms in the world teaches new hires poker before it lets them near a book. A newspaper brought a camera to the table and sat down to play. The men across the felt are working Wall Street traders. The firm is Susquehanna, which built poker into its training programme decades ago and still runs it that way, on the argument that the card room teaches something no finance degree does. Gunjan Banerji from the Wall Street Journal plays the hands herself rather than interviewing them about it. A green table, a dealer, chips, 4 people who do this for a living. No lecture hall, no slides. The teaching happens between deals, while money is actually at stake. They break it into parts on camera. Risk management first. Then bet sizing. Then patience, which sounds like the soft one and is not. Then reading the person opposite when the only data available is how they behave with money on the table. The section on patience is the one most people skip. Folding is the correct action in the overwhelming majority of hands, and almost nobody can do it for hours without inventing a reason to play. The same failure shows up in a trading account as overtrading, and it kills more people than bad analysis. Then bet sizing, which is where the video earns the watch. Being right about the odds is the easy half. How much you put behind a correct read is the part that ends careers, and they work through it hand by hand instead of describing it in the abstract. The turn is what the traders admit about being wrong. A good decision loses regularly, a bad decision wins regularly, and the only way to last is to grade the process instead of the result. Everything else in the video sits downstream of that. It matters more now than when the firm started running these tables. Every model prices probability in a second and hands it to you for free. Nothing on the screen tells you how much of your account to put behind the number, or what to do after the number was right and you lost anyway. Free on YouTube, produced by a newspaper, filmed at a real table with real hands. The maths is public. The sizing is the job. 1 table. 4 traders. It is in the video.
16:30

One of the largest trading firms in the world teaches new hires poker before it lets them near a book. A newspaper brought a camera to the table and sat down to play. The men across the felt are working Wall Street traders. The firm is Susquehanna, which built poker into its training programme decades ago and still runs it that way, on the argument that the card room teaches something no finance degree does. Gunjan Banerji from the Wall Street Journal plays the hands herself rather than interviewing them about it. A green table, a dealer, chips, 4 people who do this for a living. No lecture hall, no slides. The teaching happens between deals, while money is actually at stake. They break it into parts on camera. Risk management first. Then bet sizing. Then patience, which sounds like the soft one and is not. Then reading the person opposite when the only data available is how they behave with money on the table. The section on patience is the one most people skip. Folding is the correct action in the overwhelming majority of hands, and almost nobody can do it for hours without inventing a reason to play. The same failure shows up in a trading account as overtrading, and it kills more people than bad analysis. Then bet sizing, which is where the video earns the watch. Being right about the odds is the easy half. How much you put behind a correct read is the part that ends careers, and they work through it hand by hand instead of describing it in the abstract. The turn is what the traders admit about being wrong. A good decision loses regularly, a bad decision wins regularly, and the only way to last is to grade the process instead of the result. Everything else in the video sits downstream of that. It matters more now than when the firm started running these tables. Every model prices probability in a second and hands it to you for free. Nothing on the screen tells you how much of your account to put behind the number, or what to do after the number was right and you lost anyway. Free on YouTube, produced by a newspaper, filmed at a real table with real hands. The maths is public. The sizing is the job. 1 table. 4 traders. It is in the video.

the lich

62,689 次观看 • 2 个月前

🚨 BTC IS SETTING UP FOR ONE MORE MAJOR DROP Look at the chart Bitcoin has failed to reclaim $80K, so I don’t think the downside is finished yet The chart is pointing to one more liquidity sweep before the real recovery begins If the pattern holds, the next stop is 65k After that, 57k Then somewhere in the mid 40s, right where long term support actually sits Here's the part most people miss looking at a chart like this Every one of those earlier rejections wasn't the end of the move, it was the setup for the next one The bottom that forms after a leg like this is usually where the real trend actually starts Once that low prints, this same structure points back through 126k, and eventually toward 170k by 2027 I've watched this exact shape play out three times now, always right before the big leg up Be sure to follow me to not miss on updates
0:33

🚨 BTC IS SETTING UP FOR ONE MORE MAJOR DROP Look at the chart Bitcoin has failed to reclaim $80K, so I don’t think the downside is finished yet The chart is pointing to one more liquidity sweep before the real recovery begins If the pattern holds, the next stop is 65k After that, 57k Then somewhere in the mid 40s, right where long term support actually sits Here's the part most people miss looking at a chart like this Every one of those earlier rejections wasn't the end of the move, it was the setup for the next one The bottom that forms after a leg like this is usually where the real trend actually starts Once that low prints, this same structure points back through 126k, and eventually toward 170k by 2027 I've watched this exact shape play out three times now, always right before the big leg up Be sure to follow me to not miss on updates

NoName

23,509 次观看 • 1 个月前