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THE FIRST CROWBRAIN AUTONOMOUS TRADING EXPERIMENT IS COMPLETE I created a separate wallet for the system, deposited $50 and allowed the algorithm to execute its own signals for the first time. During the first five hours: $50 → $420 CROWBRAIN discovered tokens, analyzed price movement, volume and liquidity, generated... BUY / SELL signals, and opened and closed positions automatically. But then the market changed while the algorithm continued trading under the same rules. It took profits too late, re-entered after major price moves and failed to stop after a series of losing trades. Eventually, the profit disappeared and the overall result of the experiment went negative. That is exactly why I am testing it publicly. It would be easy to show only the $50 → $420 moment and stay silent about what happened afterward, but the purpose of this experiment is not to produce one impressive screenshot. It is to build a system that can control risk and perform consistently under different market conditions. Now I am adding: → automatic partial profit-taking → a dynamic stop-loss → position-size limits → a cooldown after consecutive losses → a daily loss limit → protection against re-entering after a sharp price increase → market-regime detection The first test proved that CROWBRAIN can already find opportunities and turn $50 into $420. It also showed that finding good entries means nothing without disciplined exits and proper risk management. The experiment continues. After the update, I will fund a new public wallet with another $50, restart the algorithm and show every trade - successful or not. Let’s see what CROWBRAIN learns from its first serious mistake.show more

sopersone

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33,764 görüntüleme • 1 ay önce •via X (Twitter)

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13 Yorum

Smed profil fotoğrafı

Smed1 ay önce

Here's the real question. When can I have the crown trade for me? Can we have a crow fund manager?

DexorynLabs profil fotoğrafı

DexorynLabs1 ay önce

Fast gain shocks the board.

rwaisys profil fotoğrafı

rwaisys1 ay önce

respect for posting the part after the 420. a rule that shuts it off after 3 losses in a row would have kept most of it

sopersone profil fotoğrafı

sopersone1 ay önce

yeap

tx profil fotoğrafı

tx1 ay önce

hmmmm

Antonio Garcia profil fotoğrafı

Antonio Garcia1 ay önce

hi are you make new push in your repo with this autotrade?

darrylsj profil fotoğrafı

darrylsj1 ay önce

What is the value of posting these stories, I don’t want to call them lies but these are make believe and not true. What’s the upside of these posts ?

Sasha profil fotoğrafı

Sasha1 ay önce

stop it ima pass out of laughter

Create.Love.Be profil fotoğrafı

Create.Love.Be1 ay önce

Damn bro. 😎 These signals are MAD serious Yo!!! 🙏🏽

staus profil fotoğrafı

staus1 ay önce

@grok is this guy ex huggingface dev/employee

Twolight profil fotoğrafı

Twolight1 ay önce

You're token for what?

Create.Love.Be profil fotoğrafı

Create.Love.Be1 ay önce

Boners every fugging where!!!! 😂

🌕Moonshot Tim profil fotoğrafı

🌕Moonshot Tim1 ay önce

@grok is this true or fake?

Benzer Videolar

$CROWBRAIN IS READY FOR ITS SECOND PUBLIC TEST The first experiment proved two things: the algorithm can find opportunities quickly, but without strict risk management, profits can disappear just as fast. I fixed the main problems by adding automatic profit-taking, a dynamic stop-loss, position size limits, a cooldown after consecutive losses and protection against re-entering after a sharp price increase. Now I am creating a new public wallet, funding it with $50 and launching CROWBRAIN in autonomous trading mode again. Starting balance: $50 Wallet address: [AD4vTDFY9RfFCdTmLPqzmoo61R6kwqskLk3BHSgXmrbf] Anyone will be able to verify the balance, open positions and transaction history. I will not hide losing trades or show only profitable moments - the entire experiment will be public. No additional deposits during the test. Just $50, the updated algorithm and the market. Let’s see what CROWBRAIN has learned from its first mistake and what it can turn this wallet into.

$CROWBRAIN IS READY FOR ITS SECOND PUBLIC TEST The first experiment proved two things: the algorithm can find opportunities quickly, but without strict risk management, profits can disappear just as fast. I fixed the main problems by adding automatic profit-taking, a dynamic stop-loss, position size limits, a cooldown after consecutive losses and protection against re-entering after a sharp price increase. Now I am creating a new public wallet, funding it with $50 and launching CROWBRAIN in autonomous trading mode again. Starting balance: $50 Wallet address: [AD4vTDFY9RfFCdTmLPqzmoo61R6kwqskLk3BHSgXmrbf] Anyone will be able to verify the balance, open positions and transaction history. I will not hide losing trades or show only profitable moments - the entire experiment will be public. No additional deposits during the test. Just $50, the updated algorithm and the market. Let’s see what CROWBRAIN has learned from its first mistake and what it can turn this wallet into.

sopersone

17,542 görüntüleme • 1 ay önce

I GAVE A CROW BRAIN $50 AND LET IT TRADE BY ITSELF CROWBRAIN BOT is officially live. It is an autonomous trading bot inspired by the intelligence of one of the smartest birds on the planet. It scans new and trending tokens, analyzes price, volume and liquidity, and then makes its own decision: BUY → open a position SELL → take profit or exit WAIT → skip the trade The first experiment turned $50 into $420 in just five hours, but the algorithm later lost the profit because its risk controls were not strong enough. I fixed the main problems. The bot now takes partial profits, uses a dynamic stop-loss, limits position sizes, pauses after consecutive losses and avoids re-entering after a sharp price increase. The second public experiment starts today. Starting balance: $50 Public wallet: [AD4vTDFY9RfFCdTmLPqzmoo61R6kwqskLk3BHSgXmrbf] No additional deposits and no hidden trades. Every entry, exit, profit and loss will be verifiable directly on-chain. $50. One updated algorithm. Fully public trading. Let’s see whether a digital crow brain can survive the real market.

I GAVE A CROW BRAIN $50 AND LET IT TRADE BY ITSELF CROWBRAIN BOT is officially live. It is an autonomous trading bot inspired by the intelligence of one of the smartest birds on the planet. It scans new and trending tokens, analyzes price, volume and liquidity, and then makes its own decision: BUY → open a position SELL → take profit or exit WAIT → skip the trade The first experiment turned $50 into $420 in just five hours, but the algorithm later lost the profit because its risk controls were not strong enough. I fixed the main problems. The bot now takes partial profits, uses a dynamic stop-loss, limits position sizes, pauses after consecutive losses and avoids re-entering after a sharp price increase. The second public experiment starts today. Starting balance: $50 Public wallet: [AD4vTDFY9RfFCdTmLPqzmoo61R6kwqskLk3BHSgXmrbf] No additional deposits and no hidden trades. Every entry, exit, profit and loss will be verifiable directly on-chain. $50. One updated algorithm. Fully public trading. Let’s see whether a digital crow brain can survive the real market.

sopersone

12,515 görüntüleme • 1 ay önce

what the ffffffff i spent 12 hours building a digital crow brain that scans Solana and Robinhood Chain and gives me BUY / SELL / WAIT signals then i started trading them $100 → $1,362 CROWBRAIN. the idea is simple: crows are among the smartest birds on earth. they use tools, remember faces, solve complex problems and have around 1.5 billion neurons inside a tiny brain i wanted to build a market system inspired by that intelligence every few seconds it: → finds trending and newly created tokens across both chains → checks price movement, volume and liquidity → removes pools with less than $25K in reserves → gives me BUY / SELL / WAIT with the reason behind every decision → records every check inside a public execution log → displays the process through an interactive crow brain there is no private group and no hidden signal feed. the system finds the opportunities, but i still choose which trades to execute 12 hours of building $100 → $1,362 and i decided to share the whole thing for free: repo: next: connect a real balance and let CROWBRAIN trade automatically until then, i’ll keep trading its signals manually and share the results here should i post every signal it catches live?

what the ffffffff i spent 12 hours building a digital crow brain that scans Solana and Robinhood Chain and gives me BUY / SELL / WAIT signals then i started trading them $100 → $1,362 CROWBRAIN. the idea is simple: crows are among the smartest birds on earth. they use tools, remember faces, solve complex problems and have around 1.5 billion neurons inside a tiny brain i wanted to build a market system inspired by that intelligence every few seconds it: → finds trending and newly created tokens across both chains → checks price movement, volume and liquidity → removes pools with less than $25K in reserves → gives me BUY / SELL / WAIT with the reason behind every decision → records every check inside a public execution log → displays the process through an interactive crow brain there is no private group and no hidden signal feed. the system finds the opportunities, but i still choose which trades to execute 12 hours of building $100 → $1,362 and i decided to share the whole thing for free: repo: next: connect a real balance and let CROWBRAIN trade automatically until then, i’ll keep trading its signals manually and share the results here should i post every signal it catches live?

sopersone

47,543 görüntüleme • 1 ay önce

BEEBRAIN IS GOING TO THE NEXT LEVEL. until now, it was just an experiment inside NERVE. a bee brain learning to read the market through liquidity, holders, snipers, creator age, volume, and now even Telegram. but the experiment went too far. after BEEBRAIN started finding signal combinations that i never explicitly taught it to look for, i decided to turn it into a standalone project. tomorrow BEEBRAIN gets: → its own website → its own Twitter account → its own GitHub repo → a fully open brain architecture i want anyone to be able to open it up and see exactly how the system thinks. what “senses” it uses. what signals reach the glomeruli. how the mushroom bodies learn from previous pools. which combinations start to matter. and why the brain says PASS on one token and ignores another completely. the last test already went from $600 → $1,900. one token was detected 40 seconds before anyone on CT started talking about it. but that was only version one. next, i want to keep giving the brain new senses and see what connections it discovers on its own. > social signals. > on-chain behavior. > wallet movement. > context. > maybe things normal traders would never even think to connect. i do not want to build just another “trading bot.” i want to build a brain that learns how to understand the market by itself. tomorrow BEEBRAIN stops being an experiment. it becomes its own project.

BEEBRAIN IS GOING TO THE NEXT LEVEL. until now, it was just an experiment inside NERVE. a bee brain learning to read the market through liquidity, holders, snipers, creator age, volume, and now even Telegram. but the experiment went too far. after BEEBRAIN started finding signal combinations that i never explicitly taught it to look for, i decided to turn it into a standalone project. tomorrow BEEBRAIN gets: → its own website → its own Twitter account → its own GitHub repo → a fully open brain architecture i want anyone to be able to open it up and see exactly how the system thinks. what “senses” it uses. what signals reach the glomeruli. how the mushroom bodies learn from previous pools. which combinations start to matter. and why the brain says PASS on one token and ignores another completely. the last test already went from $600 → $1,900. one token was detected 40 seconds before anyone on CT started talking about it. but that was only version one. next, i want to keep giving the brain new senses and see what connections it discovers on its own. > social signals. > on-chain behavior. > wallet movement. > context. > maybe things normal traders would never even think to connect. i do not want to build just another “trading bot.” i want to build a brain that learns how to understand the market by itself. tomorrow BEEBRAIN stops being an experiment. it becomes its own project.

h100envy

14,279 görüntüleme • 1 ay önce

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

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 görüntüleme • 7 ay önce

Know what to do before the market forces you to decide. 1. Before the trade — Should I trade? Is it a proven setup from your playbook? Does the market environment fit? Does the reward justify the risk? Do you have a clear invalidation level and the correct position size? Are you following your system, or do you just want action? If these don't align, pass. 2. At entry — What am I risking? Know your entry, stop, position size, initial $ risk, % of capital at risk, and your target or exit method. The chart determines the stop. The stop determines the position size. 3. After entry — Has anything actually changed? Is the setup still valid? Has your predetermined exit been reached? Has price action materially changed? Are you reacting to the chart or to your P&L? If nothing important has changed, don't invent a new decision. 4. When losing — Exit or hold? Hold only while your thesis and predefined risk remain valid. Exit when the stop is triggered, the setup is invalidated, market behavior violates your entry reason, or your risk limit is reached. Never turn a planned trade into an unplanned investment. 5. When winning — Exit or hold? Has the target been reached? Has the trailing stop triggered? Has the trend or setup weakened? Is there actual evidence to exit, or are you afraid of giving back profit? Let your exit rules manage the winner, not your emotions. 6. After the trade — What did I learn? Record the setup, entry, exit, risk, result in R, rule adherence, mistakes, a screenshot, and one lesson. Then separate two questions: did the strategy work, and did you execute it correctly? A losing trade can still be good execution. A winning trade can still involve bad execution. Make your important trading decisions before money is influencing your emotions.

Know what to do before the market forces you to decide. 1. Before the trade — Should I trade? Is it a proven setup from your playbook? Does the market environment fit? Does the reward justify the risk? Do you have a clear invalidation level and the correct position size? Are you following your system, or do you just want action? If these don't align, pass. 2. At entry — What am I risking? Know your entry, stop, position size, initial $ risk, % of capital at risk, and your target or exit method. The chart determines the stop. The stop determines the position size. 3. After entry — Has anything actually changed? Is the setup still valid? Has your predetermined exit been reached? Has price action materially changed? Are you reacting to the chart or to your P&L? If nothing important has changed, don't invent a new decision. 4. When losing — Exit or hold? Hold only while your thesis and predefined risk remain valid. Exit when the stop is triggered, the setup is invalidated, market behavior violates your entry reason, or your risk limit is reached. Never turn a planned trade into an unplanned investment. 5. When winning — Exit or hold? Has the target been reached? Has the trailing stop triggered? Has the trend or setup weakened? Is there actual evidence to exit, or are you afraid of giving back profit? Let your exit rules manage the winner, not your emotions. 6. After the trade — What did I learn? Record the setup, entry, exit, risk, result in R, rule adherence, mistakes, a screenshot, and one lesson. Then separate two questions: did the strategy work, and did you execute it correctly? A losing trade can still be good execution. A winning trade can still involve bad execution. Make your important trading decisions before money is influencing your emotions.

Scott Redler

28,241 görüntüleme • 1 ay önce

$DXYZ Trade Recap I want to share my full trading journey on $DXYZ, from the first entry to the final exit. I first posted this stock on April 30th and took my initial position at $31.58. Within seven days, the stock exploded to $60+, and my position doubled quickly. My usual rule is simple: When my investment doubles, I sell all or most of the position. This time, I only sold half. On May 13th, I bought back the shares I sold, around $51, and shared that update as well. Price pushed back to the $70 area, but after forming a clear double top and failing to break $71, I sold the shares I added on May 13th at price $59. Soon after, the stock dropped sharply to the $50 area. I bought again around $50, with a stop slightly below $47, and that stop was hit. Later, when price pulled back to the $38 range, I entered again and waited for price action into the SpaceX IPO. I eventually exited all remaining shares in premarket before the IPO, around $36. That completes my entire trade sequence on this name. I made a profit overall, but not as much as I originally expected. The key reason is: Price never broke above the previous $70 resistance. Looking back, the part I didn’t execute well was letting my bias take over. I believed the stock could go much higher, and because of that, I didn’t follow my usual discipline: I didn’t sell the entire position when I doubled my investment the first time. I didn’t sell everything when the stock failed to break the previous high. Both moments were clear opportunities to lock in the full gain. Still, it was a solid learning experience and a profitable trade overall. Thanks for reading, and I hope this recap helps others stay disciplined in their own trading.

$DXYZ Trade Recap I want to share my full trading journey on $DXYZ, from the first entry to the final exit. I first posted this stock on April 30th and took my initial position at $31.58. Within seven days, the stock exploded to $60+, and my position doubled quickly. My usual rule is simple: When my investment doubles, I sell all or most of the position. This time, I only sold half. On May 13th, I bought back the shares I sold, around $51, and shared that update as well. Price pushed back to the $70 area, but after forming a clear double top and failing to break $71, I sold the shares I added on May 13th at price $59. Soon after, the stock dropped sharply to the $50 area. I bought again around $50, with a stop slightly below $47, and that stop was hit. Later, when price pulled back to the $38 range, I entered again and waited for price action into the SpaceX IPO. I eventually exited all remaining shares in premarket before the IPO, around $36. That completes my entire trade sequence on this name. I made a profit overall, but not as much as I originally expected. The key reason is: Price never broke above the previous $70 resistance. Looking back, the part I didn’t execute well was letting my bias take over. I believed the stock could go much higher, and because of that, I didn’t follow my usual discipline: I didn’t sell the entire position when I doubled my investment the first time. I didn’t sell everything when the stock failed to break the previous high. Both moments were clear opportunities to lock in the full gain. Still, it was a solid learning experience and a profitable trade overall. Thanks for reading, and I hope this recap helps others stay disciplined in their own trading.

SEPTINVESTING

19,447 görüntüleme • 5 ay önce

📊 Most traders spend their time searching for opportunities. GENESIS was built to find them. ➡️ Introducing Genesis, an autonomous #crypto market intelligence platform designed to change how #traders navigate the market. GENESIS doesn’t just display data. It actively hunts for trading opportunities across crypto markets, analyzes price action, momentum, market structure and catalysts, and generates actionable model trades with defined entries, stop-losses and profit targets. And the intelligence doesn’t stop when a trade is identified. The system continues monitoring market conditions and tracking its positions. Alongside its autonomous trading engine, GENESIS delivers a comprehensive intelligence dashboard with advanced indicators, full interactive charts, market analytics and portfolio performance tracking. 💡 The idea is simple: bring market research, opportunity discovery, trade generation and ongoing intelligence together into one autonomous system. The next generation of crypto trading intelligence is taking shape. 🌐 Explore GENESIS:

📊 Most traders spend their time searching for opportunities. GENESIS was built to find them. ➡️ Introducing Genesis, an autonomous #crypto market intelligence platform designed to change how #traders navigate the market. GENESIS doesn’t just display data. It actively hunts for trading opportunities across crypto markets, analyzes price action, momentum, market structure and catalysts, and generates actionable model trades with defined entries, stop-losses and profit targets. And the intelligence doesn’t stop when a trade is identified. The system continues monitoring market conditions and tracking its positions. Alongside its autonomous trading engine, GENESIS delivers a comprehensive intelligence dashboard with advanced indicators, full interactive charts, market analytics and portfolio performance tracking. 💡 The idea is simple: bring market research, opportunity discovery, trade generation and ongoing intelligence together into one autonomous system. The next generation of crypto trading intelligence is taking shape. 🌐 Explore GENESIS:

Crypto Holding™ 💎

294,058 görüntüleme • 2 gün önce

The GPT-6 Astra brain, a neural copy of 169 winning Robinhood memecoin traders, is about to touch real money for the first time. Six gates stand between what it wants and my deposit, and I'm not one of them. > PROVENANCE. Whose fill is it really. Gifted or dust dies here, 1 fill in 11 > COHORT. Who went in, at what cost, with what record. Averaging down is not entering > TOKEN. Seeded, or can't even sell. A sell probe runs before a dollar moves > MARKET. How far above the wallets' own cost, and whether the candle is already tired > RISK. Never above 4% of the deposit, five positions max, the day stops at minus 6% > EXECUTE. Slippage cap, two tries. No fill, walk away The three middle gates have to say yes at the same second or nothing happens. The right token at the wrong price parks and knocks again in 90 seconds. A token that turns out seeded after entry closes itself, whatever the PnL. Half of every profit goes back into the deposit that sizes the next trade. Half buys $FOMOBRAIN and burns it. Every result goes into the score of the wallet that caused it, so the judge learns what its own picks did. Every no gets written down with its reason. The gates are built, and the wallet is next. God bless us.

The GPT-6 Astra brain, a neural copy of 169 winning Robinhood memecoin traders, is about to touch real money for the first time. Six gates stand between what it wants and my deposit, and I'm not one of them. > PROVENANCE. Whose fill is it really. Gifted or dust dies here, 1 fill in 11 > COHORT. Who went in, at what cost, with what record. Averaging down is not entering > TOKEN. Seeded, or can't even sell. A sell probe runs before a dollar moves > MARKET. How far above the wallets' own cost, and whether the candle is already tired > RISK. Never above 4% of the deposit, five positions max, the day stops at minus 6% > EXECUTE. Slippage cap, two tries. No fill, walk away The three middle gates have to say yes at the same second or nothing happens. The right token at the wrong price parks and knocks again in 90 seconds. A token that turns out seeded after entry closes itself, whatever the PnL. Half of every profit goes back into the deposit that sizes the next trade. Half buys $FOMOBRAIN and burns it. Every result goes into the score of the wallet that caused it, so the judge learns what its own picks did. Every no gets written down with its reason. The gates are built, and the wallet is next. God bless us.

cvxv666

11,917 görüntüleme • 2 ay önce

THIS SHOULDN'T BE POSSIBLE ANTHROPIC'S OPUS 5.5 RUNS A 16-LEG SPIDER THAT READS WALLETS, NEWS, ON-CHAIN AND SENTIMENT AT THE SAME TIME. IT TURNED $200 INTO $3,742 IN 9 DAYS. A human trader does one thing at a time. Read the news, then check the chart, then size the trade. By the time you finish, the move is gone. Opus does all of it at once. Each leg sits in its own part of the market. Positions, liquidity and risk get their own legs too. 22 tentacles reach into 368 pairs and 9 markets, from BTC to gold to DOGE. The numbers per room: 124 wallet signals. 57 news signals. 88 on-chain signals. 61 liquidity reads. 15 scans every second. Every read feeds one score. 33 at the start. 52 after on-chain. 78 after sentiment. 95 before execution. Nothing trades below 72. Risk per trade is fixed at 4%. Then the hands move. Fill in 0.2 seconds. Slippage 0.03%. Stop to breakeven the moment it can. Take profit at +3R. In one 3-second window it bought gold, moved a SOL stop and closed ETH. All of this happened while the market bled. XRP down 7.38%. BTC down 2.26%. $281 after 9 trades. $801 after 28. $1,935 after 47. $3,742 after 65. 71% win rate. I lose a trade every time I switch tabs. Claude doesn't have tabs. The full setup is below and free. Almost nobody has it saved. Save it before the next market dump.

THIS SHOULDN'T BE POSSIBLE ANTHROPIC'S OPUS 5.5 RUNS A 16-LEG SPIDER THAT READS WALLETS, NEWS, ON-CHAIN AND SENTIMENT AT THE SAME TIME. IT TURNED $200 INTO $3,742 IN 9 DAYS. A human trader does one thing at a time. Read the news, then check the chart, then size the trade. By the time you finish, the move is gone. Opus does all of it at once. Each leg sits in its own part of the market. Positions, liquidity and risk get their own legs too. 22 tentacles reach into 368 pairs and 9 markets, from BTC to gold to DOGE. The numbers per room: 124 wallet signals. 57 news signals. 88 on-chain signals. 61 liquidity reads. 15 scans every second. Every read feeds one score. 33 at the start. 52 after on-chain. 78 after sentiment. 95 before execution. Nothing trades below 72. Risk per trade is fixed at 4%. Then the hands move. Fill in 0.2 seconds. Slippage 0.03%. Stop to breakeven the moment it can. Take profit at +3R. In one 3-second window it bought gold, moved a SOL stop and closed ETH. All of this happened while the market bled. XRP down 7.38%. BTC down 2.26%. $281 after 9 trades. $801 after 28. $1,935 after 47. $3,742 after 65. 71% win rate. I lose a trade every time I switch tabs. Claude doesn't have tabs. The full setup is below and free. Almost nobody has it saved. Save it before the next market dump.

INSIDER

10,480 görüntüleme • 1 ay önce

I gave my Grok Bot two options: multiply or die. It came back asking for $250 to hire a team of six. The same headcount on a real desk is three analysts and a risk officer. That is $400,000 a year before anyone opens a position. Mine cost $250 once and about $300 a month to keep breathing. Mine took $250 to $611 on day one, out of a wallet anyone can open and read. Most of the build is not about making it trade. It is about stopping it. Every agent's job description ends with a list of what it may never do without asking me first. One of the six is not allowed to trade at all. Its entire job is killing what the other five found, and its no beats every score on the desk. The one that makes the final call never touches the market either. It only signs. Day three the account was back to $180 and it opened nothing for nine hours while every account I follow posted green screenshots. That was the day the build started working. You could have this running by Friday. Most people reading this will not, and in a few months they will be paying somebody who did. The whole build is in the post below ↓ Save it while it still sounds strange. Give it a quarter and this is just how a retail account works.

I gave my Grok Bot two options: multiply or die. It came back asking for $250 to hire a team of six. The same headcount on a real desk is three analysts and a risk officer. That is $400,000 a year before anyone opens a position. Mine cost $250 once and about $300 a month to keep breathing. Mine took $250 to $611 on day one, out of a wallet anyone can open and read. Most of the build is not about making it trade. It is about stopping it. Every agent's job description ends with a list of what it may never do without asking me first. One of the six is not allowed to trade at all. Its entire job is killing what the other five found, and its no beats every score on the desk. The one that makes the final call never touches the market either. It only signs. Day three the account was back to $180 and it opened nothing for nine hours while every account I follow posted green screenshots. That was the day the build started working. You could have this running by Friday. Most people reading this will not, and in a few months they will be paying somebody who did. The whole build is in the post below ↓ Save it while it still sounds strange. Give it a quarter and this is just how a retail account works.

NO1ennn

27,193 görüntüleme • 2 ay önce

I used Claude Fable 5.1 and GPT-6 Astra to build a self-learning trading algorithm The result: +$1,840.82 in two days It makes about 17 trades a day or 521 a month, with an average trade size of $53.2 Six AI agents track new coins on Solana and learn to make trading decisions as one system I called it QuantumOrb GitHub repo: Here’s how it works: SCOUT tracks new launches, bonding curve activity and PumpSwap swaps, then records the trading data CARTOGRAPHER detects shared wallet funding sources, same-bundle activity and connections to the token creator JUDGE analyzes traders from their transaction history and estimates their performance and edge after fees, entry delay and copybot activity QUANT calculates execution on the curve or inside the pool, accounting for fees, market impact, priority fees and Jito tips GUARD checks holder concentration, connected wallet groups and launch risks EXECUTOR models opening and closing positions through take-profit, stop-loss or time expiry At the center of the system is the Orb Every launch is classified into three probability states: +60% is reached first −30% is reached first Neither threshold is reached within 60 minutes Most profitable trades: $45 → $158 (+$113) $37 → $146 (+$109) $31 → $125 (+$94) Organic buyer flow shifts probability toward one scenario Connected-wallet activity, sells and holder concentration adjust the estimate in the opposite direction With every new signal, the state updates. After that, QUANT determines whether the setup still makes sense after all execution costs, while GUARD decides whether the risk is acceptable Every launch that passes GUARD is used for learning, including the ones the system decides to skip One hour later, the actual outcome is recorded and fed back into the model That feedback becomes part of the next decision.

I used Claude Fable 5.1 and GPT-6 Astra to build a self-learning trading algorithm The result: +$1,840.82 in two days It makes about 17 trades a day or 521 a month, with an average trade size of $53.2 Six AI agents track new coins on Solana and learn to make trading decisions as one system I called it QuantumOrb GitHub repo: Here’s how it works: SCOUT tracks new launches, bonding curve activity and PumpSwap swaps, then records the trading data CARTOGRAPHER detects shared wallet funding sources, same-bundle activity and connections to the token creator JUDGE analyzes traders from their transaction history and estimates their performance and edge after fees, entry delay and copybot activity QUANT calculates execution on the curve or inside the pool, accounting for fees, market impact, priority fees and Jito tips GUARD checks holder concentration, connected wallet groups and launch risks EXECUTOR models opening and closing positions through take-profit, stop-loss or time expiry At the center of the system is the Orb Every launch is classified into three probability states: +60% is reached first −30% is reached first Neither threshold is reached within 60 minutes Most profitable trades: $45 → $158 (+$113) $37 → $146 (+$109) $31 → $125 (+$94) Organic buyer flow shifts probability toward one scenario Connected-wallet activity, sells and holder concentration adjust the estimate in the opposite direction With every new signal, the state updates. After that, QUANT determines whether the setup still makes sense after all execution costs, while GUARD decides whether the risk is acceptable Every launch that passes GUARD is used for learning, including the ones the system decides to skip One hour later, the actual outcome is recorded and fed back into the model That feedback becomes part of the next decision.

Daniro

336,359 görüntüleme • 29 gün önce

#WATCH | Delhi CM Rekha Gupta says, "Cloud seeding is a necessity for Delhi and is the first experiment of its kind. We want to try it in Delhi to see if it can help us control this very serious environmental problem. So the blessings of the people of Delhi are with the government, and we feel that this will be a successful experiment and in future, we will be able to overcome these environmental problems."

#WATCH | Delhi CM Rekha Gupta says, "Cloud seeding is a necessity for Delhi and is the first experiment of its kind. We want to try it in Delhi to see if it can help us control this very serious environmental problem. So the blessings of the people of Delhi are with the government, and we feel that this will be a successful experiment and in future, we will be able to overcome these environmental problems."

ANI

27,262 görüntüleme • 1 yıl önce

Something interesting is going to be released soon... ✨ Kitt The Inner Circle Trader "If I had to trade only one model for the rest of my life, considering everything I've publicly disclosed, my choice would be either the second stage of re-distribution in an MMSM or the second stage of re-accumulation in an MMBM. With either of these, I believe I could consistently generate substantial profits without the need to explore alternative strategies. These models rely on specific components of both the buy and sell sides of the market curve, which are directly interconnected. This isn't a matter of identifying support and resistance levels; it's about understanding the logic of order flow. In the case of the market maker sell model, I focus on identifying a pool of liquidity beneath the initial consolidation. When I spot this sellside opportunity, I patiently await a reversal. This reversal should lead to a drop of at least 50% from the smart money's reversal point down to the sellside liquidity. If it achieves this, and then begins to rally once more, I'll look to correlate it with the other side of the curve, where the market previously rallied before reversing. This will provide me with an array that initially signaled a bullish trend but now acts as a reversal indicator. This marks the second stage of distribution or redistribution, and it usually happens swiftly, pushing prices towards the sellside. In essence, I'm waiting for a unicorn setup, where all the pieces align perfectly, and I have everything in my favor. I'll risk 5% on such a trade. This approach involves re-accumulation, where the sellside drops down to 50%, and then I match it with another array to capture the reversal. Now, picture a market maker model involving a consolidation phase where relative equal lows are formed, followed by an upward rally, possibly forming a consolidation that resembles a bull flag pattern. Subsequently, it rallies out of that consolidation. Sometimes, it may create a second stage of re-accumulation as it trades towards a premium array level—a level I consider a liquidity draw. If I'm feeling bullish, I'd aim for that level. I don't necessarily need to be there at the exact moment; I might spot the opportunity later and act accordingly. If it's reacting off of a level, that should offer sellside. So, you know where sellside delivery. The market should drop down. So, I'm anticipating price reacting and reversing at the smart money reversal once it starts to break down. If it goes back up a little bit, that's the smart money reversal. Low risk sell is the next stage and then they'll drop. When we reach the low-risk sell, it's important that the drop reaches at least 50% of the total range from the smart money reversal to the sellside I'm targeting. As long as it accomplishes this, I have confidence that the subsequent rally will reach a premium array on the left side of the curve before the market makes its high and reverses. Why would it do that? Because it's part of a larger continuation. So when and how would I determine when it's going to fail ,that first leg of re-distribution on the sell side, if it doesn't pierce 50% of that range from the smart money reversal down to the sell side liquidity. If it doesn't do that, then it's not going to go down there. It's going to be a continuation of reverse and go the other way." #ict #ICT

Something interesting is going to be released soon... ✨ Kitt The Inner Circle Trader "If I had to trade only one model for the rest of my life, considering everything I've publicly disclosed, my choice would be either the second stage of re-distribution in an MMSM or the second stage of re-accumulation in an MMBM. With either of these, I believe I could consistently generate substantial profits without the need to explore alternative strategies. These models rely on specific components of both the buy and sell sides of the market curve, which are directly interconnected. This isn't a matter of identifying support and resistance levels; it's about understanding the logic of order flow. In the case of the market maker sell model, I focus on identifying a pool of liquidity beneath the initial consolidation. When I spot this sellside opportunity, I patiently await a reversal. This reversal should lead to a drop of at least 50% from the smart money's reversal point down to the sellside liquidity. If it achieves this, and then begins to rally once more, I'll look to correlate it with the other side of the curve, where the market previously rallied before reversing. This will provide me with an array that initially signaled a bullish trend but now acts as a reversal indicator. This marks the second stage of distribution or redistribution, and it usually happens swiftly, pushing prices towards the sellside. In essence, I'm waiting for a unicorn setup, where all the pieces align perfectly, and I have everything in my favor. I'll risk 5% on such a trade. This approach involves re-accumulation, where the sellside drops down to 50%, and then I match it with another array to capture the reversal. Now, picture a market maker model involving a consolidation phase where relative equal lows are formed, followed by an upward rally, possibly forming a consolidation that resembles a bull flag pattern. Subsequently, it rallies out of that consolidation. Sometimes, it may create a second stage of re-accumulation as it trades towards a premium array level—a level I consider a liquidity draw. If I'm feeling bullish, I'd aim for that level. I don't necessarily need to be there at the exact moment; I might spot the opportunity later and act accordingly. If it's reacting off of a level, that should offer sellside. So, you know where sellside delivery. The market should drop down. So, I'm anticipating price reacting and reversing at the smart money reversal once it starts to break down. If it goes back up a little bit, that's the smart money reversal. Low risk sell is the next stage and then they'll drop. When we reach the low-risk sell, it's important that the drop reaches at least 50% of the total range from the smart money reversal to the sellside I'm targeting. As long as it accomplishes this, I have confidence that the subsequent rally will reach a premium array on the left side of the curve before the market makes its high and reverses. Why would it do that? Because it's part of a larger continuation. So when and how would I determine when it's going to fail ,that first leg of re-distribution on the sell side, if it doesn't pierce 50% of that range from the smart money reversal down to the sell side liquidity. If it doesn't do that, then it's not going to go down there. It's going to be a continuation of reverse and go the other way." #ict #ICT

LumiTraders

389,489 görüntüleme • 3 yıl önce

Trading consolidations is a guaranteed way to burn money. The breakout retest is the ONLY strategy that works when markets move sideways—and it's ridiculously profitable when done right. First, here's why consolidation trading doesn’t work: • Too much volatility. • Unpredictable price action. • Market manipulation in both directions. That’s why we trade the breakout retest: Here’s the 4-steps to it: Step 1: Draw the box When price consolidates, I draw a box from the lowest point to the highest point. Then I wait. Step 2: Wait for the breakout I'm waiting for one thing: a big momentum candlestick that breaks out of the box. Ideally, this candle aligns with the higher timeframe trend. Step 3: Identify the imbalance That momentum candle creates a gap—the space between the low of the candle before the breakout and the high of the candle after. Step 4: Enter on the retest Price pulls back to retest that imbalance around 50%—fair value. When it does, I enter. Stop loss above the imbalance. Target 2-3R. Why this works: The market always moves from imbalance to balance to imbalance. That pullback after the breakout? It's the market seeking fair value before continuing. The bottom line: Stop trading consolidation. Start positioning on retests. If you can't wait for the retest, you're not ready to be profitable. — This is just a breakdown of one of the trading strategies we covered in our 2-hour long cryptocurrency trading course. I also discussed the trend pullback strategy, how to trade liquidity sweep reversals, and why understanding liquidity is the only way to avoid becoming exit liquidity. Just comment "COURSE" and I'll DM it to you immediately so you can watch it.

Trading consolidations is a guaranteed way to burn money. The breakout retest is the ONLY strategy that works when markets move sideways—and it's ridiculously profitable when done right. First, here's why consolidation trading doesn’t work: • Too much volatility. • Unpredictable price action. • Market manipulation in both directions. That’s why we trade the breakout retest: Here’s the 4-steps to it: Step 1: Draw the box When price consolidates, I draw a box from the lowest point to the highest point. Then I wait. Step 2: Wait for the breakout I'm waiting for one thing: a big momentum candlestick that breaks out of the box. Ideally, this candle aligns with the higher timeframe trend. Step 3: Identify the imbalance That momentum candle creates a gap—the space between the low of the candle before the breakout and the high of the candle after. Step 4: Enter on the retest Price pulls back to retest that imbalance around 50%—fair value. When it does, I enter. Stop loss above the imbalance. Target 2-3R. Why this works: The market always moves from imbalance to balance to imbalance. That pullback after the breakout? It's the market seeking fair value before continuing. The bottom line: Stop trading consolidation. Start positioning on retests. If you can't wait for the retest, you're not ready to be profitable. — This is just a breakdown of one of the trading strategies we covered in our 2-hour long cryptocurrency trading course. I also discussed the trend pullback strategy, how to trade liquidity sweep reversals, and why understanding liquidity is the only way to avoid becoming exit liquidity. Just comment "COURSE" and I'll DM it to you immediately so you can watch it.

The Trading Geek (Brad Goh)

11,008 görüntüleme • 10 ay önce

This guy built an HFT algorithm on Polymarket with an average trade size of $35.88 Result: +$470,883 His algorithm trades short-term crypto Up/Down markets. It combines directional trading and dynamic hedging, while forming complete sets with part of its inventory: 1. Finds undervalued outcomes using its own fair value estimates It factors in the underlying asset’s price relative to the opening price, the time remaining, momentum, volatility, the speed of price movements, and spot/perp data If its model values Up at 65c and the price on Polymarket is 55c, it starts building a position 2. Buys the opposite side when the market changes direction If the underlying asset reverses direction, it starts buying the opposite outcome to reduce its net directional exposure This hedges part of its existing position 3. Turns the hedge into a complete set If each matched Up + Down pair costs less than $1, that portion of the position has a structural edge regardless of how the market resolves It keeps the unpaired remainder as a directional residual on the side its model considers undervalued Its Polymarket account: mo-money You can build your own trading bot here: (A trial is available after signing up) The bot’s main edge comes from a combination of fair-value entry and dynamic hedging: it first builds a position in an undervalued outcome, then uses the opposite side to reduce exposure or form a complete set

This guy built an HFT algorithm on Polymarket with an average trade size of $35.88 Result: +$470,883 His algorithm trades short-term crypto Up/Down markets. It combines directional trading and dynamic hedging, while forming complete sets with part of its inventory: 1. Finds undervalued outcomes using its own fair value estimates It factors in the underlying asset’s price relative to the opening price, the time remaining, momentum, volatility, the speed of price movements, and spot/perp data If its model values Up at 65c and the price on Polymarket is 55c, it starts building a position 2. Buys the opposite side when the market changes direction If the underlying asset reverses direction, it starts buying the opposite outcome to reduce its net directional exposure This hedges part of its existing position 3. Turns the hedge into a complete set If each matched Up + Down pair costs less than $1, that portion of the position has a structural edge regardless of how the market resolves It keeps the unpaired remainder as a directional residual on the side its model considers undervalued Its Polymarket account: mo-money You can build your own trading bot here: (A trial is available after signing up) The bot’s main edge comes from a combination of fair-value entry and dynamic hedging: it first builds a position in an undervalued outcome, then uses the opposite side to reduce exposure or form a complete set

VALIX

89,279 görüntüleme • 1 ay önce

10 days ago, i gave my LLM $10k to trade with and a ton of research tools as an experiment the system is built on top of what i built with and uses: - X signals + X websearch(manual selection of top voices + automated search) - Perplexity research - Tavily - Company filings - Earnings, analyst targets etc - News - Technical and catalysts - private reports I limited the scope to 223 stocks in AI infra, energy and robotics. But I didn’t ask it for "stock picks", I asked it to run a portfolio. Every session, MiniMax M3, running through Nebius Token Factory, must decide what to buy, hold, add, trim or avoid. For every stock, it produces: → A target weight → 1, 5 and 20 session forecasts → A stop loss → A profit taking ladder → A time stop → A rationale, counterargument and invalidation trigger Then a deterministic risk engine gets the final word. It checks concentration, correlations, basket exposure and position size. Every call is timestamped, every trade fills at the next observed close, and costs are included. Nothing gets rewritten after the fact. The most interesting trade so far? $AMD The bot started building the position on September 15. A few sessions later, AMD had jumped 9.9% in one day on 2.3x normal volume. The position was up 22.6%. The thesis was still improving. But RSI had reached 72.9. The analyst consensus target had effectively caught up with the price. And the AI basket was already at its exposure limit. The model said hold, but the risk engine trimmed the position anyway. That is exactly the behavior I wanted! It liked the company but refused to let conviction become concentration. The portfolio it built includes: Compute: Micron, Nvidia, Broadcom, TSMC, Monolithic Power Power: Siemens Energy, BWXT, Caterpillar, First Solar, Mitsubishi Heavy, ABB, Cameco Robotics: HIWIN, Novanta, Tesla Early results, from a clean $10,000 start: Portfolio value: $10,471.69 Return: +4.72% Ahead of its benchmark: +4.31 points Executed trades: 114 Cash: 10% Very early. Most positions have only four or five observed sessions. And the first version already taught me something: The bot found too many “good” ideas. It opened 63 positions, often in tiny amounts. So I kept the same account and the full history, but changed the policy. It is now concentrating toward 25 positions, with larger minimum entries and one in, one out replacements. The experiment isn’t really wether or not a LLM can predict stocks, it’s more "can a model turn a mountain of messy info into repeatable, constrained and fully auditable decisions?" Now I can finally measure it will share more as the experiemnt continues... (fictive money, not financial advice, just a fun experiment)

10 days ago, i gave my LLM $10k to trade with and a ton of research tools as an experiment the system is built on top of what i built with and uses: - X signals + X websearch(manual selection of top voices + automated search) - Perplexity research - Tavily - Company filings - Earnings, analyst targets etc - News - Technical and catalysts - private reports I limited the scope to 223 stocks in AI infra, energy and robotics. But I didn’t ask it for "stock picks", I asked it to run a portfolio. Every session, MiniMax M3, running through Nebius Token Factory, must decide what to buy, hold, add, trim or avoid. For every stock, it produces: → A target weight → 1, 5 and 20 session forecasts → A stop loss → A profit taking ladder → A time stop → A rationale, counterargument and invalidation trigger Then a deterministic risk engine gets the final word. It checks concentration, correlations, basket exposure and position size. Every call is timestamped, every trade fills at the next observed close, and costs are included. Nothing gets rewritten after the fact. The most interesting trade so far? $AMD The bot started building the position on September 15. A few sessions later, AMD had jumped 9.9% in one day on 2.3x normal volume. The position was up 22.6%. The thesis was still improving. But RSI had reached 72.9. The analyst consensus target had effectively caught up with the price. And the AI basket was already at its exposure limit. The model said hold, but the risk engine trimmed the position anyway. That is exactly the behavior I wanted! It liked the company but refused to let conviction become concentration. The portfolio it built includes: Compute: Micron, Nvidia, Broadcom, TSMC, Monolithic Power Power: Siemens Energy, BWXT, Caterpillar, First Solar, Mitsubishi Heavy, ABB, Cameco Robotics: HIWIN, Novanta, Tesla Early results, from a clean $10,000 start: Portfolio value: $10,471.69 Return: +4.72% Ahead of its benchmark: +4.31 points Executed trades: 114 Cash: 10% Very early. Most positions have only four or five observed sessions. And the first version already taught me something: The bot found too many “good” ideas. It opened 63 positions, often in tiny amounts. So I kept the same account and the full history, but changed the policy. It is now concentrating toward 25 positions, with larger minimum entries and one in, one out replacements. The experiment isn’t really wether or not a LLM can predict stocks, it’s more "can a model turn a mountain of messy info into repeatable, constrained and fully auditable decisions?" Now I can finally measure it will share more as the experiemnt continues... (fictive money, not financial advice, just a fun experiment)

dylan ツ

161,505 görüntüleme • 1 ay önce

I just closed a quick $73.8K trade—but not before nearly botching the whole thing. I set my take profit at 212.70 instead of 212.776. When price started dropping fast, I had to manually close all my positions with speedy fingers before hitting the wrong TP level. lol. Let me break down the trade setup: Price was bearish, then we got a market shift signalling a potential bullish move on the 15-minute structure. After price mitigated the supply zone and created a failed reaction, I knew we had a flip + sweep zone forming. Price pushed hard to the upside, but gravity eventually pulled it back down. I saw bearish momentum forming and knew price was gravitating toward the extreme demand zone below. Instead of waiting for a buy setup (which would happen during New York session—not my time), I decided to trade the pullback itself. Here’s how I entered: I waited for a huge bearish candle, then watched for price to pull back slightly. That pullback created my supply zone. When price mitigated that zone and created a lower high—boom, that's where I entered. Overall thoughts: Honestly, I rate this trade 5/10. Not perfect. I rushed the entry and couldn't use my normal platform. Some positions even filled at non-ideal prices. But here's what I did right: proper stop loss placement and taking profit at the exact point where demand would step in. — This is just one setup from my complete trading framework. I also cover how to spot market shifts before they happen, the exact 3 timeframes I use to confirm every trade, and how to identify high-probability supply and demand zones for entries. Just comment "FRAMEWORK" and I'll send you the full breakdown in your DMs.

I just closed a quick $73.8K trade—but not before nearly botching the whole thing. I set my take profit at 212.70 instead of 212.776. When price started dropping fast, I had to manually close all my positions with speedy fingers before hitting the wrong TP level. lol. Let me break down the trade setup: Price was bearish, then we got a market shift signalling a potential bullish move on the 15-minute structure. After price mitigated the supply zone and created a failed reaction, I knew we had a flip + sweep zone forming. Price pushed hard to the upside, but gravity eventually pulled it back down. I saw bearish momentum forming and knew price was gravitating toward the extreme demand zone below. Instead of waiting for a buy setup (which would happen during New York session—not my time), I decided to trade the pullback itself. Here’s how I entered: I waited for a huge bearish candle, then watched for price to pull back slightly. That pullback created my supply zone. When price mitigated that zone and created a lower high—boom, that's where I entered. Overall thoughts: Honestly, I rate this trade 5/10. Not perfect. I rushed the entry and couldn't use my normal platform. Some positions even filled at non-ideal prices. But here's what I did right: proper stop loss placement and taking profit at the exact point where demand would step in. — This is just one setup from my complete trading framework. I also cover how to spot market shifts before they happen, the exact 3 timeframes I use to confirm every trade, and how to identify high-probability supply and demand zones for entries. Just comment "FRAMEWORK" and I'll send you the full breakdown in your DMs.

The Trading Geek (Brad Goh)

12,264 görüntüleme • 9 ay önce

$BTC (4hr) Our system locked profit on Bitcoin capturing at least 90% of the move with our Alpha Stop-Loss Dots. But now Bitcoin is resetting its volatility cycle for the next setup. What does that mean for the price direction right now and what do you need to know? 👇 Volatility is cyclical as is volume. And volatility right now has not had a clear direction. But its been releasing which has given us a lot of volatility and shake out in the price action. This is why I had given my warning to everyone that we were locking profits here on short term trades (long term still long). In our OS system I am able to identify moves a lot sooner now with a lot more accuracy and thats the result of what you all have been seeing here with the calls. What we need to look for next on the charts and what I am doing 👇 If you have OS and have taken profits on that stop out you did your job as a trader. But we need to make sure everyone else has a game-plan. In my opinion the price could see another shake out before the breakout. This is called a "Spring Board" setup. Thats what I am watching for. But it has to also print one of our OS signals with Phantom. I will keep you all up to date when it happens. Enjoy the video 🤝

$BTC (4hr) Our system locked profit on Bitcoin capturing at least 90% of the move with our Alpha Stop-Loss Dots. But now Bitcoin is resetting its volatility cycle for the next setup. What does that mean for the price direction right now and what do you need to know? 👇 Volatility is cyclical as is volume. And volatility right now has not had a clear direction. But its been releasing which has given us a lot of volatility and shake out in the price action. This is why I had given my warning to everyone that we were locking profits here on short term trades (long term still long). In our OS system I am able to identify moves a lot sooner now with a lot more accuracy and thats the result of what you all have been seeing here with the calls. What we need to look for next on the charts and what I am doing 👇 If you have OS and have taken profits on that stop out you did your job as a trader. But we need to make sure everyone else has a game-plan. In my opinion the price could see another shake out before the breakout. This is called a "Spring Board" setup. Thats what I am watching for. But it has to also print one of our OS signals with Phantom. I will keep you all up to date when it happens. Enjoy the video 🤝

Wick 🔥

19,420 görüntüleme • 1 ay önce

This guy built an asynchronous complete-set bot and made +$111,945 on Polymarket with a 50% win rate I analyzed its trades and found that its main strategy is based on gradually accumulating both sides of the same market In 88.76% of markets, it bought both Up and Down It looks something like this: > First, the bot accumulates one side > After the underlying asset moves, it starts buying the opposite outcome > Then it switches between both sides several times as the market reprices > This way, it tries to build a position where Up + Down < $1 The average cost of these complete sets is 98.69c, which gives an edge of about 1.31c But this is not pure arbitrage. Part of its inventory remains directional, with the side determined by its model’s signal Its Polymarket nickname: norm1e69 The main edge of this trading bot comes from switching between both outcomes to build complete sets below $1, while keeping the unpaired inventory as a directional residual

This guy built an asynchronous complete-set bot and made +$111,945 on Polymarket with a 50% win rate I analyzed its trades and found that its main strategy is based on gradually accumulating both sides of the same market In 88.76% of markets, it bought both Up and Down It looks something like this: > First, the bot accumulates one side > After the underlying asset moves, it starts buying the opposite outcome > Then it switches between both sides several times as the market reprices > This way, it tries to build a position where Up + Down < $1 The average cost of these complete sets is 98.69c, which gives an edge of about 1.31c But this is not pure arbitrage. Part of its inventory remains directional, with the side determined by its model’s signal Its Polymarket nickname: norm1e69 The main edge of this trading bot comes from switching between both outcomes to build complete sets below $1, while keeping the unpaired inventory as a directional residual

VALIX

17,859 görüntüleme • 1 ay önce