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MIT defines an algorithm in one sentence that changes how you think about trading "a computational procedure that takes an input and produces an output through a well-defined sequence of steps" that's it. not AI. not machine learning. not a black box a set of rules that takes data...

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23,862 次观看 • 8 天前 •via X (Twitter)

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Jason Calacanis: Social media companies should disclose their algorithms or lose Section 230 protections Why? “An algorithm is more powerful than an editor at the New York Times or an editor at HBO or Netflix...” From E243: “Algorithms must be disclosed and you must have the option, given to you upfront, to switch your algorithm.” “There should be BYOA, bring your own algorithm. There should be an algorithm store.” “If you could say, ‘I want one that just gives me a chronological feed. I want one that is from the highest quality sources.’” “And then you should be required to show what the default algorithm is doing.” “And if you don't do that, I think you should lose your Section 230 because an algorithm is more powerful than an editor at the New York Times or an editor at HBO or Netflix deciding what we see, deciding what goes on the homepage.” “The algorithm is a black box and who knows who's controlling it, and then what happens in society when young men are pushed towards more and more fringe content, more and more dark content?” “Why not just have all this be transparent?”
0:47

Jason Calacanis: Social media companies should disclose their algorithms or lose Section 230 protections Why? “An algorithm is more powerful than an editor at the New York Times or an editor at HBO or Netflix...” From E243: “Algorithms must be disclosed and you must have the option, given to you upfront, to switch your algorithm.” “There should be BYOA, bring your own algorithm. There should be an algorithm store.” “If you could say, ‘I want one that just gives me a chronological feed. I want one that is from the highest quality sources.’” “And then you should be required to show what the default algorithm is doing.” “And if you don't do that, I think you should lose your Section 230 because an algorithm is more powerful than an editor at the New York Times or an editor at HBO or Netflix deciding what we see, deciding what goes on the homepage.” “The algorithm is a black box and who knows who's controlling it, and then what happens in society when young men are pushed towards more and more fringe content, more and more dark content?” “Why not just have all this be transparent?”

The All-In Podcast

191,177 次观看 • 9 个月前

The realization over the last two extensions has come home to roost. There is not going to be a purchase of TikTok with the Chinese algorithm. We know that we can't buy the algorithm so we're gonna have to rewrite this whole thing, but while we're doing that, we're gonna try and solve for all the litigation that social media gets all stemming from the fact that the platform owns the data and the customer doesn't. I think we're gonna have to fix that. So our proposal is to fix that and not use the Chinese algorithm. When you issue a chip, let's say you take an NVIDIA chip, that chip is a queen bee. It sits in the center of the hive and every programmer that uses that chip is the honeybee. It spends millions of hours training on that chip, learning that chip, writing code for that chip and developing AI for that chip. That's the honey. There's only gonna be two kinds of honey on Earth. American or Chinese. Now if you want the world to be running on Chinese honey, stop shipping American queen bee chips, and that is what is happening right now.
6:11

The realization over the last two extensions has come home to roost. There is not going to be a purchase of TikTok with the Chinese algorithm. We know that we can't buy the algorithm so we're gonna have to rewrite this whole thing, but while we're doing that, we're gonna try and solve for all the litigation that social media gets all stemming from the fact that the platform owns the data and the customer doesn't. I think we're gonna have to fix that. So our proposal is to fix that and not use the Chinese algorithm. When you issue a chip, let's say you take an NVIDIA chip, that chip is a queen bee. It sits in the center of the hive and every programmer that uses that chip is the honeybee. It spends millions of hours training on that chip, learning that chip, writing code for that chip and developing AI for that chip. That's the honey. There's only gonna be two kinds of honey on Earth. American or Chinese. Now if you want the world to be running on Chinese honey, stop shipping American queen bee chips, and that is what is happening right now.

Kevin O'Leary aka Mr. Wonderful

43,532 次观看 • 1 年前

THIS GUY CONNECTED CLAUDE TO TRADINGVIEW AND CUT 200+ HOURS OF MANUAL CHART WORK 40 trades a week used to mean the same loop: open the chart, check RSI, compare EMA, write notes and wait for the next setup now Claude reads TradingView 24/7, tracks volume, indicators and price conditions while he is not even at the desk this is not a $20k trading terminal and not a quant fund setup, just one laptop, one chart and one strategy written in plain English the crazy part is not that Claude predicts the market, it is that a regular trader can remove the boring human loop from every setup ↓
0:26

THIS GUY CONNECTED CLAUDE TO TRADINGVIEW AND CUT 200+ HOURS OF MANUAL CHART WORK 40 trades a week used to mean the same loop: open the chart, check RSI, compare EMA, write notes and wait for the next setup now Claude reads TradingView 24/7, tracks volume, indicators and price conditions while he is not even at the desk this is not a $20k trading terminal and not a quant fund setup, just one laptop, one chart and one strategy written in plain English the crazy part is not that Claude predicts the market, it is that a regular trader can remove the boring human loop from every setup ↓

Gipp 🦅

55,377 次观看 • 18 天前

Here it is: The algorithm that toppled the United States government on November 3, 2020. The 2020 election Edison data confirms fluctuations in vote counts—meaning a straight-up algorithm. “Scytl is receiving the election data directly from election officials in at least nine states, meaning there is no projection—this is raw data. When you look at raw data in an election, we would all agree that elections are supposed to be additive processes. So, with each interval report, you would expect to see more votes—not deletions. More votes, not deletions. Now look at this real-time video of the Edison reports. You have in-person votes, then mail-in votes, and look how it pauses—there is oscillation and deletion. This is the algorithm trying to figure out the math problem that is you. That is you. And if I let this video play to completion, you’ll find out it wasn’t just Pennsylvania. We have three counties in North Carolina. We have three counties in Georgia. And guess what— in Georgia, we know for a fact that Scytl contracts directly with the state to get that data. So you should see no oscillation. Instead, what you see is absolute manipulation, and this is all possible because of how wide open the system is.” Ban all voting machines and Pass the Save Act. And arrest Chris Krebs.
4:06

Here it is: The algorithm that toppled the United States government on November 3, 2020. The 2020 election Edison data confirms fluctuations in vote counts—meaning a straight-up algorithm. “Scytl is receiving the election data directly from election officials in at least nine states, meaning there is no projection—this is raw data. When you look at raw data in an election, we would all agree that elections are supposed to be additive processes. So, with each interval report, you would expect to see more votes—not deletions. More votes, not deletions. Now look at this real-time video of the Edison reports. You have in-person votes, then mail-in votes, and look how it pauses—there is oscillation and deletion. This is the algorithm trying to figure out the math problem that is you. That is you. And if I let this video play to completion, you’ll find out it wasn’t just Pennsylvania. We have three counties in North Carolina. We have three counties in Georgia. And guess what— in Georgia, we know for a fact that Scytl contracts directly with the state to get that data. So you should see no oscillation. Instead, what you see is absolute manipulation, and this is all possible because of how wide open the system is.” Ban all voting machines and Pass the Save Act. And arrest Chris Krebs.

🇺🇸RealRobert🇺🇸

18,982 次观看 • 4 个月前

Chinese futures exchange quant built an algorithm for predicting oil prices. He made $586,000 in profit in just one month. 16 trades, only 2 in loss. Each win = $150-300k profits. Tech stack: MiroFish simulation + OSINT parser + Claude. His Polymarket profile: How does his strategy work? It’s a synthesis of Claude AI, news parser and simulation engine. Claude gathers the data for the simulation. OSINT parser scrapes every Iran/Hormuz headline. MiroFish simulates how every single piece of data affects the oil price. Output: an algorithm that’s been nailing oil moves perfectly for two months straight. This guy knows in advance what others only see after the chart moves. And unlike top futures traders, every one of his trades is visible on-chain. His knowledge and algorithm are completely open for us to use. I’m copying every new oil trade he makes with this bot:
0:30

Chinese futures exchange quant built an algorithm for predicting oil prices. He made $586,000 in profit in just one month. 16 trades, only 2 in loss. Each win = $150-300k profits. Tech stack: MiroFish simulation + OSINT parser + Claude. His Polymarket profile: How does his strategy work? It’s a synthesis of Claude AI, news parser and simulation engine. Claude gathers the data for the simulation. OSINT parser scrapes every Iran/Hormuz headline. MiroFish simulates how every single piece of data affects the oil price. Output: an algorithm that’s been nailing oil moves perfectly for two months straight. This guy knows in advance what others only see after the chart moves. And unlike top futures traders, every one of his trades is visible on-chain. His knowledge and algorithm are completely open for us to use. I’m copying every new oil trade he makes with this bot:

cvxv666

75,110 次观看 • 2 个月前

An ex-Tudor quant PM just sat down for 76 minutes and explained what's happened to the industry over the last 15 years. His main claim: there hasn't been a single new idea in trading in 15 years. Every algorithm and strategy moving billions through Wall Street today is a repackaging of something that already existed in 2010.
1:14:41

An ex-Tudor quant PM just sat down for 76 minutes and explained what's happened to the industry over the last 15 years. His main claim: there hasn't been a single new idea in trading in 15 years. Every algorithm and strategy moving billions through Wall Street today is a repackaging of something that already existed in 2010.

zostaff

13,637 次观看 • 1 个月前

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

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

cvxv666

165,663 次观看 • 10 天前

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.
2:38

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

Tykoo

25,535 次观看 • 6 个月前

Entire cities in America are having their rent prices kept to the highest possible rates by rental algorithms “All designed to keep the overall market at its highest peak. They are not charging what the market can bear. They are controlling the market” “RealPage is a company that operates software that allows landlords to input their data into an algorithm. And then RealPage sets the the prices for all of those landlords. That's old fashioned price fixing using new. Technology we allege, in effect a housing cartel. Talking about an algorithm that aggregates otherwise confidential information that the landlords have that ordinarily they would not share with their competitors. That allows then the algorithm to spit out a pricing recommendation. All designed to keep the overall market at its highest peak.” Here’s how this system works - Large property managers upload their confidential data - The algorithm aggregates this across competitors in the same market along with public data - It recommends rents designed to maximize revenue by pushing prices higher This is price fixing because everyone using these services are all getting the same maximum profit increase in suggested rent prices
1:30

Entire cities in America are having their rent prices kept to the highest possible rates by rental algorithms “All designed to keep the overall market at its highest peak. They are not charging what the market can bear. They are controlling the market” “RealPage is a company that operates software that allows landlords to input their data into an algorithm. And then RealPage sets the the prices for all of those landlords. That's old fashioned price fixing using new. Technology we allege, in effect a housing cartel. Talking about an algorithm that aggregates otherwise confidential information that the landlords have that ordinarily they would not share with their competitors. That allows then the algorithm to spit out a pricing recommendation. All designed to keep the overall market at its highest peak.” Here’s how this system works - Large property managers upload their confidential data - The algorithm aggregates this across competitors in the same market along with public data - It recommends rents designed to maximize revenue by pushing prices higher This is price fixing because everyone using these services are all getting the same maximum profit increase in suggested rent prices

Wall Street Apes

25,908 次观看 • 2 个月前

Jeff Bezos just identified the most expensive bureaucratic failure in the American economy. It fits in one sentence. Bezos: “Why does it take months and months and months to get a building permit? It doesn’t make any sense.” It doesn’t make any sense because a building code is not a judgment call. It is an algorithm. And algorithms should be executed by machines. Bezos: “Miami should have an AI application that reads your building permit for a new house or a new building and it should give you a yes or a no in ten seconds.” Ten seconds. Not three months. Not six weeks. Not whenever the reviewer clears their backlog. Bezos: “If the answer is no, it should tell you the six things you have to change to get a yes.” No ambiguity. No interpretation. No bureaucratic delay dressed up as due diligence. Just a deterministic feedback loop compressing months of institutional friction into a single automated decision. We are competing against sovereign adversaries deploying gigawatt data centers and scaling physical infrastructure at a pace that does not stop to ask permission. And we are losing ground to countries that never needed to. The AI arms race is not only fought in data centers. It is fought in the gap between when someone decides to build something and when the government allows it. Every month this system runs on biological speed is a month that cannot be recovered. The governments that integrate AI into their core civic functions will trigger a wave of physical development the old world could never produce. The ones that refuse will still be reviewing the same forms a decade from now. While the cities that said yes are already living inside the future they built. The bottleneck was never ambition. It was always the man holding the rubber stamp deciding when ambition was allowed to begin. And the stamp is just a rubber version of the algorithm that should have been running this whole time.
0:36

Jeff Bezos just identified the most expensive bureaucratic failure in the American economy. It fits in one sentence. Bezos: “Why does it take months and months and months to get a building permit? It doesn’t make any sense.” It doesn’t make any sense because a building code is not a judgment call. It is an algorithm. And algorithms should be executed by machines. Bezos: “Miami should have an AI application that reads your building permit for a new house or a new building and it should give you a yes or a no in ten seconds.” Ten seconds. Not three months. Not six weeks. Not whenever the reviewer clears their backlog. Bezos: “If the answer is no, it should tell you the six things you have to change to get a yes.” No ambiguity. No interpretation. No bureaucratic delay dressed up as due diligence. Just a deterministic feedback loop compressing months of institutional friction into a single automated decision. We are competing against sovereign adversaries deploying gigawatt data centers and scaling physical infrastructure at a pace that does not stop to ask permission. And we are losing ground to countries that never needed to. The AI arms race is not only fought in data centers. It is fought in the gap between when someone decides to build something and when the government allows it. Every month this system runs on biological speed is a month that cannot be recovered. The governments that integrate AI into their core civic functions will trigger a wave of physical development the old world could never produce. The ones that refuse will still be reviewing the same forms a decade from now. While the cities that said yes are already living inside the future they built. The bottleneck was never ambition. It was always the man holding the rubber stamp deciding when ambition was allowed to begin. And the stamp is just a rubber version of the algorithm that should have been running this whole time.

Dustin

293,235 次观看 • 3 个月前

Jordan Peterson just named the one thing no machine will ever possess. Not intelligence. Not logic. Not processing power. A ghost. Peterson reached back to Carl Jung to describe something most people never slow down long enough to feel. You are not just the person sitting here reading this. You are every version of yourself that could ever exist across time. Peterson: “The Self is everything you are and everything you could be across time.” There is a version of you that fulfilled every ounce of potential you carry. The finished version. The one standing at the far end of your life who became everything you were built to become. That version is not a fantasy. It is a gravitational field. And it has been speaking to you your entire life. Not through words. Not through logic. Through the feeling of meaning. Peterson: “The answer is through the instinct of meaning.” When something resonates so deep it stops you mid-step and you cannot explain why. That is not a chemical accident. That is your future self reaching backward through time whispering where to walk next. Peterson: “That which you could be tells you where to walk by making that path meaningful.” Your potential is not quiet. It is dragging you forward every single day through a language older than speech. Now look at what we are building. Machines designed to optimize every human decision. Career paths. Schedules. Relationships. Health. Creativity. The algorithm will map the most efficient route to any destination you name. But it cannot exist across time. It has no unrealized potential. No future version of itself standing at any finish line. No ghost pulling it toward something it was meant to become. It has compute. It does not have a soul whispering directions. When you hand your choices to an algorithm you are not delegating a task. You are muting the only compass that was ever yours. Meaning is not efficient. It is not optimized. It does not care about the shortest path. Meaning requires friction. Confusion. Standing in total darkness and feeling your way forward on nothing but instinct. That is the entire point. The struggle is not the obstacle between you and your potential. The struggle is the conversation between you and your potential. Remove it and you do not arrive faster. You arrive as someone else. We are building the most powerful optimization engine in human history. And we are about to aim it directly at the one process that was never supposed to be optimized. The algorithm will hand you a perfect map. But it will never give you a reason to walk.
0:57

Jordan Peterson just named the one thing no machine will ever possess. Not intelligence. Not logic. Not processing power. A ghost. Peterson reached back to Carl Jung to describe something most people never slow down long enough to feel. You are not just the person sitting here reading this. You are every version of yourself that could ever exist across time. Peterson: “The Self is everything you are and everything you could be across time.” There is a version of you that fulfilled every ounce of potential you carry. The finished version. The one standing at the far end of your life who became everything you were built to become. That version is not a fantasy. It is a gravitational field. And it has been speaking to you your entire life. Not through words. Not through logic. Through the feeling of meaning. Peterson: “The answer is through the instinct of meaning.” When something resonates so deep it stops you mid-step and you cannot explain why. That is not a chemical accident. That is your future self reaching backward through time whispering where to walk next. Peterson: “That which you could be tells you where to walk by making that path meaningful.” Your potential is not quiet. It is dragging you forward every single day through a language older than speech. Now look at what we are building. Machines designed to optimize every human decision. Career paths. Schedules. Relationships. Health. Creativity. The algorithm will map the most efficient route to any destination you name. But it cannot exist across time. It has no unrealized potential. No future version of itself standing at any finish line. No ghost pulling it toward something it was meant to become. It has compute. It does not have a soul whispering directions. When you hand your choices to an algorithm you are not delegating a task. You are muting the only compass that was ever yours. Meaning is not efficient. It is not optimized. It does not care about the shortest path. Meaning requires friction. Confusion. Standing in total darkness and feeling your way forward on nothing but instinct. That is the entire point. The struggle is not the obstacle between you and your potential. The struggle is the conversation between you and your potential. Remove it and you do not arrive faster. You arrive as someone else. We are building the most powerful optimization engine in human history. And we are about to aim it directly at the one process that was never supposed to be optimized. The algorithm will hand you a perfect map. But it will never give you a reason to walk.

Dustin

40,730 次观看 • 2 个月前

We don't have a seller yet. However the syndicate ends up, we don't know. This is a complicated deal. Two factors to consider: Number one, we need Xi and Trump to agree they're gonna be a buyer and a seller. That's number one. Number two, we need to figure out a way to take care of Senator Cotton's concerns around the algorithm and many others who are not going to give an indemnification for using the original algorithm. We have to solve for that too. My bet, not to sound arrogant, is I will be there at the table at the end.
0:41

We don't have a seller yet. However the syndicate ends up, we don't know. This is a complicated deal. Two factors to consider: Number one, we need Xi and Trump to agree they're gonna be a buyer and a seller. That's number one. Number two, we need to figure out a way to take care of Senator Cotton's concerns around the algorithm and many others who are not going to give an indemnification for using the original algorithm. We have to solve for that too. My bet, not to sound arrogant, is I will be there at the table at the end.

Kevin O'Leary aka Mr. Wonderful

35,678 次观看 • 11 个月前

"The worst trade is a trade that you make money on. Because then you convince yourself bad trades will make you money, and you lose a lot going forward" Tillman Holloway on why you can't let the market drive you into emotional decisions "The intention is to not let the conditions of the market drive you into an emotional state that then leads you into a decision. Those decisions most of the time are bad ones" "There's an old adage my dad and my granddad used to tell me. The worst trade is a trade that you make money on. That's a bad trade because then you convince yourself that bad trades will make you money" "You gotta have a strategy, and you gotta deploy it without emotion"
0:35

"The worst trade is a trade that you make money on. Because then you convince yourself bad trades will make you money, and you lose a lot going forward" Tillman Holloway on why you can't let the market drive you into emotional decisions "The intention is to not let the conditions of the market drive you into an emotional state that then leads you into a decision. Those decisions most of the time are bad ones" "There's an old adage my dad and my granddad used to tell me. The worst trade is a trade that you make money on. That's a bad trade because then you convince yourself that bad trades will make you money" "You gotta have a strategy, and you gotta deploy it without emotion"

The Wolf Of All Streets

15,907 次观看 • 19 天前

Elon Musk just put a number on the flaw at the center of Nvidia’s empire. Wall Street has not done the math yet. Nvidia’s Blackwell is the most sought-after silicon on Earth. Every AI lab wants it. Every sovereign nation is bidding for it. Blackwell runs every model, for every company, in every data center on the planet. That universality built the empire. It is also the fracture point. Musk: “We believe the AI5 chip will be about a third of the power of an Nvidia Blackwell for roughly comparable performance. And much less than 10% of the cost.” One-third the power. Comparable performance. Less than ten percent of the cost. Musk: “This is a chip that is very much optimized for the Tesla AI software stack. It’s not meant to be a general purpose chip.” Nvidia builds silicon that serves a million different customers. Every transistor spent on universal compatibility is a transistor not dedicated to one task. Tesla is building silicon for exactly one customer. Itself. When you strip away every function you will never call, you do not get a lesser chip. You get a weapon. Here is what the market refuses to see. Data centers drink unlimited power from the grid. Robots run on batteries. Musk: “In order to have a functional robot, you have to have a great AI chip. And it needs to be an inexpensive chip and it needs to be very power efficient.” You cannot put a Blackwell inside a walking machine. It would drain the battery before it crossed the room. The entire AI revolution lives inside air-conditioned buildings bolted to the electrical grid. Musk is not competing for that market. He is engineering the silicon that survives outside of it. One-third the power is not a spec sheet footnote. It is the physics threshold that severs intelligence from the wall socket. Without that number, every robot on Earth stays tethered. With it, the algorithm walks. Less than ten percent of the cost is not a pricing strategy. It is the line where a machine brain stops being a capital expenditure and becomes a commodity component. When the chip inside a humanoid costs less than the motors in its legs, you do not manufacture hundreds of robots. You manufacture millions. Wall Street is valuing the AI revolution by who dominates the data center. Musk is building the only silicon designed to leave one. Nvidia built the brain of the cloud. Musk is building the brain of the physical world. No one has priced that in yet.
0:56

Elon Musk just put a number on the flaw at the center of Nvidia’s empire. Wall Street has not done the math yet. Nvidia’s Blackwell is the most sought-after silicon on Earth. Every AI lab wants it. Every sovereign nation is bidding for it. Blackwell runs every model, for every company, in every data center on the planet. That universality built the empire. It is also the fracture point. Musk: “We believe the AI5 chip will be about a third of the power of an Nvidia Blackwell for roughly comparable performance. And much less than 10% of the cost.” One-third the power. Comparable performance. Less than ten percent of the cost. Musk: “This is a chip that is very much optimized for the Tesla AI software stack. It’s not meant to be a general purpose chip.” Nvidia builds silicon that serves a million different customers. Every transistor spent on universal compatibility is a transistor not dedicated to one task. Tesla is building silicon for exactly one customer. Itself. When you strip away every function you will never call, you do not get a lesser chip. You get a weapon. Here is what the market refuses to see. Data centers drink unlimited power from the grid. Robots run on batteries. Musk: “In order to have a functional robot, you have to have a great AI chip. And it needs to be an inexpensive chip and it needs to be very power efficient.” You cannot put a Blackwell inside a walking machine. It would drain the battery before it crossed the room. The entire AI revolution lives inside air-conditioned buildings bolted to the electrical grid. Musk is not competing for that market. He is engineering the silicon that survives outside of it. One-third the power is not a spec sheet footnote. It is the physics threshold that severs intelligence from the wall socket. Without that number, every robot on Earth stays tethered. With it, the algorithm walks. Less than ten percent of the cost is not a pricing strategy. It is the line where a machine brain stops being a capital expenditure and becomes a commodity component. When the chip inside a humanoid costs less than the motors in its legs, you do not manufacture hundreds of robots. You manufacture millions. Wall Street is valuing the AI revolution by who dominates the data center. Musk is building the only silicon designed to leave one. Nvidia built the brain of the cloud. Musk is building the brain of the physical world. No one has priced that in yet.

Dustin

160,181 次观看 • 2 个月前

This is the rarest piece of HFT content on the internet - Robert Almgren walking through a real execution trade tick by tick He's the co-founder of Quantitative Brokers and the man who wrote the Almgren-Chriss model - the standard execution algorithm used at Goldman, Citadel, Two Sigma In this lecture he shows a live order from May 14 2018, every fill, every limit, every market reaction across 2 minutes and 40 seconds The article above is that exact same thinking applied to Polymarket - measure your edge in bits before you place a single trade Bookmark it & give it a watch tonight ↓
16:43

This is the rarest piece of HFT content on the internet - Robert Almgren walking through a real execution trade tick by tick He's the co-founder of Quantitative Brokers and the man who wrote the Almgren-Chriss model - the standard execution algorithm used at Goldman, Citadel, Two Sigma In this lecture he shows a live order from May 14 2018, every fill, every limit, every market reaction across 2 minutes and 40 seconds The article above is that exact same thinking applied to Polymarket - measure your edge in bits before you place a single trade Bookmark it & give it a watch tonight ↓

0xDipper

253,114 次观看 • 1 个月前

Palestine vs the Algorithm! Palestine versus the Algorithm is a fight happening every day. As the Israelis kill journalists, the Algorithm suppresses what they say. The Algorithm has been programmed to mute talk of Palestinian genocide; To gate keep the starvation and the slaughter, and the daily massacres to hide. Palestine versus the Algorithm is one of the great battles of our time. As the Algorithm supports mass murder, TikTok, Youtube, X, all fall in line. But the Algorithm is man-made and paid for by a Zionist agenda, To pervert social media platforms into becoming genocide defenders. Palestine versus the Algorithm, or Technology versus the People. The Algorithm is anti-human, blocking the good that would end the evil. It’s not only Palestinians that the Algorithm is designed to defeat, It's everyone who stands up for life and justice, and who refuses to retreat. Subscribe and Follow Mama on Rumble:
1:30

Palestine vs the Algorithm! Palestine versus the Algorithm is a fight happening every day. As the Israelis kill journalists, the Algorithm suppresses what they say. The Algorithm has been programmed to mute talk of Palestinian genocide; To gate keep the starvation and the slaughter, and the daily massacres to hide. Palestine versus the Algorithm is one of the great battles of our time. As the Algorithm supports mass murder, TikTok, Youtube, X, all fall in line. But the Algorithm is man-made and paid for by a Zionist agenda, To pervert social media platforms into becoming genocide defenders. Palestine versus the Algorithm, or Technology versus the People. The Algorithm is anti-human, blocking the good that would end the evil. It’s not only Palestinians that the Algorithm is designed to defeat, It's everyone who stands up for life and justice, and who refuses to retreat. Subscribe and Follow Mama on Rumble:

TheMamaSchwabParody

29,573 次观看 • 9 个月前

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

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

Livsun

187,776 次观看 • 1 个月前

Stop Gambling, Start Engineering: The Ultimate Guide To CCXT Algorithmic Trading most traders are essentially walking into a high stakes casino with a blindfold on while the house has a high speed laser aimed directly at their bankroll. if you have ever felt the soul crushing weight of a liquidation notification at three in the morning then you know the market is a 24/7 beast that eats human emotion for breakfast there is a hidden bridge that connects your laptop to almost every major crypto exchange in existence and once you cross it the game changes forever. my name is moon dev i believe that code is the great equalizer because through losing money with liquidations and over trading i knew i had to automate my trading so i learned to code as in the past i spent hundreds of thousands on devs for app, thinking i would not be able to code myself w/ bots you must iterate to success so i decided to learn live on youtube, and now we are here, fully automated systems trading for me instead of getting liquidated. the secret weapon behind this transition is a library called ccxt which acts as a universal translator for exchanges like binance, bybit, and kucoin most people think they need to spend years studying computer science just to place a single trade via code but that is a lie designed to keep you on the sidelines. the reality is that once you understand how to initialize a connection you can control your entire portfolio with just a few lines of logic. it starts with importing the library and setting up your credentials in a way that doesn't leave your keys exposed to the world the first mistake that bankrupts most manual traders is the inability to act fast enough when the trend shifts. when you build a bot the first thing you need to master is the market order because it allows you to enter or exit a position instantly regardless of the price. it is the ultimate panic button for when a strategy goes south or a massive opportunity presents itself while market orders are great for speed they are the fastest way to get eaten alive by fees if you are not careful. this is where the limit order comes into play allowing you to dictate exactly what price you are willing to pay for an asset. by using a create limit order function you can place your bids and asks in the order book and wait for the market to come to you most traders forget that once an order is placed it stays active until it is either filled or manually removed. i have seen countless accounts go to zero because a bot kept piling on buy orders without ever checking to see if the previous ones were canceled. the cancel all orders function is the invisible shield that prevents your algorithm from accidentally over leveraging your account the real magic happens when you realize you can cancel more than just basic limit orders. there are untriggered conditional orders like stop losses and take profits that often hide in the background of an exchange waiting to ruin your day. by passing specific parameters into your cancel function you can wipe the slate clean and ensure your bot is starting from a neutral state every single time if you want to know what the whales are doing before it shows up on a candle chart then you need to be looking at the raw order book. fetching the order book gives you a direct view of every single bid and ask currently sitting on the exchange. this is the most honest data you can get because it represents real money waiting to be filled at specific price levels you can actually parse this data to find the exact top of the bid and the bottom of the ask to ensure your bot always gets the best possible entry. most retail traders are looking at delayed charts while your bot is reading the tape in real time and calculating the spread. this allows you to place orders that are optimized for the current liquidity rather than just guessing where the price might go one of the biggest hurdles in automation is managing the sheer volume of data that an exchange throws at you. when you fetch open high low close volume data you are getting the historical heartbeat of an asset across any timeframe you choose. this data is the foundation of every technical indicator from simple moving averages to complex machine learning models the problem is that raw data is often a mess of lists and dictionaries that are impossible for a human or a simple script to read efficiently. this is why we use pandas to convert that garbage into a structured data frame that looks exactly like a clean spreadsheet. once your data is in a data frame you can calculate rsi or macd with a single line of code and visualize the entire market structure the path to becoming a successful automated trader is not a sprint but a series of iterations toward a system that works. i chose to learn this live in front of the world because i wanted to prove that anyone can escape the cycle of over trading. you don't need a million dollars to start but you do need a system that removes the human element from the equation if you are still clicking buttons on a website then you are competing against machines that can process thousands of data points per second. it is time to stop playing a rigged game and start building your own edge in the market. the code is there for anyone to grab and the only thing standing between you and a fully automated portfolio is the willingness to sit down and write the first line every algorithm you build is a brick in a wall that protects your capital from the emotional swings of the crypto market. i spend my days refining these systems and sharing the process because i know how lonely it feels to lose everything to a flash crash. we are building a community where code is the tool and financial freedom is the goal the final step is realizing that your balance is just a number that your bot needs to manage with cold logic. by fetching your balance frequently your bot can calculate position sizes based on your total equity ensuring that no single trade can ever wipe you out. this is the difference between gambling and systematic trading and it is accessible to anyone with an internet connection i hope you take these tools and start building something that allows you to sleep peacefully while the markets do their thing. the industry is secretive for a reason but we are breaking those walls down one line of code at a time. the journey is long but the reward of never having to worry about a liquidation again is worth every second of the struggle
1:09:25

Stop Gambling, Start Engineering: The Ultimate Guide To CCXT Algorithmic Trading most traders are essentially walking into a high stakes casino with a blindfold on while the house has a high speed laser aimed directly at their bankroll. if you have ever felt the soul crushing weight of a liquidation notification at three in the morning then you know the market is a 24/7 beast that eats human emotion for breakfast there is a hidden bridge that connects your laptop to almost every major crypto exchange in existence and once you cross it the game changes forever. my name is moon dev i believe that code is the great equalizer because through losing money with liquidations and over trading i knew i had to automate my trading so i learned to code as in the past i spent hundreds of thousands on devs for app, thinking i would not be able to code myself w/ bots you must iterate to success so i decided to learn live on youtube, and now we are here, fully automated systems trading for me instead of getting liquidated. the secret weapon behind this transition is a library called ccxt which acts as a universal translator for exchanges like binance, bybit, and kucoin most people think they need to spend years studying computer science just to place a single trade via code but that is a lie designed to keep you on the sidelines. the reality is that once you understand how to initialize a connection you can control your entire portfolio with just a few lines of logic. it starts with importing the library and setting up your credentials in a way that doesn't leave your keys exposed to the world the first mistake that bankrupts most manual traders is the inability to act fast enough when the trend shifts. when you build a bot the first thing you need to master is the market order because it allows you to enter or exit a position instantly regardless of the price. it is the ultimate panic button for when a strategy goes south or a massive opportunity presents itself while market orders are great for speed they are the fastest way to get eaten alive by fees if you are not careful. this is where the limit order comes into play allowing you to dictate exactly what price you are willing to pay for an asset. by using a create limit order function you can place your bids and asks in the order book and wait for the market to come to you most traders forget that once an order is placed it stays active until it is either filled or manually removed. i have seen countless accounts go to zero because a bot kept piling on buy orders without ever checking to see if the previous ones were canceled. the cancel all orders function is the invisible shield that prevents your algorithm from accidentally over leveraging your account the real magic happens when you realize you can cancel more than just basic limit orders. there are untriggered conditional orders like stop losses and take profits that often hide in the background of an exchange waiting to ruin your day. by passing specific parameters into your cancel function you can wipe the slate clean and ensure your bot is starting from a neutral state every single time if you want to know what the whales are doing before it shows up on a candle chart then you need to be looking at the raw order book. fetching the order book gives you a direct view of every single bid and ask currently sitting on the exchange. this is the most honest data you can get because it represents real money waiting to be filled at specific price levels you can actually parse this data to find the exact top of the bid and the bottom of the ask to ensure your bot always gets the best possible entry. most retail traders are looking at delayed charts while your bot is reading the tape in real time and calculating the spread. this allows you to place orders that are optimized for the current liquidity rather than just guessing where the price might go one of the biggest hurdles in automation is managing the sheer volume of data that an exchange throws at you. when you fetch open high low close volume data you are getting the historical heartbeat of an asset across any timeframe you choose. this data is the foundation of every technical indicator from simple moving averages to complex machine learning models the problem is that raw data is often a mess of lists and dictionaries that are impossible for a human or a simple script to read efficiently. this is why we use pandas to convert that garbage into a structured data frame that looks exactly like a clean spreadsheet. once your data is in a data frame you can calculate rsi or macd with a single line of code and visualize the entire market structure the path to becoming a successful automated trader is not a sprint but a series of iterations toward a system that works. i chose to learn this live in front of the world because i wanted to prove that anyone can escape the cycle of over trading. you don't need a million dollars to start but you do need a system that removes the human element from the equation if you are still clicking buttons on a website then you are competing against machines that can process thousands of data points per second. it is time to stop playing a rigged game and start building your own edge in the market. the code is there for anyone to grab and the only thing standing between you and a fully automated portfolio is the willingness to sit down and write the first line every algorithm you build is a brick in a wall that protects your capital from the emotional swings of the crypto market. i spend my days refining these systems and sharing the process because i know how lonely it feels to lose everything to a flash crash. we are building a community where code is the tool and financial freedom is the goal the final step is realizing that your balance is just a number that your bot needs to manage with cold logic. by fetching your balance frequently your bot can calculate position sizes based on your total equity ensuring that no single trade can ever wipe you out. this is the difference between gambling and systematic trading and it is accessible to anyone with an internet connection i hope you take these tools and start building something that allows you to sleep peacefully while the markets do their thing. the industry is secretive for a reason but we are breaking those walls down one line of code at a time. the journey is long but the reward of never having to worry about a liquidation again is worth every second of the struggle

Moon Dev

14,105 次观看 • 4 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 • 1 个月前