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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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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 ↓
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John von Neumann designed a self-replicating machine in a cellular automaton in the 1950s - without a computer to run it. Every modern reinforcement learning algorithm that discovers emergent behavior traces back to that same idea: define the rules, not the outcome. The holy grail of artificial life is open-ended evolution - an algorithm you could run for a million years and it would keep producing surprising complexity. Nobody has built it yet. But every time an LLM develops unexpected capabilities from scale alone, it looks a little closer. 30 minutes. Bookmark & watch today. The most underrated field in all of computer science.
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The company behind DeepSeek makes more money from trading than from AI. Their weapon isn't a secret model, it's one principle that's now available to everyone Few people know this, but DeepSeek didn't grow out of an AI lab. It grew out of a quant fund that spent years making money in the markets, then poured that money into the model And the principle they profit from is more boring than everyone thinks They don't search for one genius trading idea. They take a thousand ideas, run each through a machine, and only let the ones that pass survive to trade While a human tests one idea a week, their system kills 999 a night That's the whole difference between them and a retail trader. Not brains. The speed and ruthlessness of the testing Because here's what few understand about their own losses. You find a strategy with a beautiful chart and you believe it But a beautiful chart is no proof it works. Run enough versions and a profitable curve will appear even on random numbers One of the dozens just got lucky, and you decided you found gold This filtering used to be locked inside funds like these. It took a team and millions. This year it was compressed into AI I described an idea in plain words, and AI built the strategy itself, ran it on years of real data, and showed whether it was real or just luck I took two of the most-cited quant papers, so I wouldn't take it on faith Time Series Momentum on Bitcoin: +1,537%. Alive Carry trade, a strategy cited for years: Sharpe dropped from 0.61 to 0.11. The premium almost dead One still prints money, the other dried up long ago. And trading blind, you'd never know which is which until it took your deposit The same principle DeepSeek paid millions for is now one prompt. You can test or build your own strategy here (free trial after signing up): The full breakdown of both strategies is in the article below
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qwinsi

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An MIT professor finds the prize behind a thousand doors by opening ten. no faster running. he just skips every door it cannot be behind. the lecture is called algorithmic thinking. 6 million people have watched it, and it has been free the whole time. brute force checks every door, one by one. a real algorithm throws away half of them with every step. same answer, a thousand times fewer moves, zero new hardware. now read the post above. that is Wall Street paying $300 million for the opposite. they drilled a cable through a mountain to open the same thousand doors 3 milliseconds faster. Devadas just stopped opening most of them. that same move is every search engine, every trading model, every small AI that beats a bigger one. the edge was never the speed of the machine. it was the shape of the idea. no cable. no mountain. one better idea. a quant I know says one algorithm rewrite beat a full year of latency upgrades on his desk. they spent $300 million to run the long way faster. the shortcut has been free for over a decade.
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An MIT professor finds the prize behind a thousand doors by opening ten. no faster running. he just skips every door it cannot be behind. the lecture is called algorithmic thinking. 6 million people have watched it, and it has been free the whole time. brute force checks every door, one by one. a real algorithm throws away half of them with every step. same answer, a thousand times fewer moves, zero new hardware. now read the post above. that is Wall Street paying $300 million for the opposite. they drilled a cable through a mountain to open the same thousand doors 3 milliseconds faster. Devadas just stopped opening most of them. that same move is every search engine, every trading model, every small AI that beats a bigger one. the edge was never the speed of the machine. it was the shape of the idea. no cable. no mountain. one better idea. a quant I know says one algorithm rewrite beat a full year of latency upgrades on his desk. they spent $300 million to run the long way faster. the shortcut has been free for over a decade.

Rossst.03

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Raoul Pal

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McDonald’s is facing a federal class-action lawsuit over an AI pricing tool. Filed October 2 in Chicago, Thomas v. McDonald’s claims that since about 2019 the company has pooled nonpublic sales data from roughly 14,000 U.S. restaurants, about 95% independently owned, and used a machine-learning “pricing engine” to recommend menu prices. The suit alleges a “30% rule,” where an item’s price is suggested higher once 30% of stores have already raised it, and that franchisees are pressured to follow it. The plaintiff calls that algorithmic price-fixing under the Sherman Act and blames it for higher menu prices. McDonald’s says the complaint is inaccurate. Franchisees set prices. The tools are optional and do not automate, coordinate, or fix prices. AI does not set the price of a Big Mac. This is not McDonald’s, Wendy’s, and Burger King colluding. It is one brand and its own operators. A franchisee can drive around, write down public menu boards, and match prices legally. The plaintiff’s bet is that sharing nonpublic data through a common algorithm turns ordinary franchise coordination into an antitrust violation. No ruling yet. This is an allegation, not a finding.

Gunther Eagleman™

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Ken Griffin, who runs Citadel, just admitted that the most expensive skill of his quants, the one he paid them millions for, now costs nothing And for the first time in history, that skill belongs not just to a hedge fund, but to anyone with access to AI A fund's entire quant team runs one cycle: come up with a strategy, turn it into code, test it on history, deploy it to the market Every step used to cost separate people and years of training. Now AI does the whole cycle You describe an idea in words, it writes the strategy itself, runs it on years of real data, and if it's alive, deploys it straight to a broker But the speed isn't the point. The point is what the retail trader doesn't understand You take a strategy with a beautiful chart and you believe it. But a beautiful chart is no proof Run enough versions and a profitable curve will appear even on random numbers. One just got lucky, and you decided you found gold Funds don't trust any curve until they test it on data the strategy has never seen. There the fit falls apart, and a real edge survives I ran two of the most-cited quant papers through this cycle Time Series Momentum on Bitcoin: +1,537%. Alive Betting Against Beta, a classic from every textbook: minus 7.78% a year while the market rose 14.86%. A corpse One still prints money, the other died long ago. And trading blind, you'd never know which is which until it took your deposit If you want to run your own idea through the same test, it takes minutes here (free trial after signing up): The entire hedge fund is six steps. AI already ate four of them. Which ones, I broke down in the article below
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Ken Griffin, who runs Citadel, just admitted that the most expensive skill of his quants, the one he paid them millions for, now costs nothing And for the first time in history, that skill belongs not just to a hedge fund, but to anyone with access to AI A fund's entire quant team runs one cycle: come up with a strategy, turn it into code, test it on history, deploy it to the market Every step used to cost separate people and years of training. Now AI does the whole cycle You describe an idea in words, it writes the strategy itself, runs it on years of real data, and if it's alive, deploys it straight to a broker But the speed isn't the point. The point is what the retail trader doesn't understand You take a strategy with a beautiful chart and you believe it. But a beautiful chart is no proof Run enough versions and a profitable curve will appear even on random numbers. One just got lucky, and you decided you found gold Funds don't trust any curve until they test it on data the strategy has never seen. There the fit falls apart, and a real edge survives I ran two of the most-cited quant papers through this cycle Time Series Momentum on Bitcoin: +1,537%. Alive Betting Against Beta, a classic from every textbook: minus 7.78% a year while the market rose 14.86%. A corpse One still prints money, the other died long ago. And trading blind, you'd never know which is which until it took your deposit If you want to run your own idea through the same test, it takes minutes here (free trial after signing up): The entire hedge fund is six steps. AI already ate four of them. Which ones, I broke down in the article below

qwinsi

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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 Aufrufe • vor 8 Monaten

this is absolutely f*cking insane Black Rock just leaked 5 GitHub repos that give you the trading desk a fund pays millions for: want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want the engine serious quants run in production? -> NautilusTrader: a Rust-native trading engine where the backtest and the live run use the same code want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + these five is the setup to test this month. bookmark and share with a friend
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this is absolutely f*cking insane Black Rock just leaked 5 GitHub repos that give you the trading desk a fund pays millions for: want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want the engine serious quants run in production? -> NautilusTrader: a Rust-native trading engine where the backtest and the live run use the same code want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + these five is the setup to test this month. bookmark and share with a friend

kiosa

28,500 Aufrufe • vor 10 Tagen

Marvin Minsky, MIT professor and father of AI: "The best trader on Wall Street is no longer human. I spent fifty years proving the mind is just a machine built from smaller machines, and once that's true, nothing stops a machine from being the house. it already is." this free lecture holds the entire "AI edge" the funds and the gurus sell, and the man teaching it founded the field itself and put his whole MIT course online for free. at the board it's simple. Minsky's idea is that there is no magic in thinking. a mind is a huge pile of small, mindless parts, each doing something simple, and intelligence is just what happens when enough of them work together. a trading machine is the same trick: thousands of simple rules stacked until an edge appears. that's the whole "AI", minus the mystery. Minsky taught this at MIT and published "The Society of Mind" in 1986. the course has been free ever since. same point as my casino piece above: the house wins with many small mindless advantages stacked, not with one born genius. the ideas are free and every AI lab and every fund already has them. what they cannot sell you is the data and the compute to actually build the machine. that is the real moat now, and no lecture can hand it to you.
1:56:06

Marvin Minsky, MIT professor and father of AI: "The best trader on Wall Street is no longer human. I spent fifty years proving the mind is just a machine built from smaller machines, and once that's true, nothing stops a machine from being the house. it already is." this free lecture holds the entire "AI edge" the funds and the gurus sell, and the man teaching it founded the field itself and put his whole MIT course online for free. at the board it's simple. Minsky's idea is that there is no magic in thinking. a mind is a huge pile of small, mindless parts, each doing something simple, and intelligence is just what happens when enough of them work together. a trading machine is the same trick: thousands of simple rules stacked until an edge appears. that's the whole "AI", minus the mystery. Minsky taught this at MIT and published "The Society of Mind" in 1986. the course has been free ever since. same point as my casino piece above: the house wins with many small mindless advantages stacked, not with one born genius. the ideas are free and every AI lab and every fund already has them. what they cannot sell you is the data and the compute to actually build the machine. that is the real moat now, and no lecture can hand it to you.

Voltex

27,976 Aufrufe • vor 3 Monaten

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

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

qwinsi

47,864 Aufrufe • vor 27 Tagen

this is absolutely f*cking insane Black Rock just leaked 4 GitHub repos that give you the trading desk a fund pays millions for: want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + these five is the setup to test this month. bookmark and share with a friend
0:23

this is absolutely f*cking insane Black Rock just leaked 4 GitHub repos that give you the trading desk a fund pays millions for: want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + these five is the setup to test this month. bookmark and share with a friend

Beaver

40,185 Aufrufe • vor 1 Tag

This guy built a trading bot on Polymarket with an average trade size of $9 Result: +$108,248 I analyzed its trades and found that its core strategy is based on dynamically accumulating both outcomes at different points throughout 5 minute BTC Up/Down markets It uses price swings within each market to buy each outcome when it becomes cheaper Here’s what that looks like in practice: > BTC declines sharply > Up falls to 42c, and the bot starts accumulating it > Then BTC rebounds > Down becomes cheaper, and the algorithm buys it at 55c Up + Down = 97c -> 3c edge These paired positions have an average edge of 2.7% But it does not always keep Up and Down perfectly balanced at a 1:1 ratio On top of its paired inventory, the algorithm often keeps a directional residual, which adds extra upside if that outcome wins Its Polymarket account: Jarvisen You can build your own trading bot here: (A trial is available after signing up) Stats: > Trades per active hour: 680 > Average trade size: $9.11 > Win rate: 69% The main edge of this HFT algorithm comes from volatility harvesting: it alternates between accumulating Up and Down, building paired inventory with a combined cost below $1 while keeping a small directional skew toward the cheaper side
0:28

This guy built a trading bot on Polymarket with an average trade size of $9 Result: +$108,248 I analyzed its trades and found that its core strategy is based on dynamically accumulating both outcomes at different points throughout 5 minute BTC Up/Down markets It uses price swings within each market to buy each outcome when it becomes cheaper Here’s what that looks like in practice: > BTC declines sharply > Up falls to 42c, and the bot starts accumulating it > Then BTC rebounds > Down becomes cheaper, and the algorithm buys it at 55c Up + Down = 97c -> 3c edge These paired positions have an average edge of 2.7% But it does not always keep Up and Down perfectly balanced at a 1:1 ratio On top of its paired inventory, the algorithm often keeps a directional residual, which adds extra upside if that outcome wins Its Polymarket account: Jarvisen You can build your own trading bot here: (A trial is available after signing up) Stats: > Trades per active hour: 680 > Average trade size: $9.11 > Win rate: 69% The main edge of this HFT algorithm comes from volatility harvesting: it alternates between accumulating Up and Down, building paired inventory with a combined cost below $1 while keeping a small directional skew toward the cheaper side

VALIX

22,562 Aufrufe • vor 21 Tagen

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

77,128 Aufrufe • vor 6 Monaten

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

175,981 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 10 Monaten

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 Aufrufe • vor 6 Monaten

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

293,573 Aufrufe • vor 7 Monaten