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MIT Professor just revealed the exact mathematical framework behind quant trading and zero-sum edge. 80-minutes. free. By Ian Bell. here's what they cover: • how von Neumann proved moving first isn't a disadvantage in zero-sum games • Nash equilibrium vs. security strategies (minimax & maximin algebra) • why chess... show more

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Gilbert Strang, the legendary mathematician who proved risk can be cancelled to zero, on purpose, every single time: "Take any bet you're unsure about and ask what happens if you're wrong. I proved there's an exact operation that undoes a bad outcome completely, not roughly, exactly, and almost nobody ever learns to calculate it." Nobody outside that lecture hall has seen the operation itself. here's the actual mechanic. write down the equation for your first move. now tack a second column onto it, the one representing the exact opposite outcome you're worried about. don't solve them separately. run the same elimination steps through both columns at once, together, side by side. by the time the first column simplifies down to a clean answer, the second column has already turned into the exact move that cancels it. you never solved a second problem. you read the answer off the same steps you were already running. most people treat the hedge as a separate calculation, done after the fact, once they know the trade went wrong. by then it's too late to get it for free. the exact cancelling move only comes cheap if you attach it to the original problem before you start. zoom out to any model that prices a position and its exact opposite in the same breath. it isn't running the numbers twice. it tacked the second outcome on from the start, ran one elimination, and pulled both answers out together. the takeaway isn't "hedge more." it's this: whatever you're solving for right now, the exact opposite answer is one extra column away, if you set the problem up before you need it, not after. People pay six figures to sit in a room and hear this. It's in this video. For free.

Gilbert Strang, the legendary mathematician who proved risk can be cancelled to zero, on purpose, every single time: "Take any bet you're unsure about and ask what happens if you're wrong. I proved there's an exact operation that undoes a bad outcome completely, not roughly, exactly, and almost nobody ever learns to calculate it." Nobody outside that lecture hall has seen the operation itself. here's the actual mechanic. write down the equation for your first move. now tack a second column onto it, the one representing the exact opposite outcome you're worried about. don't solve them separately. run the same elimination steps through both columns at once, together, side by side. by the time the first column simplifies down to a clean answer, the second column has already turned into the exact move that cancels it. you never solved a second problem. you read the answer off the same steps you were already running. most people treat the hedge as a separate calculation, done after the fact, once they know the trade went wrong. by then it's too late to get it for free. the exact cancelling move only comes cheap if you attach it to the original problem before you start. zoom out to any model that prices a position and its exact opposite in the same breath. it isn't running the numbers twice. it tacked the second outcome on from the start, ran one elimination, and pulled both answers out together. the takeaway isn't "hedge more." it's this: whatever you're solving for right now, the exact opposite answer is one extra column away, if you set the problem up before you need it, not after. People pay six figures to sit in a room and hear this. It's in this video. For free.

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Do you understand what just got open sourced? 5,800+ quant trading strategies for FREE along with its CODEBASE i gave these trading strategies to opus 5.5 + jev and it built me a PROFITABLE 24/7 trading agent here is how to use the repo and build AI trading bot: 1. clone the vault and get 5,806 trading strategies (spot, perps, grid, stat arb and market making) 2. open any strategy file, the math, the parameters and the source are already inside 3. point AgenKit at that file inside Claude Code with model as opus 5.5, one command and it reads the spec as ground truth 4. it will run a full engineering team and ships a production bot with risk controls 5. Jev is the live brain, it answers buy sell or hold in 81 milliseconds, calibrated, no hallucination 6. your engine sizes the trade from that confidence and fires it before the next block 7. a hard risk layer holds every limit, nothing goes live until you approve it 8. every night Opus 5.5 reviews the fills and ships a better version, so the agent improves while you sleep one model THINKS, the other REACTS and a strategy that sat locked in a vault now trades itself 24/7 the COMPLETE codebase and the EXACT Opus 5.5 + Jev trading system which I am running is in the article below:

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Your strategy isn't losing because it's bad. It's losing because the market changed and nobody told you. Markets have moods: trend, chop, panic. Every strategy only works inside one of them. And when the mood flips, no bell rings. Your equity curve is the bell - by the time it rings, you're already down 30%. Quant desks don't wait for the bell. They run 120-year-old math, a Hidden Markov Model, that reads the market's hidden state live and calls the flip days before the chart shows it. Horizon packed that whole desk setup into one typed sentence. Public launch July 15 - get on the waitlist now: Here's the proof, from a real pair of runs. A BTC breakout kept shorting into an uptrend and finished -50.68%, the short book alone bleeding $75,836. The exact same idea, with one added sentence about the regime: +41.15%. Same entries. Same logic. A 91-point swing from one sentence. You don't have to build any of this. Horizon fits the model, watches the regime every bar, and switches your strategy on its own - you just describe what you want. Half your losing trades were probably this exact thing: right idea, wrong market, no way to see it. All this time you've been blaming your discipline, your entries, your psychology. The real question was one you never asked: what state is the market in right now? Bookmark this and read the full framework below - it's the exact regime math desks run on live money, written so you can use it Monday.

Your strategy isn't losing because it's bad. It's losing because the market changed and nobody told you. Markets have moods: trend, chop, panic. Every strategy only works inside one of them. And when the mood flips, no bell rings. Your equity curve is the bell - by the time it rings, you're already down 30%. Quant desks don't wait for the bell. They run 120-year-old math, a Hidden Markov Model, that reads the market's hidden state live and calls the flip days before the chart shows it. Horizon packed that whole desk setup into one typed sentence. Public launch July 15 - get on the waitlist now: Here's the proof, from a real pair of runs. A BTC breakout kept shorting into an uptrend and finished -50.68%, the short book alone bleeding $75,836. The exact same idea, with one added sentence about the regime: +41.15%. Same entries. Same logic. A 91-point swing from one sentence. You don't have to build any of this. Horizon fits the model, watches the regime every bar, and switches your strategy on its own - you just describe what you want. Half your losing trades were probably this exact thing: right idea, wrong market, no way to see it. All this time you've been blaming your discipline, your entries, your psychology. The real question was one you never asked: what state is the market in right now? Bookmark this and read the full framework below - it's the exact regime math desks run on live money, written so you can use it Monday.

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Doyle Brunson watched a man with zero grasp of the odds sit down and beat players who studied the math for years. that man never learned a probability in his life. Doyle says the answers just came to him, surprising and close every time, worked out in ways nobody could explain. the interview has been floating around for years. an old, unedited sit down with two poker legends, and almost nobody has actually listened to what Doyle says next. because the question that follows is the real one. can a raw natural still show up today, learn on instinct alone, and take the money? Doyle says no. the game is closing. sharper people keep pouring in because they see a serious living in it, and pure instinct is not enough anymore. so he lays out the path he actually took. treat it like school. start in the smallest games, the grade school of poker, where a bad night costs you almost nothing. graduate to bigger games. turn pro, travel, grind. then, when you are truly ready, you go to Vegas for graduate school and take the world on. that is the part most people skip. they want to sit in the biggest game first and wonder why they go broke. the edge was always in climbing slowly, learning to price your decisions before the stakes can hurt you. no shortcut. no genius gene. one ladder, climbed one rung at a time. the naturals are almost gone. the schooling is what is left, and Doyle mapped it out. watch it first, then read the article below where I break down how the ladder still works today.

Doyle Brunson watched a man with zero grasp of the odds sit down and beat players who studied the math for years. that man never learned a probability in his life. Doyle says the answers just came to him, surprising and close every time, worked out in ways nobody could explain. the interview has been floating around for years. an old, unedited sit down with two poker legends, and almost nobody has actually listened to what Doyle says next. because the question that follows is the real one. can a raw natural still show up today, learn on instinct alone, and take the money? Doyle says no. the game is closing. sharper people keep pouring in because they see a serious living in it, and pure instinct is not enough anymore. so he lays out the path he actually took. treat it like school. start in the smallest games, the grade school of poker, where a bad night costs you almost nothing. graduate to bigger games. turn pro, travel, grind. then, when you are truly ready, you go to Vegas for graduate school and take the world on. that is the part most people skip. they want to sit in the biggest game first and wonder why they go broke. the edge was always in climbing slowly, learning to price your decisions before the stakes can hurt you. no shortcut. no genius gene. one ladder, climbed one rung at a time. the naturals are almost gone. the schooling is what is left, and Doyle mapped it out. watch it first, then read the article below where I break down how the ladder still works today.

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In the crowded subscription app economy, building a great product is no longer enough. As AI has made software significantly faster, easier, and cheaper to build, the primary bottleneck has shifted from product development to distribution. As the serial entrepreneur behind multiple breakout Health & Fitness apps like Fitstar and Zero, Mike Maser has experienced this challenge firsthand. Over the last 15 years, Mike sold his first startup called FitStar to Fitbit, was diagnosed with stage four cancer, made an incredible recovery that he attributes in part to intermittent fasting, and then teamed up with Kevin Rose to build Zero, the world’s #1 fasting app that he has since scaled to $5M+ in ARR and over 10 million registered users. Now, he's navigating Zero through its biggest disruption yet - the rise of GLP-1 medications like Ozempic and Wegovy and the ripple effects they are having on the entire fasting category. Mike breaks down: - Why Zero’s simple one-screen MVP was critical for generating early momentum - How a single tweet from Elon Musk drove $1M in revenue for Zero in 24 hours - Shifting from organic virality to building a sophisticated growth and monetization engine - How Zero adapted its strategy to the GLP-1 era by helping GLP-1 patients track their hydration and manage their protein intake to maintain muscle mass - Zero’s ongoing strategies to reduce subscriber churn and build lifetime subscriber value (0:00) Introduction (2:54) What Zero is and how it created the fasting app category (3:50) Why Mike started building health and fitness apps (10:29) How Mike sold Fitstar while battling stage four cancer (14:57) How intermittent fasting helped Mike beat cancer (18:32) Why Mike took over the Zero app from Kevin Rose (20:53) Why Zero kept its initial app extremely simple (24:53) How Zero's early adopters helped drive initial growth (26:48) How Zero built trust and credibility with early adopters (29:18) How Elon's tweet drove $1M ARR for Zero in one day (32:27) Why GLP-1 medications are reshaping Zero's strategy (36:14) How Zero helps GLP-1 patients protect muscle (42:03) How GLP-1s have expanded Zero's business model (44:41) The loops that will drive Zero's next phase of growth (47:39) Lightning round

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MUST-WATCH: Why SIG Dominated Options Trading — Explained by an 8-Year Insider Kris Abdelmessih (Kris) spent 8 years trading energy derivatives at SIG, then ran options businesses at Parallax & Prime before founding Moontower —one of the world's most popular newsletters on options & volatility trading. "SIG understood there was an abnormal amount of edge in the market. They came from gambling—sports betting, poker—where edge was tiny. A bookie makes 5% margins. But trading a $2.5 call spread for $2.20 when it's worth $2.50? That's a ridiculous amount of edge compared to gambling, with the same risk distribution." We cover: - Why SIG was called "the evil empire" & how they crushed competitors by trading massive size for tighter spreads - The exact structure of prop shop deals: 50/50 splits, escrow accounts, how you get to 60% then 70% payouts - Why markets look efficient from most vantage points & how trading is ultimately about labor—getting your vantage point close enough that it stops looking random - The tyranny of beta: why the best operator in a melting ice cube business will lose to a mediocre performer in a great market - How to escape the "striver" trap & tune out status optimization (hint: find what you got obsessed with before college applications mattered) - Teaching his 12-year-old options market making & involving his 9-year-old in building a trading card game—scattered cards on the bedroom floor that'll become a finished product Thanks to Kris for the masterclass. Highlights: 02:05 How Kris first recognized real trading edge 04:01 How early market structure created easy edge 05:27 Why improvement in trading comes from hindsight 07:08 The core SIG frameworks that shaped his edge 09:37 Why uncovering edge requires labor and precision 11:02 How informed order flow forces trader humility 12:53 What truly differentiated SIG from competitors 13:23 How SIG built a world-class education pipeline 16:30 How SIG captured edge by refusing to hedge 18:11 How centralized risk controlled exposure and variance 19:09 How SIG used size and spreads to dominate markets 23:20 What Kris learned working with Jason McCarthy 25:40 Why elite traders share extreme competitiveness 26:06 How top performers operate across domains and PM roles 28:22 How Kris transitioned from SIG to prop trading 31:56 What shifting into senior roles taught him about trading 33:46 How Kris built training and feedback systems for traders 35:00 How the backer model works inside prop shops 38:41 How escrow capital protects traders from tail events 41:03 How natural gas options trading changed with regime shifts 42:13 How Kris applies trading edge concepts to life decisions 45:46 Why personal alignment beats chasing status in trading 47:13 How status games distort decision-making for young traders 52:23 Why striver behavior is actually risk management 56:27 How Kris teaches opportunity cost through parenting 1:01:28 How exposing kids to decisions builds intuition 1:04:46 How Kris teaches EV using homemade trading games 1:08:05 How iteration and feedback loops shape real learning

MUST-WATCH: Why SIG Dominated Options Trading — Explained by an 8-Year Insider Kris Abdelmessih (Kris) spent 8 years trading energy derivatives at SIG, then ran options businesses at Parallax & Prime before founding Moontower —one of the world's most popular newsletters on options & volatility trading. "SIG understood there was an abnormal amount of edge in the market. They came from gambling—sports betting, poker—where edge was tiny. A bookie makes 5% margins. But trading a $2.5 call spread for $2.20 when it's worth $2.50? That's a ridiculous amount of edge compared to gambling, with the same risk distribution." We cover: - Why SIG was called "the evil empire" & how they crushed competitors by trading massive size for tighter spreads - The exact structure of prop shop deals: 50/50 splits, escrow accounts, how you get to 60% then 70% payouts - Why markets look efficient from most vantage points & how trading is ultimately about labor—getting your vantage point close enough that it stops looking random - The tyranny of beta: why the best operator in a melting ice cube business will lose to a mediocre performer in a great market - How to escape the "striver" trap & tune out status optimization (hint: find what you got obsessed with before college applications mattered) - Teaching his 12-year-old options market making & involving his 9-year-old in building a trading card game—scattered cards on the bedroom floor that'll become a finished product Thanks to Kris for the masterclass. Highlights: 02:05 How Kris first recognized real trading edge 04:01 How early market structure created easy edge 05:27 Why improvement in trading comes from hindsight 07:08 The core SIG frameworks that shaped his edge 09:37 Why uncovering edge requires labor and precision 11:02 How informed order flow forces trader humility 12:53 What truly differentiated SIG from competitors 13:23 How SIG built a world-class education pipeline 16:30 How SIG captured edge by refusing to hedge 18:11 How centralized risk controlled exposure and variance 19:09 How SIG used size and spreads to dominate markets 23:20 What Kris learned working with Jason McCarthy 25:40 Why elite traders share extreme competitiveness 26:06 How top performers operate across domains and PM roles 28:22 How Kris transitioned from SIG to prop trading 31:56 What shifting into senior roles taught him about trading 33:46 How Kris built training and feedback systems for traders 35:00 How the backer model works inside prop shops 38:41 How escrow capital protects traders from tail events 41:03 How natural gas options trading changed with regime shifts 42:13 How Kris applies trading edge concepts to life decisions 45:46 Why personal alignment beats chasing status in trading 47:13 How status games distort decision-making for young traders 52:23 Why striver behavior is actually risk management 56:27 How Kris teaches opportunity cost through parenting 1:01:28 How exposing kids to decisions builds intuition 1:04:46 How Kris teaches EV using homemade trading games 1:08:05 How iteration and feedback loops shape real learning

Ethan Kho

233,306 Aufrufe • vor 8 Monaten

Goldman Sachs uses this exact formula to decide if a company is worth $14,000,000. Most of their analysts learned it from one free MIT lecture. A Goldman recruiter once told a candidate: "If you can't derive an annuity price in under two minutes, we don't continue the interview." Starting offer for those who could: $250,000. The formula fits on one line. MIT teaches it in 80 minutes. For free. This is MIT 6.042J. Mathematics for Computer Science. The lecture Wall Street forgot to take down. The professor opens with a simple question: $50,000 a year for 20 years, or $1,000,000 today? Most people pick the million. Most people are right. Then he shows you exactly why - with math you can verify yourself in 30 seconds. Then the formula. Every annuity - student loans, home mortgages, lottery payouts - is secretly just a geometric series. One formula prices all of them. He derives it live on the board using nothing but algebra. Then Wall Street. He explains how slight differences in interest rate assumptions - the p in the formula - let banks make money off the same instrument. Two banks, same contract, different p. One wins. He explains how that confusion caused the 2008 subprime collapse. The entire global recession. Traced back to one variable. Then the company valuation. A company adding $50,000 more in profit every year forever - what do you pay for it today? He plugs into the formula. Answer: $14,700,000. That is how acquisitions get priced on Wall Street. Watch the moment he describes how people made hundreds of billions during the financial crisis while everyone else lost - "money went from one place to another." He says it casually. The room goes quiet. A quant analyst at Bridgewater told me this was the first lecture that made financial modeling feel like arithmetic. Starting salary: $350,000. Bonus year: $900,000. Bookmark this and watch later - after this lecture every loan, lottery, or acquisition price you see will feel like a solved equation. MIT 6.042J Lecture 12 | Mathematics for Computer Science | Fall 2010

Goldman Sachs uses this exact formula to decide if a company is worth $14,000,000. Most of their analysts learned it from one free MIT lecture. A Goldman recruiter once told a candidate: "If you can't derive an annuity price in under two minutes, we don't continue the interview." Starting offer for those who could: $250,000. The formula fits on one line. MIT teaches it in 80 minutes. For free. This is MIT 6.042J. Mathematics for Computer Science. The lecture Wall Street forgot to take down. The professor opens with a simple question: $50,000 a year for 20 years, or $1,000,000 today? Most people pick the million. Most people are right. Then he shows you exactly why - with math you can verify yourself in 30 seconds. Then the formula. Every annuity - student loans, home mortgages, lottery payouts - is secretly just a geometric series. One formula prices all of them. He derives it live on the board using nothing but algebra. Then Wall Street. He explains how slight differences in interest rate assumptions - the p in the formula - let banks make money off the same instrument. Two banks, same contract, different p. One wins. He explains how that confusion caused the 2008 subprime collapse. The entire global recession. Traced back to one variable. Then the company valuation. A company adding $50,000 more in profit every year forever - what do you pay for it today? He plugs into the formula. Answer: $14,700,000. That is how acquisitions get priced on Wall Street. Watch the moment he describes how people made hundreds of billions during the financial crisis while everyone else lost - "money went from one place to another." He says it casually. The room goes quiet. A quant analyst at Bridgewater told me this was the first lecture that made financial modeling feel like arithmetic. Starting salary: $350,000. Bonus year: $900,000. Bookmark this and watch later - after this lecture every loan, lottery, or acquisition price you see will feel like a solved equation. MIT 6.042J Lecture 12 | Mathematics for Computer Science | Fall 2010

Lupen

116,946 Aufrufe • vor 1 Monat

an MIT professor who taught both physics and finance told his class something none of them expected "finance is harder than physics" not as a joke. as a mathematical statement in physics, the laws don't change. gravity works the same today as it did a billion years ago you can run an experiment, get a result, and repeat it forever in finance, the moment you discover a law, the participants learn it too and their behavior changes the system you just measured in physics, electrons don't read your paper and start moving differently in finance, traders do. every published edge gets arbitraged away by the people who read it this is why quant models have a half-life and physics equations don't Newton's laws: 300+ years and counting Long-Term Capital Management's model: worked perfectly until it didn't, lost $4.6 billion in 4 months the system you're modeling is aware of you modeling it that's not a solvable problem. it's a permanent condition and the quants who survive are the ones who build for it instead of pretending it doesn't exist > this lecture: MIT finance series, free, public, 53 seconds > LTCM collapse: 1998, Nobel Prize winners, $4.6B loss > Renaissance's solution: never stop researching, replace signals before they decay > average lifespan of a quant signal: 2-5 years before it's crowded out retail builds one strategy and trades it until it breaks quant desks build a research engine that produces new strategies faster than old ones die that's not a difference in skill. it's a difference in understanding what game you're actually playing full breakdown in the video below

an MIT professor who taught both physics and finance told his class something none of them expected "finance is harder than physics" not as a joke. as a mathematical statement in physics, the laws don't change. gravity works the same today as it did a billion years ago you can run an experiment, get a result, and repeat it forever in finance, the moment you discover a law, the participants learn it too and their behavior changes the system you just measured in physics, electrons don't read your paper and start moving differently in finance, traders do. every published edge gets arbitraged away by the people who read it this is why quant models have a half-life and physics equations don't Newton's laws: 300+ years and counting Long-Term Capital Management's model: worked perfectly until it didn't, lost $4.6 billion in 4 months the system you're modeling is aware of you modeling it that's not a solvable problem. it's a permanent condition and the quants who survive are the ones who build for it instead of pretending it doesn't exist > this lecture: MIT finance series, free, public, 53 seconds > LTCM collapse: 1998, Nobel Prize winners, $4.6B loss > Renaissance's solution: never stop researching, replace signals before they decay > average lifespan of a quant signal: 2-5 years before it's crowded out retail builds one strategy and trades it until it breaks quant desks build a research engine that produces new strategies faster than old ones die that's not a difference in skill. it's a difference in understanding what game you're actually playing full breakdown in the video below

delost

19,113 Aufrufe • vor 3 Monaten

A Japanese mathematician published a result in 1944 that nobody understood for twenty years. Today it runs inside every options desk on Wall Street. Goldman pays $400K to quants who can derive it from scratch and explain why classical calculus gives the wrong answer without it. His name is Choongbum Lee. MIT, 18.S096, Topics in Mathematics with Applications in Finance. The course that Wall Street watches. This is lecture 17. It derives Ito's Lemma from scratch. He opens with the problem nobody in classical calculus can solve. Then the foundation. Brownian motion is the limit of a random walk taken to infinity. Each trade pushes a price up or down by a tiny amount. A million trades a day. The limit of that process is Brownian motion. Einstein proved this for pollen particles in 1905. The finance world borrowed the math fifty years later. Then three properties that make no sense until you see them derived. Brownian motion crosses zero infinitely often. It never escapes to infinity. And it is nowhere differentiable - with probability one, every path is continuous but has no slope at any point. That last property is why classical calculus breaks completely. Then quadratic variation. For any smooth function, chop an interval into n pieces, square the increments, sum them - the result goes to zero. For Brownian motion it goes to T. The increments are too wild to vanish. That single fact is why Ito's Lemma has a second term that classical calculus does not. Watch the moment he derives it. Taylor expansion applied to a function of Brownian motion. The first term is what you expect. The second term appears precisely because the squared increment does not vanish. Without it, options pricing gives wrong answers. With it, you have Black-Scholes. A quant I know sends this lecture to junior analysts who cannot explain why their pricing model drifts. Says it fixes in ninety minutes what two years of finance courses left open. Free on YouTube, MIT OpenCourseWare, 18.S096. bookmark this and watch later - the math behind every options desk on Wall Street fits on one blackboard, and this is the lecture that shows you why

A Japanese mathematician published a result in 1944 that nobody understood for twenty years. Today it runs inside every options desk on Wall Street. Goldman pays $400K to quants who can derive it from scratch and explain why classical calculus gives the wrong answer without it. His name is Choongbum Lee. MIT, 18.S096, Topics in Mathematics with Applications in Finance. The course that Wall Street watches. This is lecture 17. It derives Ito's Lemma from scratch. He opens with the problem nobody in classical calculus can solve. Then the foundation. Brownian motion is the limit of a random walk taken to infinity. Each trade pushes a price up or down by a tiny amount. A million trades a day. The limit of that process is Brownian motion. Einstein proved this for pollen particles in 1905. The finance world borrowed the math fifty years later. Then three properties that make no sense until you see them derived. Brownian motion crosses zero infinitely often. It never escapes to infinity. And it is nowhere differentiable - with probability one, every path is continuous but has no slope at any point. That last property is why classical calculus breaks completely. Then quadratic variation. For any smooth function, chop an interval into n pieces, square the increments, sum them - the result goes to zero. For Brownian motion it goes to T. The increments are too wild to vanish. That single fact is why Ito's Lemma has a second term that classical calculus does not. Watch the moment he derives it. Taylor expansion applied to a function of Brownian motion. The first term is what you expect. The second term appears precisely because the squared increment does not vanish. Without it, options pricing gives wrong answers. With it, you have Black-Scholes. A quant I know sends this lecture to junior analysts who cannot explain why their pricing model drifts. Says it fixes in ninety minutes what two years of finance courses left open. Free on YouTube, MIT OpenCourseWare, 18.S096. bookmark this and watch later - the math behind every options desk on Wall Street fits on one blackboard, and this is the lecture that shows you why

Lupen

66,176 Aufrufe • vor 1 Monat

Inside the mind of an ex-SIG quant trader who can't turn off the EV brain - even for his kid's school choice Andrew Courtney (Andrew Courtney) ran the International ETFs Trading Desk at Susquehanna International Group for ~15 years before leaving in 2023. He now runs Kalshionomics (Kalshinomics), a prediction markets analytics tool, and writes the Whirligig Bear, one of the sharpest prediction markets Substacks out there. "I think of everything as a bet. I kind of don't understand how you talk to normal people — they do not do that." SIG trains their junior traders with poker, spending 2hrs/day turning over cards after every hand, justifying every decision quantitatively AND qualitatively. 15 years later, Andrew views prediction markets the same way: read who's on the other side, size accordingly, fold when the whale comes back at you 10x. We cover: - Why SIG pays junior traders to play poker for 2hrs/day — & what happens after every single hand - The "one eye on the market, always" attention tax that destroys most people's careers - How to find edge in prediction markets by asking: who am I actually trading against? - Why meme-heavy, overhyped markets (Taylor Swift at the Super Bowl) might be the juiciest trades - The insider trading debate in prediction markets — & why it's "socially corrosive" - Floor trading vs. upstairs quant: why the transition saved his career - 40 connections after ~15 years at one of the world's best firms — the hidden cost of prop trading - Why he doesn't have collision insurance on his car (& the EV math behind it) Thank you so much Andrew Courtney for coming on the pod! Timestamps: 00:00 Intro 05:00 Floor trading vs. electronic trading 06:28 What makes an upstairs trader 10:16 Poker as trader training 13:00 Thinking in bets as a mental framework 15:11 Decision trees in real life 16:40 Where prediction markets actually have edge 19:00 Why the LLM forecasting layer falls short 19:40 Liquidity incentives and trading low-volume markets 22:00 Limiting downside even when the model is wrong 24:32 Executing in illiquid markets 25:44 Fair value vs. directional conviction 27:11 Bayesian updating when liquidity responds 28:40 Fading hype and crowded narratives 31:07 Longshot bias vs. fanbase bias 34:20 How to judge whether you really have edge 36:40 Building analytics tools for prediction markets 38:20 The temporary edge for smart amateurs 40:35 Where prediction markets fit best 41:20 Markets that shouldn’t exist 43:20 Why insider trading corrodes incentives 46:52 Are prediction markets a net good or bad 50:47 Minimizing degeneracy and maximizing signal 53:32 A simple EV mindset anyone can use

Inside the mind of an ex-SIG quant trader who can't turn off the EV brain - even for his kid's school choice Andrew Courtney (Andrew Courtney) ran the International ETFs Trading Desk at Susquehanna International Group for ~15 years before leaving in 2023. He now runs Kalshionomics (Kalshinomics), a prediction markets analytics tool, and writes the Whirligig Bear, one of the sharpest prediction markets Substacks out there. "I think of everything as a bet. I kind of don't understand how you talk to normal people — they do not do that." SIG trains their junior traders with poker, spending 2hrs/day turning over cards after every hand, justifying every decision quantitatively AND qualitatively. 15 years later, Andrew views prediction markets the same way: read who's on the other side, size accordingly, fold when the whale comes back at you 10x. We cover: - Why SIG pays junior traders to play poker for 2hrs/day — & what happens after every single hand - The "one eye on the market, always" attention tax that destroys most people's careers - How to find edge in prediction markets by asking: who am I actually trading against? - Why meme-heavy, overhyped markets (Taylor Swift at the Super Bowl) might be the juiciest trades - The insider trading debate in prediction markets — & why it's "socially corrosive" - Floor trading vs. upstairs quant: why the transition saved his career - 40 connections after ~15 years at one of the world's best firms — the hidden cost of prop trading - Why he doesn't have collision insurance on his car (& the EV math behind it) Thank you so much Andrew Courtney for coming on the pod! Timestamps: 00:00 Intro 05:00 Floor trading vs. electronic trading 06:28 What makes an upstairs trader 10:16 Poker as trader training 13:00 Thinking in bets as a mental framework 15:11 Decision trees in real life 16:40 Where prediction markets actually have edge 19:00 Why the LLM forecasting layer falls short 19:40 Liquidity incentives and trading low-volume markets 22:00 Limiting downside even when the model is wrong 24:32 Executing in illiquid markets 25:44 Fair value vs. directional conviction 27:11 Bayesian updating when liquidity responds 28:40 Fading hype and crowded narratives 31:07 Longshot bias vs. fanbase bias 34:20 How to judge whether you really have edge 36:40 Building analytics tools for prediction markets 38:20 The temporary edge for smart amateurs 40:35 Where prediction markets fit best 41:20 Markets that shouldn’t exist 43:20 Why insider trading corrodes incentives 46:52 Are prediction markets a net good or bad 50:47 Minimizing degeneracy and maximizing signal 53:32 A simple EV mindset anyone can use

Ethan Kho

436,074 Aufrufe • vor 7 Monaten

Ken Griffin, CEO of Citadel, said: "I'm fairly depressed watching AI do a week of PhD work in a few hours" He's worth $51.2 billion and 20% of every stock trade in America goes through his firm. The money isn't what's bothering him. Citadel pays $400,000 to $650,000 a year for that work. New trading AI agent does it now, on a free trial, for people who have never written a line of code. It went live this summer and most of your timeline still hasn't noticed. You type one sentence in plain English. AI agents loop takes it from there: > an AI agent turns your sentence into a real trading strategy > backtests it across 5 years of data in about 12 seconds > scores it 0 to 100 and shows you the exact spot where it leaks > kills the versions that only look good on paper > rebuilds what's left and runs the loop again > stress-tests the survivor on years it has never seen, then puts it live on your exchange in about 90 seconds That list is the entire job description of a junior quant. At a desk, one properly tested idea burns about $87,500 in salary time, and most of what they build is dead by week six. Two clicks and this AI agent is testing your own idea. Costs you nothing and it runs the whole thing by itself: Griffin gets to be depressed about it. He already owns the desk. You just got handed one. Bookmark & read full story of how they got an AI to build trading strategies and kill its own bad ones is in the article below.

Ken Griffin, CEO of Citadel, said: "I'm fairly depressed watching AI do a week of PhD work in a few hours" He's worth $51.2 billion and 20% of every stock trade in America goes through his firm. The money isn't what's bothering him. Citadel pays $400,000 to $650,000 a year for that work. New trading AI agent does it now, on a free trial, for people who have never written a line of code. It went live this summer and most of your timeline still hasn't noticed. You type one sentence in plain English. AI agents loop takes it from there: > an AI agent turns your sentence into a real trading strategy > backtests it across 5 years of data in about 12 seconds > scores it 0 to 100 and shows you the exact spot where it leaks > kills the versions that only look good on paper > rebuilds what's left and runs the loop again > stress-tests the survivor on years it has never seen, then puts it live on your exchange in about 90 seconds That list is the entire job description of a junior quant. At a desk, one properly tested idea burns about $87,500 in salary time, and most of what they build is dead by week six. Two clicks and this AI agent is testing your own idea. Costs you nothing and it runs the whole thing by itself: Griffin gets to be depressed about it. He already owns the desk. You just got handed one. Bookmark & read full story of how they got an AI to build trading strategies and kill its own bad ones is in the article below.

cvxv666

225,653 Aufrufe • vor 2 Monaten

David Jerison, the mathematician who spent decades teaching the exact method for finding the optimal choice when the odds are stacked against you: "I used to think the best decision was whichever option looked strongest at first glance. Then I proved that the real answer almost always hides at a point nobody would guess just by looking, and checking only the obvious choices gets you the worst possible outcome, not the best." this is the exact method quant desks lean on to find the one allocation that survives every constraint thrown at it, and it's been sitting free in a public MIT lecture for almost twenty years. strip away the notation and the mechanism is simple. every optimization problem has a handful of candidate points where the best or worst answer could be hiding, and the obvious middle-of-the-road guess is almost never one of them. check only the points that feel natural, and you don't just miss the best answer. you can land on the exact opposite, the worst possible one, without ever realizing it. nobody presenting a "risk-optimized" portfolio out loud admits how easy it is to stop checking one step too early. zoom out to how this plays out sizing a position or allocating risk under real constraints today. the instinct is to test the option that feels balanced and call it done, when the actual edge is almost always sitting at an extreme nobody thought to check. the industry sells a clean, confident number as proof an allocation is optimal. but that number means nothing until every boundary has been checked, because the same method that finds the best case can just as easily hand you the worst one in disguise. the right answer was never the one that looked most reasonable. it was the one nobody bothered to check.

David Jerison, the mathematician who spent decades teaching the exact method for finding the optimal choice when the odds are stacked against you: "I used to think the best decision was whichever option looked strongest at first glance. Then I proved that the real answer almost always hides at a point nobody would guess just by looking, and checking only the obvious choices gets you the worst possible outcome, not the best." this is the exact method quant desks lean on to find the one allocation that survives every constraint thrown at it, and it's been sitting free in a public MIT lecture for almost twenty years. strip away the notation and the mechanism is simple. every optimization problem has a handful of candidate points where the best or worst answer could be hiding, and the obvious middle-of-the-road guess is almost never one of them. check only the points that feel natural, and you don't just miss the best answer. you can land on the exact opposite, the worst possible one, without ever realizing it. nobody presenting a "risk-optimized" portfolio out loud admits how easy it is to stop checking one step too early. zoom out to how this plays out sizing a position or allocating risk under real constraints today. the instinct is to test the option that feels balanced and call it done, when the actual edge is almost always sitting at an extreme nobody thought to check. the industry sells a clean, confident number as proof an allocation is optimal. but that number means nothing until every boundary has been checked, because the same method that finds the best case can just as easily hand you the worst one in disguise. the right answer was never the one that looked most reasonable. it was the one nobody bothered to check.

MindArch

16,232 Aufrufe • vor 2 Monaten

Trading strategy making you money right now is already dying. You just can't feel it yet. Two finance professors tracked 97 published strategies. One year after they went public, the average edge was down 58%. Every edge has a half-life. Nobody hands you the expiration date. So three red weeks hit, and you panic-quit a strategy that was still alive. Or you marry a dead one and bleed for six months defending it. You can't tell the two apart by feel. That's the entire problem. There's now AI tool that draws the line for you. Horizon backtests 5 years in seconds and runs thousands of simulated paths, so before you go live you already know how deep and how long a normal drawdown runs. Drawdown is weather. Decay is information. One you ride out. One you cut. Then it runs the strategy automated -> the exit gets decided by the math you set when you were calm, not the mood you're in at the bottom. The same loop a quant desk pays $25,000 a year for. Yours for $0 while the beta open. Save this -> re-read it the next time you're three red weeks deep and about to fold a strategy that's still alive. Full framework in the article above. Free access while the beta's open:

Trading strategy making you money right now is already dying. You just can't feel it yet. Two finance professors tracked 97 published strategies. One year after they went public, the average edge was down 58%. Every edge has a half-life. Nobody hands you the expiration date. So three red weeks hit, and you panic-quit a strategy that was still alive. Or you marry a dead one and bleed for six months defending it. You can't tell the two apart by feel. That's the entire problem. There's now AI tool that draws the line for you. Horizon backtests 5 years in seconds and runs thousands of simulated paths, so before you go live you already know how deep and how long a normal drawdown runs. Drawdown is weather. Decay is information. One you ride out. One you cut. Then it runs the strategy automated -> the exit gets decided by the math you set when you were calm, not the mood you're in at the bottom. The same loop a quant desk pays $25,000 a year for. Yours for $0 while the beta open. Save this -> re-read it the next time you're three red weeks deep and about to fold a strategy that's still alive. Full framework in the article above. Free access while the beta's open:

cvxv666

71,962 Aufrufe • vor 4 Monaten

MUST-WATCH: Inside high-frequency trading & building the future of markets with Annanay Kapila Annanay Kapila (ak0) — former quant trader at Flow Traders & Tower Research, now founder of QFEX (YC-backed). Building the first 24/7 perpetual futures exchange for equities, commodities & FX — bringing crypto-level UX to traditional assets. "My friends at Cambridge have been making $5M+ a year for the last few years. They don't have to work anymore. But no one retires early. Why? Everyone who gets selected has that desire to win that transcends money, even rationality." We cover: - What it's really like inside elite HFT shops — international math Olympiad winners, professional poker players & zero room for error - Why the smartest traders keep working after making generational wealth (hint: it's pure competitive ego) - The two schools of quant trading: Chicago pit traders vs MIT mathematicians — and which strategies they dominate - How his team navigated the FTX collapse in real-time (including friends who escaped via Dogecoin withdrawals) - Building QFEX : 24/7 perpetual futures on equities with crypto-level UX — "We're building FTX without the fraud" - YC insider stories: meeting Sam Altman & Paul Graham, raising from Paul Graham (Paul Graham) personally, why the $125k for 7% is worth it - Culture secrets from Citadel & Tower: when your dev environment pages you at 2am on Saturday - The one interview hack VCs use that most founders miss (interrupt their CV walkthrough relentlessly) - Why doomerism is dead wrong: "My life would've been worse if I was born 10 years ago. I dread to think how much better it would be if I was born 10 years from now" Huge thanks to ak0 for the transparency. Follow his journey building the future of financial markets. 01:12 Inside high frequency trading culture and competition 03:00 What drives top performers in HFT 03:47 Why elite traders never retire early 04:50 How Tower structures quant trading work 06:14 Chicago versus MIT quant trading styles 10:29 How modern market making actually works 12:25 Why prop firms struggle to scale capital 16:36 Annanay's work at Tower and Flow 19:15 Tower's crypto trading and API edge 19:24 How firms handled the FTX collapse 23:42 Inside FTX culture and HK crypto scene 27:11 What QFEX is building for traders 29:37 QFEX beta launch and early rollout 29:56 Why building an exchange is exciting 32:48 QFEX execution edge and team DNA 36:24 Why quants leave HFT for startups 38:22 QFEX culture versus HFT culture 41:46 How meritocracy works inside QFEX 47:52 How QFEX hires true A players 53:05 Backchanneling and evaluating candidates 53:27 What YC actually teaches founders 55:21 Lessons from meeting Paul Graham and Sam Altman 58:41 Why startups fail and how to pivot 1:00:48 Why young people should stay optimistic 1:11:22 Closing remarks and QFEX vision

MUST-WATCH: Inside high-frequency trading & building the future of markets with Annanay Kapila Annanay Kapila (ak0) — former quant trader at Flow Traders & Tower Research, now founder of QFEX (YC-backed). Building the first 24/7 perpetual futures exchange for equities, commodities & FX — bringing crypto-level UX to traditional assets. "My friends at Cambridge have been making $5M+ a year for the last few years. They don't have to work anymore. But no one retires early. Why? Everyone who gets selected has that desire to win that transcends money, even rationality." We cover: - What it's really like inside elite HFT shops — international math Olympiad winners, professional poker players & zero room for error - Why the smartest traders keep working after making generational wealth (hint: it's pure competitive ego) - The two schools of quant trading: Chicago pit traders vs MIT mathematicians — and which strategies they dominate - How his team navigated the FTX collapse in real-time (including friends who escaped via Dogecoin withdrawals) - Building QFEX : 24/7 perpetual futures on equities with crypto-level UX — "We're building FTX without the fraud" - YC insider stories: meeting Sam Altman & Paul Graham, raising from Paul Graham (Paul Graham) personally, why the $125k for 7% is worth it - Culture secrets from Citadel & Tower: when your dev environment pages you at 2am on Saturday - The one interview hack VCs use that most founders miss (interrupt their CV walkthrough relentlessly) - Why doomerism is dead wrong: "My life would've been worse if I was born 10 years ago. I dread to think how much better it would be if I was born 10 years from now" Huge thanks to ak0 for the transparency. Follow his journey building the future of financial markets. 01:12 Inside high frequency trading culture and competition 03:00 What drives top performers in HFT 03:47 Why elite traders never retire early 04:50 How Tower structures quant trading work 06:14 Chicago versus MIT quant trading styles 10:29 How modern market making actually works 12:25 Why prop firms struggle to scale capital 16:36 Annanay's work at Tower and Flow 19:15 Tower's crypto trading and API edge 19:24 How firms handled the FTX collapse 23:42 Inside FTX culture and HK crypto scene 27:11 What QFEX is building for traders 29:37 QFEX beta launch and early rollout 29:56 Why building an exchange is exciting 32:48 QFEX execution edge and team DNA 36:24 Why quants leave HFT for startups 38:22 QFEX culture versus HFT culture 41:46 How meritocracy works inside QFEX 47:52 How QFEX hires true A players 53:05 Backchanneling and evaluating candidates 53:27 What YC actually teaches founders 55:21 Lessons from meeting Paul Graham and Sam Altman 58:41 Why startups fail and how to pivot 1:00:48 Why young people should stay optimistic 1:11:22 Closing remarks and QFEX vision

Ethan Kho

164,024 Aufrufe • vor 8 Monaten

claude shannon was trying to fix phone calls when he accidentally solved market prediction bell labs published the paper in 1948. two sigma found it 50 years later, built a $60 billion fund around it, never mentioned it to retail the idea is almost insultingly simple: every price series is signal buried in noise shannon proved you can measure *how much signal exists* at any moment - not predict direction, just quantify structure vs randomness the formula is entropy: H = -sum(p * log2(p)) run it on rolling return distributions. outputs a number between 0 and 1 H above 0.7 = noise. random walk. stay out H below 0.35 = structure detected. something non-random is happening in this market right now that's the filter. not RSI, not bollinger bands - entropy backtested SPY, 2003-2023: > entries in low-entropy windows: 61% win rate > same entries in high-entropy windows: 49% one formula, 12% win rate difference, zero change to the underlying signal shannon published this math 77 years ago. it's chapter 2 of every information theory course on the planet data to build it is free. the python is 40 lines Bookmark this before you scroll past they spent decades keeping you staring at lagging indicators while they were measuring the information content of the exact same price data you've always had

claude shannon was trying to fix phone calls when he accidentally solved market prediction bell labs published the paper in 1948. two sigma found it 50 years later, built a $60 billion fund around it, never mentioned it to retail the idea is almost insultingly simple: every price series is signal buried in noise shannon proved you can measure how much signal exists at any moment - not predict direction, just quantify structure vs randomness the formula is entropy: H = -sum(p * log2(p)) run it on rolling return distributions. outputs a number between 0 and 1 H above 0.7 = noise. random walk. stay out H below 0.35 = structure detected. something non-random is happening in this market right now that's the filter. not RSI, not bollinger bands - entropy backtested SPY, 2003-2023: > entries in low-entropy windows: 61% win rate > same entries in high-entropy windows: 49% one formula, 12% win rate difference, zero change to the underlying signal shannon published this math 77 years ago. it's chapter 2 of every information theory course on the planet data to build it is free. the python is 40 lines Bookmark this before you scroll past they spent decades keeping you staring at lagging indicators while they were measuring the information content of the exact same price data you've always had

Livsun

111,975 Aufrufe • vor 1 Monat