Loading video...

Video Failed to Load

Go Home

Inside Susquehanna International Group with Todd Simkin Todd Simkin led trader education & development at SIG, one of the most elite prop trading firms on the planet, for decades. He's now Head of Insurance at River's Edge, Susquehanna's managing general underwriter. We cover: - Why SIG trains traders with...

ethanrkho's profile picture

Ethan Kho

17,773 subscribers

34,855 views • 6 months ago •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

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
1:09:23

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 views • 8 months ago

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
56:48

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 views • 7 months ago

INSIDE JUPITER: TRADING THE NEWS WITH PREDICTION MARKETS This episode with Julian breaks down why prediction markets are becoming a core primitive of Web4, how the Polymarket partnership fits the Jupiter full-stack vision, and why the future isn’t a separate product it’s prediction embedded everywhere. time stamps : 00:00 Intro Inside Jupiter HQ 00:18 Who's Julian and what's his role 00:44 What JuPredict actually is: “trading the news” 01:11 Polymarket × Jupiter 02:01 Why prediction markets are emerging 03:00 Markets vs commentators 04:24 Local & social prediction markets 05:15 On-chain transparency & global accessibility 06:27 Prediction markets in the Jupiter Web4 stack 07:48 The vision: predicting from any interface 09:28 Why people misread market prices 10:20 The real edge: studying trade flow before events 11:05 What prediction markets are best at predicting 11:40 Consistency & accuracy of the markets 12:10 Message to the community
13:01

INSIDE JUPITER: TRADING THE NEWS WITH PREDICTION MARKETS This episode with Julian breaks down why prediction markets are becoming a core primitive of Web4, how the Polymarket partnership fits the Jupiter full-stack vision, and why the future isn’t a separate product it’s prediction embedded everywhere. time stamps : 00:00 Intro Inside Jupiter HQ 00:18 Who's Julian and what's his role 00:44 What JuPredict actually is: “trading the news” 01:11 Polymarket × Jupiter 02:01 Why prediction markets are emerging 03:00 Markets vs commentators 04:24 Local & social prediction markets 05:15 On-chain transparency & global accessibility 06:27 Prediction markets in the Jupiter Web4 stack 07:48 The vision: predicting from any interface 09:28 Why people misread market prices 10:20 The real edge: studying trade flow before events 11:05 What prediction markets are best at predicting 11:40 Consistency & accuracy of the markets 12:10 Message to the community

JUP AND JUICE (🧃)

20,990 views • 7 months ago

Inside Goldman Sachs' natural gas trading desk with John Knorring John Knorring — former Managing Director & Head of Natural Gas Trading at Goldman Sachs. Traded through Katrina, Amaranth's collapse & the financial crisis. Now building electricity hedging markets in the emerging markets. "We priced Lehman's entire options book — 500,000 options, 17 million vega — in 30 minutes during the collapse. At Goldman, you had to know your position at all times or you got fired." We cover: - Trading in the actual pits vs today's algo-dominated markets — why electronic trading pushed him out - How shale dropped US nat gas from $16 to $1.50 & why Henry Hub futures unlocked the entire buildout - Absorbing Lehman's book during the crisis & managing massive dislocated positions - The information edge at Goldman vs pure price-taking at DRW — what you lose without client flow - Why discretionary trading can't be fully automated (gold hitting $4,000 wasn't in any model's history) - Building Green Tiger Markets — bringing forward curves to Philippine electricity (zero public pricing until 2 years ago) ~$2B notional in hedging orders processed, liquidity still early but growing - Manila vs New York business culture — "nobody wants to be first, everyone wants to be second" Timestamps: 00:00 Intro 01:35 Leading natural gas trading at Goldman Sachs 04:06 Trading hurricanes, Amaranth, and 2008 crisis volatility 04:48 How pit trading and voice execution worked 06:21 How Goldman risk systems handled massive positions 06:55 How electronic trading transformed energy markets 08:31 Did algorithmic trading kill discretionary edge? 09:59 Why coding became essential for commodity traders 11:10 What pit-era trading psychology felt like 12:34 How Dodd-Frank changed bank trading desks 14:19 Why John left Goldman for DRW prop trading 16:16 What discretionary traders actually did at DRW 19:14 How losing client flow changed information edges 21:20 What data powered discretionary energy strategies 24:19 Can discretionary trading ever be automated? 28:45 How traders detect paradigm shifts in commodities 30:21 John’s framework: ideas, execution, money management 34:30 John’s current trades in silver and gold 34:49 Why he built Green Tiger Markets in PH 36:30 How electricity forward hedging works in emerging markets 41:52 Growth outlook for Philippines electricity hedging 45:25 Are PH market participants sophisticated enough to hedge? 48:20 Cultural realities trading and doing business in Manila 51:20 How GTM differs from ICE and CME structures 59:20 Origin story: Carlos, technology, and building GTM 1:00:34 From Goldman trader to emerging-market exchange builder
1:11:26

Inside Goldman Sachs' natural gas trading desk with John Knorring John Knorring — former Managing Director & Head of Natural Gas Trading at Goldman Sachs. Traded through Katrina, Amaranth's collapse & the financial crisis. Now building electricity hedging markets in the emerging markets. "We priced Lehman's entire options book — 500,000 options, 17 million vega — in 30 minutes during the collapse. At Goldman, you had to know your position at all times or you got fired." We cover: - Trading in the actual pits vs today's algo-dominated markets — why electronic trading pushed him out - How shale dropped US nat gas from $16 to $1.50 & why Henry Hub futures unlocked the entire buildout - Absorbing Lehman's book during the crisis & managing massive dislocated positions - The information edge at Goldman vs pure price-taking at DRW — what you lose without client flow - Why discretionary trading can't be fully automated (gold hitting $4,000 wasn't in any model's history) - Building Green Tiger Markets — bringing forward curves to Philippine electricity (zero public pricing until 2 years ago) ~$2B notional in hedging orders processed, liquidity still early but growing - Manila vs New York business culture — "nobody wants to be first, everyone wants to be second" Timestamps: 00:00 Intro 01:35 Leading natural gas trading at Goldman Sachs 04:06 Trading hurricanes, Amaranth, and 2008 crisis volatility 04:48 How pit trading and voice execution worked 06:21 How Goldman risk systems handled massive positions 06:55 How electronic trading transformed energy markets 08:31 Did algorithmic trading kill discretionary edge? 09:59 Why coding became essential for commodity traders 11:10 What pit-era trading psychology felt like 12:34 How Dodd-Frank changed bank trading desks 14:19 Why John left Goldman for DRW prop trading 16:16 What discretionary traders actually did at DRW 19:14 How losing client flow changed information edges 21:20 What data powered discretionary energy strategies 24:19 Can discretionary trading ever be automated? 28:45 How traders detect paradigm shifts in commodities 30:21 John’s framework: ideas, execution, money management 34:30 John’s current trades in silver and gold 34:49 Why he built Green Tiger Markets in PH 36:30 How electricity forward hedging works in emerging markets 41:52 Growth outlook for Philippines electricity hedging 45:25 Are PH market participants sophisticated enough to hedge? 48:20 Cultural realities trading and doing business in Manila 51:20 How GTM differs from ICE and CME structures 59:20 Origin story: Carlos, technology, and building GTM 1:00:34 From Goldman trader to emerging-market exchange builder

Ethan Kho

70,455 views • 8 months ago

Ex-Global Trading & Risk Director at Cargill on how edge is formed in commodities markets Kristine Engman spent 20+ years at Cargill—one of America's largest agriculture conglomerates—trading billions in physical & financial commodities across energy & agriculture. "Edge in commodities? It's a lot about relationships. The intel you get through those relationships—that's information not readily accessible to the rest of the marketplace that can give you an edge." We cover: - How edge actually works in commodities—relationships, capital access & information arbitrage - Why you can trade on "insider information" in commodities (unlike equities) - Managing physical risk—forecast accuracy, weather shocks & transportation nightmares - The negative power trade—selling electricity below zero & getting hauled into the boss's office - Why Cargill traders performed best when equities markets performed worst - Quantitative (and even technical) vs fundamental trading styles—finding what works & sticking to it - The Ukraine war position—going extended limit long before the invasion - Life lessons from trading—"have your house in order" & taking risks with incomplete information Timestamps: 00:00 Intro 01:15 Edge in commodities trading? Relationships, capital, information 04:40 Commodities market efficiency, information flows, and AI 08:32 Hedge funds vs. ABCDs, commodity trading strategies 13:34 When commodities outperform equities, the 2022 boom 17:45 Headline risk, social media impact on markets 19:08 Risk management strategies in physical commodities trading 26:14 Probability, forecasting, and scenarios for trading decisions 31:00 What makes a great commodities trader today 37:53 Contrarian trading strategies, alpha generation in commodities 42:24 Russia–Ukraine war impact on commodity markets, trading 45:35 Life and career lessons from commodities trading 51:30 Careers, uncertainty, and learning in commodities markets
56:21

Ex-Global Trading & Risk Director at Cargill on how edge is formed in commodities markets Kristine Engman spent 20+ years at Cargill—one of America's largest agriculture conglomerates—trading billions in physical & financial commodities across energy & agriculture. "Edge in commodities? It's a lot about relationships. The intel you get through those relationships—that's information not readily accessible to the rest of the marketplace that can give you an edge." We cover: - How edge actually works in commodities—relationships, capital access & information arbitrage - Why you can trade on "insider information" in commodities (unlike equities) - Managing physical risk—forecast accuracy, weather shocks & transportation nightmares - The negative power trade—selling electricity below zero & getting hauled into the boss's office - Why Cargill traders performed best when equities markets performed worst - Quantitative (and even technical) vs fundamental trading styles—finding what works & sticking to it - The Ukraine war position—going extended limit long before the invasion - Life lessons from trading—"have your house in order" & taking risks with incomplete information Timestamps: 00:00 Intro 01:15 Edge in commodities trading? Relationships, capital, information 04:40 Commodities market efficiency, information flows, and AI 08:32 Hedge funds vs. ABCDs, commodity trading strategies 13:34 When commodities outperform equities, the 2022 boom 17:45 Headline risk, social media impact on markets 19:08 Risk management strategies in physical commodities trading 26:14 Probability, forecasting, and scenarios for trading decisions 31:00 What makes a great commodities trader today 37:53 Contrarian trading strategies, alpha generation in commodities 42:24 Russia–Ukraine war impact on commodity markets, trading 45:35 Life and career lessons from commodities trading 51:30 Careers, uncertainty, and learning in commodities markets

Ethan Kho

82,254 views • 7 months ago

EDGE REVEALED: How an Ex-Jane Street Trader Finds Edge in Markets & Life Agustin Lebron Agustin Lebron (former Jane Street trader, author of The Laws of Trading, now working at an AI startup applying reinforcement learning to market execution) breaks down what edge really means — and how to find yours in trading, careers & life. “Edge is something that either you know or you can do that the marginal participant in that market either doesn’t or can’t.” We cover: - What edge actually means & how Jane Street builds organizational edge (their worst skill is still "pretty decent") - Why you can never truly know if you have edge — the statistical vs intuitive approaches - The consolidation of quant trading: from dozens of options firms to a handful of giants - The gamblification of everything — retail trading, sports betting & prediction markets fueling quant profits - What it was like having Sam Bankman-Fried (SBF) as a Jane Street intern: "Day one, I'm going to ask all the questions" - How to apply edge thinking to your own career: find what you're differentially good at - Raising teenagers in the AI age: why the traditional path still works, but other paths are opening up 00:00 Introduction 00:44 What is edge in financial markets 01:43 Jane Street and organizational structures for quant trading 03:46 Identifying and validating edge in trading 06:22 Navigating extreme market events and volatility 09:37 Future of quant trading and consolidation 12:15 Why quant firm profits have increased 14:42 The gamblization of everything 15:55 Who should pursue a career in quant trading 18:19 Applying the concept of edge to career and life decisions 20:56 Advice for interns to excel in quant trading 23:44 Predicting long-term success in trading interns 25:08 Sam Bankman-Fried as an intern and FTX reflections 28:15 Reasons people leave Jane Street and what they do next 31:19 Advice for young people in a changing world 36:09 Navigating job insecurity in tech-driven roles 38:55 Where to live if you want to be successful 40:28 Raising kids for a rapidly changing future 42:55 Questions young people should ask themselves 44:45 Outro and book recommendation
44:46

EDGE REVEALED: How an Ex-Jane Street Trader Finds Edge in Markets & Life Agustin Lebron Agustin Lebron (former Jane Street trader, author of The Laws of Trading, now working at an AI startup applying reinforcement learning to market execution) breaks down what edge really means — and how to find yours in trading, careers & life. “Edge is something that either you know or you can do that the marginal participant in that market either doesn’t or can’t.” We cover: - What edge actually means & how Jane Street builds organizational edge (their worst skill is still "pretty decent") - Why you can never truly know if you have edge — the statistical vs intuitive approaches - The consolidation of quant trading: from dozens of options firms to a handful of giants - The gamblification of everything — retail trading, sports betting & prediction markets fueling quant profits - What it was like having Sam Bankman-Fried (SBF) as a Jane Street intern: "Day one, I'm going to ask all the questions" - How to apply edge thinking to your own career: find what you're differentially good at - Raising teenagers in the AI age: why the traditional path still works, but other paths are opening up 00:00 Introduction 00:44 What is edge in financial markets 01:43 Jane Street and organizational structures for quant trading 03:46 Identifying and validating edge in trading 06:22 Navigating extreme market events and volatility 09:37 Future of quant trading and consolidation 12:15 Why quant firm profits have increased 14:42 The gamblization of everything 15:55 Who should pursue a career in quant trading 18:19 Applying the concept of edge to career and life decisions 20:56 Advice for interns to excel in quant trading 23:44 Predicting long-term success in trading interns 25:08 Sam Bankman-Fried as an intern and FTX reflections 28:15 Reasons people leave Jane Street and what they do next 31:19 Advice for young people in a changing world 36:09 Navigating job insecurity in tech-driven roles 38:55 Where to live if you want to be successful 40:28 Raising kids for a rapidly changing future 42:55 Questions young people should ask themselves 44:45 Outro and book recommendation

Ethan Kho

214,514 views • 8 months ago

DROPS E38: Vanta Trading - The Best Traders Won't Be Human Arrash is the founder and CEO of Vanta Trading, a decentralized prop trading platform built on Bittensor. He spent years as a quant trader building his own strategies before deciding the entire funded-account industry needed to be rebuilt from the ground up. We talk about: - Why most "funded accounts" trade on money that doesn't exist - Why your payout was never real - How prop firms intentionally change rules and spreads before you cash out - Why the best traders of the future won't be human And much more… Timestamps: 0:00 Introduction 1:48 Founder of Vanta 3:08 Explaining Vanta to an Uber Driver 3:26 Unfair vs Fair Funding 6:37 How do they make money? 8:33 How a Legit Prop Firm Makes Money 9:43 Founder's Journey Into Entrepreneurship 10:42 Discovering Crypto & Blockchain 13:03 From LinkedIn Engineer to Quant Trader 15:22 Trading Strategies 16:13 How Trading Is Changing? 18:39 Why TradFi Should Fear Hyperliquid 19:16 Building on BitTensor 21:43 Explaining BitTensor Simply 22:03 How BitTensor Creates Value 23:17 BitTensor's Structure 24:36 BitTensor as Crypto AI 25:35 Is the BitTensor Hype Justified? 27:09 Revenue & Profitability in BitTensor 29:17 Role of TAO 29:51 Advantages & Limitations of BitTensor ecosystem 31:56 What Is Vanta? 32:26 Why No One Fixed Prop Trading Before 33:12 Is the Entire Industry a Scam? 34:17 How Prop Firms Really Make Money 35:35 How Vanta Is Different 38:33 Copy Trading Explained 39:30 Vanta's Business Model 40:28 Dark Reality Behind Funded Accounts 43:48 Long-Term Vision for Vanta 44:50 What Happens If Too Many Traders Win? 46:25 Future Belongs to AI Traders 47:59 Vanta's Endgame 49:14 Conclusion
50:55

DROPS E38: Vanta Trading - The Best Traders Won't Be Human Arrash is the founder and CEO of Vanta Trading, a decentralized prop trading platform built on Bittensor. He spent years as a quant trader building his own strategies before deciding the entire funded-account industry needed to be rebuilt from the ground up. We talk about: - Why most "funded accounts" trade on money that doesn't exist - Why your payout was never real - How prop firms intentionally change rules and spreads before you cash out - Why the best traders of the future won't be human And much more… Timestamps: 0:00 Introduction 1:48 Founder of Vanta 3:08 Explaining Vanta to an Uber Driver 3:26 Unfair vs Fair Funding 6:37 How do they make money? 8:33 How a Legit Prop Firm Makes Money 9:43 Founder's Journey Into Entrepreneurship 10:42 Discovering Crypto & Blockchain 13:03 From LinkedIn Engineer to Quant Trader 15:22 Trading Strategies 16:13 How Trading Is Changing? 18:39 Why TradFi Should Fear Hyperliquid 19:16 Building on BitTensor 21:43 Explaining BitTensor Simply 22:03 How BitTensor Creates Value 23:17 BitTensor's Structure 24:36 BitTensor as Crypto AI 25:35 Is the BitTensor Hype Justified? 27:09 Revenue & Profitability in BitTensor 29:17 Role of TAO 29:51 Advantages & Limitations of BitTensor ecosystem 31:56 What Is Vanta? 32:26 Why No One Fixed Prop Trading Before 33:12 Is the Entire Industry a Scam? 34:17 How Prop Firms Really Make Money 35:35 How Vanta Is Different 38:33 Copy Trading Explained 39:30 Vanta's Business Model 40:28 Dark Reality Behind Funded Accounts 43:48 Long-Term Vision for Vanta 44:50 What Happens If Too Many Traders Win? 46:25 Future Belongs to AI Traders 47:59 Vanta's Endgame 49:14 Conclusion

MR SHIFT 🦁

37,896 views • 3 months ago

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
1:12:32

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 views • 8 months ago

I CAUGHT THE $436,000 INSIDER Episode 3 of PredictTime is live - and you'll hear this story straight from the source tre 🇨🇦, founder of Polysights. We go deep - into how his platform catches insiders on Polymarket before the news drops and into his life journey Everything you need to know about insider trading in prediction markets - is in this episode Timecodes: 00:00 — Cold open / Highlights 00:51 — The $436k profit case: how a $34k order paid out before the news 01:05 — Meet Tre, founder of Polysights 01:35 — How Polysights spots anomalies: radar score and wallet clusters 03:17 — What Polysights actually does: advanced analytics for prediction markets 04:00 — From aircraft engineering to the Canadian Air Force 05:01 — Entrepreneurial roots: e-commerce and dropshipping 06:13 — Selling the first company at 23 and getting paid in crypto 08:21 — Were meme coins detrimental or a launchpad into Web3? 09:46 — The "Fatherhood Buff": how a son changes drive and risk 12:03 — Rank 1 in WoW: gaming as training for founders 14:35 — From PredictFun on Blast to Polymarket: trader-first, builder-second 16:35 — Building Insider Finder: scoring every trade on Polymarket 19:34 — The Maduro case in detail: why it wasn't posted publicly 21:18 — Mayor Eric Adams dropout: prediction markets vs mainstream media 22:24 — MicroStrategy markets: 6–0 on recycled wallets 26:50 — Philosophy: why insider trading accelerates truth 30:56 — Disagreeing with Kalshi's CEO: the centralized vs decentralized divide 35:19 — If the CFTC bans insider trading: validation or death? 48:09 — Arbitrage across Polymarket, Kalshi and sportsbooks 50:48 — Why prediction markets finally boomed in 2024–2025 54:39 — Alpha decay and growing to 45,000 users 55:59 — The Bloomberg / WSJ / NYT moment 58:08 — Fundraising $1.5M from Halifax: the lead VC drama 01:02:26 — Business model: moving away from subscriptions 01:04:21 — No-code automated trading strategies as the future 01:13:20 — Roadmap: V1 by Q2 and a regulated prediction market in Canada 01:16:24 — Solo founder reality: from intern-only to an ex-Meta tech lead 01:18:46 — Crucial advice: don't build another trading terminal 01:20:27 — Breaking in with no connections, no VCs, no hub 01:24:58 — Rapid-fire: Ferrari, Bitcoin to $1M, Hyperliquid vs Kalshi 01:27:35 — The biggest insider trade he can't prove: Lord Miles 01:28:12 — Role models: CZ, Kobe, Shane, Elon 01:30:00 — Final advice: stop overthinking and take the swing
1:32:53

I CAUGHT THE $436,000 INSIDER Episode 3 of PredictTime is live - and you'll hear this story straight from the source tre 🇨🇦, founder of Polysights. We go deep - into how his platform catches insiders on Polymarket before the news drops and into his life journey Everything you need to know about insider trading in prediction markets - is in this episode Timecodes: 00:00 — Cold open / Highlights 00:51 — The $436k profit case: how a $34k order paid out before the news 01:05 — Meet Tre, founder of Polysights 01:35 — How Polysights spots anomalies: radar score and wallet clusters 03:17 — What Polysights actually does: advanced analytics for prediction markets 04:00 — From aircraft engineering to the Canadian Air Force 05:01 — Entrepreneurial roots: e-commerce and dropshipping 06:13 — Selling the first company at 23 and getting paid in crypto 08:21 — Were meme coins detrimental or a launchpad into Web3? 09:46 — The "Fatherhood Buff": how a son changes drive and risk 12:03 — Rank 1 in WoW: gaming as training for founders 14:35 — From PredictFun on Blast to Polymarket: trader-first, builder-second 16:35 — Building Insider Finder: scoring every trade on Polymarket 19:34 — The Maduro case in detail: why it wasn't posted publicly 21:18 — Mayor Eric Adams dropout: prediction markets vs mainstream media 22:24 — MicroStrategy markets: 6–0 on recycled wallets 26:50 — Philosophy: why insider trading accelerates truth 30:56 — Disagreeing with Kalshi's CEO: the centralized vs decentralized divide 35:19 — If the CFTC bans insider trading: validation or death? 48:09 — Arbitrage across Polymarket, Kalshi and sportsbooks 50:48 — Why prediction markets finally boomed in 2024–2025 54:39 — Alpha decay and growing to 45,000 users 55:59 — The Bloomberg / WSJ / NYT moment 58:08 — Fundraising $1.5M from Halifax: the lead VC drama 01:02:26 — Business model: moving away from subscriptions 01:04:21 — No-code automated trading strategies as the future 01:13:20 — Roadmap: V1 by Q2 and a regulated prediction market in Canada 01:16:24 — Solo founder reality: from intern-only to an ex-Meta tech lead 01:18:46 — Crucial advice: don't build another trading terminal 01:20:27 — Breaking in with no connections, no VCs, no hub 01:24:58 — Rapid-fire: Ferrari, Bitcoin to $1M, Hyperliquid vs Kalshi 01:27:35 — The biggest insider trade he can't prove: Lord Miles 01:28:12 — Role models: CZ, Kobe, Shane, Elon 01:30:00 — Final advice: stop overthinking and take the swing

Predict Time

58,555 views • 5 months ago

Inside the Billionaire Backed Prediction Markets Hedge Fund. Run by a 24-Year-Old. Camilo Saravia (camilo), founder of BlueWalker Capital, a systematic prediction markets fund backed by Daniel Howard of Halo Capital. "I don't want more capital. I'm extremely long our equity." We cover: - Why insider trading in prediction markets is terrible for liquidity and GOOD for society - Prediction markets as cash-backed truth in a world of AI slop and disinformation - Why he turned down the allocator question entirely, and the "Goldilocks zone" that makes a fund this size work - His research team's actual mission statement: "collapse the entropy of the internet into signal" - Trading Spotify streams and measuring how fast Mamdani viralizes vs Cuomo on TikTok - Why beating earnings has almost no correlation with the stock going up, and why only testing reveals that - Mention markets as literal next-word prediction, and how makers got sniped out - Hiring missionaries with a mercenary work style, and why every hire takes a pay cut vs Citadel, Jane Street, Wintermute - Daniel Howard's mandate: faster, more risk, more aggression. "They haven't backed me to print 7% APY" - The abundance mindset, from a kid with immigrant parents sitting across from generational wealth - A venture mindset applied to public equities: pulling the thread from free cash flow down to Glassdoor culture - Why you never need to be binarily right: buy at 20, sell at 40, never wait for resolution Highlights: (00:00) Intro (00:56) Taker vs maker, reflexive vs proactive: the strategy map (03:08) What makes an event contract different from an equity (04:53) Insider trading in prediction markets: bug or feature (08:18) Cash-backed truth in a world of AI slop (12:43) Where edge actually comes from (14:19) The dataset: billions of records a day (17:44) Building a money management business from an empty office (21:34) Why asset management competes with software as a business model (23:51) How to underwrite elite talent (25:50) Missionaries vs mercenaries, and why the tension is the point (30:54) Recruiting against Citadel money (and losing on salary every time) (37:32) "We can't compromise speed": the Daniel Howard mandate (44:45) The abundance mindset (48:28) "Why should I invest?" / "I don't want more capital" (50:12) Collapsing the entropy of the internet (51:04) Spotify streams, TikTok virality, and mention markets (53:17) The one data provider he'd long if he could (55:49) There's a business behind everything (01:00:07) How to find the real drivers in any market (01:04:04) A venture mindset applied to public equities (01:10:06) Prediction markets 101: where to actually start (01:12:05) Why you don't need to be binarily right (01:13:19) Final question: building personal edge against the models
1:16:45

Inside the Billionaire Backed Prediction Markets Hedge Fund. Run by a 24-Year-Old. Camilo Saravia (camilo), founder of BlueWalker Capital, a systematic prediction markets fund backed by Daniel Howard of Halo Capital. "I don't want more capital. I'm extremely long our equity." We cover: - Why insider trading in prediction markets is terrible for liquidity and GOOD for society - Prediction markets as cash-backed truth in a world of AI slop and disinformation - Why he turned down the allocator question entirely, and the "Goldilocks zone" that makes a fund this size work - His research team's actual mission statement: "collapse the entropy of the internet into signal" - Trading Spotify streams and measuring how fast Mamdani viralizes vs Cuomo on TikTok - Why beating earnings has almost no correlation with the stock going up, and why only testing reveals that - Mention markets as literal next-word prediction, and how makers got sniped out - Hiring missionaries with a mercenary work style, and why every hire takes a pay cut vs Citadel, Jane Street, Wintermute - Daniel Howard's mandate: faster, more risk, more aggression. "They haven't backed me to print 7% APY" - The abundance mindset, from a kid with immigrant parents sitting across from generational wealth - A venture mindset applied to public equities: pulling the thread from free cash flow down to Glassdoor culture - Why you never need to be binarily right: buy at 20, sell at 40, never wait for resolution Highlights: (00:00) Intro (00:56) Taker vs maker, reflexive vs proactive: the strategy map (03:08) What makes an event contract different from an equity (04:53) Insider trading in prediction markets: bug or feature (08:18) Cash-backed truth in a world of AI slop (12:43) Where edge actually comes from (14:19) The dataset: billions of records a day (17:44) Building a money management business from an empty office (21:34) Why asset management competes with software as a business model (23:51) How to underwrite elite talent (25:50) Missionaries vs mercenaries, and why the tension is the point (30:54) Recruiting against Citadel money (and losing on salary every time) (37:32) "We can't compromise speed": the Daniel Howard mandate (44:45) The abundance mindset (48:28) "Why should I invest?" / "I don't want more capital" (50:12) Collapsing the entropy of the internet (51:04) Spotify streams, TikTok virality, and mention markets (53:17) The one data provider he'd long if he could (55:49) There's a business behind everything (01:00:07) How to find the real drivers in any market (01:04:04) A venture mindset applied to public equities (01:10:06) Prediction markets 101: where to actually start (01:12:05) Why you don't need to be binarily right (01:13:19) Final question: building personal edge against the models

Ethan Kho

128,657 views • 1 month ago

E174: Tarek Mansour - Launching the First Regulated Perps In The U.S and building the next generation of financial markets Tarek Mansour is the co-founder and CEO of Kalshi, the first regulated prediction market exchange in the US, valued at $22 Billion. He grew up in Lebanon with a single mom, studied at MIT, worked at Citadel, and spent 6 years years building a company most people ignored before it finally took off. We talk about what it actually takes to not give up, why markets are better at finding truth than experts, why Kalshi is launching the first regulated Perps in the US, and how his team is building what he calls the next generation of financial markets. Timestamps: 0:00 Intro 1:54 Urgency 3:11 Anime 4:53 Who is Tarek? 7:02 Mathematics & Certainty 9:10 Tarek's chip on the shoulder 13:33 Partnerships: Trezor Bitwise 15:19 Resilience 16:31 Entrepreneurship is Therapy 18:23 The startup emotional rollercoaster 22:16 Showing up for 2000 days with no results 24:45 First time Founder advantage 26:40 When Kalshi almost made it, but did not 29:54 Partnerships: KAST 31:19 Focus Inputs, Not Results 33:16 The Kalshi beginnings story 36:00 The True Innovation Of Prediction Markets 38:28 How Prediction Markets Revolutionize The Media 41:37 Prediction Markets and Hedging explained simply 44:55 Leverage In Prediction Markets 47:16 Partnerships: Jupiter Ethena 48:00 Insider Trading 51:19 Insider Trading Rules enforcement: who is responsible? 55:26 How Kalshi spots Suspicious Behavior 56:59 Tarek's Honest View On Crypto 59:41 Launching the first Regulated Perps In The U.S 1:00:30 What Does Regulated Perps Mean? 1:02:22 Was Kalshi perps launch inspired by Hyperliquid? 1:03:41 Competition 1:05:58 Kalshi Endgame 1:07:06 What is Kalshi doing with the billions of $ they raised 1:08:31 Happiness and engagement 1:13:08 One Thing Tarek Should Let Go Of 1:14:18 Closing Thoughts
1:15:03

E174: Tarek Mansour - Launching the First Regulated Perps In The U.S and building the next generation of financial markets Tarek Mansour is the co-founder and CEO of Kalshi, the first regulated prediction market exchange in the US, valued at $22 Billion. He grew up in Lebanon with a single mom, studied at MIT, worked at Citadel, and spent 6 years years building a company most people ignored before it finally took off. We talk about what it actually takes to not give up, why markets are better at finding truth than experts, why Kalshi is launching the first regulated Perps in the US, and how his team is building what he calls the next generation of financial markets. Timestamps: 0:00 Intro 1:54 Urgency 3:11 Anime 4:53 Who is Tarek? 7:02 Mathematics & Certainty 9:10 Tarek's chip on the shoulder 13:33 Partnerships: Trezor Bitwise 15:19 Resilience 16:31 Entrepreneurship is Therapy 18:23 The startup emotional rollercoaster 22:16 Showing up for 2000 days with no results 24:45 First time Founder advantage 26:40 When Kalshi almost made it, but did not 29:54 Partnerships: KAST 31:19 Focus Inputs, Not Results 33:16 The Kalshi beginnings story 36:00 The True Innovation Of Prediction Markets 38:28 How Prediction Markets Revolutionize The Media 41:37 Prediction Markets and Hedging explained simply 44:55 Leverage In Prediction Markets 47:16 Partnerships: Jupiter Ethena 48:00 Insider Trading 51:19 Insider Trading Rules enforcement: who is responsible? 55:26 How Kalshi spots Suspicious Behavior 56:59 Tarek's Honest View On Crypto 59:41 Launching the first Regulated Perps In The U.S 1:00:30 What Does Regulated Perps Mean? 1:02:22 Was Kalshi perps launch inspired by Hyperliquid? 1:03:41 Competition 1:05:58 Kalshi Endgame 1:07:06 What is Kalshi doing with the billions of $ they raised 1:08:31 Happiness and engagement 1:13:08 One Thing Tarek Should Let Go Of 1:14:18 Closing Thoughts

MR SHIFT 🦁

609,534 views • 4 months ago

What It's Like Building a Proprietary Power Trading Firm with Cory Paddock Cory Paddock (Cory Paddock) has been trading the physical power grid since the early 2000s. He built his own firm from scratch in 2014 — trading his own capital — and has navigated every major paradigm shift in U.S. energy markets since coal dominated the grid. "It's the perfect amount of darkness. The information is public — but it's in the dark." We cover: - How Cory built a point of view on paradigm shifts before the market caught up - Why power trading sits at the intersection of economics & physics — and why that matters for edge - Why backtesting more than a few years of power data is basically useless — the grid isn't the same grid - LMP & locational marginal pricing: how physical grid constraints turn into alpha if you know where to look - The 5–10 trades that make your year — and why forcing setups in the lean periods kills you - How GBE stripped pay uncertainty out of trader comp so people can just focus on trading well - What it actually feels like watching your own money swing in real-time — and when it stops feeling that way Thanks so much Cory for coming on Odds on Open! Timestamps: 00:00 Intro 01:09 Starting GBE and finding trading edge 01:38 Overview of electricity markets and pricing 02:05 Power trading structure and deregulated markets 04:11 Research pipeline for market data analysis 04:56 Domain knowledge and renewable energy trading 06:39 LLMs and AI tools for quants 07:49 Grid data and intraday market signals 09:25 Finding alpha in electricity markets 10:13 Paradigm shifts and regime change insights 13:19 Coal to gas, wind, and solar trends 14:39 Data centers, EVs, and load growth 16:47 Recruiting talent in energy trading firms 17:38 Gen Z quants and algorithmic trading skills 21:55 Outliers, agency, and Gen Z traders 24:11 Culture and innovation in quant finance 27:29 Trading personal capital and risk management 28:06 PJM West Hub and market dynamics 32:19 Five-minute tick data and volatility 34:49 High-conviction trades and alpha generation 35:35 Incentive alignment and trader performance 36:42 Pay structure and removing stress capital 39:22 Motivation and purpose in trading careers 40:00 Passing knowledge to the next generation 42:04 Host reflections on electricity trading 42:14 Closing thoughts and sign-off
42:18

What It's Like Building a Proprietary Power Trading Firm with Cory Paddock Cory Paddock (Cory Paddock) has been trading the physical power grid since the early 2000s. He built his own firm from scratch in 2014 — trading his own capital — and has navigated every major paradigm shift in U.S. energy markets since coal dominated the grid. "It's the perfect amount of darkness. The information is public — but it's in the dark." We cover: - How Cory built a point of view on paradigm shifts before the market caught up - Why power trading sits at the intersection of economics & physics — and why that matters for edge - Why backtesting more than a few years of power data is basically useless — the grid isn't the same grid - LMP & locational marginal pricing: how physical grid constraints turn into alpha if you know where to look - The 5–10 trades that make your year — and why forcing setups in the lean periods kills you - How GBE stripped pay uncertainty out of trader comp so people can just focus on trading well - What it actually feels like watching your own money swing in real-time — and when it stops feeling that way Thanks so much Cory for coming on Odds on Open! Timestamps: 00:00 Intro 01:09 Starting GBE and finding trading edge 01:38 Overview of electricity markets and pricing 02:05 Power trading structure and deregulated markets 04:11 Research pipeline for market data analysis 04:56 Domain knowledge and renewable energy trading 06:39 LLMs and AI tools for quants 07:49 Grid data and intraday market signals 09:25 Finding alpha in electricity markets 10:13 Paradigm shifts and regime change insights 13:19 Coal to gas, wind, and solar trends 14:39 Data centers, EVs, and load growth 16:47 Recruiting talent in energy trading firms 17:38 Gen Z quants and algorithmic trading skills 21:55 Outliers, agency, and Gen Z traders 24:11 Culture and innovation in quant finance 27:29 Trading personal capital and risk management 28:06 PJM West Hub and market dynamics 32:19 Five-minute tick data and volatility 34:49 High-conviction trades and alpha generation 35:35 Incentive alignment and trader performance 36:42 Pay structure and removing stress capital 39:22 Motivation and purpose in trading careers 40:00 Passing knowledge to the next generation 42:04 Host reflections on electricity trading 42:14 Closing thoughts and sign-off

Ethan Kho

17,408 views • 7 months ago

Ex-Equity Crypto Trader Reveals His Systematic Trading Mindset To Conquer the Market w/ pedma 00:00 Pedma intro: Viewing trading as a business rather than predicting market cycles 01:32 Rejecting the 4-year cycle theory to focus on structural risk premia 02:24 Starting in 2017 trading US equities penny stocks pumping 50% to 80% 03:34 Surviving tail risk blowups, trading halts, and expensive borrow fees 04:40 The adverse selection trap of short borrow locates at 7:00 AM 05:25 Transitioning to large caps in 2020 and moving into crypto in 2021 06:00 Running a hybrid systematic model across 9 exchanges and 13 wallets 06:54 Defining systematic trading versus discretionary decision fatigue 09:50 Understanding why you get paid and the core mechanics of risk premia 11:15 Why classic momentum and trend models persist across decades 12:21 Harvesting short term alpha on Hyperliquid weekend oil contracts 14:12 Finding structural arbitrage across decentralized and centralized venues 16:00 Pricing inefficiencies in illiquid crypto contracts 17:42 The hidden reality of quant trading: infrastructure and bookkeeping demands 19:18 The coding bug that sorted trades alphabetically instead of by signal strength 21:12 The capital scaling bottleneck and the reality of a 30% to 50% CAGR 22:05 Why bucket shop exchanges ban profitable accounts that exploit mispricings 23:27 Starting a newsletter in 2022 to expose logical gaps through writing 25:35 Networking with high-signal quant traders 28:03 Replacing social media chart slop with Robert Carver's systematic books 29:11 Navigating the Dunning-Kruger curve and 4 consecutive winning years 32:01 Final advice for beginners: understand your counterparty and keep models simple 33:51 Outro
34:09

Ex-Equity Crypto Trader Reveals His Systematic Trading Mindset To Conquer the Market w/ pedma 00:00 Pedma intro: Viewing trading as a business rather than predicting market cycles 01:32 Rejecting the 4-year cycle theory to focus on structural risk premia 02:24 Starting in 2017 trading US equities penny stocks pumping 50% to 80% 03:34 Surviving tail risk blowups, trading halts, and expensive borrow fees 04:40 The adverse selection trap of short borrow locates at 7:00 AM 05:25 Transitioning to large caps in 2020 and moving into crypto in 2021 06:00 Running a hybrid systematic model across 9 exchanges and 13 wallets 06:54 Defining systematic trading versus discretionary decision fatigue 09:50 Understanding why you get paid and the core mechanics of risk premia 11:15 Why classic momentum and trend models persist across decades 12:21 Harvesting short term alpha on Hyperliquid weekend oil contracts 14:12 Finding structural arbitrage across decentralized and centralized venues 16:00 Pricing inefficiencies in illiquid crypto contracts 17:42 The hidden reality of quant trading: infrastructure and bookkeeping demands 19:18 The coding bug that sorted trades alphabetically instead of by signal strength 21:12 The capital scaling bottleneck and the reality of a 30% to 50% CAGR 22:05 Why bucket shop exchanges ban profitable accounts that exploit mispricings 23:27 Starting a newsletter in 2022 to expose logical gaps through writing 25:35 Networking with high-signal quant traders 28:03 Replacing social media chart slop with Robert Carver's systematic books 29:11 Navigating the Dunning-Kruger curve and 4 consecutive winning years 32:01 Final advice for beginners: understand your counterparty and keep models simple 33:51 Outro

Lock In

14,131 views • 1 month ago

Susquehanna was founded by professional gamblers who turned card-counting and poker into one of the largest options trading firms on earth. the director who ran their trader training program for 30+ years on what the job actually is in 2026: "there's almost no decision-making about a single trade anymore. it's our starting point, it's where we build intuition. but ultimately what our traders are doing is parameterizing and building systems." "seeing how those systems are running, seeing the outcome, and then adjusting parameters to interact in a changing environment." why? speed: "trading happens faster than human speed. if you relied on somebody turning the page of theoretical values to find what an option is worth, the trade's long gone by the time they find it." the edge is rarely in making the single call, it's building the thing that makes the call, and knowing which parameters to use. clip from "Odds on Open"
1:12

Susquehanna was founded by professional gamblers who turned card-counting and poker into one of the largest options trading firms on earth. the director who ran their trader training program for 30+ years on what the job actually is in 2026: "there's almost no decision-making about a single trade anymore. it's our starting point, it's where we build intuition. but ultimately what our traders are doing is parameterizing and building systems." "seeing how those systems are running, seeing the outcome, and then adjusting parameters to interact in a changing environment." why? speed: "trading happens faster than human speed. if you relied on somebody turning the page of theoretical values to find what an option is worth, the trade's long gone by the time they find it." the edge is rarely in making the single call, it's building the thing that makes the call, and knowing which parameters to use. clip from "Odds on Open"

Goshawk Trades

23,391 views • 2 months ago

Today I sat down with Jordan Levi, the Kosher Cowboy - a Jewish kid from the Chicago suburbs who became the largest cattle feeder in America. Jordan runs Five Rivers Cattle Feeding with a capacity of nearly a million head. He started as a runner on the Chicago Board of Trade at 13 and found his way to cattle through a hedge fund that sent him to a feedlot in Amarillo. He showed up in Gucci loafers and never left the industry. We go deep on how he trades the curve instead of making binary bets, why the cattle supply is the tightest since the 1950s, and what it takes to manage risk on nearly a million animals across 13 feedlots. I hope you enjoy this episode as much as I did. 02:16 - The Belted Galloway 03:51 - The Kosher Cowboy 08:05 - Pulling value of the futures forward 11:43 - Learning Cattle trading 16:48 - Daily average gain in cattle 18:20 - Jordan’s Eureka moment in the cattle industry 21:08 - What does trading in animals actually look like? 27:05 - How Jordan defines his ROI in trading cattle 31:17 - The Cattle curve 33:37 - The state of the cattle market 42:10 - Buying the largest cattle feeder in the world 46:09 - Grass vs. grain fed cattle 49:15 - Predictions for the cattle supply over the next 10 years 50:54 - The international market 53:17 - Trading frequencies and macro thesis 01:00:27 - USA beef vs. international beef 01:05:21 - The cattle supply chain 01:07:32 - The future of auction yards and ranchers 01:09:40 - AI in AgTech 01:12:52 - The biggest problem facing the industry 01:14:42 - Livestock as a commodity that dies and how that impacts trading theory 01:20:51 - Is there a market for new entrants into cattle? 01:21:41 - Beef prices and the impact of a closed border on the industry 01:24:39 - Jordan’s biggest ideas for the industry 01:26:49 - Philanthropic efforts 01:31:24 - a day in the life of Jordan 01:26:42 - Risk management in cattle 01:41:17 - Does what you do show a leading indicator to the broader health of the American economy?
1:42:29

Today I sat down with Jordan Levi, the Kosher Cowboy - a Jewish kid from the Chicago suburbs who became the largest cattle feeder in America. Jordan runs Five Rivers Cattle Feeding with a capacity of nearly a million head. He started as a runner on the Chicago Board of Trade at 13 and found his way to cattle through a hedge fund that sent him to a feedlot in Amarillo. He showed up in Gucci loafers and never left the industry. We go deep on how he trades the curve instead of making binary bets, why the cattle supply is the tightest since the 1950s, and what it takes to manage risk on nearly a million animals across 13 feedlots. I hope you enjoy this episode as much as I did. 02:16 - The Belted Galloway 03:51 - The Kosher Cowboy 08:05 - Pulling value of the futures forward 11:43 - Learning Cattle trading 16:48 - Daily average gain in cattle 18:20 - Jordan’s Eureka moment in the cattle industry 21:08 - What does trading in animals actually look like? 27:05 - How Jordan defines his ROI in trading cattle 31:17 - The Cattle curve 33:37 - The state of the cattle market 42:10 - Buying the largest cattle feeder in the world 46:09 - Grass vs. grain fed cattle 49:15 - Predictions for the cattle supply over the next 10 years 50:54 - The international market 53:17 - Trading frequencies and macro thesis 01:00:27 - USA beef vs. international beef 01:05:21 - The cattle supply chain 01:07:32 - The future of auction yards and ranchers 01:09:40 - AI in AgTech 01:12:52 - The biggest problem facing the industry 01:14:42 - Livestock as a commodity that dies and how that impacts trading theory 01:20:51 - Is there a market for new entrants into cattle? 01:21:41 - Beef prices and the impact of a closed border on the industry 01:24:39 - Jordan’s biggest ideas for the industry 01:26:49 - Philanthropic efforts 01:31:24 - a day in the life of Jordan 01:26:42 - Risk management in cattle 01:41:17 - Does what you do show a leading indicator to the broader health of the American economy?

Chris Powers

822,631 views • 6 months ago

How Losing $68k in 1 Night Helped To Find Trading Edge w/ PILTR 00:00 PILTR intro: 4yrs of trading, German Bitcoin podcasts as first touchpoint & early Bitcoin automated savings 01:48 Leveraged trading and why beginners underestimate risk 02:55 Increasing leverage from 2x to 12x without stop losses 03:37 Getting greedy after winning trades and DCA failure 04:25 Blowing $68K in 1 night and keeping only $2K 05:02 Repeating the liquidation cycle 3 times before changing behaviors 05:25 Backtesting like a freak and paper trading to find an edge 05:44 Defining profitable trading as the constant execution of positive expectancy 06:06 Discovering a 65% win rate setup in backtesting logs 07:09 What he would tell his 6 year younger beginner self 07:56 Why there is no holy grail in trading technicals 08:50 Building a holistic confluence system using economic and liquidity data 09:15 Starting analysis with macro data and liquidity positioning 11:00 Risking what you are comfortable losing in absolute dollar size 11:26 Placing stop losses where positive expectancy thesis ends 11:42 Aiming for 1.5R to 2R rewards on high probability setups 12:48 Rejecting algorithms and AI to maintain discretionary mechanical setups 13:35 Why trading psychology represents 90% of execution 14:30 The misconception of chasing a home run 16:25 The social media trap of letting YouTube opinions influence your own trades 18:02 Differentiating legit traders from fake lifestyle gurus 18:50 Traders PILTR recommends on Twitter (Jordi, @joel_sabugal) 21:36 Using prop accounts to trade complex low-volume markets 22:57 Respecting drawdown rules on prop firms to protect entry fees 23:19 Prop firm salary payouts versus live account purchasing power 23:56 Why the long-term goal of every trader should be having a live account 24:50 Final Advice
26:43

How Losing $68k in 1 Night Helped To Find Trading Edge w/ PILTR 00:00 PILTR intro: 4yrs of trading, German Bitcoin podcasts as first touchpoint & early Bitcoin automated savings 01:48 Leveraged trading and why beginners underestimate risk 02:55 Increasing leverage from 2x to 12x without stop losses 03:37 Getting greedy after winning trades and DCA failure 04:25 Blowing $68K in 1 night and keeping only $2K 05:02 Repeating the liquidation cycle 3 times before changing behaviors 05:25 Backtesting like a freak and paper trading to find an edge 05:44 Defining profitable trading as the constant execution of positive expectancy 06:06 Discovering a 65% win rate setup in backtesting logs 07:09 What he would tell his 6 year younger beginner self 07:56 Why there is no holy grail in trading technicals 08:50 Building a holistic confluence system using economic and liquidity data 09:15 Starting analysis with macro data and liquidity positioning 11:00 Risking what you are comfortable losing in absolute dollar size 11:26 Placing stop losses where positive expectancy thesis ends 11:42 Aiming for 1.5R to 2R rewards on high probability setups 12:48 Rejecting algorithms and AI to maintain discretionary mechanical setups 13:35 Why trading psychology represents 90% of execution 14:30 The misconception of chasing a home run 16:25 The social media trap of letting YouTube opinions influence your own trades 18:02 Differentiating legit traders from fake lifestyle gurus 18:50 Traders PILTR recommends on Twitter (Jordi, @joel_sabugal) 21:36 Using prop accounts to trade complex low-volume markets 22:57 Respecting drawdown rules on prop firms to protect entry fees 23:19 Prop firm salary payouts versus live account purchasing power 23:56 Why the long-term goal of every trader should be having a live account 24:50 Final Advice

Lock In

13,718 views • 2 months ago

My dear friend, Vlad Tenev, changed the landscape of investing forever! The rise of the retail investor is largely due to Robinhood's success... and in this new Journey Man, we discuss it all... Enjoy! 00:00 - Intro 00:53 - Introducing Vlad Tenev of Robinhood 01:27 - Why Take on Wall Street? 01:54 - Robinhood’s Zero-Fee Origin Story 02:53 - Inspiration from Instagram and Uber 04:24 - Reimagining Trading for Mobile 05:05 - The Challenge of Disrupting Finance 05:42 - Why Everything Is Hard 06:34 - Early Wrong Assumptions 07:42 - Raising Capital with a Small Vision 08:48 - Funding Robinhood on AngelList 09:50 - Early Investors Changed Their Lives 10:38 - The Crypto Explosion Begins 11:07 - Considering a Bitcoin Exchange First 12:17 - Bitcoin’s Early Skepticism and Growth 13:08 - Robinhood Launches Crypto in 2018 14:03 - 2020: Crypto Revenue Surges Overnight 15:04 - The Challenge of Crypto Cyclicality 16:11 - Staffing a Volatile Business 17:10 - Building Robinhood’s Lean Crypto Team 18:46 - Robinhood’s First Crypto Event Coming 19:38 - Where TradFi Meets DeFi 20:34 - Tokenizing Everything 21:09 - Robinhood’s Vision for Crypto + Finance 21:47 - Thoughts on Crypto Options Demand 23:04 - Why Crypto Options Haven’t Taken Off 24:09 - Millennials and the Speculative Economy 25:22 - Democratizing Trading for Everyone 26:08 - Why Buy-and-Hold Doesn’t Work for All 27:15 - Trading vs Investing: A Matter of Wealth 28:01 - Trading as a Skill Anyone Can Build 29:13 - Robinhood’s Role in Onboarding Millions 30:06 - The Fed's Role and Retail Insight 31:03 - The Rise of the Retail Macro Trader 32:17 - Helping Users Succeed with Robinhood Strategies 33:35 - Power of Community and the Hive Mind 34:55 - Will AI Disrupt Community Too? 36:14 - Technological Waves and Investor Opportunity 37:10 - Human Purpose in an AI World 37:52 - Tokenizing Human Connection 38:28 - Creators, Platforms, and Future-Proofing 39:26 - Vlad’s Long-Term View of the Future 40:05 - Financial Services at the Heart of Disruption 41:14 - If AI Replaces Jobs, What Happens to Investing? 42:25 - Entering the Economic Singularity 43:31 - What Happens When AIs Win the Markets? 44:16 - AI's Role in Capital and Markets 45:07 - Will AI Eliminate Human Emotion from Markets? 46:06 - HFT: The Original AI Traders 47:20 - AI and Long-Term Probabilistic Forecasting 48:48 - GPUs, Gaming, and the Origins of AI 50:01 - Nvidia, CUDA, and Wall Street Arms Races 51:04 - Flash Boys and Microwave Trading 51:54 - Will AI Costs Go to Zero? 52:52 - Lower Cost, Higher Usage 53:41 - Robinhood’s UX Won’t Be Just a Chatbox 55:16 - Cortex: AI-Powered Features at Robinhood 56:54 - Tokenization and the Future of Asset Management 57:44 - Crowdsourced, Tokenized Hedge Funds 58:48 - Portability of Tokenized Assets 59:39 - Blockchain as the New Rails of Finance 01:00:09 - The Trump Token and Capital Formation 01:01:00 - Capital Access Unlocks Innovation 01:01:49 - Why Crypto Needs Regulatory Clarity 01:03:17 - From Meme Coins to Real Assets 01:04:17 - Crypto's Path to $100 Trillion? 01:05:15 - The Financial System Will Run on Blockchains 01:06:00 - Platform Layer vs Application Layer Wealth 01:06:29 - AI Raises Money and Launches Tokens 01:07:39 - AIs Creating Software and Capital Formation 01:08:00 - Final Thoughts: A Wild Future Ahead 01:08:20 - When Will Vlad Buy a CryptoPunk? 01:08:51 - Wrapping Up: AI, Crypto, and the Road Ahead
1:09:53

My dear friend, Vlad Tenev, changed the landscape of investing forever! The rise of the retail investor is largely due to Robinhood's success... and in this new Journey Man, we discuss it all... Enjoy! 00:00 - Intro 00:53 - Introducing Vlad Tenev of Robinhood 01:27 - Why Take on Wall Street? 01:54 - Robinhood’s Zero-Fee Origin Story 02:53 - Inspiration from Instagram and Uber 04:24 - Reimagining Trading for Mobile 05:05 - The Challenge of Disrupting Finance 05:42 - Why Everything Is Hard 06:34 - Early Wrong Assumptions 07:42 - Raising Capital with a Small Vision 08:48 - Funding Robinhood on AngelList 09:50 - Early Investors Changed Their Lives 10:38 - The Crypto Explosion Begins 11:07 - Considering a Bitcoin Exchange First 12:17 - Bitcoin’s Early Skepticism and Growth 13:08 - Robinhood Launches Crypto in 2018 14:03 - 2020: Crypto Revenue Surges Overnight 15:04 - The Challenge of Crypto Cyclicality 16:11 - Staffing a Volatile Business 17:10 - Building Robinhood’s Lean Crypto Team 18:46 - Robinhood’s First Crypto Event Coming 19:38 - Where TradFi Meets DeFi 20:34 - Tokenizing Everything 21:09 - Robinhood’s Vision for Crypto + Finance 21:47 - Thoughts on Crypto Options Demand 23:04 - Why Crypto Options Haven’t Taken Off 24:09 - Millennials and the Speculative Economy 25:22 - Democratizing Trading for Everyone 26:08 - Why Buy-and-Hold Doesn’t Work for All 27:15 - Trading vs Investing: A Matter of Wealth 28:01 - Trading as a Skill Anyone Can Build 29:13 - Robinhood’s Role in Onboarding Millions 30:06 - The Fed's Role and Retail Insight 31:03 - The Rise of the Retail Macro Trader 32:17 - Helping Users Succeed with Robinhood Strategies 33:35 - Power of Community and the Hive Mind 34:55 - Will AI Disrupt Community Too? 36:14 - Technological Waves and Investor Opportunity 37:10 - Human Purpose in an AI World 37:52 - Tokenizing Human Connection 38:28 - Creators, Platforms, and Future-Proofing 39:26 - Vlad’s Long-Term View of the Future 40:05 - Financial Services at the Heart of Disruption 41:14 - If AI Replaces Jobs, What Happens to Investing? 42:25 - Entering the Economic Singularity 43:31 - What Happens When AIs Win the Markets? 44:16 - AI's Role in Capital and Markets 45:07 - Will AI Eliminate Human Emotion from Markets? 46:06 - HFT: The Original AI Traders 47:20 - AI and Long-Term Probabilistic Forecasting 48:48 - GPUs, Gaming, and the Origins of AI 50:01 - Nvidia, CUDA, and Wall Street Arms Races 51:04 - Flash Boys and Microwave Trading 51:54 - Will AI Costs Go to Zero? 52:52 - Lower Cost, Higher Usage 53:41 - Robinhood’s UX Won’t Be Just a Chatbox 55:16 - Cortex: AI-Powered Features at Robinhood 56:54 - Tokenization and the Future of Asset Management 57:44 - Crowdsourced, Tokenized Hedge Funds 58:48 - Portability of Tokenized Assets 59:39 - Blockchain as the New Rails of Finance 01:00:09 - The Trump Token and Capital Formation 01:01:00 - Capital Access Unlocks Innovation 01:01:49 - Why Crypto Needs Regulatory Clarity 01:03:17 - From Meme Coins to Real Assets 01:04:17 - Crypto's Path to $100 Trillion? 01:05:15 - The Financial System Will Run on Blockchains 01:06:00 - Platform Layer vs Application Layer Wealth 01:06:29 - AI Raises Money and Launches Tokens 01:07:39 - AIs Creating Software and Capital Formation 01:08:00 - Final Thoughts: A Wild Future Ahead 01:08:20 - When Will Vlad Buy a CryptoPunk? 01:08:51 - Wrapping Up: AI, Crypto, and the Road Ahead

Raoul Pal

172,640 views • 1 year ago

Inside Kalshi : How a Prediction Market Hit $22B in 2 Years 65 lawyers said it couldn't be done. They sued their own regulator instead. Eight years later, Kalshi just raised $1B at a $22B valuation, and co-founders Tarek Mansour and Luana Lopes Lara took us inside the bet that got them here. 00:00 Why prediction markets? 01:35 We sue our regulator 02:31 From Ballet to Building a $22B Company 03:19 Discipline & delayed gratification 04:20 Getting out of a comfort zone 04:56 65 Lawyers said No 05:05 Tarek's story 06:04 How the idea starts - The Trump trade 07:45 No one says it's possible 09:00 Four Years to Earn the Right to Launch 10:22 Stagnating while everyone else grew 11:53 Cutting through the noise 12:43 "Forget product-market fit" 13:28 Why we sued the regulator 14:45 We won. Then they appealed 16:43 The 4-week sprint to the election 18:03 The clearinghouse crisis 18:42 6 months of migration in one weekend 19:14 Calling the election before the media 19:31 Prediction markets vs gambling 20:51 Money where your mouth is 22:06 What "regulated" actually means 23:10 It's not just sports 24:05 The trader who studies Trump's speeches 26:40 The teacher who made $8K from source code 27:42 Hurricane markets as insurance 28:40 Where Kalshi goes next
31:10

Inside Kalshi : How a Prediction Market Hit $22B in 2 Years 65 lawyers said it couldn't be done. They sued their own regulator instead. Eight years later, Kalshi just raised $1B at a $22B valuation, and co-founders Tarek Mansour and Luana Lopes Lara took us inside the bet that got them here. 00:00 Why prediction markets? 01:35 We sue our regulator 02:31 From Ballet to Building a $22B Company 03:19 Discipline & delayed gratification 04:20 Getting out of a comfort zone 04:56 65 Lawyers said No 05:05 Tarek's story 06:04 How the idea starts - The Trump trade 07:45 No one says it's possible 09:00 Four Years to Earn the Right to Launch 10:22 Stagnating while everyone else grew 11:53 Cutting through the noise 12:43 "Forget product-market fit" 13:28 Why we sued the regulator 14:45 We won. Then they appealed 16:43 The 4-week sprint to the election 18:03 The clearinghouse crisis 18:42 6 months of migration in one weekend 19:14 Calling the election before the media 19:31 Prediction markets vs gambling 20:51 Money where your mouth is 22:06 What "regulated" actually means 23:10 It's not just sports 24:05 The trader who studies Trump's speeches 26:40 The teacher who made $8K from source code 27:42 Hurricane markets as insurance 28:40 Where Kalshi goes next

EO

5,277,622 views • 4 months ago

Ex-IMC semiconductor options trader Lihong Wang on seeing the whole market's flow, betting the entire AI stack, & why foundation models are good enough to beat the S&P 500. Lihong Wang (Lihong) | Discretionary semiconductor options trader @ IMC Trading | Now co-founder of Freeport, a YC-backed event-driven perps exchange "You see what everyone's doing, you trade against the people who are stupid, and you get out of the way or you follow the people who are smart." We cover: - How a bet actually gets made on a vol desk: not predicting the future, just selling what's expensive and buying what's cheap - The edge of the seat: being counterparty to 20-40% of flow in certain options markets, a view "maybe a few dozen people on the entire planet" have - Knowing who HAS to trade: reverse-engineering banks' structured-product hedging from issuance data - How desks blow up: overestimating correlation, and why NVDA -17% on DeepSeek didn't mean AMD -12% - The trader group chats: ~3x beta to the S&P, median 30% drawdown in July, and everyone still bullish - His portfolio: 50 chip/AI stocks covering the entire stack, up 470% bottom-to-peak, then a 70% drawdown ("I'm literally down an entire Ferrari today") - Why trading firms may be the third-largest consumers of AI tokens on the planet - Moats vs. growth: why Cursor got funded with no moat, and what that means for careers in the age of AI - His hot take: foundation models can already beat the S&P 500 risk-adjusted. The model isn't the hard part, the harness is Highlights: 00:00 Intro 01:09 How discretionary options bets get made — and blow up 10:23 A message from Onyx 10:53 Warehousing benign flow through July's deleveraging 16:40 Seat leverage and how trading firms allocate talent 22:03 Why quant traders are levered long the AI stack 26:27 Playing the AI hand: leverage, moats, personal brand 36:42 From IMC to Freeport: AI agents for event-driven trading 45:26 Perp DEX endgame: liquidity, fragmentation, and SEC rulemaking 51:21 Sourcing semiconductor alpha from trader dinners 56:05 Narrative edge, DeepSeek puts, and a 70% drawdown 01:04:24 Is AI a bubble? Compounding, path, and Kelly leverage 01:08:39 US, China, and comparative advantage under ASI 01:12:17 Risk-taking when you're post-economic
1:14:10

Ex-IMC semiconductor options trader Lihong Wang on seeing the whole market's flow, betting the entire AI stack, & why foundation models are good enough to beat the S&P 500. Lihong Wang (Lihong) | Discretionary semiconductor options trader @ IMC Trading | Now co-founder of Freeport, a YC-backed event-driven perps exchange "You see what everyone's doing, you trade against the people who are stupid, and you get out of the way or you follow the people who are smart." We cover: - How a bet actually gets made on a vol desk: not predicting the future, just selling what's expensive and buying what's cheap - The edge of the seat: being counterparty to 20-40% of flow in certain options markets, a view "maybe a few dozen people on the entire planet" have - Knowing who HAS to trade: reverse-engineering banks' structured-product hedging from issuance data - How desks blow up: overestimating correlation, and why NVDA -17% on DeepSeek didn't mean AMD -12% - The trader group chats: ~3x beta to the S&P, median 30% drawdown in July, and everyone still bullish - His portfolio: 50 chip/AI stocks covering the entire stack, up 470% bottom-to-peak, then a 70% drawdown ("I'm literally down an entire Ferrari today") - Why trading firms may be the third-largest consumers of AI tokens on the planet - Moats vs. growth: why Cursor got funded with no moat, and what that means for careers in the age of AI - His hot take: foundation models can already beat the S&P 500 risk-adjusted. The model isn't the hard part, the harness is Highlights: 00:00 Intro 01:09 How discretionary options bets get made — and blow up 10:23 A message from Onyx 10:53 Warehousing benign flow through July's deleveraging 16:40 Seat leverage and how trading firms allocate talent 22:03 Why quant traders are levered long the AI stack 26:27 Playing the AI hand: leverage, moats, personal brand 36:42 From IMC to Freeport: AI agents for event-driven trading 45:26 Perp DEX endgame: liquidity, fragmentation, and SEC rulemaking 51:21 Sourcing semiconductor alpha from trader dinners 56:05 Narrative edge, DeepSeek puts, and a 70% drawdown 01:04:24 Is AI a bubble? Compounding, path, and Kelly leverage 01:08:39 US, China, and comparative advantage under ASI 01:12:17 Risk-taking when you're post-economic

Ethan Kho

182,771 views • 24 days ago

🎙️ New The Edge Podcast 🐂 Kool Krypto: The Story Behind The Anon DeFi Investor & Trader | What He's Betting On This Cycle 0:00 - Intro 2:25 - From mining BTC at 13 to fund manager 5:25 - Early to ETHLend and DeFi 7:38 - The poker game that led to a fund 10:26 - Is TradFi “in the arena” with DeFi? 14:17 - Early to onchain perps like GMX, HL 20:13 - Liquid fund, yield gen, and a little VC 23:28 - Why Kool doesn’t trade memecoins 28:01 - The DRV bull thesis 41:14 - Kool’s winning BTC call spread 45:58 - Pros/cons of Derive’s RFQ trading 50:14 - AI agents, a tailwind for options 52:50 - His onchain options trading tools 56:29 - Pros/cons of options vs perps 1:02:37 - Managing risk and closing trades 1:05:35 - Why Opulent October makes sense 1:11:12 - His viral ETH call spread on Derive 1:17:38 - Bull thesis for KNTQ 1:25:07 - Derive V3’s ability to list new markets 1:28:31 - What if HL/Lighter launch options? 1:34:19 - Why Kool remains bullish on LIT 1:38:24 - Closing 🙏 Thanks to KoolKrypto for joining us!
1:41:52

🎙️ New The Edge Podcast 🐂 Kool Krypto: The Story Behind The Anon DeFi Investor & Trader | What He's Betting On This Cycle 0:00 - Intro 2:25 - From mining BTC at 13 to fund manager 5:25 - Early to ETHLend and DeFi 7:38 - The poker game that led to a fund 10:26 - Is TradFi “in the arena” with DeFi? 14:17 - Early to onchain perps like GMX, HL 20:13 - Liquid fund, yield gen, and a little VC 23:28 - Why Kool doesn’t trade memecoins 28:01 - The DRV bull thesis 41:14 - Kool’s winning BTC call spread 45:58 - Pros/cons of Derive’s RFQ trading 50:14 - AI agents, a tailwind for options 52:50 - His onchain options trading tools 56:29 - Pros/cons of options vs perps 1:02:37 - Managing risk and closing trades 1:05:35 - Why Opulent October makes sense 1:11:12 - His viral ETH call spread on Derive 1:17:38 - Bull thesis for KNTQ 1:25:07 - Derive V3’s ability to list new markets 1:28:31 - What if HL/Lighter launch options? 1:34:19 - Why Kool remains bullish on LIT 1:38:24 - Closing 🙏 Thanks to KoolKrypto for joining us!

DeFi Dad ⟠ defidad.eth

112,007 views • 23 days ago