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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...

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Ethan Kho

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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
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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

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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

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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 Aufrufe • vor 6 Monaten

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

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Ex-Citadel Quant Researcher on Trading Power & Gas — One of the Most Asymmetric Markets in the World Neel Somani (Neel Somani) — ex-Citadel commodities QR. Built the models the discretionary traders used to price power. "It's table stakes to put down seven figures of collateral in order to seriously trade power." We cover: - What a commodities QR actually does — building models traders use, sitting in PM meetings & how "slope" (your real cut of P&L) works - Where power edge comes from: congestion — the physics of a wire that heats up, droops, and can't carry more - How a hub trade gets built from the ground up: weather → demand → which units switch on → your price vs. the market's - Why blindly going long power is a structurally losing trade — skew assets always price above expected value - The anatomy of a blow-up: doubling down into the Feb 2021 Texas freeze as the price ran to $9,000/MWh - Why hedge funds trade power & gas but mostly steer clear of oil — geopolitics & risk you can't model - "Binding constraints" — the pricing model he carried off the grid and into startups, AI & supply chains - Why the guys who take risk for a living buy index funds with their own money Highlights: (00:00) Intro (01:12) Quant researcher execution models within multi-manager hedge funds (07:35) How transmission line congestion drives alpha in power markets (13:24) Capital intensity and managing risk profiles of high-skew assets (19:19) Why commodity desks prefer domestic power over geopolitical oil risk (22:55) Portfolio construction and risk mitigation during tail-risk freeze events (31:36) Capitalizing on the physical infrastructure constraints of AI data centers (36:25) How agentic architecture redefines software engineering and technical moats (43:04) Quant career opportunity cost relative to the AI paradigm shift (56:15) Variant views on venture multiples and agentic customer acquisition economics
1:00:13

Ex-Citadel Quant Researcher on Trading Power & Gas — One of the Most Asymmetric Markets in the World Neel Somani (Neel Somani) — ex-Citadel commodities QR. Built the models the discretionary traders used to price power. "It's table stakes to put down seven figures of collateral in order to seriously trade power." We cover: - What a commodities QR actually does — building models traders use, sitting in PM meetings & how "slope" (your real cut of P&L) works - Where power edge comes from: congestion — the physics of a wire that heats up, droops, and can't carry more - How a hub trade gets built from the ground up: weather → demand → which units switch on → your price vs. the market's - Why blindly going long power is a structurally losing trade — skew assets always price above expected value - The anatomy of a blow-up: doubling down into the Feb 2021 Texas freeze as the price ran to $9,000/MWh - Why hedge funds trade power & gas but mostly steer clear of oil — geopolitics & risk you can't model - "Binding constraints" — the pricing model he carried off the grid and into startups, AI & supply chains - Why the guys who take risk for a living buy index funds with their own money Highlights: (00:00) Intro (01:12) Quant researcher execution models within multi-manager hedge funds (07:35) How transmission line congestion drives alpha in power markets (13:24) Capital intensity and managing risk profiles of high-skew assets (19:19) Why commodity desks prefer domestic power over geopolitical oil risk (22:55) Portfolio construction and risk mitigation during tail-risk freeze events (31:36) Capitalizing on the physical infrastructure constraints of AI data centers (36:25) How agentic architecture redefines software engineering and technical moats (43:04) Quant career opportunity cost relative to the AI paradigm shift (56:15) Variant views on venture multiples and agentic customer acquisition economics

Ethan Kho

116,875 Aufrufe • vor 1 Monat

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 Aufrufe • vor 6 Monaten

“Quant trading is simpler than people think.” Goon joins the Insilico Terminal Podcast to talk about finding crypto edges, market making, and why the best trades often come from clicking around broken markets before anyone else notices. We covered TradFi, early DeFi rugs, solo trading, perp DEXs, Hyperliquid, prediction markets, bad bots, points farming, and the on-chain casino arb that turned into PvP. 00:00 Intro + CryptoGoon’s background 04:31 Getting rugged and discovering early DeFi 07:08 Why TradFi-style edges worked in 2021 crypto 11:03 Solo trading vs trading inside a firm 15:25 The on-chain casino arb that became PvP 24:13 Finding edge early on new perp DEXs 31:10 Why quant trading is simpler than people think 38:39 Why points farming can reveal real edge 53:01 Crypto morale, HYPE, and where the market is now
59:58

“Quant trading is simpler than people think.” Goon joins the Insilico Terminal Podcast to talk about finding crypto edges, market making, and why the best trades often come from clicking around broken markets before anyone else notices. We covered TradFi, early DeFi rugs, solo trading, perp DEXs, Hyperliquid, prediction markets, bad bots, points farming, and the on-chain casino arb that turned into PvP. 00:00 Intro + CryptoGoon’s background 04:31 Getting rugged and discovering early DeFi 07:08 Why TradFi-style edges worked in 2021 crypto 11:03 Solo trading vs trading inside a firm 15:25 The on-chain casino arb that became PvP 24:13 Finding edge early on new perp DEXs 31:10 Why quant trading is simpler than people think 38:39 Why points farming can reveal real edge 53:01 Crypto morale, HYPE, and where the market is now

Insilico Terminal

19,138 Aufrufe • vor 2 Monaten

The next perp winner probably won't be decided by incentives. It'll be decided by what assets users can trade. And the narrative doesn't lack users; it lacks new markets to bring on-chain. BTC or ETH are not enough; users need something familiar. Ondo Perps is filling exactly that gap: RWA perps (commodities + tokenized equities). It's not a coincidence that recent platforms choose RWAs for perp trading: + 24/7 access to assets that are normally locked behind market hours (equities, commodities, etc) + Tokenized equities are no longer just spot assets, they become viable collateral and underlyings for perpetual markets. In fact, you can find RWAs perp in multiple places: + Hyperliquid (via HIP-3) + Aster + Grvt + Lighter + Ostium Ondo Perps has one real edge: it's built on top of Ondo Global Markets, which just reportedly crossed $1b TVL, $2b in cumulative trading volume, and around 70% share of the tokenized equity issuer market. Instead of building an RWA ecosystem from scratch, Ondo Perps can leverage the existing asset base and distribution of Ondo Global Markets as that ecosystem grows. Distribution brought users. Asset coverage may decide who keeps them. For early participants, there's also a $150k reward pool during week one.
1:11

The next perp winner probably won't be decided by incentives. It'll be decided by what assets users can trade. And the narrative doesn't lack users; it lacks new markets to bring on-chain. BTC or ETH are not enough; users need something familiar. Ondo Perps is filling exactly that gap: RWA perps (commodities + tokenized equities). It's not a coincidence that recent platforms choose RWAs for perp trading: + 24/7 access to assets that are normally locked behind market hours (equities, commodities, etc) + Tokenized equities are no longer just spot assets, they become viable collateral and underlyings for perpetual markets. In fact, you can find RWAs perp in multiple places: + Hyperliquid (via HIP-3) + Aster + Grvt + Lighter + Ostium Ondo Perps has one real edge: it's built on top of Ondo Global Markets, which just reportedly crossed $1b TVL, $2b in cumulative trading volume, and around 70% share of the tokenized equity issuer market. Instead of building an RWA ecosystem from scratch, Ondo Perps can leverage the existing asset base and distribution of Ondo Global Markets as that ecosystem grows. Distribution brought users. Asset coverage may decide who keeps them. For early participants, there's also a $150k reward pool during week one.

andrew.moh

14,195 Aufrufe • vor 1 Monat

Insilico Terminal Podcast Episode 29 - Worst Contrarian - BACK OFFICE @ LARP CAPITAL 00:00 Intro, WC’s background, why he views trading as a business not speculation 04:07 First major arbitrage breakthrough, Binance AUD tether trade and going full-time 08:30 Why most people approach markets wrong, repeatable arbitrage vs lucky directional bets 19:40 The “exploiter mindset”, how to think about protocols, oracles and metagame risk 30:05 Building a trader network, why one phone call can unlock the next unusual trade 40:54 Sports betting exploits, getting banned everywhere and finding edge in inefficiency 53:33 Crypto vs TradFi today, where opportunity still exists and why chaos creates edge 01:06:22 Final advice, relationships, martial arts, Education of a Speculator and closing
1:12:30

Insilico Terminal Podcast Episode 29 - Worst Contrarian - BACK OFFICE @ LARP CAPITAL 00:00 Intro, WC’s background, why he views trading as a business not speculation 04:07 First major arbitrage breakthrough, Binance AUD tether trade and going full-time 08:30 Why most people approach markets wrong, repeatable arbitrage vs lucky directional bets 19:40 The “exploiter mindset”, how to think about protocols, oracles and metagame risk 30:05 Building a trader network, why one phone call can unlock the next unusual trade 40:54 Sports betting exploits, getting banned everywhere and finding edge in inefficiency 53:33 Crypto vs TradFi today, where opportunity still exists and why chaos creates edge 01:06:22 Final advice, relationships, martial arts, Education of a Speculator and closing

Insilico Terminal

25,403 Aufrufe • vor 4 Monaten

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 poker before they touch a market - The one trait great traders have that can't be taught - How SIG's flat org structure (3 titles, total) creates true meritocracy - Why the flop in poker maps directly to the top of the order book - SIG's expansion into sports trading, private equity, venture & reinsurance - How River's Edge prices NIL deals, Eagles Super Bowl promos & catastrophe risk - SIG's 25-year bet on prediction markets — & their relationship with Kalshi - Why titles are cheap & what SIG rewards instead Highlights: 00:00 Intro 01:01 What makes a great quant trader 02:26 How Susquehanna trains decision-making under uncertainty 03:05 Susquehanna interview philosophy: process, updating, communication 03:41 Teaching derivatives, options pricing, and risk from scratch 04:21 Using games to model trading concepts 05:11 Poker as a controlled model of asymmetric information 05:41 Internal Susquehanna trading games and mock trading structure 06:51 How mock trading reveals signal vs. noise 11:30 What traits can’t be trained 16:19 How Susquehanna screens for untrainable traits in interviews 20:41 Why internship performance is noisy 24:41 How pods work inside Susquehanna 26:11 Titles are meaningless at Susquehanna 34:01 Building SEG RE and expanding insurance capacity 43:31 25-year internal belief in prediction markets at Susquehanna 55:01 Closing remarks
55:13

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 poker before they touch a market - The one trait great traders have that can't be taught - How SIG's flat org structure (3 titles, total) creates true meritocracy - Why the flop in poker maps directly to the top of the order book - SIG's expansion into sports trading, private equity, venture & reinsurance - How River's Edge prices NIL deals, Eagles Super Bowl promos & catastrophe risk - SIG's 25-year bet on prediction markets — & their relationship with Kalshi - Why titles are cheap & what SIG rewards instead Highlights: 00:00 Intro 01:01 What makes a great quant trader 02:26 How Susquehanna trains decision-making under uncertainty 03:05 Susquehanna interview philosophy: process, updating, communication 03:41 Teaching derivatives, options pricing, and risk from scratch 04:21 Using games to model trading concepts 05:11 Poker as a controlled model of asymmetric information 05:41 Internal Susquehanna trading games and mock trading structure 06:51 How mock trading reveals signal vs. noise 11:30 What traits can’t be trained 16:19 How Susquehanna screens for untrainable traits in interviews 20:41 Why internship performance is noisy 24:41 How pods work inside Susquehanna 26:11 Titles are meaningless at Susquehanna 34:01 Building SEG RE and expanding insurance capacity 43:31 25-year internal belief in prediction markets at Susquehanna 55:01 Closing remarks

Ethan Kho

34,855 Aufrufe • vor 5 Monaten

I sat down with Arthur Hayes to discuss the Iran conflict, bitcoin, gold, oil, inflation, deflation, decentralized exchanges, prediction markets, privacy coins, and why he thinks insider trading should be legal. YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:49 - Iran war & oil market reaction 5:30 - Inflation vs deflation breakdown 7:42 - Why the Fed will always print money 9:08 - Gold vs bitcoin & global trade shift 14:20 - Arthur Hayes portfolio strategy 17:54 - Hyperliquid & the future of trading 24:20 - Insider trading should be legal? 27:59 - Why bitcoin doesn’t need institutions 32:09 - Zcash & privacy in crypto 35:23 - What Arthur is bearish on in crypto 40:30 - How big Hyperliquid can get 42:24 - Will prediction markets be successful? 46:45 - Final market outlook
49:52

I sat down with Arthur Hayes to discuss the Iran conflict, bitcoin, gold, oil, inflation, deflation, decentralized exchanges, prediction markets, privacy coins, and why he thinks insider trading should be legal. YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:49 - Iran war & oil market reaction 5:30 - Inflation vs deflation breakdown 7:42 - Why the Fed will always print money 9:08 - Gold vs bitcoin & global trade shift 14:20 - Arthur Hayes portfolio strategy 17:54 - Hyperliquid & the future of trading 24:20 - Insider trading should be legal? 27:59 - Why bitcoin doesn’t need institutions 32:09 - Zcash & privacy in crypto 35:23 - What Arthur is bearish on in crypto 40:30 - How big Hyperliquid can get 42:24 - Will prediction markets be successful? 46:45 - Final market outlook

Anthony Pompliano 🌪

74,847 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 6 Monaten

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

113,281 Aufrufe • vor 9 Tagen

Debunking Coffeezilla’s prediction market claims In his recent video “prediction markets aren’t just gambling,” Coffeezilla made the three untrue or misleading claims below: 1) “The only way you get the news early, by the way, is if it’s insider trading.” 2) “All these prediction markets are doing is aggregating sentiment on the news.” 3) “The only way you can get something not in the news from these markets is if someone with non-news information trades, which is AKA inside information.” I want to start by saying that I’ve really enjoyed Coffeezilla’s content in the past, and I appreciate him blacking out my name in the tweet he screenshared. I also watched the full video and agreed with parts of it (and disagreed with other parts). That said, these three claims are egregiously incorrect, and I want to correct the record. I hope Coffeezilla reads this and reconsiders these points 1) “The only way you get the news early, by the way, is if it’s insider trading.” Merriam-Webster defines insider trading as “the illegal use of information available only to insiders in order to make a profit in financial trading.” This claim is wrong because it is clearly possible to get information early in entirely legal ways. For example, in CPI inflation markets, someone might notice prices rising on goods they regularly buy, or a sophisticated trader might aggregate pricing data across many products and form a forecast before the CPI release. Journalists may later report on inflation, but the information existed beforehand. Markets also react faster to sudden events, like a Trump Truth Social post or an earthquake, than journalists do. Traders are financially incentivized to react in seconds; journalists are not. Recent high-profile examples include Nobel Peace Prize, Spotify, and Time POTY markets, where traders had information before the news broke. In the Nobel case, there was disinformation claiming insider trading, but as far as most observers can tell, the information was obtained legally via web-scraping. Even if the Nobel Committee disliked it, legally obtained information is fair game. 2) “All these prediction markets are doing is aggregating sentiment on the news.” This is easy to debunk. Prediction markets do aggregate information, but not merely sentiment or headlines. That’s likely why CNN and CNBC partnered with Kalshi. News is filtered through editors, incentives, and bias. Taking headlines at face value is not a winning trading strategy. Savvy traders treat news as one input among many variables. If a headline says “Poll X shows Clinton up 10 points,” markets may adjust, but they don’t blindly price the headline. They factor in other variables. I’d argue markets are often smarter than the news. Domer❤️‍🔥 has even argued that Fed markets on Kalshi are more accurate than CME due to traders like himself making them more efficient, and I think he’s right. 3) “The only way you can get something not in the news from these markets is if someone with non-news information trades, which is AKA inside information.” Merriam-Webster defines insider information as “information not known to the public that one has obtained by virtue of being an insider.” You can obtain non-news information without being an insider. This overlaps with point one, but here’s a concrete example. For the recent TN-07 special election, I traveled to TN-07 and spoke with voters leaving early-voting sites and with everyday residents. I learned how little awareness there was that a special election was even happening, and how voters were thinking about the race. That information wasn’t in the news, but it informed how I traded. I’m not an insider. This was “alpha hunting” through firsthand observation. Almost every serious prediction market trader has similar stories. This is certainly not "insider trading." I’m genuinely curious to hear your thoughts, Coffeezilla, and hope for a good-faith dialogue. I hope you are doing well!
0:24

Debunking Coffeezilla’s prediction market claims In his recent video “prediction markets aren’t just gambling,” Coffeezilla made the three untrue or misleading claims below: 1) “The only way you get the news early, by the way, is if it’s insider trading.” 2) “All these prediction markets are doing is aggregating sentiment on the news.” 3) “The only way you can get something not in the news from these markets is if someone with non-news information trades, which is AKA inside information.” I want to start by saying that I’ve really enjoyed Coffeezilla’s content in the past, and I appreciate him blacking out my name in the tweet he screenshared. I also watched the full video and agreed with parts of it (and disagreed with other parts). That said, these three claims are egregiously incorrect, and I want to correct the record. I hope Coffeezilla reads this and reconsiders these points 1) “The only way you get the news early, by the way, is if it’s insider trading.” Merriam-Webster defines insider trading as “the illegal use of information available only to insiders in order to make a profit in financial trading.” This claim is wrong because it is clearly possible to get information early in entirely legal ways. For example, in CPI inflation markets, someone might notice prices rising on goods they regularly buy, or a sophisticated trader might aggregate pricing data across many products and form a forecast before the CPI release. Journalists may later report on inflation, but the information existed beforehand. Markets also react faster to sudden events, like a Trump Truth Social post or an earthquake, than journalists do. Traders are financially incentivized to react in seconds; journalists are not. Recent high-profile examples include Nobel Peace Prize, Spotify, and Time POTY markets, where traders had information before the news broke. In the Nobel case, there was disinformation claiming insider trading, but as far as most observers can tell, the information was obtained legally via web-scraping. Even if the Nobel Committee disliked it, legally obtained information is fair game. 2) “All these prediction markets are doing is aggregating sentiment on the news.” This is easy to debunk. Prediction markets do aggregate information, but not merely sentiment or headlines. That’s likely why CNN and CNBC partnered with Kalshi. News is filtered through editors, incentives, and bias. Taking headlines at face value is not a winning trading strategy. Savvy traders treat news as one input among many variables. If a headline says “Poll X shows Clinton up 10 points,” markets may adjust, but they don’t blindly price the headline. They factor in other variables. I’d argue markets are often smarter than the news. Domer❤️‍🔥 has even argued that Fed markets on Kalshi are more accurate than CME due to traders like himself making them more efficient, and I think he’s right. 3) “The only way you can get something not in the news from these markets is if someone with non-news information trades, which is AKA inside information.” Merriam-Webster defines insider information as “information not known to the public that one has obtained by virtue of being an insider.” You can obtain non-news information without being an insider. This overlaps with point one, but here’s a concrete example. For the recent TN-07 special election, I traveled to TN-07 and spoke with voters leaving early-voting sites and with everyday residents. I learned how little awareness there was that a special election was even happening, and how voters were thinking about the race. That information wasn’t in the news, but it informed how I traded. I’m not an insider. This was “alpha hunting” through firsthand observation. Almost every serious prediction market trader has similar stories. This is certainly not "insider trading." I’m genuinely curious to hear your thoughts, Coffeezilla, and hope for a good-faith dialogue. I hope you are doing well!

Benjamin Freeman

80,021 Aufrufe • vor 8 Monaten

E83: XBorg : The Future of Gaming Lou is the founder and CEO of XBorg, a SwissBorg spin off developing the world’s largest player identity protocol in gaming. Thank you to our Partners Jupiter , SwissBorg , Pudgy Penguins , Astar Network , dVIN Labs (🍷, ⛓) , and Coinsilium for helping us make this happen. Timestamps 0:00 Introduction 2:14 Introducing Louis 3:40 From Hospitality to Crypto 5:48 Escaping the 9-to-5 7:26 Leverage: Code and Media 9:19 Investing in In-House Teams 10:39 Trading Success 14:13 How to Make Money Trading Crypto 17:00 Meme Coins 20:27 Meme Coins: Lottery of Crypto 25:44 Primary VS Secondary Markets 27:32 Primary Market Insights and Investments 30:21 Building a Gaming Protocol 33:10 Social Impact of Crypto Trading 37:57 Trading and Building Simultaneously 41:10 What is XBorg? 42:45 Online Identity Importance 45:18 Holy Grail for XBorg 46:10 Inside the Crypto Token Listing Game 53:33 KOL Frenzy in Crypto 58:27 Prediction for the Next 12 Months
1:01:10

E83: XBorg : The Future of Gaming Lou is the founder and CEO of XBorg, a SwissBorg spin off developing the world’s largest player identity protocol in gaming. Thank you to our Partners Jupiter , SwissBorg , Pudgy Penguins , Astar Network , dVIN Labs (🍷, ⛓) , and Coinsilium for helping us make this happen. Timestamps 0:00 Introduction 2:14 Introducing Louis 3:40 From Hospitality to Crypto 5:48 Escaping the 9-to-5 7:26 Leverage: Code and Media 9:19 Investing in In-House Teams 10:39 Trading Success 14:13 How to Make Money Trading Crypto 17:00 Meme Coins 20:27 Meme Coins: Lottery of Crypto 25:44 Primary VS Secondary Markets 27:32 Primary Market Insights and Investments 30:21 Building a Gaming Protocol 33:10 Social Impact of Crypto Trading 37:57 Trading and Building Simultaneously 41:10 What is XBorg? 42:45 Online Identity Importance 45:18 Holy Grail for XBorg 46:10 Inside the Crypto Token Listing Game 53:33 KOL Frenzy in Crypto 58:27 Prediction for the Next 12 Months

MR SHIFT 🦁

118,158 Aufrufe • vor 2 Jahren

Celebrating 2 years of smarter markets 🧮 Since trading commenced on Abaxx Exchange on June 28, 2024, our pursuit of building smarter markets has brought us the privilege of sharing many memorable 'firsts' with our growing network of market participants, including: 📍 First physically deliverable LNG futures to trade globally 📍 First delivery of a regulated, exchange-cleared voluntary carbon contract 📍 First USD-denominated, physically-deliverable benchmarks for nickel sulphate and lithium carbonate outside of China 📍 First physically deliverable kilobar gold futures deliverable into Singapore 📍 First co-located futures and spot market for physical gold in Asia 📍 First FBOT registration issued since 2022 📍 First wind and solar futures indexed to installed generation capacity 📍 First delivery under the Abaxx Gold Singapore futures contract 📍 First globally-accessible 1,000 oz., four-nines silver futures These market 'firsts' within our first 730 days of trading created the momentum that resulted in: 18 new commodity futures benchmarks 1M+ contracts traded 40 connected clearing firms, brokers and ISVs There is no shortage of market problems and opportunities in need of new plumbing and new price signals, and we look forward to another year building smarter markets for global commodities trading. Thank you to our clearing members, front-office, trading, back-office, and market surveillance vendors, market data distribution partners -- and to the teams, partners and investors contributing every day to the growth of smarter markets.
2:39

Celebrating 2 years of smarter markets 🧮 Since trading commenced on Abaxx Exchange on June 28, 2024, our pursuit of building smarter markets has brought us the privilege of sharing many memorable 'firsts' with our growing network of market participants, including: 📍 First physically deliverable LNG futures to trade globally 📍 First delivery of a regulated, exchange-cleared voluntary carbon contract 📍 First USD-denominated, physically-deliverable benchmarks for nickel sulphate and lithium carbonate outside of China 📍 First physically deliverable kilobar gold futures deliverable into Singapore 📍 First co-located futures and spot market for physical gold in Asia 📍 First FBOT registration issued since 2022 📍 First wind and solar futures indexed to installed generation capacity 📍 First delivery under the Abaxx Gold Singapore futures contract 📍 First globally-accessible 1,000 oz., four-nines silver futures These market 'firsts' within our first 730 days of trading created the momentum that resulted in: 18 new commodity futures benchmarks 1M+ contracts traded 40 connected clearing firms, brokers and ISVs There is no shortage of market problems and opportunities in need of new plumbing and new price signals, and we look forward to another year building smarter markets for global commodities trading. Thank you to our clearing members, front-office, trading, back-office, and market surveillance vendors, market data distribution partners -- and to the teams, partners and investors contributing every day to the growth of smarter markets.

Abaxx Exchange

10,763 Aufrufe • vor 1 Monat

Your ICT Strategy Is Incomplete Without This | Casper SMC In this weeks Order Book episode I sit down with Casper SMC to break down how he combines ICT concepts with orderflow to sharpen his entries and reads. We get into why ICT alone leaves gaps, how orderflow fills them, and the exact way Casper layers the two on live charts. If you've been trading SMC/ICT and feel like something's missing, this conversation is for you. Timestamps: 00:00 - Intro with Casper SMC 2:09 - Caspers Backstory (Legal trouble, found trading, hardship to success) 6:55 - Managing Risk, The Perception Of Money, Dissociative Trading 10:09 - The Evolution of Caspers Trading Strategy and View On Markets 16:10 - Taking Advantage of All Available Market Information 19:09 - Casper Explains How Heatmaps Work 26:12 - Casper Explains Volume Profiles 31:55 - Putting Together The Flow and Direction Data To Form A Trade 35:22 - Thinking Like An Institution (and Using Their Tools) 37:55 - The Book That Changed Caspers Life and How Your Mindset Shapes Your Reality 46:24 - Maynes Polymarket Picks (World Cup, SpaceX IPO, Crude Oil, Gold)
57:08

Your ICT Strategy Is Incomplete Without This | Casper SMC In this weeks Order Book episode I sit down with Casper SMC to break down how he combines ICT concepts with orderflow to sharpen his entries and reads. We get into why ICT alone leaves gaps, how orderflow fills them, and the exact way Casper layers the two on live charts. If you've been trading SMC/ICT and feel like something's missing, this conversation is for you. Timestamps: 00:00 - Intro with Casper SMC 2:09 - Caspers Backstory (Legal trouble, found trading, hardship to success) 6:55 - Managing Risk, The Perception Of Money, Dissociative Trading 10:09 - The Evolution of Caspers Trading Strategy and View On Markets 16:10 - Taking Advantage of All Available Market Information 19:09 - Casper Explains How Heatmaps Work 26:12 - Casper Explains Volume Profiles 31:55 - Putting Together The Flow and Direction Data To Form A Trade 35:22 - Thinking Like An Institution (and Using Their Tools) 37:55 - The Book That Changed Caspers Life and How Your Mindset Shapes Your Reality 46:24 - Maynes Polymarket Picks (World Cup, SpaceX IPO, Crude Oil, Gold)

Mayne

26,582 Aufrufe • vor 3 Monaten

I spoke with CFTC Chairman Mike Selig about artificial intelligence, prediction markets, and crypto. He is a pro-innovation regulator, but he also understands the important nuances of many of these technologies and their impact. This was fun to hear from him directly. YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:59 - SEC & CFTC cooperation & new policy direction 3:44 - AI, crypto & prediction markets shaping the future 7:49 - Insider information, manipulation & market risks 13:23 - Decentralization & regulating on-chain markets 17:14 - The rise of AI trading agents 21:30 - AI, memes & misinformation in financial markets 23:04 - Should regulators be allowed to use crypto? 25:00 - Innovation task force & new policy initiatives 27:18 - Bridging the gap between lawmakers & builders 29:30 - Perpetual futures and new crypto market products 31:16 - The role of regulators in managing market risk
32:34

I spoke with CFTC Chairman Mike Selig about artificial intelligence, prediction markets, and crypto. He is a pro-innovation regulator, but he also understands the important nuances of many of these technologies and their impact. This was fun to hear from him directly. YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:59 - SEC & CFTC cooperation & new policy direction 3:44 - AI, crypto & prediction markets shaping the future 7:49 - Insider information, manipulation & market risks 13:23 - Decentralization & regulating on-chain markets 17:14 - The rise of AI trading agents 21:30 - AI, memes & misinformation in financial markets 23:04 - Should regulators be allowed to use crypto? 25:00 - Innovation task force & new policy initiatives 27:18 - Bridging the gap between lawmakers & builders 29:30 - Perpetual futures and new crypto market products 31:16 - The role of regulators in managing market risk

Anthony Pompliano 🌪

29,226 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 2 Monaten