Odds on Open

Former Goldman Sachs head of natural gas trading John Knorring delivers a lecture on commodities trading from first principles, adapted from the one he gives to Princeton financial engineering students. Starting from one equation, supply minus demand equals the change in inventory, he explains how storage constraints drive price, why crude oil went negative in 2020, and why US natural gas is so volatile. Topics include Henry Hub futures, physical delivery and arbitrage, basis risk, the storability of coal, oil, gas, and power, and how weather shocks can double nat gas prices in a week. Essential viewing for energy traders and quants.Knorring then covers the shale revolution and Aubrey McClendon, how low prices built demand through power burn, LNG exports, and AI data centers, and the Goldman trading framework: idea generation, trade expression, execution, and money management. He walks through his bullish silver trade, from the structural deficit and gold-silver ratio to buying futures and cheap upside calls, and what he got wrong at $120. The episode closes with market trends, including position sizing lessons from Amaranth, the solar duck curve, the US LNG export wave, and why physical gas traders will outlast automated derivatives desks, plus timeless career advice for Wall Street.

Former Goldman Sachs oil derivatives trader and GSR Head of DeFi Simranjeet Singh breaks down market microstructure, liquidity provision, and risk management across traditional commodities and crypto assets. Simran recounts navigating the historic April 2020 WTI crude collapse to -$37.63, detailing how desks adapted option pricing models when log-normality broke under 400% annualized volatility, shifted margining frameworks, and managed balance sheet risk across massive sovereign hedging flows like Mexico’s Hacienda Hedge. He breaks down how sell-side dealers monetize flow, warehouse basis risk on bespoke structured products such as producer three-way collars, and coordinate with credit and CVA desks when sharp market drawdowns trigger counterparty liquidity shocks.Transitioning from sell-side commodity derivatives to systematic buy-side trading at GSR, Simran explains why all trading strategies ultimately reduce to momentum or mean reversion. He unpacks the mechanics of delta-neutral perpetual futures funding rate arbitrage, navigating liquidity and market impact as a taker, and the operational risk lessons learned from a $3.1M single-day drawdown. Exploring current market regimes, Simran details where macro alpha is shifting—highlighting the opaque, emerging OTC complex around power, electricity, and AI compute—and outlines the market design thesis behind Monaco Exchange to disintermediate perpetual future listings and democratize 24/7 global market access.

David Orr returns to Odds on Open to unpack how he sustains a 50% annualized return and a 2.1 Sharpe ratio by stripping the multi-manager model down to raw alpha generation. We deconstruct his firm’s AI-driven risk architecture, detailing how proprietary factor models recalculate custom basket correlations in real-time to manage tail risk across a 400% levered portfolio. David also breaks down his personal discretionary process, explaining his strict two-hour cap on single-name equity research to eliminate sunk-cost bias, the fatal mechanics of shorting revenue growth, and why institutional pod shops structurally sacrifice top-tier returns to optimize for AUM fee harvesting.Beyond portfolio construction, we explore the unit economics of scaling a high-performance fund through unconventional talent sourcing. David explains how he evaluates pure live track records to recruit non-consensus PMs, utilizing the fund’s long/short vehicle to offer massive tax deferral advantages for incoming high-frequency traders. We also examine his structural framework for the AI trade—specifically why hyperscalers offer superior downside protection and take-or-pay ROIC compared to hardware manufacturers—and his thesis on why unplugging from consensus narrative feeds is required to isolate variant views in shifting market regimes.

Checkout Flux 4.0 here: https://www.flux.live/flux4/index.html On this episode of Odds on Open, hedge fund manager Joe Walsh joins Ethan Kho to break down how probabilistic decision-making and bankroll management from high-stakes poker translate into institutional equity outperformance. Walsh outlines his framework for game selection across liquid markets, detailing why he deliberately avoids the crowded, factor-constrained time horizons of multi-manager pod shops and indiscriminate passive ETF flows to capture mispriced fundamentals. From exploiting negative gamma events, forced de-grossing cascades, and liquidity cycle dislocations to balancing expected value against variance, this conversation explores how disciplined capital preservation and downside underwriting unlock high-compounding, low-drawdown portfolio construction.The discussion then pivots to tactical execution, asymmetric bet sizing, and the mechanics of buyside alpha generation. Walsh breaks down why he scales positions on the “right side of the V” rather than fighting price trends, his strict risk limits and borrow cost considerations on the short side, and how George Soros’s reflexivity framework dictates narrative-driven multiple expansion and terminal value compression. Bringing the framework to life, he maps out value chains and shifting profit pools across emerging asset classes—dissecting the entire AI infrastructure trade from hyperscaler CapEx sustainability and data center power grid constraints to thermal cooling bottlenecks and specialized offshore industrial monopolies. This masterclass is essential viewing for long/short equity analysts, portfolio managers, quantitative traders, and allocators seeking durable, structural edge in modern markets.

Geoffrey Williams spent more than a decade at Goldman Sachs, where he traded structured products through the 2008 financial crisis and later co-ran the CMBS trading business. In this episode of Odds on Open, he takes Ethan Kho inside the Goldman mortgage desk as Bear Stearns and Lehman collapsed. He explains how banks sold tens of billions of super senior protection on ABS CDOs for 10 basis points a year, and what it was like to trade TABX and ABS CDOs as AAA paper repriced from basis points to points upfront. He also breaks down how conduit CMBS is structured and tranched, why commercial real estate moves in cycles across malls, office, and life science, and how post-GFC capital rules pushed securitized credit risk off dealer balance sheets and onto hedge funds.Geoffrey now runs 1543 Capital LP, an opportunistic structured finance hedge fund, with a co-CIO who came from Brevan Howard. The fund focuses on end-of-life CMBS, residential solar ABS, and other dislocated credit. He walks through how he underwrites a CMBS bond loan by loan, when desktop tools like Google Earth are enough and when you need to see the property, and how he sizes positions to survive unknown unknowns while targeting mid-teens unlevered returns. The conversation also covers hedging macro tail risk from deficits, rates, and AI disruption, how populist politics and the proposed California wealth tax shape his real estate exposure, luck versus skill in finance careers, thinking in bets as an entrepreneur, and the biggest mistake structured credit PMs make: forgetting that credit markets move in step functions.

Checkout Flux 4.0 here: https://www.flux.live/flux4/index.html

In this episode of Odds on Open, Alpaca Co-Founder and CEO Yoshi Yokokawa breaks down the changing microstructure of systematic trading, the democratization of quantitative R&D, and why AI agents alone will never grant you a defensible market edge. As LLMs compress strategy iteration and backtesting feedback loops, the traditional line between retail quants and institutional fund managers continues to blur. Yoshi shares high-signal data from Alpaca’s API platform—tracking a 4x explosion in active algorithmic developers—and explains why execution discipline, behavioral risk control, and operational frameworks remain the true drivers of alpha in liquid markets.The conversation dives deep into the structural economics of modern asset management, analyzing the recent surge of YC-backed AI hedge funds, the reality of competing against multi-manager scale, and why top-tier trading firms spend aggressively on compute. Drawing from his background as a former trader at Lehman Brothers and his journey building global brokerage infrastructure, Yoshi explores the rise of entity-based solopreneurs, tokenized equities, and MCP-ready financial services. Whether you are a portfolio manager, quant developer, or market allocator, this episode provides actionable frameworks for handling strategy recalibration through regime shifts and cultivating defensibility in an increasingly commoditized financial landscape.

Checkout Flux 4.0 here: https://www.flux.live/flux4/index.html Chris Camillo and Tom Costello go head-to-head on Odds on Open's first every debate episode. They answer a question every trader eventually confronts: who is actually better positioned to take risk — a concentrated retail investor with no LPs, mandates, or career risk, or an institutional hedge fund manager with a two-and-twenty product to protect? Chris, who compounded $20K into roughly $80 million over 18 years using his "social arb" strategy, argues that a 50–70% drawdown is rational inside a properly bucketed, levered, long-horizon risk account. Tom, a former Tudor, Moore, and Caxton PM who now runs a market-neutral fund targeting ~20% annual returns with a 1.4% peak drawdown, counters with the Pareto distribution of hedge fund and day-trader returns, the beta-versus-alpha logic behind institutional portfolio construction, and why allocators refuse to pay for beta they can get on margin themselves. Along the way they debate 3x leveraged S&P ETFs and the hidden roll and theta costs inside them, "too big to fail" AI concentration, the Situational Awareness blowup, crypto's 98% correlation trap, and why overfit backtests stopped landing in Tom's inbox.The second half is a live stress test of social arb as an edge: Chris walks through the Beacon Roofing hailstorm trade built on Google Trends, the Hunger Games and Stranger Things trades built on conversational data, and the e.l.f. Cosmetics trade triggered by a single Jeffree Star video that no sell-side cosmetics analyst had seen. Tom critiques it as an institutional money manager — alpha decay, crowding, discretionary-desk constraints, and whether Citadel plus LLMs will systematize TikTok sentiment before retail's information asymmetry is arbitraged away. If you care about risk management, leverage, position sizing, uncorrelated returns, alternative data, market microstructure, and where retail traders still hold a structural advantage over multi-manager pod shops, this episode is for you.

Lihong Wang spent his career as a discretionary options trader at IMC, one of the world's largest market makers, trading semiconductor volatility on names like Nvidia, AMD, and Broadcom. In this episode, he breaks down how a prop trading desk actually makes money: reading order flow to identify counterparties, trading against structured product hedging flows from Asian banks, warehousing benign risk instead of externalizing it, and why correlation blowups — like the DeepSeek selloff and July's deleveraging cascade — are how options market makers get hurt. He explains why the seat at a top trading firm is one of the most leveraged information positions in markets, how firms like IMC, Jane Street, SIG, and HRT train and allocate quant trader talent, and what separates market making from prediction.Now the founder of Freeport, a YC-backed perpetual futures exchange, Lihong shares his AI investing thesis: why he holds a 2x levered portfolio of 50 stocks across the entire AI and semiconductor supply chain — from TSMC and ASML to optics, connectivity, and memory names like SK Hynix — and why nearly every quant trader he knows is doing the same. We cover how narratives move from private conversations to Twitter to Bloomberg (and where the edge dies), Leopold Aschenbrenner's Situational Awareness fund, whether AI is a dot-com-style bubble, Kelly criterion leverage sizing, the future of perp DEXs like Hyperliquid, and how young people should think about risk, career moats, and personal brand in the age of AI. Essential listening for hedge fund analysts, quants, options traders, and anyone trading the AI supply chain.

Checkout Flux 4.0 here: https://www.flux.live/flux4/index.html Alix Pasquet, hedge fund manager and Managing Partner at Prime Macaya Capital, breaks down how fundamental investors build durable edge in modern markets. In this episode, Alix explains the three sources of competitive advantage in the investment business — analytical, informational, and behavioral — and why exploiting group behavior beats behavioral finance checklists. He covers what he learned inside a top quant fund (data scrubbing, transaction costs, temporal stops), how smaller hedge funds compete against multi-manager pods like Citadel and Millennium, and his full idea generation process: investor networks, 13F tracking, thematic collisions, and the customer-investor mismatch. He also makes the contrarian case for technical analysis as a behavioral tool, explains why moats and chart strength are connected, and shares why buying stocks at new highs beats bottom-fishing in a post-2022 market.Later in the conversation, Alix delivers a warning on AI in investing: why over-reliance on AI tools will produce the worst portfolio managers of the next decade, the five core analytical skills every analyst must protect (pattern recall, visualization, reading between the lines, leap of judgment, synthesis), and how to become a "bionic" investor who combines analog training with digital tools. He closes with career advice for young analysts and aspiring PMs — building a personal moat, the power pair concept, network as competitive advantage, and applying margin of safety to your personal life. Essential listening for hedge fund analysts, portfolio managers, long-short equity investors, and anyone pursuing a career in active management, stock picking, or the hedge fund industry.

How does market making actually work? In this lecture, former high-frequency trader Annanay Kapila (Flow Traders, Tower Research Capital; founder & CEO of QFEX) breaks down the mechanics of market making from first principles. He maps the systematic trading landscape—prop trading vs hedge funds, HFT vs mid-frequency—and explains why market making sits in the high-Sharpe, proprietary quadrant. You'll learn how a central limit order book (CLOB) works, how arbitrage-free pricing pins down fair value (theo), and why the mid-price between best bid and best ask is the market's best estimate of a stock's true price.From there, the lecture gets into the real edge mechanics: quoting around fair value, edge per share, queue priority, and adverse selection—the hidden cost that punishes market makers when their fair value model is wrong. Annanay walks through how competition between market makers compresses the bid-offer spread, why thick books (large-cap US equities trading $10B+ a day) offer lower transaction costs than thin books, and how quant firms size their quotes based on confidence in their fair value models. Essential viewing for aspiring quants, traders, MFE students, and anyone who wants to understand market microstructure, liquidity provision, and how high-frequency trading firms actually make money.