Explore every episode of the podcast Odds on Open

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.

Checkout Flux 4.0 here: https://www.flux.live/flux4/index.html Noah Kann is 22, running his third firm, and managing capital raised from some of the wealthiest families in America. On this episode of Odds on Open, Ethan Kho sits down with the co-CEO of Venari Asset Management to unpack what a 22-year-old hedge fund manager knows that 20-year Wall Street veterans miss: why running a hedge fund is the least sexy business in finance, why compliance and a well-written PPM are an emerging manager's competitive advantage, and why complacency, not inexperience, is the real driver of alpha decay. Noah walks through his path from buying HCA at $15 during the COVID drawdown at 16, to a leveraged crypto trading firm at 17, to launching SageTech Capital at 19, and explains how behavioral finance and studying past cycles like the Great Depression substitute for market reps he hasn't lived through.The conversation then moves into Venari's macro discretionary playbook: trading government intervention and defense spending, a top-down process that turns a geopolitical thesis into a position, non-consensus indicators like data-center HVAC suppliers, and how the firm handles crowded trades and momentum with hard stops, max drawdown limits, and disciplined profit-taking. Noah breaks down the multi-strategy structure across long/short equity, LEAPS options, and ETF baskets, explains why mindfulness is the single most important risk control for a discretionary macro fund, and shares what family offices actually diligence when an emerging manager raises capital — stewardship, lockups, and the next generation. The episode closes on what people miss about Jane Street, SIG, and WorldQuant, hiring for rate of improvement over credentials, pod-shop culture, and why differentiated inputs produce differentiated returns. Essential listening for hedge fund analysts, PMs, quants, allocators, emerging managers, and MFE and MBA students building an edge in liquid markets.

Checkout Flux 4.0 here: https://www.flux.live/flux4/index.html Lucas Schuermann started a market-neutral quant fund in his Columbia dorm room, trading stat arb across FX and early crypto markets, before dropping out to scale it into Q Capital. In this episode of Odds on Open, he breaks down how he electronified Genesis Trading's OTC desk as VP of Engineering — taking a phone-and-Telegram trading operation to a fully electronic market-making system with HFT execution — and why flow, capital, and trust are the real moats in market making, not speed. He explains the biggest misconception about HFT firms and market makers like Jane Street, Jump, and Citadel Securities, and why having flow and economies of scale matters more than latency.Lucas then dives into building Variational, first as a crypto prop shop trading DeFi and OTC derivatives, and now as one of the largest on-chain perps trading platforms — a broker-like model with zero-fee trading, aggregated liquidity, and a new swaps instrument that fixes the funding rate problem with perpetual futures. We cover perps vs swaps vs spot mechanics, total return swaps, internal vs external market makers, RWA perps, and why he believes the cypherpunks already won. Plus: how to identify trends worth riding using growth-curve data, why asset prices are uncorrelated with industry durability, how to build expertise in a technical domain fast, and the role of hubris in career differentiation for young quants, traders, and founders.

Checkout Flux 4.0 here: https://www.flux.live/flux4/index.htmlCamilo Saravia is the founder of BlueWalker Capital, a systematic hedge fund trading prediction markets — and possibly the only fund dedicated exclusively to the asset class. Backed by Daniel Howard, son of Brevan Howard co-founder Alan Howard, Camilo breaks down how the fund prints alpha across taker and maker strategies, reflexive vs. proactive pricing, and why event contracts carry different adverse selection and binary risk than equities. He makes the contrarian case that insider flow is a feature, not a bug — the mechanism that makes prediction markets a money-backed source of truth — and maps where systematic edge actually comes from: proprietary order book and on-chain fill data, vertical integration, execution speed, and a team hungry enough to make unit economics work in a market Citadel and Jane Street won't touch.The back half is a blueprint for launching an emerging fund from scratch: underwriting talent, hiring quants who turn down Citadel and Wintermute offers, missionaries vs. mercenaries, and why speed is a startup fund's structural edge. Camilo details his research philosophy — collapsing internet entropy into tradable signal, mining exotic alternative data from TikTok virality to Spotify streams, and applying a venture-style lens to price what markets can't: unstructured data, operational KPIs, and execution quality. The episode closes with prediction markets 101 — order books, market microstructure, narrative risk, and why the best trades exit at 50 rather than waiting for resolution on Polymarket — plus how to build durable personal edge as AI commoditizes technical skills. Essential listening for quants, PMs, traders, allocators, and anyone tracking prediction markets as the next institutional asset class.

Checkout Flux 4.0 here: https://www.flux.live/flux4/index.htmlJosh Young (Bison Capital) on Energy Alpha, Deep Value, and Activism in Small-Cap Oil & Gas: Josh Young runs a concentrated, long-only public equities energy fund that's up 200% since inception while the energy sector is down 60% — a spread he calls a statistical impossibility. In this episode, Josh breaks down the process behind that outperformance: why he screens for large discounts to liquidation value and third-party reserve appraisals, how being chairman of a public E&P rewired his view of oil and gas as a capital allocation business, and why returns on invested capital and inflection points matter more than static free cash flow yields. He explains why he refuses to short stocks, how he sizes positions across speculative, medium, and high-conviction buckets, and why deleveraging setups — companies going from 4x debt/EBITDA to under 1x — have driven the bulk of his idiosyncratic returns. He also walks through the Abqaiq attack in real time as an example of why almost nobody has edge on short-term crude direction.The conversation goes deep on where alpha actually lives in energy markets: activist campaigns and proxy fights in small-cap E&Ps, co-investment vehicles for concentrated activist positions, the principal-agent problem that keeps allocators, endowments, and ESG-constrained institutions out of oil and gas, and the Fama-French small-cap illiquidity premium that makes an out-of-favor sector fertile ground. Josh discusses CTA and commitment-of-traders positioning as a timing input, energy's collapse to 4% of the S&P 500 versus a four-decade average above 10%, the long-cycle case for $250 oil, and why finding public-but-undisseminated data in state and provincial filings still produces real edge. He closes on the limits of AI in fundamental energy research, why oil companies bragging about AI-driven operations tend to underperform, and what most energy traders get wrong about left-tail risk, volatility, and buying the fundamentals instead of the macro. Essential viewing for hedge fund analysts, portfolio managers, commodity traders, allocators, and anyone studying deep value investing, portfolio construction, and edge in liquid markets.

Checkout Flux 4.0 here: https://www.flux.live/Suhaimi Zainul-Abidi is the CEO of Quantedge, the Singapore-based systematic hedge fund that has compounded at roughly 20% net annualized returns for 20 years — growing from $3 million raised from friends and family to over $6 billion in AUM, with close to $5 billion of that from investment gains rather than fundraising. In this episode of Odds on Open, Suhaimi breaks down Quantedge's founding thesis and why the firm's real edge isn't informational: with close to 300 distinct markets traded across equities, commodities, rates and FX, breadth and independent bets let them target ~25% annualized volatility without risk of ruin. We go deep on risk premia and factor-based market-neutral strategies, why economic rationale must come before empirical evidence in the research pipeline, why they refuse to run a black box they can't explain, and how they think about signal versus noise in alternative data, narratives and news flow. Suhaimi also gives a clear-eyed view on generative AI in portfolio management — transformational for research productivity, data cleaning and execution, but deliberately kept out of the production models.The second half is essential listening for emerging managers, allocators and anyone building an asset management business. Suhaimi explains why Quantedge turned down the obvious scaling path — a lower-vol, allocator-friendly product — and why he believes chasing capital on someone else's terms is the mistake that kills early-stage funds. He details the 2018 decision to introduce fixed-term and semi-perpetual share classes, the estimated cost of that call (he thinks they'd be a $20 billion fund without it), and the deeper insight behind it: funds rarely die from drawdowns, they die from redemptions arriving at the worst possible moment. We also cover Class Q and permanent capital as employee alignment, why Quantedge hires almost exclusively out of school rather than poaching mid-career PMs, what a broad definition of meritocracy looks like inside a quant fund, his own path from law to running the firm, capital consolidation and the rise of AI-native allocators, and the case for patience and conviction in building a 50-year compounding machine.00:00 Intro1:34 Founding thesis: $3M to a $6B quant fund6:06 Why the edge is breadth, not information10:40 A Message from ONYX11:20 Scaling to $6B without chasing allocator capital15:25 Why they refuse an allocator-friendly low-vol version18:21 No black boxes: holding conviction at 25% vol23:22 News flow, narratives, and where AI actually helps30:29 Hiring fresh grads and retaining quant talent38:51 From lawyer to CEO of a quant fund49:19 Capital consolidation and the AI-native allocator54:52 The capital trap that kills emerging managers58:54 Fixed-term lockups and why funds actually die1:04:48 Class Q, permanent capital, and employee alignment1:18:59 How to build a 50-year compounding business

In this episode of Odds on Open, former Balyasny Asset Management (BAM) quantamental portfolio manager and Imply founder Ying Hua breaks down how top multi-manager hedge funds synthesize quantitative discipline with discretionary analysis to extract repeatable market alpha. Ying details the mechanics of constructing a systematic quantamental framework—ranging from automating volatility-adjusted position sizing to eliminate behavioral bias, to scraping granular alternative data sets like state highway patrol records and geospatial tracking for asymmetric earnings trades. The conversation draws a sharp line between quantitative pattern matching and fundamental situational judgment, exposing how institutional investors capture edge where pure quants and traditional fundamental analysts both miss the mark.The deep dive extends into the frontier of AI in portfolio management, dissecting why off-the-shelf LLMs fall short without ticker-level financial knowledge graphs and specialized domain context. Ying analyzes how automated data workflows impact earnings print volatility, why pod shop equity trading increasingly mirrors high-stakes poker dictated by positioning dynamics rather than static valuations, and how junior analysts can identify high-alpha sectors. Tailored for hedge fund PMs, quants, equity research analysts, allocators, and MFE/MBA candidates, this episode delivers rigorous mental models on market microstructure, regime shifts, and process automation in liquid markets.00:00 Intro01:07 Positioning and position sizing in a quantamental framework02:30 Quant pattern matching vs fundamental situational edge04:47 Automating position sizing to remove emotional bias06:35 A message from ONYX07:03 Extracting alpha from alternative data: Scraped highway and disaster mapping11:54 Which parts of the fundamental investment process can AI automate?16:53 Why market automation increases earnings print volatility20:29 Structural limitations of using general LLMs for portfolio management27:32 Building ticker-level domain knowledge graphs for AI workflows30:29 Why specialized finance workflows beat commoditized AI wrappers36:48 Will AI make liquid markets more efficient?39:34 How fundamental PMs should redesign workflows for the AI era42:08 What core competencies define elite talent in modern pod shops?48:52 Why multi-manager equity trading resembles high-stakes poker52:29 How junior analysts should evaluate sector alpha and career edge58:23 Evaluating personal drawdowns, self-awareness, and P&L meritocracy01:08:51 Reconstructing market narratives from first principles

In this episode of *Odds on Open*, hedge fund risk manager and derivatives trader Erik Smolinski deconstructs the structural mechanics of alpha generation, portfolio construction, and risk mitigation in liquid markets. Boasting a near-100% return last year and a flawless annual track record since 2008, Erik details how he transitions from profiling persistent market effects to engineering scalable profit mechanisms. The discussion explores the practical realities of the variance risk premium (VRP), sector rotation dynamics, and advanced derivatives structures—such as ratio call diagonals—designed to capture convex upside across shifting volatility regimes while minimizing exposure to tail risk and adverse market microstructure.Tailored specifically for hedge fund analysts, portfolio managers, quants, allocators, and MFE students, this episode isolates the institutional edge of book agility and systematic process execution over retail bias. Erik breaks down the microstructural nuances of navigating illiquid options chains, the mathematical frameworks behind quantitative momentum, and the strict process constraints required to prevent spectacular portfolio blowups during unexpected regime shifts. Listeners will gain a deep understanding of how to desensitize P&L tracking, align execution models with individual psychological risk profiles, and accurately price implied volatility to build a sustainable, credibility-forward trading framework.00:00 Intro00:01:26 How thematic volatility and objective execution drove triple-digit returns00:04:38 Profiling market effects to construct actionable profit mechanisms00:07:19 Sponsor break00:10:29 Identifying sector leaders and structuring ratio call diagonals00:16:42 The path from military discipline to options trading00:28:05 Formulating systematic trading plans and pricing implied volatility00:39:59 Aligning options strategy with individual psychological risk profiles00:42:57 Structural agility and liquidity capture in retail books00:55:42 Developing discretionary intuition through structured market observation01:01:12 The mechanics of spectacular portfolio blowups and scaling risks01:03:00 The macroeconomic foundations of edge and risk premia persistence

In this episode of Odds on Open, we dissect the mechanics of institutional alpha generation with Rich Falk-Wallace, founder of Arcana and former portfolio manager at Citadel. Rich explains why the vast majority of hedge fund failures stem from flawed portfolio construction and mismanaged risk leakage rather than a lack of fundamental insights. We dive deep into how elite multi-strat platforms mathematically isolate idiosyncratic alpha by systematically neutralizing ex-ante correlation and factor exposures across trading books. For PMs, quants, and allocators looking to understand market structure, this conversation provides a rigorous framework for evaluating the cost of volatility, managing crowding, and building attribute-perfect benchmarks that keep investment teams intellectually honest.The discussion also charts the structural shifts redefining the front office, tracing how sophisticated risk architecture is migrating from the back office directly into the hands of fundamental stock pickers and concentrated long-only asset managers. Rich offers an insider’s perspective on the secular evolution of buy-side talent, explaining how artificial intelligence and mock portfolio trackers are accelerating the career progression of junior analysts into active risk-takers. Finally, we analyze the current macro regime of capital allocation, detailing the ongoing fragmentation of hedge fund capital via separately managed accounts (SMAs) and why the future of active management belongs to firms that treat factor constraints with the same diligence as single-stock theses.00:00 Intro01:10 Defining the fundamental role of a hedge fund portfolio manager02:52 How top portfolio managers isolate alpha and avoid risk leakage09:57 A message from ONYX10:25 Risk management frameworks at elite multi-manager platforms18:17 The mechanics of neutralizing factor risk in multi-strat books27:33 Why institutional allocators are adopting advanced factor modeling tools38:57 How veteran investors adapt to modern systematic risk architecture44:48 Integrating factor constraints into fundamental portfolio construction processes55:29 Decomposing idiosyncratic returns in concentrated long-only portfolios01:05:34 The secular evolution and future of buy-side junior analyst roles01:15:52 Contrarian theses on multi-manager asset aggregation and market fragmentation01:19:42 Operational pitfalls and capital allocation mistakes in scaling funds

Apply to Onyx’s trading event here: https://www.onyxcapitalgroup.com/uni-studentsIn this episode of Odds on Open, former Two Sigma quant Omer Cedar joins host Ethan to deconstruct how top-tier quantitative hedge funds systematically aggregate discretionary signals to isolate pure alpha. Cedar reveals the inner workings of institutional alpha capture programs, detailing how premier multi-manager pods map the information propagation curve and exploit crowded consensus sentiment to capitalize on structural mispricings. The conversation provides a rigorous, finance-native breakdown of market microstructure, analyzing the statistical deltas between asset pricing and expert variant perceptions during high-surprise macro and corporate catalysts. For portfolio managers, quantitative researchers, and buy-side analysts, this discussion offers a masterclass in data validation, situational weighting, and the mechanics of separating idiosyncratic returns from passive factor premiums.The dialogue transitions into the future of market structure, exploring how the proliferation of generative AI and autonomous digital analysts will reshape liquidity and market efficiency across equities, commodities, and secondary private markets. Cedar delivers a framework-first outlook on the scaling hedge fund ecosystem, explaining how large language models alter competitive advantage by shifting the alpha premium from commoditized data crunching to proprietary context curation. Crucially, he exposes the most persistent behavioral pitfalls observed across sophisticated institutional desks, specifically unpacking how the conservatism bias hampers optimal sizing during initial portfolio construction. Whether you are an asset allocator evaluating systematic strategies or an MFE student analyzing modern trading frameworks, this episode delivers actionable insights into balancing algorithmic risk management with human judgment.00:00 Intro00:01:14 Why systematic quant models require discretionary human judgment00:06:27 A message from Onyx00:07:06 How Two Sigma engineered an institutional alpha capture pipeline00:13:25 Why extreme consensus sentiment creates contrarian trading opportunities00:18:53 Aligning buy-side and sell-side incentives through informational edge00:25:36 How to extract alpha from the information propagation curve00:34:54 Navigating analyst mean reversion and situational weighting00:39:08 How generative AI and digital analysts reshape alpha capture00:48:13 Why a fragmented hedge fund ecosystem ensures market efficiency00:56:17 How modern LLMs accelerate data validation and market entropy01:03:47 Overcoming the conservatism bias in initial portfolio construction

Apply to Onyx’s Junior Tech Graduate Scheme here: https://verichain.io/apply/0aa1debe-ac6c-452f-9421-da6cbf4a3e8cIn this episode of Odds on Open, former Citadel quant researcher Neel Somani breaks down the opaque market structure and alpha generation mechanisms driving institutional power and natural gas trading. Neel explores the foundations of competitive edge within power markets, detailing how transmission line congestion, binding physical grid constraints, and localized supply-demand dynamics create highly asymmetric, high-skew assets. The conversation dives into the operational reality of central research desks within multi-manager commodity platforms, focusing on how quants model weather variance, fuel costs, and thermal generation outages to inform relative-value basis trades. Neel also provides a masterclass on institutional risk management and portfolio construction during extreme tail-risk events, using the 2021 Texas freeze to illustrate how top PMs navigate position sizing and delta-neutral execution when illiquid power grids face catastrophic supply shocks.Shifting from liquid macro markets to the frontier of technology, Neel analyzes the massive infrastructure constraints and power demand scaling driven by AI data centers, outlining the site selection economics and temporary generation plays dictating the space. He evaluates the structural career opportunity cost of entering quantitative finance today relative to the AI paradigm shift, challenging junior talent and MFE students to build defensible technical moats in hardware and GPU kernel optimization. Finally, the discussion delivers a sharp variant view on venture capital valuation models, predicting a severe compressed pricing event for digital assets and software companies as low-switching-cost agentic architectures fundamentally disrupt traditional growth economics, customer retention metrics, and customer acquisition costs (CAC).00:00 Intro00:01:12 Quant researcher execution models within multi-manager hedge funds00:06:17 A message from ONYX00:07:35 How transmission line congestion drives alpha in power markets00:13:24 Capital intensity and managing risk profiles of high-skew assets00:19:19 Why commodity desks prefer domestic power over geopolitical oil risk00:22:55 Portfolio construction and risk mitigation during tail-risk freeze events00:31:36 Capitalizing on the physical infrastructure constraints of AI data centers00:36:25 How agentic architecture redefines software engineering and technical moats00:43:04 Quant career opportunity cost relative to the AI paradigm shift00:56:15 Variant views on venture multiples and agentic customer acquisition economics

Apply to Onyx’s Junior Tech Graduate Scheme here: https://verichain.io/apply/0aa1debe-ac6c-452f-9421-da6cbf4a3e8cIn this episode of Odds on Open, we sit down with Victor Haghani, co-founder of Long-Term Capital Management (LTCM) and founder of Elm Wealth, to dissect why institutional alpha frequently breaks down during the position sizing phase. While standard market commentary focuses heavily on asset selection, Haghani establishes that optimizing your risk-adjusted return is an independent, non-zero-sum discipline that dictates long-term survival. We explore the structural friction between expected value and compound return, the misapplication of the Kelly criterion by sophisticated Wall Street portfolio managers, and how treating variance as an internal financial fee reshapes quantitative portfolio construction and risk management.The discussion shifts to edge verification through Haghani’s famous "Crystal Ball" experiment, analyzing how elite macro traders and advanced LLMs process information asymmetry against historical market regimes. Designed for hedge fund analysts, quants, allocators, and advanced finance students, this section provides a rigorous framework for isolating compensated systematic risk from uncompensated idiosyncratic risk. We close with an actionable breakdown of how practitioners should mathematically model and discount their own human capital, offering a definitive blueprint for maximizing lifetime smooth capital accumulation without succumbing to high-volatility ruin.00:00 Intro01:27 LTCM: Why sizing matters more than selection03:00 Expected value vs. risk-adjusted value in portfolios05:15 Why sophisticated investors struggle with sizing bets08:20 The zero-sum reality of beating the market09:30 A message from Onyx10:35 Why most firms lack a risk-adjusted return rubric12:55 Risk as an internal "fee" in portfolio construction16:30 The math of sizing concentrated stock positions20:50 The hidden danger of high-volatility wealth22:20 "How to become a billionaire" is the wrong question27:25 Testing the "Crystal Ball" hypothesis with Wall Street Journal data34:55 How LLMs perform at macro trading games40:35 Can individual investors generate alpha sustainably?47:40 Solving for optimal sizing at Elm Wealth50:25 Risk limits for young investors and human capital57:55 How to estimate the value of your human capital1:02:45 Why changing minds on investing is nearly impossible1:07:35 The most critical factor for a stable wealth curve

Apply to Onyx’s Junior Tech Graduate Scheme here: https://verichain.io/apply/0aa1debe-ac6c-452f-9421-da6cbf4a3e8cDeWayne Louis (Versor Investments) returns to break down the part of the business almost no one explains: not generating returns, but raising the capital to scale them. After Versor pulled in half a billion dollars for an event-driven strategy with just two and a half years of track record, DeWayne walks through exactly how allocations from multi-managers and managed-account platforms actually get done — and why he argues raising capital is harder than making money.We get into the screening hurdles multi-strats apply (Sharpe thresholds, team, factor orthogonality), how to pitch a secretive pod without knowing its book, and the systematic, data-driven machine Versa built to quantify merger arb across 26 years of catalyst events. Then the conversation turns to the capital-raising playbook itself: external vs internal allocations, fee structures, fund-of-funds vs pods, and why branding and storytelling — not buzzwords like "uncorrelated," "quantamental," or "AI" — are what move an allocator from apathy to conviction.A sharp, tactical episode for emerging and mid-sized managers, allocators, and anyone trying to understand how capital really flows through the multi-manager ecosystem.00:00 Intro01:22 Sourcing capital from multi-strategy managed accounts06:45 Pitching alpha relative to common hedge fund factor exposures09:21 A message from Onyx09:55 Designing systematic models for fundamental event-driven catalysts14:25 The multi-manager due diligence and verification process20:14 Structural mechanics of internal versus external balance sheet allocations28:28 Portfolio transparency differences: Multi-managers versus fund of funds31:55 Why institutional branding is harder than generating returns37:01 Strategic branding mistakes made by emerging fund managers46:46 Applying systematic data frameworks to the capital raising process53:33 Quantifying risk profiles to match multi-manager attributes58:03 Why separately managed accounts dominate institutional allocations01:06:12 Operational skill sets that build robust asset management firms01:12:23 Retaining institutional capital through transparent variance communication

In this episode of Odds on Open, we deconstruct the evolution of alternative data and alpha generation with Matt Ober, former Head of Data Strategy at WorldQuant and Chief Data Scientist at Third Point. We dive deep into the institutional framework required to scale systematic trading strategies, the cultural friction of implementing quantamental processes within long-short equity pods, and the specific mechanisms used by portfolio managers to extract a variant view from massive datasets. From the factory-floor automation of PhD-led quant shops to the high-stakes risk management of activist fundamental funds, Matt reveals how market microstructure and data-driven edge define success in liquid markets.Explore the shift toward the "degenerate economy" and the rising institutional utility of prediction markets like Kalshi and Polymarket for hedging structured KPIs. We analyze the future of decision intelligence through the lens of LLMs and MCPs (Model Context Protocol), discussing how traders, analysts, and allocators can maintain differentiation in a regime of rapid alpha erosion. Whether you are optimizing portfolio construction, refining factor exposure, or seeking a competitive advantage in venture capital, this conversation provides a masterclass in leveraging social networks and information symmetry to secure uncorrelated returns.00:00 Intro01:18 The WorldQuant thesis: Mining alternative data for systematic alpha05:06 Integrating finance expertise into PhD-heavy quant factory pipelines07:00 Scaling the quant factory: Automation and the researcher pipeline09:46 Monitoring dataset performance: Risk controls and alpha decay11:07 A message from ONYX16:38 Quantamental shifts: Transitioning data strategy to fundamental funds22:29 Institutionalizing "Old Guard" firms via top-down data buy-in26:38 Networking for quants: Comparing Tulchinsky and Loeb’s sources of edge32:44 The Degenerate Economy: Prediction markets and the volatility of attention39:42 Non-consensus career bets: Why selling beta is a stickier strategy45:01 Sourcing venture alpha: Identifying exceptional founders and GTM wedges52:30 Institutionalizing prediction markets via structured KPI hedging01:00:39 How MCPs and LLMs democratize decision intelligence01:03:08 The single source of edge: Leveraging social network information

Apply to Onyx’s Junior Tech Graduate Scheme here: https://verichain.io/apply/0aa1debe-ac6c-452f-9421-da6cbf4a3e8cLeigh Drogen, CIO of Starkiller Capital, joins the podcast to dissect the mechanics of a market neutral DeFi strategy currently operating at a 4 Sharpe ratio. We move past surface-level crypto narratives to analyze the quantitative scoring of protocol risk, code provenance, and the identification of incentivized spreads in carry trades. Drogen outlines a rigorous framework for position sizing based on a 1% max-loss rule and explains how Starkiller modulates risk across market regimes to extract uncorrelated alpha while avoiding the pitfalls of unsustainable yield and "fuckery risk" in liquid digital asset markets.The discussion shifts to the persistent alpha of cross-sectional momentum and why Starkiller views block space as a commoditized asset, drawing parallels to the fiber optic glut of the late 90s. From the market structure of token unlock schedules to the evolution of prediction markets like Estimize and Polymarket, we explore the intersection of regulatory arbitrage and table selection. This episode provides institutional-grade insights into portfolio construction, trend following, and the risk management frameworks required to navigate the liquidity and volatility of the modern crypto regime.00:00 Intro01:09 Mechanics of a 4 Sharpe market neutral DeFi strategy03:24 Quantifying protocol risk and code provenance06:40 Case study: Exploiting incentivized spreads in carry trades09:51 A message from ONYX10:53 Three primary sources of alpha in liquid crypto markets14:28 Capacity constraints and institutional yield compression18:54 Position sizing via the 1% max loss rule21:38 Pro-cyclical returns and the risk modulation framework26:44 Compounding capital through trend following and cross-sectional momentum33:35 Why momentum is the only persistent behavioral alpha38:52 Why block space is worthless: The fiber optic analogy42:30 Mitigating "fuckery risk" and vampire attacks in shorts48:39 Extracting alpha from token unlock schedules and market structure51:20 Lessons from building Estimize and the SEC/ForceRank fight55:00 The Polymarket origin story: Arbitraging regulatory hurdles01:01:45 Career risk premia and the value of "eating shit"01:05:34 Table selection: Positioning your career on the right macro curve

Apply here: https://onyxcapitalgroup.com/uni-studentsGeorge Livadas, Portfolio Manager and founder of Peregrine Capital, joins the show to break down the mechanics of running a concentrated, defensive long/short strategy as a solo PM. We explore how George generates alpha by systematically avoiding "hedge fund hotel" crowding, focusing instead on microstructure edges within niche sectors like non-bank financials and packaging to maintain a variant view. George details his portfolio construction framework—balancing core quality compounders with tactical value—and explains the math behind delivering equity-like returns while maintaining a beta-adjusted net exposure of approximately 35%.The conversation shifts to the evolving market regime, specifically how the dominance of multi-manager pods has created liquidity opportunities for patient, independent traders to exploit short-term data noise. George shares his technical survival guide for short selling, from managing volatility in "fraud and fad" names to recalibrating his process following the 2021 SPAC boom. We conclude with a deep dive into macro risk management, discussing how to insulate a portfolio against geopolitical tail risks and the psychological discipline required to develop a professional PM skill set without a traditional institutional pedigree.How do you stay independent in a market dominated by pods?00:00 Intro01:18 Selection criteria and sizing for defensive longs06:04 Sponsor break: Onyx Capital Group anniversary event06:42 Portfolio construction: Balancing quality and value factors09:13 How the SPAC boom changed short-side portfolio construction13:42 Why nimbleness and independent thinking generate alpha20:06 Capitalizing on the short-termism of multi-manager pods28:18 Managing macro tail risks without being wrong-footed38:26 Why defensive strategies offer an "inverse pod" return stream42:04 How to develop a professional PM skill set54:37 Circle of competence: Identifying disastrous longs and shorts

In this episode of Odds on Open, TD Quant Matt Schrager discusses the microstructure of municipal bond market making and the technical challenges of extracting alpha from illiquid fixed income instruments. We analyze the shift from low-latency HFT frameworks to the probabilistic modeling and statistical pricing required for securities with fragmented liquidity. Matt details the mechanics of systematic inventory management, risk-adjusted P&L optimization, and the cultural integration of elite proprietary trading teams within institutional balance sheets.Schrager outlines a variant view on finding edge in "ugly," inefficient markets, focusing on the structural opacity of private credit and the electronification of commodities. The discussion covers the evolution of market efficiency, the role of LLMs in credit due diligence, and recruiting strategies for resilient quantitative talent. This episode provides actionable insights for hedge fund analysts, quants, and PMs on the relentless process required to maintain a competitive advantage in evolving market regimes.00:00 Intro00:01:29 Announcing OOO's Newest Sponsor00:02:20 Liquidity and latency differentials in the municipal bond market00:06:37 Probabilistic modeling and statistical pricing for low-frequency instruments00:10:50 Adapting HFT simulation and backtesting to illiquid fixed income00:20:33 Systematic inventory management and risk-adjusted P&L optimization00:27:36 Transitioning proprietary trading culture into a global bank infrastructure00:34:10 Scaling electronic market making into commodities and investment-grade credit00:41:24 Identifying edge in gnarly and inefficient corners of the market00:45:23 Structural opacity and the liquidity evolution in private credit00:56:21 Why elite trading organizations prioritize relentless process over magic01:04:16 Recruiting for resilience and the velocity of fundamental improvement01:11:02 How AI-native skillsets redefine talent in liquid market regimes

In this episode of Odds on Open, we go deep into the mechanics of edge, credibility, and the structural evolution of the hedge fund industry. Host Ethan sits down with Tom, a veteran Quant PM formerly of Tudor Investment Corp and Moore Capital, to deconstruct what separates the top-tier "pod shops" from the bottom 40% of funds that fail to preserve capital.Tom challenges the common perception of market randomness, arguing instead for a deterministic view of market structure where alpha is captured by modeling participant incentives rather than just price action. We discuss the "Unified Field Theory of Finance," the operational reality of running a billion-dollar book, and why the most dangerous trap for a PM is the "gamma trap"—trading steady returns for catastrophic tail risk.00:00 Intro01:18 Building institutional credibility for early-stage managers03:01 The Pareto distribution of hedge fund returns04:25 Applying the Unified Field Theory of Finance to fair value08:14 Trading against human incentives in a deterministic market13:54 Why allocators don’t steal alpha from prospective PMs18:26 Organizational advantages and risk management in pod shops25:16 Evaluating career edge in quantitative finance for 202630:48 Paul Tudor Jones and the art of game selection33:42 Analyzing the economic viability of starting a new fund35:16 Identifying common retail pitfalls: Mean reversion and arbitrage38:55 Why there hasn't been a new trading idea in 15 years43:22 Case study: Building NLP systems and managing strategy decay50:33 Managing tail risk: Physics vs. deterministic financial distributions55:33 Identifying the gamma trap in short-volatility strategies59:10 Career pathing for PMs after a fund blow-up1:07:53 SBF and FTX: Credibility vs. the "Founder-Genius" archetype1:13:44 Establishing proof-of-concept through audited multi-year returns

Sean Emory of Avery discusses the evolution of edge in liquid markets, specifically how to leverage alternative data—from App Store analytics to digital exhaust—to identify fundamental inflection points before they are reflected in the price. We dive deep into Sean’s underwriting process, exploring how institutional investors can use granular data sets to track thesis confirmation and identify a margin of safety in real-time. This conversation provides a technical breakdown of how to separate signal from noise in a market regime increasingly dominated by ultra-short-term microstructure and passive flows.

Sean also breaks down his approach to portfolio construction, comparing the risk-return profiles of highly concentrated strategies versus diversified books. He explains why his firm prioritizes "the Six Ms" over standard volatility metrics to mitigate the risk of permanent capital impairment, offering a variant view on traditional risk management. The discussion concludes with the operational realities of the active ETF landscape, the impact of generative AI on market efficiency, and the psychological discipline required to maintain alpha when storytelling and euphoria distort traditional valuation frameworks.

In this episode of Odds on Open, quant trader Scott Phillips joins the pod to break down why crypto remains "the dumbest market in the world" and a goldmine for systematic edge. We dive deep into table selection and why the lack of institutional competition allows for Sharpe ratios exceeding 2.0 through basic trend following and momentum strategies. Phillips explains the mechanics of market inefficiencies, from the reflexivity of on-chain liquidity to the alpha found in tracking price-insensitive buyers and VC exit liquidity. For hedge fund analysts and quants, this is a masterclass in identifying liquid market anomalies that TradFi has long since arbitraged away.The conversation shifts to the technicalities of portfolio construction and risk management within the "dark forest" of DeFi. Scott details his transition from click trading to launching Hyper Trend, a tokenized on-chain hedge fund executing mid-frequency crypto strategies on Hyperliquid. We explore the microstructure of funding rates, the carry trade, and how to model counterparty risk when dealing with exchange-specific incentives and North Korean state actors. Whether you are a PM focused on factor analysis or a trader looking to exploit mean reversion in altcoins, this episode provides a raw, credibility-forward look at capturing beta-neutral returns in the world’s most volatile regime.00:43 Table selection and the math of competitive alpha06:21 Why basic trend following yields outsized Sharpe in crypto08:49 Why market inefficiency persists despite institutional inflows14:58 Price insensitive buyers: Cults, VCs, and North Korean hackers17:17 Factor analysis and the size-decay effect in shitcoins25:40 The structural edge in mid-frequency crypto strategies32:43 Tokenized DeFi vaults and on-chain hedge fund governance40:43 Designing a robust portfolio: Equal weighting vs. MVO44:21 Sourcing alpha from ghost chains and VC exit liquidity49:58 Exploiting market maker contracts and post-listing drift53:55 Operational alpha: Managing margin and manipulated funding rates01:01:13 Shifting from quant to CEO: Identity fluidity and mastery01:11:28 How to bridge the mentorship gap with elite traders01:22:38 Building network triads: The secret to compounding social capital01:29:23 Why 10x goals require total identity transformation

Get 10% off on Fundamental Edge: https://www.fundamentedge.com/odds-on-open-podcastIn this episode of Odds on Open, Ethan Kho sits down with Brett Caughran, founder of Fundamental Edge and a former Portfolio Manager at elite Tiger Cub and Multi-Manager (MM) firms.As generative AI and agentic workflows commoditize the "desktop research" layer of investing, the bar for generating idiosyncratic alpha has never been higher. Brett breaks down the specific frameworks—including ETIC (Everything There Is To Know) and the Focus 5—that top-tier pods use to identify mispriced securities and isolate key drivers before they are priced in by the market.We dive deep into the market microstructure shifts caused by the rise of indexers and factor-based quants, explaining why increased volatility is a gift for fundamental investors with the stomach for Bayesian updating. Brett also provides a roadmap for the "New Junior Analyst," shifting the focus from manual model-cranking to high-leverage primary research and AI orchestration.00:00 Intro01:29 Frameworks for developing a differentiated variant perception05:16 Financial drivers vs. narrative cycles: The Focus 5 framework08:29 Analyzing the stock vs. business: Bayesian updating in public markets12:52 AI as an intellectual power tool vs. consensus "alpha slop"17:21 Accelerating the hunch-to-hypothesis pipeline with AI sniff tests21:52 The evolution of junior analysts: From data entry to primary research28:46 Why market microstructure and behavioral alpha prevent index efficiency34:48 New meta-skills: Debugging models and the expectations gap muscle38:44 Training junior analysts: Earning the right to use power tools44:34 High-value workflows: CEO credibility analysis and guidance tracking48:28 LLMs as orchestration tools for human primary research54:55 Teachable scientific process vs. revealed investment judgment57:54 Common threads across Multi-Managers, Single Managers, and Tiger Cubs59:49 Curiosity as a meta-skill and the art of system thinking

In this episode of Odds on Open, Ethan Kho sits down with Derek Pilecki, founder of Gator Capital Management, to deconstruct his 20%+ annualized track record in the financial sector. While many generalist PMs view financials as a "sleepy backwater" or overly complex, Derek explains how he extracts alpha from regional banks, brokerages, and insurance companies by identifying fundamental business changes before they are reflected in the tape.The conversation moves from the microstructure of bank underwriting in a post-Dodd-Frank regime to the practicalities of portfolio construction, including why Derek has expanded his concentration from 25 to 40 names and his strict discipline against "averaging down" on losers. We also dive into the private credit narrative, the actual risk of systemic leverage in non-bank financials, and how generative AI is shifting the valuation multiples of moaty info-service businesses like Morningstar and FactSet.00:00 Intro01:06 Derek's +21% annualized return track record02:50 Fundamental business change vs market noise in Robinhood05:25 Portfolio construction: Concentration limits and adding to winners09:09 Sourcing alpha and identifying three-year doubles in financials12:44 Developing edge through repetition and management team cycles14:16 Why the post-GFC regime fundamentally changed bank underwriting17:07 Assessing tail risk and leverage in the private credit market21:23 AI-driven market dispersion and identifying moaty businesses24:11 Why shareholder base turnover matters for timing broken charts25:57 AI disruption vs trust-based moats in financial services29:37 Integrating AI into fundamental research and SEC filing analysis32:31 Scaling regional bank positions and managing liquidity constraints35:39 Risk management: Permanent capital loss vs mark-to-market volatility37:12 Capacity constraints: Optimizing for returns over AUM scale44:14 Behavioral edge and avoiding the "degree of difficulty" trap50:39 Career risk and the reality of active money management54:18 Breaking into the industry via public stock write-ups

In this episode of Odds on Open, we analyze the technical architecture of the data science layer within fundamental hedge funds. Guest Matei Zatreanu, founder of System2, discusses the tension between generative AI and the search for outlier-driven alpha. We move beyond the hype of LLMs to discuss the practicalities of expert network automation, the causal mapping of second-order macro effects, and why the most successful PMs treat their investment process as a craft rather than a business operation. The conversation also explores the structural shift from single-manager funds to multi-manager platforms and the specific incentive alignment strategies used to retain quant talent in high-stakes environments.(00:00:00) Intro(00:00:53) Talent constraints and outlier detection in the data science layer(00:05:38) LLM customization: Differentiated alpha vs. the consensus echo chamber(00:10:18) Automating the mosaic: AI interview agents and qualitative data synthesis(00:20:33) Mapping causal relationships and second-order macro effects via graphs(00:26:33) Curiosity as the ultimate constraint for information-rich investors(00:31:43) Multi-manager platforms vs. the rise of independent single managers(00:37:58) Solving incentive alignment and analyst retention via internal fund-of-funds(00:44:03) Managing negative network effects and custom research one-offs(00:48:33) Whale hunting: High-ticket pricing and the billionaire value mindset(00:54:58) Zero-to-one incubation: Leveraging unique market access for business spin-outs(00:59:08) Romanian roots to billionaire circles: Mentorship and aiming high(01:07:48) PM as "Doctor": Why founders prioritize craft over business operations

This episode was filmed on Thursday, March 12, 2026.

Greg Newman, founder and CEO of Onyx Capital Group and the largest liquidity provider in oil derivatives markets, on what it actually looks like to run a market-making book when liquidity breaks down entirely. What most participants misunderstand about oil vol is that the outright price — Brent, WTI — is a proxy, not the market; the real information lives in time spreads, regional diffs, and niche contracts that only a handful of firms have visibility into. Why fair value discovery in a dislocated market requires abandoning automation, reverting to manual process, and using physical market participant behavior — refiners, producers, airlines — as a real-time signal rather than a lagged one.

Greg co-founded Onyx Capital Group in 2016 after a decade trading crude and refined products across increasingly niche oil derivatives contracts. Onyx built its position by stepping into the vacuum left by banks exiting commodities post-Volcker, becoming the dominant liquidity provider across European, Middle Eastern, and Asian oil markets with an estimated 20–50% market share in several key contracts. The firm now operates market-making desks across London, New York, Dubai, and Singapore, and has expanded into data services, a single-dealer platform, retail brokerage, and physical trade finance — building a vertically integrated oil markets infrastructure business from a pure prop-trading foundation.

In this episode we cover:

•⁠ ⁠Why trading oil outrights during the dislocation was a losing game — and where the real edge was

•⁠ ⁠Fair value discovery on Sunday night: how Onyx priced contracts when every historical model broke

•⁠ ⁠Physical market reflexivity: how refiners, producers, and airlines all become forced actors at key price levels

•⁠ ⁠Geopolitical signal extraction: options open interest and off-hours order flow as an information edge over Polymarket

•⁠ ⁠Regime-break risk: why government intervention in exchange mechanisms is the tail risk that keeps Greg up at night

•⁠ ⁠Countercyclical talent investment and why Onyx's worst years built its best crisis infrastructure

•⁠ ⁠From prop shop to platform: data, single-dealer, retail brokerage, and credit as extensions of liquidity edge

•⁠ ⁠Why Onyx is building toward a hedge fund — and why track record discipline is holding them back

Timestamps:

00:00 Intro

00:48 Oil market volatility: making sense of the dislocation

04:05 Outright vs. spread positioning: where the real edge was

05:10 How Onyx manages process when liquidity breaks down

10:18 Pricing fair value on Sunday night with no precedent

16:48 Physical market participants and the reflexivity of hedging behavior

20:22 Prediction markets as an information signal — and why Onyx stopped using them

25:26 Options flow as the real tell for informed geopolitical positioning

28:17 What it feels like running a global market-making book through a crisis

33:05 Regime-break risk: when exchange mechanisms themselves fail

36:25 Countercyclical investment in talent and infrastructure

42:20 From prop shop to liquidity infrastructure: building a durable valuation

48:50 Why Onyx is building a hedge fund — and what's holding them back

57:50 Media, brand, and market disruption as compounding assets

01:05:47 The most surprising thing after 14 years of building Onyx

Legendary poker champion, decision scientist, and author of "Thinking in Bets," Annie Duke deconstructs the mechanics of decision-making under uncertainty, shifting the focus from high-variance outcomes to the rigor of positive expectancy and robust process. Leveraging her background in professional poker and cognitive psychology, Duke explores how loss aversion and resulting—the cognitive trap of equating outcome quality with decision quality—can degrade a trader's edge and lead to suboptimal portfolio construction. The conversation moves beyond theory into the practical application of base rates, reference classes, and mental time travel to combat temporal discounting, providing a masterclass for quants, PMs, and analysts on how to refine their probabilistic worldview and neutralize the noise of short-term volatility.00:00 Intro01:12 Defining bets as resource allocation under uncertainty04:52 Positive expectancy vs. outcome-based evaluation06:11 Resulting: Why outcomes are not proxies for decision quality15:19 Calculating expected value in high-variance career paths18:55 Moving from implicit intuition to explicit decision modeling24:27 Using base rates and reference classes for startups30:26 Psychological traits of elite risk takers and traders31:33 How prospect theory and loss aversion distort risk45:12 Deconstructing gut feel and the role of intuition49:36 Evaluating optionality and impact in fast-moving environments57:13 Mental time travel: Tools for managing temporal discounting01:01:31 Quantifying the intersection of luck and hard work01:04:43 Internalizing a probabilistic worldview for long-term edge

Zachary A. Levitt joins the pod to break down the architecture of a capacity-constrained multi-manager platform designed to harvest high alpha loads in niche, idiosyncratic markets. We dive deep into portfolio construction beyond the "Big Four" pod model, focusing on inverse-volatility weighting, discretionary risk overlays during regime shifts, and the mechanics of screening for relative value arbitrage strategies with minimal factor exposure. Zach explains his transition from a data-driven biotech alpha capture book to running a center book, detailing how he identifies micro-regime persistence and manages the microstructure of a lean, performance-aligned firm. This conversation is a masterclass for allocators and quants on building a non-correlated return stream by targeting the liquidity gaps and specialized incentives that larger, multi-billion dollar funds are forced to ignore.00:00 Intro01:02 The primary constraint for a young multi-manager03:13 Screening for niche strategies and consistent track records06:03 Maximizing idiosyncratic P&L through relative value arbitrage08:19 Tactical sizing and capturing micro-regime persistence12:43 Balancing inverse-vol weighting with discretionary risk overlays15:41 Case study: Rebalancing small-cap L/S during market corrections17:37 Distilling signal from noise in multi-manager portfolio oversight22:02 Coachability and removing emotion from the PM feedback loop25:52 Alpha capture in biotech via options market data30:20 Scaling the boutique multi-manager business model34:02 Disrupting the "Big Four" pods with capacity-constrained strategies42:21 Unit economics of a lean, performance-driven platform53:09 LP management and optimizing the business development funnel1:00:19 Moving from portfolio management to operational process efficiency1:05:10 Future of the industry: Consolidation vs. niche boutiques1:08:53 Roadmap for launching a niche multi-manager fund

Check out Carbon Arc here: https://www.carbonarc.co/

Kirk McKeown, founder and CEO of Carbon Arc and former senior investor-facing operator across Glenview and Point72, on how alpha migrates as market structure, tooling, and competition evolve. What most investors misunderstand about “edge” is that it is rarely static and often lives in process design, information capture, and interpretation of small narrative inflections. Why hit-rate systems, decision trees, and data structure matter now as models commoditize and the marginal advantage shifts toward differentiated inputs and synthesis.Kirk started his career at Tudor Investments during the late-1990s cycle, then worked at Glenview Capital under Larry Robbins where he built and led primary research capabilities supporting a concentrated, long-horizon portfolio process. He later spent 8.5 years at Point72 supporting a multi-manager environment optimized around catalyst-driven, variant-view investing, high at-bat volume, and repeatable organizational process. Across these seats, he worked directly with investment teams on improving idea generation, hit-rate, and conviction through compliant information collection, supply chain and value chain work, and rigorous feedback loops.

In this episode we cover:- Why alpha “moves” over time and how competitive advantage migrates with market structure and tooling- Hit-rate vs slugging frameworks across concentrated portfolios and multi-manager platforms- A research function’s only mandate: lift idea flow, hit-rate, or conviction without contaminating decision-making- Building edge via compounding domain knowledge, field research, and leading indicators before consensus data prints- “Main Street becomes Wall Street”: model-driven decisioning, data decimalization, and pricing data like a utility- Inventory as the core causal variable behind boom-bust cycles in fundamentals and supply chains- Factor frameworks as a scaling mechanism for research: market structure, business model, and decision-tree priors
Timestamps:(00:00) Intro(04:47) Tutor vs Glenview vs Point72: how edge differs(12:29) How to build “lift” for PMs: at-bats, hit-rate, sizing(18:44) Building research edge: outwork, read, fieldwork(27:16) Personal moat in 2026: analogs, history, decision trees(40:08) “Main Street becomes Wall Street”: what that actually means(44:30) Carbon Arc thesis: “decimalization” of data market structure(46:43) Why the edge migrates to data plus domain context(51:00) How to win in commoditized research: sample size beats anecdotes(01:03:26) Factorizing everything: themes, market structure, business models(01:08:37) Pruning decision trees: signals, scale points, inventory dynamics(01:14:18) Contrarian 2026 take: hedge funds launching enterprise AI labs(01:23:32) Final question: one habit to build career alpha

Follow Kirk McKeown:LinkedIn – https://www.linkedin.com/in/kirk-mckeown-400607214/

My Substack: https://ethankho.substack.com/

Alfonso Pecatiello — known as "Alf" and founder of The Macro Compass and founder of Palinuro Capital, a macro hedge fund— joins Ethan Kho to break down the frameworks behind global macro trading, real economy money creation, and what it truly takes to build a macro hedge fund from the ground up.

Alfonso Pecatiello spent years as a senior portfolio manager at ING overseeing a multi-billion dollar fixed income portfolio before founding Palinuro Capital. In this episode, Alf shares the macro investing edge that drives his process: why central bank QE and bank reserves are largely irrelevant to real economic outcomes, how commercial bank lending and government fiscal deficits are the true engines of money creation, and why tracking the second derivative of real economy money printing is one of the most powerful signals in global macro trading today.

But Alfonso Pecatiello doesn't stop at markets. The Macro Compass founder opens up about the brutal reality of launching a macro hedge fund with no seed money, no GP stake deal, and an 80% industry failure rate. He shares the moment Palinuro Capital nearly didn't survive — and the risk management mindset that carried him through.

This episode covers global macro trading strategy, hedge fund position sizing, portfolio diversification, tail risk management, factor-neutral mandates, and the real process behind founding a hedge fund from scratch.

If you're interested in macro investing, hedge fund careers, global macro strategy, money creation, central bank policy, or fund management — this is essential listening.

Former Susquehanna International Group (SIG) Head Trader Andrew Courtney breaks down the reality of being a quant trader and market maker at one of the world's elite proprietary trading firms. He reveals what trading floors actually look like—multiple monitors covered with flashing numbers, signals, and price movements that traders analyze all day with zero lunch breaks and constant attention on market microstructure. 

Andrew explains how SIG's legendary poker training culture shapes traders' ability to think probabilistically, make decisions under uncertainty, and justify every bet both quantitatively and qualitatively. He shares candid insights about who should (and shouldn't) pursue trading careers, the transition from floor trading to electronic markets, and how the tight-knit network at prop trading firms differs dramatically from consulting or investment banking paths.

Andrew now runs Kalshinomics, a prediction markets analytics tool, and writes The Whirligig Bear on Substack where he analyzes opportunities in Kalshi, Polymarket, and emerging prediction market platforms. He goes deep on finding edge in prediction markets—from identifying inefficient markets with liquidity incentives to using ChatGPT and AI tools for handicapping obscure Grammy categories. 

Andrew explains market efficiency frameworks, how to assess who you're trading against, and why some markets (like low-volume Grammy categories) offer better opportunities than hyped meme markets. He also tackles the casino-ification of America debate, insider trading concerns in prediction markets, and whether these platforms are a net good or bad for society.

We also talk about...

• The real day-to-day of quant trading and market making at SIG: staring at screens all day, monitoring signals, and staying alert for when markets go off the rails
• Why SIG's poker training program—playing for hours daily, turning over cards after every hand, and defending each decision quantitatively—builds world-class traders
• How thinking in bets becomes second nature and why Andrew now frames every decision (like private school vs public school) as an expected value calculation
• The cultural differences between floor trading (loud voices, physical presence in the pit) versus upstairs electronic trading (surrounded by sharp peers and data)
• Why prop trading careers build narrow, dense networks compared to consulting or investment banking, and what that means for long-term career optionality
• Finding edge in prediction markets: liquidity incentives, identifying who you're trading against, and why some markets are wildly inefficient

• Trading strategy and bet sizing: when to use Kelly criterion, how to scale into positions, and Bayesian updating based on how the market reacts to your trades

• The insider trading debate in prediction markets and why Andrew thinks it's corrosive to incentives, trust, and long-term market quality
• Risk transfer opportunities: using prediction markets for insurance-like hedging (Florida hurricane risk, California earthquake exposure) rather than pure speculation
• Whether prediction markets are good for society: the value of probabilistic news context versus the risk of casino-ification and degenerate gambling
• Career advice for aspiring traders: evaluating if you can handle constant screen time, limited networks, and high-variance outcomes
• How to apply expected value thinking to everyday life: insurance decisions, risk tolerance, and when not to over-optimize (don't EV calculate marriage)
• The future of prediction markets: institutional adoption, regulatory uncertainty, and whether amateurs can still compete before professionals crowd out edge
• Why Kalshinomics focuses on analytics and custom interfaces for serious traders rather than trying to be the "Bloomberg Terminal" of prediction markets
• Lessons from SIG on decision-making, probability, and building systems that extract signal from noise in high-frequency, high-stakes environments

Sinan Xin manages an emerging markets tech hedge fund from New York, investing across China, Latin America, Southeast Asia, and beyond. In this conversation, he shares how he builds edge in some of the world's most volatile markets.

We discuss:

• Why conviction can become bias—and how to tell the difference
• Building durable relationships across geographies you're not from
• The evolution of edge: from reading 10-Qs at the library to AI
• Why understanding your own behavior matters more than any tool
• How to think about career decisions when everyone's chasing the same thing
• Portfolio construction strategies for managing emerging market risks
• Why the best English-speaking management teams often underperform

Sinan explains how his background—born in China, raised in the US, working in tech M&A at Lehman Brothers before it collapsed—shaped his investment philosophy. He reveals how standing up a dropshipping website taught him about e-commerce software, why he visits cattle farms at 4am, and how private market relationships help him spot public market inflection points.

The conversation turns personal as we explore career alpha vs. beta. Sinan pushes back on the idea that smart people should simply pick "the most liquid market" (like AI today), arguing that true edge comes from self-knowledge, not chasing prestigious outcomes.

For anyone thinking about investing, careers, or how to build differentiated views in efficient markets, this is a masterclass in independent thinking.

In this episode, hedge fund manager Alix returns to Odds on Open to tackle what he calls the most important problem facing young investors today: the complete loss of analog training skills that created the greatest investors of previous generations. Alix runs a hedge fund that deliberately avoids AI tools for analysts, believing they're "extremely dangerous" because they optimize analysts in ways that sub-optimize fund performance. He breaks down why creativity comes from constraints, not abundance—why getting to consensus faster with AI actually makes you worse at generating alpha. The core issues: Gen X is the last generation trained with analog tools, the "junior-senior problem" where hedge funds realize they don't need junior analysts anymore, the attention span crisis, and why Silicon Valley executives don't let their kids use the products they built. Alix introduces the "10K and a pencil" approach, inspectional reading techniques to extract 80% of a book's value, frameworks from Charlie Munger and Peter Kaufman, Art of War principles applied to predator-prey business dynamics, and why reading trains every critical investment skill—pattern recognition, visualization, reading between the lines, leaps of judgment.

Disclaimer: This is not a financial promotion and must not be seen as advice, but only as an educational piece

Cláudia Quintela has spent 25 years connecting early-stage hedge fund managers with institutional capital. She's worked across FX, macro, and systematic strategies at State Street, UBS, Morgan Stanley, and Blenheim Capital, one of the world's largest commodity managers at its peak.

In 2017, she founded Vibe Advisors, an independent advisory boutique focused exclusively on helping emerging managers—particularly systematic, CTA, and macro funds—navigate the hardest part of launch: raising that first $50 to $200 million when you have limited track record, tight capacity constraints, and institutional investors demanding day-one infrastructure.

She specialises in the messy reality of early-stage fundraising: fee pressure, seed negotiations, managed account structures, positioning for allocators who need to sell your strategy internally, and translating complex quant models into language that gets you through the door. Her client base skews heavily toward liquid macro and model-driven managers.

Today, Cláudia runs a portfolio career: advising fund managers and investors, writing weekly about entrepreneurship and capital raising, hosting webinars on AI tools for investor relations and marketing automation, and speaking on panels about women in finance. She's an advocate for the sisterhood and believes the next generation of emerging managers will look different from the last.

Based in London and originally from Porto, she holds an MSc in Finance from LSE and is a CFA charterholder. She's here to talk about what actually works when you're trying to raise institutional capital at the hardest stage—and how emerging managers can build smarter, leaner operations using the tools that didn't exist when she started.

In November 2025, I hosted a fireside chat at Columbia University with Deepak Gurnani, founder of Versor Investments, a $1.4 billion [1] quantitative hedge fund based in New York with offices in New York and Mumbai. Deepak spent two decades at Investcorp, where he built and led the firm’s hedge fund division.

In 2013, he stepped away to found Versor with a singular goal: to build a research-driven quantitative firm focused on leveraging alternative data. This conversation is a continuation of the story we began on Odds on Open with Nishant Gurnani and DeWayne Louis, two of Versor’s partners. In that episode, we explored the systematic strategies that Versor runs. In this fireside chat, we go upstream to understand how it all began.

We talk about:

- Deepak’s journey from IIT to Citigroup to Investcorp

- How the hedge fund industry looked in the 1990s versus today

- What it really takes to spin out and build a quant firm from scratch

- Why Versor adopted cloud computing and alternative data years before most peers

- How small firms compete with giants like Citadel, Millennium, and Jane Street

- What Deepak looks for when hiring researchers

- Why “value proposition” is the starting point for any new fund

- The mindset required to build something that lasts

Versor LinkedIn Page: https://www.linkedin.com/company/versorinvestments/

Research Repository (“Athenaeum”): https://www.versorinvest.com/athenaeum/

1. Data as of December 31, 2024. AUM as per SEC definition for the purposes of item 5F on the ADV Part 1a. For important disclosures, please visit: https://www.versorinvest.com/terms-and-conditions/

Ethan Kho interviews David Orr, a former professional poker player turned hedge fund manager. They discuss David's journey from poker to founding Militia Capital, his investment philosophy, and the lessons learned from both industries. David shares insights on risk management, the importance of finding asymmetric bets, and the challenges of the hedge fund industry. He also offers advice for aspiring investors and reflects on the future of hedge funds.

Discover how top multi-manager hedge funds like Citadel and Millennium attract the brightest minds, the evolving talent strategies in the industry, and the unique traits that set successful candidates apart. Whether you're an aspiring finance professional or just curious about the inner workings of hedge funds, this episode offers valuable insights into the competitive landscape of financial services.

What’s the difference between prediction markets trading and equities trading? On Odds on Open, the world’s #1 prediction markets trader Domer explains how prediction markets work as a form of information-based trading, where news and signals can arrive at any moment, forcing continuous price discovery and repricing. Unlike stock markets, where returns often depend on long-term growth, valuation multiples, and market beta, prediction market strategy focuses on information timing, news flow, and market reaction to new data. Rather than forecasting final outcomes, traders focus on event-driven trading, short-term price movement, and probability trading, exploiting mispriced probabilities and trading event contracts instead of holding positions to resolution. This approach allows traders to generate expected value (EV) and highlights the difference between active trading vs passive investing.Domer also explains how many participants concentrate on high-volume headline markets, while traders look for prediction market edge in event contracts trading across smaller markets. On platforms like Polymarket and Kalshi, opportunities exist in alternative markets and micro events that are less crowded and prone to pricing errors. By specializing in specific market categories and focusing on liquidity, volume, and time horizon, traders can adjust position sizing and holding periods to match their edge. This approach mirrors quantitative trading and event-driven strategies, where domain knowledge and execution outperform broad speculation.Other subjects discussed...How prediction markets trading focuses on short-term price movement and active trading rather than holding event contracts to resolution.Prediction market strategy is based on exploiting mispriced probabilities to generate expected value (EV).Prediction market edge is most common in micro markets and sub-events with lower liquidity and attention.Event contracts trading rewards traders who identify information-driven repricing before markets adjust.Information-based trading in prediction markets reacts to discrete news rather than continuous market noise.Probability trading requires distinguishing mean reversion from true regime shifts after breaking news.Losses in prediction markets are often caused by crowded trades and poor position sizing, not direction.Position sizing must scale with edge and uncertainty to preserve long-term expected value (EV).Platforms like Polymarket and Kalshi allow large traders to temporarily distort prices.Capital concentration in alternative markets can create opportunity for smaller traders.Long-term success depends on repeatable decision-making rather than individual outcomes.Prediction markets exhibit less random variance than equities because prices move on information.Poker develops risk tolerance and variance management applicable to prediction markets trading.Regulation is likely to limit influenceable event contracts while allowing large markets to grow.

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How do top hedge funds actually hedge trades? At SIG, traders were often told not to hedge. Most assume elite trading firms hedge every position, but Susquehanna (SIG) built its edge by avoiding hedging when trades had positive expected value (EV). Kris Abdelmessih—who later moved into portfolio management roles in energy-derivatives trading outside SIG—explains the firm’s model: centralized risk, large position size, and no hedging unless residual exposure distorted EV or created excessive P&L variance. He shows how this framework let SIG capture options market making edge, tighten spreads, and outcompete firms that hedged mechanically.This episode is definitely a new treat for viewers, linking options pricing, trade sizing, and execution to concepts used in algorithmic trading, alpha generation, portfolio optimization, multi-strategy hedge funds, pod shops, and even sports betting edge.We also discuss...How early exposure to the Amex options pits revealed structural options market making edge from wide bid-ask spreads and fragmented exchanges.Why observing senior traders shaped his understanding of expected value (EV), risk of ruin, and edge compounding in proprietary trading.How late-1990s retail flow and weak market microstructure created abnormal options edge before competition increased.The shift from easy trading to tougher environments as order flow toxicity rose and spreads tightened.Why measuring growth in trading comes from recognizing past mistakes, skill vs. luck, and the paradox of skill.SIG’s belief that markets are mostly efficient and why uncovering inefficiency requires serious labor, pattern recognition, and deliberate practice.Why traders must assume customer order flow is informed, reinforcing humility and disciplined risk models.How SIG’s structured recruiting and education flywheel accelerated mastery of options pricing and liquidity provision.Lessons from working with elite performers like Jason McCarthy and seeing extreme competitive drive and work capacity.Why outlier PMs excel across domains, linking athletic intensity to portfolio management and hedge fund strategies.How choosing between discretionary and systematic trading requires understanding personal strengths and cognitive style.Why young traders must tune out status games in high-prestige quant trading, pod shops, and multi-strategy hedge funds.How moving from SIG to a Chicago prop trading firm introduced him to backer models, P&L splits, and strict risk budgets.Managing natural gas options risk through inventory risk, vega exposure, and shifting volatility regimes.How designing options-themed games for his kids teaches expected value, open outcry trading, and real-world decision-making.

In this episode, former Tower Research and Flow Traders quant Annanay Kapila breaks down the reality of high frequency trading, HFT strategies, and top automated trading systems. He explains what quant researchers actually do inside elite firms—where world-class math talent competes in a true zero-sum market. Annanay goes deep into market making strategies, latency engineering, alpha generation, and how quant trading teams iterate models nonstop to stay competitive. It’s one of the clearest looks at quantitative finance, execution risk, and the real mechanics behind profitable automated trading systems.Annanay also gives a rare inside look at Tower Research crypto trading during the 2022 crypto winter, breaking down crypto trading strategies, API latency edge, and structural weaknesses across early exchanges. He shares first-hand insights from the FTX collapse explained—how withdrawals froze, how APIs failed under stress, and how liquidity vanished in real time. These experiences shaped how he thinks about exchange design, risk controls, and the future of fintech startups. All of this leads into QFEX, a new platform aiming to bring crypto-style perpetuals to traditional assets with real infrastructure and institutional-grade reliability—what Annanay calls “FTX without the fraud.” We also cover hedge fund strategies, automation, and the next generation of trading platforms.We also talk about:How high frequency trading, low latency trading, and market microstructure really work inside Tower and FlowThe two schools of quant trading: Chicago-style traders vs MIT-style systematic tradingHow firms build market making strategies, order book dynamics models, and short-horizon alpha signalsWhy HFT strategies have low capacity and how firms try to scale into longer-term systematic tradingThe shift from pure spread capture to predictive statistical arbitrage and directional signalsWhy prop firms want to launch hedge fund strategies and fee-based businesses like Citadel and Two SigmaWhat quant researchers actually do: feature engineering, execution algorithms, and avoiding overfitting in machine learning for tradingHow QFEX is built: exchange architecture, matching engines, and API latency for 24/7 automated trading systemsHow Annanay hires: identifying real contributors, evaluating technical depth, and filtering candidates for quantitative finance rolesInside Tower Research crypto: crypto trading strategies, API edge, and managing exchange riskFTX collapse explained: liquidity freezes, failed withdrawals, halted APIs, and real-time risk management failuresThe future of trading: fintech startups, algorithmic trading, perpetual futures, and next-generation investing platforms

Former Citadel MD Michael Watson gives a direct look inside a multi-strategy hedge fund and what it was like working under Ken Griffin. He breaks down Citadel’s pod-shop model, its high-intensity capital allocation frameworks, and how Ken built a concentration of talent across quant trading, quant research, engineering in quant, discretionary equities, Python/C++, data engineering, and alternative data. Michael explains how Ken’s recruiting philosophy and performance expectations turned Citadel into a benchmark for the entire hedge fund industry.He also outlines the reality of day-to-day portfolio management and hedge fund engineering at scale: long hours, translating PM mental models into production systems, and building infrastructure for systematic investing. Michael discusses compensation dynamics, pass-through economics, and the skills required for tech jobs in quant trading, breaking into quant finance, and high-impact quant research roles. He closes with how Hedgineer — an all-in-one technology stack for hedge funds he launched after Citadel — uses generative AI for finance to bring multi-manager-grade technology, research pipelines, and analytics to emerging managers.We also discuss...How Citadel evaluates talent across quant trading, discretionary equities, data engineering, and front-office technology within modern hedge fund infrastructureThe role of engineering in quant and why understanding PM mental models is as important as writing code for consistent edgeDeep technical dives into Python internals, performance bottlenecks, and production-grade data pipelines for researchThe difference between systematic investing and discretionary research, and how engineers support both through advanced risk modelingWhy multi-managers dominate through capital allocation frameworks, diversification, and risk controls in pod-based structuresThe economics behind pass-through compensation, PM slope, and how front-office compensation is structured inside the pod-shop modelHow Citadel organizes research, data, and infrastructure across pods in the pod-shop modelThe benefits and limitations of alternative data and how PMs convert domain expertise into alpha generationThe rise of generative AI for finance and its impact on research, risk, portfolio analytics, and model deploymentHow Hedgineer deploys forward-engineered technology stacks for hedge funds and emerging managersBuilding knowledge graphs, MCP servers, unified data pipelines, and research platforms for portfolio management and risk modelingCareer advice for breaking into quant finance, quant engineering career paths, and building leverage through engineering skillsUnderstanding allocator sophistication vs. multi-manager scale in modern hedge-fund ecosystemsThe tension between consolidation (Citadel/MLP/Baly) and the democratization of quant tooling for smaller managersHow startups and emerging managers use AI, infra modernization, and research automation to compete with large multi-managersWhy tech jobs in quant trading increasingly require both business literacy and deep engineering intuitionLessons from eight years inside Citadel’s operating model, performance expectations, and front-office compensation structuresWhat separates elite PMs and analysts in quant research, idea generation, and alpha generationHow Python for finance careers gives engineers leverage across research, analytics, and production systems

What’s it like being a trader at SIG? At Susquehanna International Group, Todd Simkin has trained some of the world’s best traders using poker strategy, probabilistic games, and decision-making under uncertainty that mirror real-world quantitative trading. In this episode, Todd breaks down how SIG teaches trading interns Bayesian updating, asymmetric information, market microstructure awareness, and communication under pressure. From reading opponents at the poker table to interpreting order flow as a market-making trader, he explains how these game-theoretic models build trading intuition, strengthen probabilistic judgment, and sharpen the edge required for systematic trading and derivatives trading.Todd also dives into the traits that distinguish exceptional traders from simply intelligent ones — humility, truth-seeking, and the ability to update beliefs quickly when new information arrives. He explains how SIG screens for these qualities in interviews, what it’s like working or interning at SIG, and why technical skill alone isn’t enough for options pricing or market-making. Todd breaks down how real meritocracy works inside a flat trading desk, how traders collaborate to refine ideas, and how the best quants learn to think critically, debate openly, and iterate their decision process rather than operate alone.How market-making works at SIG and how traders interpret order flow, liquidity, and real-time signalsWhat Bayesian updating looks like in practice during a live trading sessionHow trading systems, not just individuals, drive performance in modern quantitative tradingThe structure of SIG’s pods and how traders collaborate inside a flat trading deskWhy communication and idea-sharing matter more than hierarchy in quantitative researchInsights into SIG’s interview process, including probabilistic reasoning, game situations, and ambiguity testsWhy SIG prioritizes truth-seeking culture and “attacking ideas, not people” in decision-makingWhat it’s like interning at SIG and how long-term projects reveal real trading aptitudeHow SIG evaluates technical skill vs. judgment vs. adaptability in new hiresWhy systematic trading requires parameter tuning, model monitoring, and rapid belief updatesHow traders combine options pricing, market microstructure, and private information to form an edgeThe role of sports trading, insurance risk, and prediction markets inside SIG’s broader ecosystemHow SIG thinks about risk transfer, volatility events, and pricing uncertaintyWhy Susquehanna moves into new businesses only when it can be best-in-classThe philosophy behind SIG’s expansion into prediction markets (Kalshi, PolyMarket, etc.)The economics of risk indemnification, NIL deals, promotions, and event-driven insuranceHow traders apply game-theoretic optimal reasoning beyond poker — in pricing, hedging, and model design

John Knorring spent over a decade on the Goldman Sachs trading floor, leading natural gas trading through the 2000s—a period defined by trading in a financial crisis, Hurricane-driven volatility, the Amaranth blow-up analysis, and trading during 2008 when bank desks had to price massive option books overnight. He explains how bank trading desks, pit traders, handwritten tickets, and early prop trading shaped risk management in trading, how hedge fund risk systems evolved under stress, and why trading psychology mattered in fast-moving energy commodities trading.John then breaks down the transition to electronic markets, the rise of algorithmic trading, and how the broader electronic trading evolution compressed spreads but expanded opportunity for strong discretionary trading strategies. He contrasts Goldman’s flow-driven environment with DRW trading strategies, explains why some investment strategy decisions still require human judgment in regime shifts, and shows how his commodities background led to building Green Tiger Markets—a new platform transforming the Philippines energy market.We also discuss...Hedge fund trading on early bank trading desksHurricanes, volatility spikes, and the Amaranth blow-upPricing massive books during financial crisis tradingOpen-outcry pits, voice execution, and price discoveryHow Goldman built risk systems for huge positionsFundamentals of natural gas trading and energy marketsStorage cycles, weather models, and pipeline flow dataHow paradigm shifts shape trading psychologyEvolution of algorithmic trading and market microstructureWhen bid-ask compression increased trader P&LWhy discretionary traders lost edge to commodity algosLessons from discretionary vs systematic trading careersThe path from Goldman to DRW prop tradingBuilding Green Tiger Markets for PH electricity hedgingHow electricity forward markets unlock investment in emerging economies

How do you start a hedge fund—and where should you launch it? Daniel Xystus has done both. From Los Angeles quant to Chicago portfolio manager to CIO in Hong Kong and the Middle East, Daniel now helps new hedge fund managers navigate fund setup, regulation, and operations. We break down what it really takes to launch a hedge fund—choosing your fund domicile, building professional infrastructure, and avoiding the operational mistakes that quietly kill most funds. Daniel explains how fund structures like Cayman, UCITS, and Singapore’s VCC differ, and why getting operations, compliance, and risk management right often matters more than alpha generation itself.We also explore how global macro and quantitative trading strategies adapt across regions—from Asia ex-Japan markets to Dubai and Abu Dhabi investment funds. Daniel breaks down how Asia hedge funds deal with high shorting costs, liquidity issues, and regulatory complexity, and why Middle East family offices are emerging as powerful allocators. From Hong Kong’s finance hub to the rising Singapore hedge fund industry, Daniel shares lessons from running billion-dollar books and advising allocators worldwide—and what aspiring quants should understand about risk, execution, and building something durable in global markets.We also discuss...Why most hedge funds fail because of operational issues, not bad tradesHow to pick the right hedge fund domicile for your investorsWhat to know about hedge fund regulations and compliance when launching a fundCommon hedge fund mistakes made by first-time managersHow to evaluate fund administration, legal structure, and prime broker supportThe real difference between long-only, market-neutral, and global macro investingHow liquidity, FX exposure, and regional risk shape Asia ex-Japan strategiesWhy Middle East family offices are allocating more to alternative investmentsHow quant funds integrate portfolio construction, risk models, and execution systemsBuilding quantitative trading strategies that survive real-world transaction costsThe role of backtesting strategies in validating hedge fund modelsWhat global allocators look for before investing in Asia hedge fundsThe rise of the Dubai finance hub and Singapore hedge fund industryHow Hong Kong’s finance hub is evolving post-COVIDCultural and regulatory differences between running funds in the U.S., Asia, and the Middle EastLessons from Daniel’s transition from astrophysics to finance and global fund management00:00 Intro & special request01:49 How to start a hedge fund02:49 Why hedge funds fail operations and structure04:29 Common hedge fund mistakes new managers make05:49 Hedge fund operations and regulation explained07:19 Asia hedge funds shorting costs and liquidity08:49 Quantitative trading strategies and backtesting systems10:19 Choosing your fund domicile Cayman vs VCC12:19 Hedge fund structure explained for allocators13:49 Launching a fund in Asia ex-Japan markets15:29 Portfolio construction and risk management insights17:19 Building an Asia-focused long short strategy18:49 Emerging markets liquidity Philippines case study20:49 From astrophysics to quant hedge fund career23:19 Running billion-dollar portfolios across global markets24:49 Global macro investing in Asia and MENA26:49 Inside Hong Kong’s post-COVID finance hub28:49 Dubai and Abu Dhabi investment fund growth31:19 Middle East family offices and capital flows33:19 Comparing hedge fund regulation across regions34:49 Dubai and Abu Dhabi as finance centers36:49 Cost of living and taxes for quants38:49 Best cities for hedge fund opportunity40:19 Quant trading lessons on risk and psychology42:49 Closing thoughts building global hedge funds

Doug Garber, former Citadel hedge fund analyst and Millennium Management portfolio manager, joins the show to unpack what it’s really like inside two of the world’s top multi-manager hedge funds — and how each approaches portfolio construction, risk management, and hedge fund culture. Drawing from his years working at Citadel and working at Millennium, Doug explains why Citadel operates more like a finely tuned multi-strategy fund — centralized, structured, and process-driven — while Millennium functions more like a decentralized network of entrepreneurial pods designed for uncorrelated alpha generation. He breaks down how each environment shapes hedge fund analysts and PMs, how competition and transparency fuel performance, and what it takes to thrive in the high-performance world of hedge fund careers.We also dig into the fundamentals of long/short equity investing and hedge fund strategies — from building variant views through deep equity research and mastering the stock picking process, to balancing market neutral strategies with conviction-driven ideas. Doug shares how the best PMs train analysts, manage exposure, and develop consistent alpha generation through disciplined feedback loops and a data-informed financial markets education. We also discuss...The Citadel vs Millennium comparison: centralized discipline vs decentralized autonomy in multi-manager hedge fundsHow sell-side to buy-side transitions build domain expertise for hedge fund analystsWhy deep equity research and sector mastery are the foundation of a strong stock picking processUnderstanding IDEO (idiosyncratic risk) and how top PMs manage exposure in long/short equity portfoliosThe role of risk models, factor exposure, and quantitative overlays in multi-strategy fund frameworksHow hedge fund culture and competition drive performance and shape hedge fund careersThe differences in risk management philosophy between Citadel’s structured systems and Millennium’s entrepreneurial freedomThe analyst–PM relationship: communication, credibility, and building trust inside a hedge fund analyst teamLessons from alpha generation failures — avoiding blow-ups through discipline and post-mortem learningWhat it takes to move from analyst to PM: curiosity, resilience, and ownership of the stock picking processHow working at Citadel trains risk awareness vs how working at Millennium empowers independent thinkingBuilding market neutral strategies that hedge factor risk and emphasize true alpha generationWhy grit and curiosity can matter more than pedigree in landing top hedge fund careersBalancing ambition, burnout, and family — Doug’s reflections on life after multi-manager hedge fundsHis new podcast Pitch the PM: real hedge fund industry insights and financial markets education for the next generation

How do you find trading edge in electricity markets? Cory Paddock, co-founder of GBE, explains how real alpha generation in power trading comes from anticipating paradigm shifts before the market sees them. In a renewable energy trading market shaped by constant regime change—coal replaced by gas, wind and solar reshaping grid topology, and data centers driving new load volatility—edge belongs to those who read the grid, not the price charts. His approach blends energy infrastructure insight with algorithmic trading discipline: track locational marginal prices, study market data pipelines, and build conviction around where power will actually flow. In fast-moving electricity markets, where historical data decays quickly, the strategy is simple—trade clean, understand risk management deeply, and position early for the next market shift.Cory’s incredibly bullish on Gen Z in quant finance. He’s betting on Gen Z quants. They’re Python- and LLM-native, fluent in building tools and models that turn raw market data into live trading infrastructure. Their exposure to open-source research and self-directed learning creates a new kind of trader—one who codes faster, questions conventions, and finds alpha in overlooked niches of energy and power trading. At GBE, he builds an environment where Gen Z trading talent can experiment, own ideas, and learn risk management through real positions, not simulations. The result is a new generation of algorithmic traders redefining what edge means in modern markets.- Building a trading strategy for electricity markets and finding edge through data-driven alpha generation- Anticipating paradigm shifts in markets and adapting trading models to regime change in power trading- How renewable energy trading and grid congestion reshape price discovery and risk management- Designing a market data pipeline for real-time energy infrastructure analysis and trading execution- Why electricity markets differ from traditional quant finance and what makes power trading unique- Using algorithmic trading frameworks to process market data and identify short-term dislocations- Risk management frameworks for volatile energy markets and five-minute tick data decision-making- Recruiting Gen Z trading talent fluent in Python, machine learning, and market data engineering- How Gen Z quants approach trading edge differently—experimentation, automation, and fast iteration- Structuring incentives for traders to align P&L ownership, discipline, and long-term performance- The psychology of running a trading firm with personal capital and managing downside risk- Why historical backtests fail in energy markets due to infrastructure evolution and topology change- Market structure and locational marginal pricing (LMP) as the foundation of energy trading strategy- How physical constraints in grids create alpha opportunities for quantitative trading teams

Can you start a hedge fund as a college student? Christina Qi, co-founder of Domeyard, did—and later built Databento, a modern market data API used by top algorithmic trading and quantitative trading teams. We get into how high-frequency trading (HFT) actually works, why clean order book/tick market data matters for robust trading strategies, and how a product-led model beats “talk-to-sales.” Christina shares what it takes to compete with Bloomberg/Refinitiv, where AI in finance is headed, and how better data unlocks faster research, reliable execution, and scalable quantitative trading workflows.Christina also breaks down hedge fund fundraising as a first-time manager—what allocators look for, how to structure fees/lockups/redemptions, and why your track record is everything. We talk about 2025 algorithmic trading: easier tools, tougher alpha, and how to find edge with high-quality market data, disciplined backtesting, and strong risk management. She closes with career advice for aspiring quants: master market structure, build real trading strategies in Python, and apply machine learning trading where it truly adds value—not as hype, but as part of a rigorous AI in finance toolkit.We also discuss...

• Founding Domeyard in college and turning a summer strategy into an HFT hedge fund
• Using high-frequency trading to attract day-one allocators in hedge fund fundraising
• Why a verifiable track record matters more than terms when raising capital
• How to set fees, lockups, and redemptions as a first-time manager
• When investor relations and performance diverge and how to keep LPs during drawdowns
• Why Domeyard shut down and the scalability limits of HFT
• Building Databento as an API-first market data/market data API platform for algorithmic and quantitative trading
• Solving data licensing and usage rights with clean tick data, order book data, and better market microstructure coverage
• Competing with Bloomberg and Refinitiv by focusing upstream on raw market data (not dashboards)
• Winning with product-led growth and self-serve checkout instead of talk-to-sales
• A bottom-up purchase at a major AI company as proof that PLG works for market data APIs
• Adoption by options market makers, quant funds, and AI in finance teams for research, alternative data, and NLP for markets use cases
• Cheaper backtesting and better trading infrastructure but tougher alpha generation in 2025
• A public roadmap and user upvotes to prioritize datasets that matter to quants and quantitative trading workflows
• Advance commitments that de-risk new exchange integrations and ensure day-one usage
• Incumbents copying features as validation that Databento leads in market data APIs
• The AI-in-finance arms race and why data quality decides machine learning trading, risk management, and Sharpe ratio outcomes
• How macro conditions change fundraising outcomes for startups and hedge funds
• Career advice for aspiring quants: learn market structure/market microstructure, data engineering, rigorous backtesting, portfolio construction, and build real trading strategies