Odds on Open

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 priorsTimestamps:(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 alphaFollow 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 railsWhy SIG's poker training program—playing for hours daily, turning over cards after every hand, and defending each decision quantitatively—builds world-class tradersHow thinking in bets becomes second nature and why Andrew now frames every decision (like private school vs public school) as an expected value calculationThe 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 optionalityFinding edge in prediction markets: liquidity incentives, identifying who you're trading against, and why some markets are wildly inefficientTrading 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 tradesThe insider trading debate in prediction markets and why Andrew thinks it's corrosive to incentives, trust, and long-term market qualityRisk transfer opportunities: using prediction markets for insurance-like hedging (Florida hurricane risk, California earthquake exposure) rather than pure speculationWhether prediction markets are good for society: the value of probabilistic news context versus the risk of casino-ification and degenerate gamblingCareer advice for aspiring traders: evaluating if you can handle constant screen time, limited networks, and high-variance outcomesHow 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 edgeWhy Kalshinomics focuses on analytics and custom interfaces for serious traders rather than trying to be the "Bloomberg Terminal" of prediction marketsLessons 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 differenceBuilding durable relationships across geographies you're not fromThe evolution of edge: from reading 10-Qs at the library to AIWhy understanding your own behavior matters more than any toolHow to think about career decisions when everyone's chasing the same thingPortfolio construction strategies for managing emerging market risksWhy the best English-speaking management teams often underperformSinan 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 pieceCláudia 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 lastsVersor 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.

❗ACCESS OPPORTUNITIES EXCLUSIVE TO OOO VIEWERS: https://app.youform.com/forms/e2jpsj4z ❗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

How do top quantitative trading firms use generative AI?  @versorinvestments , a $1.4B[1] quantitative investment boutique in the asset management industry, reveals how human ingenuity drives its AI-powered investment research and machine learning in finance pipeline. Partners DeWayne Louis and Nishant Gurnani explain how they combine supervised machine learning, natural language processing, and alternative data—from credit card receipts to job postings—to generate investment insights and forecast returns across global equity markets. We discuss why strong quant trading strategies start with clean data, how to avoid data-mining traps, and why top quantitative researchers think like market scientists, not model-builders.We dive into Versor’s flagship hedge fund strategies, from its quant merger arbitrage framework that predicts competing bids to its global equities tactical trading (GETT) strategy capturing dislocations in global equity markets. Nishant and DeWayne unpack what “positive convexity” means in practice, how to design market-neutral quant trading strategies uncorrelated to CTAs, and how Versor’s 30-year research lineage from Investcorp reflects true capital markets innovation. They share lessons on quant research culture, hiring IIT-trained talent, and how disciplined portfolio construction and human-guided AI define the next generation of machine learning in finance and algorithmic trading.We also discuss...How alternative data investing drives alpha in the modern AI quant hedge fund ecosystemBuilding models for event-driven investing strategies and predicting competing bids in merger arbitrage hedge funds – read more here.How Versor’s managed futures strategy achieves diversification and positive convexity investment performanceIdentifying global dislocations through global equity index futures trading and relative value signalsConstructing market-neutral portfolios through advanced market neutral quantitative strategies – read more hereWhy Versor’s success as a research-driven hedge fund comes from blending data science with human intuitionTurning unstructured data in finance — from job postings to credit card data — into tradable insightsDesigning an algorithmic trading platform that scales across multiple asset classes and geographiesApplying machine learning hedge fund strategies to model complex market behaviorsHow disciplined portfolio construction quant strategies optimize risk-adjusted returnsThe evolution of data-driven investing hedge funds and how AI is reshaping portfolio managementThe future of quant talent recruitment in finance and why deep research skills beat brainteasersLessons from 30 years of capital markets innovation and systematic alpha generationFuture of AI in hedge funds — read more here: https://www.linkedin.com/pulse/quant-intel-agentic-ai-quantitative-investing-versorinvestments-cygxf/ Why human-guided AI remains critical in building resilient, high-Sharpe machine learning hedge fund strategies

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