Odds on Open

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

Why do smart investors lose money? Alix Pasquet, Managing Partner of Prime Macaya Capital Management, breaks down the paradox at the heart of hedge fund investing psychology—why high IQ often hurts investors more than it helps. Drawing on decades of experience allocating to top quant funds and running capital, Alix explains how hedge fund managers fall into classic strategy mistakes, why competing against other smart people is a losing game, and how temperament, meta-rationality, and emotional intelligence determine long-term returns. He shares lessons from poker, backgammon, and behavioral finance investing, showing how overconfidence, overfitting, and complexity bias cause even the most analytical investors to underperform—and what it really takes to develop a resilient hedge fund manager mindset that consistently outperforms.We also dive into how AI in finance 2025 is changing the rules of the game. Alix argues that the rise of LLMs and financial markets automation is amplifying investor laziness and creating “fantasy stocks,” where hype replaces deep work. He reveals how algorithmic trading and AI are reshaping competition, why quant fund blowups from 2007 still hold lessons today, and how complexity and systems thinking in markets help investors avoid repeating those same errors. From overreliance on automation to cognitive bias in quant funds and artificial intelligence, Alix explains how to adapt your process—combining analog judgment, data discipline, and humility—to truly understand how hedge funds make money and how smart people keep losing it.- Why smart investors lose money and how behavioral finance explains repeated hedge-fund blow-ups- Cognitive biases in investing and how even seasoned managers misread probability and risk- Investor temperament and success: why emotional discipline matters more than IQ or pedigree- Risk management lessons from hedge funds drawn from two decades of allocation experience- Quant finance insights from studying how data access, cleaning, and market impact shape alpha- Quantitative trading psychology and what separates disciplined quants from over-fit models- Why quants lose money: the hidden behavioral alpha that algorithms can’t replicate- Market microstructure investing and how execution, liquidity, and leverage drive performance- Complexity and systems thinking in markets—how to simplify chaotic systems into tradable edges- Behavioral alpha in quant strategies: exploiting human errors embedded in data- Intelligence vs wisdom investing: when deep knowledge clouds judgment and kills returns- IQ traps in decision making that cause overconfidence and portfolio blow-ups- Intellectual arrogance in hedge funds and how meta-rationality builds long-term humility- Generative AI in markets and how narrative feedback loops distort valuations- AI amplifying investor mistakes: when automation removes human judgment- Machine learning investing: where predictive models add value—and where they fail- Data-driven investing strategies and the limits of backtesting without context- Automation in portfolio management and the danger of delegating conviction to code- Network theory in investing: building multiple networks to uncover leading indicators- Analog training vs digital distraction: why reading, reflection, and deep work still create edge- Emotional self-regulation for investors—habits, routines, and recovery to sustain performance- Lessons from poker and backgammon for investing: strategy, variance, and position sizing- Mentorship and triads networking strategy—how to create compounding social capital- How to build diverse networks for success across geography, sector, and generation- Stoicism and finance mindset: developing calm under uncertainty and volatility

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 fundUsing high-frequency trading to attract day-one allocators in hedge fund fundraisingWhy a verifiable track record matters more than terms when raising capitalHow to set fees, lockups, and redemptions as a first-time managerWhen investor relations and performance diverge and how to keep LPs during drawdownsWhy Domeyard shut down and the scalability limits of HFTBuilding Databento as an API-first market data/market data API platform for algorithmic and quantitative tradingSolving data licensing and usage rights with clean tick data, order book data, and better market microstructure coverageCompeting 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-salesA bottom-up purchase at a major AI company as proof that PLG works for market data APIsAdoption by options market makers, quant funds, and AI in finance teams for research, alternative data, and NLP for markets use casesCheaper backtesting and better trading infrastructure but tougher alpha generation in 2025A public roadmap and user upvotes to prioritize datasets that matter to quants and quantitative trading workflowsAdvance commitments that de-risk new exchange integrations and ensure day-one usageIncumbents copying features as validation that Databento leads in market data APIsThe AI-in-finance arms race and why data quality decides machine learning trading, risk management, and Sharpe ratio outcomesHow macro conditions change fundraising outcomes for startups and hedge fundsCareer advice for aspiring quants: learn market structure/market microstructure, data engineering, rigorous backtesting, portfolio construction, and build real trading strategies

Should you invest in the S&P 500, or look for smarter ways to beat the market? Jason Hsu, Co-Founder of Research Affiliates ($159B AUM) and now CIO of Rayliant, explains why simply buying the index or asking “should I invest in ETFs” isn’t enough. In this episode, he breaks down smart beta vs S&P 500, systematic investing, and how factor investing strategies and fundamental indexing can deliver some of the best long-term investment strategies for investors who want to know how to beat the market beyond traditional index funds.Asian markets are less efficient than the US, Jason says. With higher retail speculation, governance risks, and volatility, opportunities open up for quant investing through Asian ETFs, China stock market investing, and emerging markets investing strategies that capture inefficiencies. As CIO of Rayliant, Hsu shows how his team builds factor-based portfolios across China, Japan, Korea, Taiwan, and other emerging markets to turn inefficiency into alpha.We also cover:- How Jason Hsu cofounded Research Affiliates, scaling systematic strategies to manage $159B AUM- Launching the PIMCO All Asset Fund in 2002 and bringing multi-asset investing strategies to retail investors- The origin of smart beta ETFs and why fundamental indexing offers a better alternative to cap-weighted indexes- How the tech bubble exposed flaws in traditional indexing and set the stage for factor investing strategies- Why governance factors and valuation discipline are especially important in emerging markets- Building Rayliant’s smart beta 2.0 products using multi-factor models and machine learning in investing- How factors in investing reveal the “nutrients” of a portfolio for long-term compounding- The difference between risk premia and behavioral biases as drivers of factor returns- Examples of behavioral investing mistakes in Asia and how professionals can capture alpha from retail flows- Why low-frequency quant strategies align better with pension funds and sovereign wealth funds than high-frequency trading (HFT)- The future of quant investing explained: machine learning, non-linear models, and portfolio construction- Jason’s career advice for young professionals navigating the hedge fund and asset management career path00:00 Intro01:42 Founding Research Affiliates and early startup days03:02 Launching the PIMCO All Asset Fund in 200204:26 Smart beta ETFs explained and how they started09:19 Spinning off Rayliant and focus on Asia10:26 Why Asian markets are less efficient than the US11:43 Opportunities from inefficiency and alpha in China13:38 Gambling analogy and retail speculation in Asia16:53 Liquidity challenges in smaller emerging markets20:41 Rayliant’s product offerings and smart beta 2.020:57 What factors reveal about markets and portfolios23:34 Risk premia vs behavioral biases in factors25:39 Governance, valuation, and smart money factors in Asia28:27 Using machine learning in Rayliant’s strategies34:05 Can discretionary managers still have edge today38:39 Poker, luck, and systematic investing advantages41:00 Future of discretionary managers and pod firms42:44 Are high-frequency trading firms sustainable long term46:22 Rayliant’s mission and value to society50:00 Career advice for young finance professionals53:14 Closing thoughts