Odds on Open

❗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

Can you trade the stock market with AI? Yes: Renee Yao launched Neo Ivy Capital, a billion-dollar** AI hedge fund that uses AI in trading and investing to generate alpha. In this episode of Odds on Open, she explains how she built a quant hedge fund from scratch, scaling to over $1B AUM** with advanced AI hedge fund strategies that adapt to markets in real time and show how to trade stocks with artificial intelligence at scale.Unlike traditional firms that rely on armies of quant researchers and static machine-learning models, Renee (who used to work as a QR Analyst at Citadel and Portfolio Manager at Millennium) reveals how machine learning in trading has evolved into true self-learning AI. She breaks down why most funds still depend on crowded factor bets, and how her fund’s approach delivers uncorrelated returns — a real edge in the hedge fund career path and a blueprint for the future of systematic investing.**Note: According to a recent Form ADV filing, Neo Ivy Capital oversees about $1.02 billion in assets under management. This figure represents total regulatory AUM, which is broader than the ~$313M reported in 13F-disclosed securities and may include additional holdings or leverage.We also discuss...- Citadel hedge fund strategy and risk management lessons after 2008- Why diversification and breadth of edge matter in a quant hedge fund explained- How Neo Ivy uses AI in trading and investing to generate uncorrelated returns- Why machine learning in trading has evolved into true self-learning AI- The three barriers to entry for AI hedge funds: modern AI, infrastructure, portfolio design- Why large funds rely on crowded factor bets while Neo Ivy delivers pure alpha- How fund size impacts scalability and alpha opportunities- What it’s like moving from Citadel and Millennium to founding a fund- How to start a hedge fund and build infrastructure from scratch- How self-evolving AI models adapted during COVID market shocks- The role of modern tools like LSTMs and transformers in AI hedge fund strategies- Career and life lessons from the hedge fund career path and staying disciplined00:00 Intro01:14 Renee Yao’s journey to founding Neo Ivy02:28 Joining Citadel after the financial crisis04:13 Hedge fund diversification and breadth of edge04:45 Why Neo Ivy trades with AI strategies07:50 How self-learning AI adapts to markets09:40 Causation vs correlation in AI hedge funds10:33 Barriers to entry for AI hedge funds14:47 Risks of crowded factor bets explained16:39 Why big funds struggle with AI talent17:29 From PM at Citadel to hedge fund founder18:47 Challenges of launching a quant hedge fund20:25 Biggest constraint for AI hedge fund startups22:08 How AI hedge funds adapted during COVID24:04 Modern AI tools used in quant trading25:13 Building hedge fund infrastructure from scratch26:26 Career advice for aspiring quants and traders28:55 Adapting career goals to changing job markets31:57 Life lessons from trading and risk management32:51 Staying disciplined while running a hedge fund34:38 Obsession and belief in AI hedge funds35:41 Closing thoughts on hedge funds and life

In this episode of Odds on Open, Ethan Kho sits down with Joe Mezrich, Founder of Metafoura LLC and former Managing Director at Nomura Quant Strategies, to reflect on nearly 40 years in quant finance. Joe’s career spans the early days at Salomon Brothers—where he helped pioneer factor models, risk modeling, and even early machine learning in finance—through senior sell-side research roles at Morgan Stanley, UBS, and Nomura.Joe shares how the sell side effectively built modern factor investing, why models like the Barra risk model failed in crises such as the Tech Bubble (2000) and the Quant Crisis (2007–08), and how market-neutral strategies and algorithmic trading continue to shape today’s buy-side. He also explains why interpretability, from CART decision trees to today’s LLMs for trading, is critical for robust risk management.We cover:- Origins of quant finance on the sell side at Salomon Brothers.- Early factor models, the Barra risk model, and portfolio risk modeling.- Use of robust statistics and CART decision trees in machine learning for finance.- Why risk models failed in the Tech Bubble (2000) and Quant Crisis (2007–08).- Growth of market-neutral strategies and interaction between sell-side research and the buy side.- Crisis lessons: liquidity concentration, model speed, and explainability.- Evolution of factor investing into overlays and ETFs.- How quant researchers balance complexity vs. interpretability with LLMs for trading.- Role of alternative data, point-in-time datasets, and data visualization in alpha.- Wall Street culture: Liar’s Poker-era Salomon, Morgan Stanley, UBS, Nomura.- Impact of interest rates, earnings vs. sales growth, and macro regimes on factors.- Sustainability of multi-manager pod shops (Citadel, Millennium) and implications for quants.- Career lessons: curiosity, humility, and finding beauty in quant models.Whether you’re a quant researcher, an aspiring algorithmic trading professional, or an allocator seeking to understand systematic funds, give this a listen.00:00 Intro and Episode Overview00:46 Origins of Quant Finance at Salomon Brothers02:56 Early Factor Models and Barra Risk Model05:51 Robust Statistics and CART Decision Trees08:58 Machine Learning in Finance 1990s Experiments12:06 Why Risk Models Failed in Tech Bubble15:31 Lessons from the 2007 Quant Crisis18:51 Rise of Market Neutral and Sell-Side Research22:26 Evolution of Factor Investing to ETFs26:01 Balancing Complexity and Explainability for Quants29:16 Alternative Data and Point-in-Time Datasets32:46 Wall Street Culture Salomon Morgan UBS Nomura38:08 Interest Rates Macro Regimes and Factor Drivers41:51 Are Multi-Manager Pod Shops Sustainable?46:04 What Makes Exceptional Quant Researchers Last49:26 Curiosity Humility and Risk Management52:56 Finding Beauty in Quant Models and Data56:16 Final Lessons from 40 Years in Quant Finance

Former Cargill Global Trading & Risk Management Director Kristine Engman Hochbaum explains how commodities trading strategies and quant trading strategies really work. On Odds on Open, she breaks down the sources of trading edge and alpha generation in today’s commodities markets, from agriculture trading to energy and metals.We cover why physical vs. financial commodities trade differently, how systematic trading and traditional players (hedge funds vs. ABCDs) approach markets, and the lessons of the 2022 commodities boom. With Ethan, Kristine unpacks real-world risk management strategies around delta, liquidity, forecast accuracy, and headline shocks — from Russia–Ukraine war trading impact to weather, inflation, and supply chain disruptions.Key topics in this episode:- How relationships and brokers create trading edge in commodities- Why capital allocation matters for building positions- Headline risk and the impact of social media on commodity prices- Managing liquidity risk and delta in physical books- Weather risk and forecast accuracy in agriculture and energy trading- The role of supply chain disruptions in commodities markets- Differences in hedge fund vs. ABCD trading strategies- Lessons from the Russia–Ukraine war trading impact and inflation and commodities- Stories from real trades: negative power prices and long oil during crisis- Career lessons: what makes a great trader and how to keep learning commodities marketsAlong the way, she shares what separates good traders from great ones, why inflation and commodities are inseparable, and how to keep learning in fast-changing markets. If you’ve ever wondered what makes a great trader or how to start learning commodities markets, this episode is for you.00:00 Intro01:15 Edge in commodities trading? Relationships, capital, information04:40 Commodities market efficiency, information flows, and AI08:32 Hedge funds vs. ABCDs, commodity trading strategies13:34 When commodities outperform equities, the 2022 boom17:45 Headline risk, social media impact on markets19:08 Risk management strategies in physical commodities trading26:14 Probability, forecasting, and scenarios for trading decisions31:00 What makes a great commodities trader today37:53 Contrarian trading strategies, alpha generation in commodities42:24 Russia–Ukraine war impact on commodity markets, trading45:35 Life and career lessons from commodities trading51:30 Careers, uncertainty, and learning in commodities markets

This week, Ethan Kho sits down with Dr. Ernest P. Chan — former quant at Millennium and Morgan Stanley, and now founder of PredictNow.ai and QTS Capital Management. Ernie is one of the best-known voices in quant finance, author of Quantitative Trading, and a pioneer in systematic trading strategies.We cover:- When machine learning trading models work in markets — and when they fail- Why financial markets suffer from data sparsity, and how regime shifts and black swan events in finance break models- How quants use AI in trading for risk management and portfolio optimization- The promise of LLMs for financial markets and how generative AI can overcome data scarcity- Semi-supervised learning explained, with real examples from analyst reports and Fed speeches- Where quants can still find alpha generation when new technologies become widely available- How PredictNow helps banks and hedge funds apply AI risk management at scale- Lessons from launching QTS Capital and running independent quant trading strategies such as crisis alpha- The role of alternative data in hedge funds and what actually drives performance post-2008- What it was like working alongside quants at Millennium, Morgan Stanley, Credit Suisse — and how Renaissance Technologies influenced Ernie’s career- The traits that make a great quant, and why creativity still matters in quantitative trading strategies= Advice for students and professionals entering quant finance in the age of financial big data and generative AI- How to spot overfitting in backtests and apply the scientific method in systematic trading strategies- Why risk awareness separates long-term success from blow-ups in post-2008 quant strategies

In this episode of Odds on Open, Ethan Kho sits down with Vinesh Jha, founder of Extract Alpha and former director of PDT Partners, to unpack lessons from the 2007 Quant Quake and how systematic investors can adapt in today’s crowded landscape.We cover:- What really happened inside PDT Partners when the firm lost $500M during the Quant Quake- Why so many quant hedge funds blew up in 2007 — and the key financial crisis lessons still relevant today- Inside the culture at PDT Partners vs the siloed world of other hedge funds- Why Vinesh Jha left the buy side to start Extract Alpha — and how alternative data reshaped quant finance- The rise of earnings transcript models, analyst accuracy signals, and Estimize’s crowdsourced forecasts- Will today’s LLMs and NLP models in finance get commoditized like old factor strategies?- The trade-offs between running a hedge fund and building a data company- How smaller systematic funds can still compete with giants like Citadel, Millennium, and DE Shaw- What it’s really like to work as a quant — and the traits that make a good quantBonus: - How quant hedge funds find alpha using alternative data and NLP- How hedge funds use earnings expectations and post-earnings drift to trade- What lessons can quants learn from market crashes and black swan events?00:00 Intro01:00 Inside PDT Partners during the Quant Quake05:11 How quants decide when models fail08:49 Culture at PDT vs other hedge funds10:38 Why Vinesh founded Extract Alpha15:25 Financial crisis lessons: crowded quant trades16:20 Will LLMs and NLP in finance get crowded?18:53 Best alternative data sets for alpha24:54 Do Estimize crowdsourced forecasts make money?28:19 Can buzzwords like AI predict returns?32:02 Why Vinesh didn’t start a hedge fund35:37 How quants should reinvent mid-career38:51 AI disruption vs creativity in quant finance40:48 Can small funds compete with Citadel, Millennium, DE Shaw?43:30 What makes a good quant stand out46:54 Closing thoughts on longevity in quant financeWhether you’re deep into quant finance, researching hedge fund strategies, or simply curious about what makes a good quant, this conversation offers rare insight into how edge is found—and lost—in modern markets.

What’s it really like working as a quant in fundamental research at Two Sigma—and how will AI, LLMs, and agentic workflows change quantitative trading strategies? Bill Mann, former Two Sigma fundamental researcher and founder of Harmonic Insights, joins Ethan Kho to break down how hedge funds build edge from widely available data and why “hacker” creativity still matters in systematic investing.Bill shares insights from AQR and Two Sigma, including how proprietary data pipelines become alpha generation engines, how to avoid crowding in popular factors, and what makes a great hedge fund strategy and the best work environment for quants. He also explains how LLMs, algorithmic trading, and automated research pipelines will transform research, engineering, and trading—and why mastering data engineering for quant finance is critical for junior quants.We answer questions like:– How do hedge funds find edge using fundamental vs. quantitative analysis?– What is point-in-time data and how does it prevent look-ahead bias?– How do proprietary data pipelines create alpha generation?– How can quants avoid crowding in value factors?– What’s it like working as a quant? What’s the culture like inside Two Sigma’s quantitative trading strategies team?– How do LLMs and AI agents change systematic investing workflows?– Which quant research tasks will be automated first?– What skills will junior quants need in the AI era?– How should aspiring quants practice creativity and problem-solving?– How does algorithmic trading intersect with data-driven investing?– What role will high-frequency trading (HFT) play in the future?– How do fintech startups work with hedge funds?– What’s the *New Barbarians* podcast about?#quantfinance #twosigma #aiinfinance #largelanguagemodels00:00 Intro00:57 Life as a Quant at Two Sigma02:36 Finding Edge in Fundamental Data07:04 Creating a Creative Quant Research Culture11:19 How LLMs Change Quantitative Trading15:52 AI’s Impact on Junior Quant Careers22:56 Using AI Tools for Learning23:57 Harmonic Insights: Advising Fintech Startups30:47 The New Barbarians Podcast Explained33:26 Crypto and Market Makers vs TradFi34:54 Career Advice for Aspiring Quants38:46 Final Takeaways

Can you analyze social media for investment decisions? How do hedge funds use Reddit posts, earnings calls, and SEC filings to find alpha? What’s the role of LLMs for financial analysis in 2025? Chris Kantos, Head of Quantitative Research at Alexandria Technology, joins us to explain how the buy side uses natural language processing (NLP), AI for investing, and text-based sentiment data to generate AI alpha signals across all asset classes—from equities to commodities.We dive deep into how Alexandria builds quantitative trading strategies from unstructured data like Reddit posts, news articles, earnings calls, and 10-Ks. Chris explains how most hedge funds get NLP wrong, why Alexandria’s document-specific classifiers give them an edge, and what makes a good social media for stock analysis dataset in a crowded and noisy world. He also tackles the myth of data commoditization and explains why alpha decay isn’t always inevitable.We answer questions like:– How do hedge funds use Reddit and social sentiment in trading?– What makes a good NLP model for financial data?– Has alternative data become commoditized?– What separates FinBERT from other finance-specific LLMs?– What’s the best way to train a sentiment model for earnings calls?– How is ChatGPT used in finance and investing workflows today?– How do professional quants cut through the noise on Twitter, Reddit, and X?– What’s the future of AI in systematic investing?– How does news sentiment impact trading strategies?– How do hedge funds use alternative data beyond equity alpha?– How do professionals use Reddit data for stock analysis without getting fooled by noise?– What’s the best path to become a quant researcher today?– What skills and experience matter in quant finance careers?Chris also shares quant finance career advice, why he left risk modeling for alt data, and what really happened in the office when Bernie Madoff got caught.

Financial markets are a game, says Grant Stenger. So how do you win in the financial markets? Grant, a former Jane Street intern and current crypto founder, believes markets are the most competitive game on earth—and he's been training to beat them since high school.From card counting in middle school to landing a hedge fund internship at QuantRes before university, Grant shares how a lifelong obsession with games, poker, and mathematical edge led him to build Kinetic—a decentralized crypto exchange and DEX aggregator on Solana trading millions of tokens.We talk about his quant trading internship experiences at QuantRes, Numerai (a crypto hedge fund), and Jane Street, and why he left it all to start his own crypto founder story building next-gen trading infrastructure.He breaks down:- His Jane Street internship experience and why they make you play Figgy- Why gambling and trading the market are similar- Poker and trading—and how poker is better prep than chess for decision making under uncertainty- Numerai’s bet-staking model and encrypted data thesis- How decentralized crypto exchanges actually make money- Trading on Solana vs Ethereum and why Solana is built for high-frequency crypto trading- Building a platform that can support 10 million tokens- Why being early in a new asset class gives you an edge—and how that edge resembles the one in gambling vs trading- And how he figured out how to get a hedge fund internship in high schoolWe discuss the collapse of FTX, the fall of Sam Bankman-Fried, the risks of centralized cryptocurrency exchanges, and why trading isn’t just gambling—but a game of edge in both gambling and trading.