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.

How do you become a solo quant trader and build your own systematic trading business? Robert Carver, ex-head of fixed income at Man AHL—a $63 billion systematic trading hedge fund—shares how he went from managing institutional capital to becoming an independent, full-time quant trader.He reveals the key skills, mindset, and tools needed to succeed in quant trading without working at a big firm, how to create your own quant trading strategies, and why a PhD isn't required to break into systematic trading. Also, he shares how to manage risk and how he runs 200+ futures trading strategies as a solo trader with a small account.He breaks down:- How to become a quant trader without working at a hedge fund- The skills and background needed to succeed in quant trading without a math degree- Whether STEM is required for quant jobs- Why the Sharpe ratio is flawed and what to use instead- What separates top performers: traits of successful quant traders- How to build a quant trading career path solo vs going the institutional route- What investing strategies to use- The best quant trading strategies for individual traders- How to properly backtest trading strategies without overfitting- How to deal with alpha decay and determine when a strategy stops working- Inside the research culture of a top hedge fund strategies team- How to get into hedge funds in today’s competitive environment- What it’s like to work at a quant fund versus a more traditional hedge fundPlus: why most quant trading strategies rely on public, simple rules—and how to apply them profitably with a skeptical mindset.Robert is also the author of several acclaimed trading guides, including Systematic Trading, Advanced Futures Trading Strategies, Smart Portfolios, and Leveraged Trading. They’re what got Ethan (our host) into quant finance to begin with.00:00 Intro00:45 How to Handle Losing $1 Billion04:05 What It’s Like Working at Man AHL06:39 Why Quant Funds Hire STEM Grads08:43 High Frequency vs. Low Frequency Quants10:44 The Most Important Trait in Quant Research12:53 Is Sharpe Ratio a Good Metric?16:41 Geometric Returns vs. Sharpe Ratio17:45 How to Avoid Overfitting in Backtests21:28 Dangers of Implicit Fitting in Models23:02 How Robert Deals With Alpha Decay25:29 Robert’s Current Quant Trading Strategies28:45 Should You Trade Independently or Join a Fund?31:19 Why Trading Your Own Capital Is Hard32:09 Does He Look for Market Inefficiencies?33:45 His Biggest Trading Innovation: Execution35:45 How Hedge Funds Approve New Strategies39:27 Will Big Quant Firms Dominate Forever?42:44 Pod Shops vs. Collaborative Quant Culture43:48 Final Thoughts + His Books on Quant FinanceFind links to Robert's books here:1. http://www.systematicmoney.org/systematic-trading/2. http://www.systematicmoney.org/smart/3. https://www.systematicmoney.org/leveraged-trading4. https://www.systematicmoney.org/advanced-futures

What is edge in trading—and how do hedge funds and quant traders find edge in 2025? According to Agustin Lebron, former quant trader at Jane Street and author of The Laws of Trading, edge is what separates average traders from those who thrive at a top hedge fund. But today, finding edge requires more than just a good model—it demands judgment, adaptability, and a deep understanding of how quant trading firms actually operate.In this episode, Agustin breaks down exactly how quant trading works, how elite firms like Jane Street maintain their edge, and how quant funds make money in both calm and chaotic markets. He shares hard-earned insights on how to become a quant trader, the realities of trading internships at hedge funds, and what it really takes to get hired at Jane Street.He breaks down:– What is edge in trading and how to know if you have it– How elite quant firms like Jane Street develop and defend edge– Why edge is statistical—but also deeply judgment-based– How to get hired at Jane Street and succeed in a Jane Street intern experience– What makes a good trader (hint: it’s not just math)– Why some trading models fail during market crises– How small quant shops compete with large firms– How to stand out in a hedge fund or quant internship– Should I become a quant? Questions every student should ask– Why quant trading firms 2025 will look different—and whether AI will replace traders in some rolesAgustin also shares how young people can find their personal edge in a world transformed by AI, automation, and rising inequality. His advice? Don’t chase status. Follow curiosity. Learn where real value comes from.

If venture capital underperforms the public markets, is it still worth the investment? For Avik Ashar, the answer is yes—but not for the reasons you think. Avik, a Principal at Riverwalk Holdings, an India-focused VC firm, argues that most people misunderstand how venture capital works and how to fairly evaluate VC fund performance.Venture capital, he explains, isn’t just about chasing unicorns or short-term IRRs. It’s a strategic investment tool, especially in Asia, used by family offices and conglomerates not only for returns but also for M&A, R&D, and market expansion. In markets like India, venture capital is helping industrial groups future-proof their businesses while tapping into innovation. He also highlights how India’s maturing public markets and mutual fund sector are making early-stage investing and startup exits far more viable than in places like Southeast Asia, where liquidity is still limited.He breaks down:– Why VC returns vs public markets often look misleading—unless you know how to analyze them– How family offices in India are using venture funding for strategic acquisitions– Why M&A is finally taking off in Asia—and what that means for founders– The key differences between venture capital in India, Southeast Asia, and the U.S.– Why India’s public markets are becoming a critical exit path– How startup exits work in markets without strong IPO pipelines– Why Southeast Asia’s VC boom from 2014–2018 underperformed– What Gen Z needs to understand about building in a noisy, AI-native world– How venture capital vs private equity differs in terms of outcomes, strategy, and timelinesHe also shares an important reminder for our age of endless short-form content: “The most expensive thing you can give today isn’t your time—it’s your attention.”00:00 Intro00:33 Why invest in venture capital?01:01 How venture returns work03:01 Venture as an R&D and acquisition pipeline04:11 The outlier nature of VC returns05:11 Why family offices invest in venture05:52 Examples of conglomerate acquisitions in India09:43 Differences in VC ecosystems: US, Singapore, and India16:29 Do family-backed VCs perform better in India?16:50 Riverwalk Holdings’ experience in India18:22 Maturity of India’s financial ecosystem for startups23:59 Where will venture gains come from?27:51 Indian conglomerates embracing startups28:52 Challenges of building companies in Asia30:22 Advice for young people in an uncertain world32:44 Tech’s share of the US market cap38:04 Staying focused amidst noise40:57 Advice for recent graduates42:24 Outro

Is venture capital dead? Not for Guy Horowitz, who boasts 20+ years in the VC ecosystem, holding the title of partner at firms like DTCP, 33N, and ESH.vc. In this episode of Build to Last, we unpack the changing face of early-stage investing and startup funding—from the 2000s hardware boom to the rise of software unicorns, and now the new frontier: AI-first companies. Guy shares battle-tested insights on identifying founder-market fit, navigating VC cycles, and why understanding capital formation is just as important as finding the next big tech innovation.We also dive into the future of work, education, and the middle class. What happens when generative AI and automation replace white-collar jobs faster than traditional schools can adapt? What happens when AI agents arrive at desks, offices, and boardrooms? With three kids of his own, Guy reflects on raising future-proof talent and what young people today really need to succeed in a world defined by machine learning, venture-backed disruption, and rapid technological change. Spoiler: it's not just about coding bootcamps—it's about curiosity, adaptability, and a willingness to learn from people who aren’t like you.Some key takeaways:– How venture capital has changed in the past two decades– Why "great ideas" don’t matter without the right founders– What makes a great startup exit– The question top VCs like Guy Horowitz ask before writing a check– What NOT to say in a pitch meeting– How DTCP became a breakout fund backed by Deutsche Telekom– Why today’s job market is rigged (and how you can stand out anyway)– Whether AI will make human investors obsolete– Why most white-collar jobs are more automatable than we think– Is now a good time to start a fund? Guy’s honest take– Advice for young people unsure about their futureAlso in this episode, we discuss how to identify REAL startup talent (even if they’re mean) and what makes a great VC (beyond capital). Subscribe for more conversations with founders, builders, and leaders.#venturecapital #investing #ai 00:00 Podcast Intro 00:43 How venture capital has changed 01:55 Guy’s early career at Gemini 05:46 Lessons from being cocky too early 09:15 What makes a great VC investor 12:49 How Guy evaluates founders 18:54 What not to say in a pitch 21:38 The story behind DTCP 27:08 Fundraising success and growth 30:11 Is venture capital dead? 36:41 Raising kids in the AI age 43:44 What happens to the middle class?51:12 Advice to young people 52:42 Outro