Odds on Open

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

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

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

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

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