The Sophron Network

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  1. 22h ago

    Euan Sinclair – The Man Who Wrote The Book On Volatility Trading

    Euan Sinclair joins The Sophron® Network to discuss where the edge in options trading has actually gone after thirty years. He explains why the mathematics he learned during a physics PhD mostly did not transfer, why a GARCH model that printed money in 1998 is priced in today, and why the edge now lies in finding situations rather than in building a better pricing model. Euan Sinclair is Portfolio Manager and Senior Financial Engineer at Hull Tactical Asset Allocation in Chicago, responsible for the research and implementation of the firm's volatility strategies. He has traded options professionally since 1995. Before Hull he traded a long and short equity volatility book at Bluefin Europe and Bluefin Trading, and was a partner at the volatility fund Talton Capital Management. He is the author of three books published by Wiley: Volatility Trading, Option Trading, and Positional Option Trading. He has sat on the editorial board of the Journal of Investment Strategies since 2012, and holds a PhD in theoretical physics from the University of Bristol, where he studied quantum chaos. We cover the philosophy Blair Hull carried from counting cards at blackjack into the option pits, why no single indicator survives transaction costs, and why the firm's advantage lies in combining fifty small signals rather than discovering new ones. Euan draws a distinction between having a model and having a situation, illustrated by the difference between a blackjack card counter and a player who has found one sloppy dealer. We also discuss hedging at implied against realized volatility, the Kelly criterion as an organizing principle rather than a formula, the variance premium as a consequence of risk aversion, what separates a genuine bubble from an expensive stock, and the two things he teaches practitioners first: that theta is not an edge, and that hedging is not a way of making money. Follow Euan SinclairLinkedIn: https://www.linkedin.com/in/euan-sinclairHull Tactical Asset Allocation: https://www.hulltactical.comHull Tactical blog: https://www.hulltactical.com/blog/ Core Timestamps00:00 - Welcome and introductions01:43 - From a physics PhD to the option pits04:42 - Blair Hull, blackjack, and find an edge, bet the edge, stay in the game07:18 - Which parts of the physics mathematics transferred09:51 - What the junior quants can do that he cannot15:07 - Why the edge moved from pricing models to risk premia18:41 - Market timing, fifty small signals, and a 2% alpha22:51 - Why the data layer is the foundation of the house24:45 - Hedging at implied versus realized volatility28:59 - Two ways to find an edge: a model or a situation34:54 - The switching option, and finding the easy game37:53 - Kelly as an organizing principle rather than a formula42:59 - The variance premium and what a real bubble looks like51:36 - Theta is not an edge58:03 - Crypto as TradFi twenty years earlier1:01:41 - Where to follow Hull Tactical Main Topics CoveredThe variance premium as a consequence of risk aversionModel based edge versus situation based edgeBet sizing, fractional Kelly, and parameter misestimationHedging at implied against realized volatilityWhy theta is not an edge and hedging is not a profit centreWhy there are no new ideas in trading, only new places to apply themConnect With UsInstagram: https://instagram.com/amsterdaminvestLinkedIn: https://linkedin.com/company/amsterdam-investment-clubX: https://x.com/amsterdaminvest Subscribe for more conversations at the intersection of markets, research, and technology.

  2. Aug 4

    Plettenberg Capital – How To Build An AI-Native Quant Fund

    Alexander Levin and Daniel van Flymen join The Sophron® Network to explain how they built Plettenberg Capital, an AI-native quantitative fund in New York run like a software company. Roughly 500 live algorithms, and no language model anywhere near the market. We take the fund apart layer by layer: the mental model they started from, the data layer everything rests on, the single engine that does both research and live execution, and the precise place where AI is allowed to sit. Alexander Levin is Founder and Chief Investment Officer for public markets at Plettenberg Capital. He previously co-founded the AI startup Beautiful Technologies, held P&L ownership across core cloud networking at Amazon Web Services, and helped launch Cisco's $400 million Digital Alpha fund, after starting in mergers and acquisitions at Houlihan Lokey. Daniel van Flymen is Co-Founder and Chief Technology Officer, and the engineer behind the research engine, cloud infrastructure and AI tooling. He has spent two decades as a technical co-founder and engineering leader in New York, including CTO roles at Spice and Foodwit and engineering leadership at Candid and BlinkRx. Levin argues that most people analyse the market zoomed all the way in, and that the useful move is the opposite: treat it as one lake of capital moving between sectors, where almost everything reduces to momentum or mean reversion. Van Flymen then takes the system apart: the data layer, where a missed corporate action invalidates every backtest above it; the simulation engine, which is the same engine that runs in production; and the execution stack, all built in house. We also discuss deliberately brittle code, two minute deploys, the Swiss cheese model of aviation safety applied to a trading system, why they use no volume or fundamentals or alternative data, whether sentiment is already in the price, and what happens to research teams once the tooling that compresses weeks of work into seconds is available to everyone. Follow Alexander Levin and Daniel van FlymenLinkedIn: https://www.linkedin.com/in/levinalexander/ LinkedIn: https://www.linkedin.com/in/danielvanflymen/ Plettenberg Capital: https://plettenbergcapital.com Enquiries, as given on the episode: ir@plettenbergcapital.com Core Timestamps00:00 - Welcome and introductions 02:58 - Two seats: the investment side and the engineering side 11:36 - The lake: liquidity, sectors and how hot money moves 18:43 - Regimes, and why an eight month downtrend is not a random walk 24:17 - A flock of birds: why they run around 500 algorithms 27:42 - The data layer, and why getting it wrong invalidates everything 32:49 - Pragmatism, brittle code and a two minute deploy 36:16 - What they deliberately do not use 43:08 - Where the AI actually sits: compression, not prediction 48:54 - The Swiss cheese model, applied to a trading system 50:41 - Is everything already in the price? 55:40 - Twenty strategies you understand, or 500 you do not 59:31 - The five year view: leaner teams, more autonomous systems Main Topics CoveredMarkets as a lake of liquidity moving between sectorsMomentum and mean reversion as the two underlying behavioursRegime identification across 500 decision treesPoint in time data, corporate actions and backtest integrityOne engine for research, validation and live executionWhere AI belongs in a quant fund, and where it does notPrice only research, and sector exposure through ETFsConnect With UsInstagram: https://instagram.com/amsterdaminvest LinkedIn: https://linkedin.com/company/amsterdam-investment-club X: https://x.com/amsterdaminvest Subscribe for more conversations at the intersection of markets, research, and technology.

  3. Jul 28

    Marc Nunes – How Do You Compete Against The Hedge Funds?

    Marc Nunes joins The Sophron® Network to argue that a weaker trading signal nobody else has is worth more than a stronger one that every fund already runs. He has sat on essentially every side of the quantitative trade, and now runs open competitions claiming a global crowd can out-signal the very research teams he used to lead. This episode puts that to him as a research question rather than a pitch. Marc Nunes is the co-founder and CEO of AlphaNova, a Singapore based, AI-native asset management platform that crowdsources trading signals through open competitions, evaluates them with proprietary methods in geometry and statistics, and deploys the survivors into a live deep learning trading system. He was previously Chief Risk Officer at BFAM Partners in Hong Kong, and for five years chief executive of Shell Street Labs, where he led a team of PhD researchers building AI trading models. He spent seventeen years at Societe Generale Corporate and Investment Banking, latterly as a Managing Director, having started as a financial engineer at C*ATS Software in New York. He holds a PhD in Mathematics from UC Berkeley. We cover why alpha decays and how it travels between funds inside people's heads, why he pays for signals no more than 50% correlated with his existing book, and how AlphaNova separates a real signal from an overfit one using leakage checks, submission limits and a live period. We also discuss the Gaussian copula that priced super senior protection at zero before 2008, correlation read as points on a sphere, who actually wins these contests, agentic AI as table stakes, and why an agentic hedge fund is going backwards. Follow Marc NunesLinkedIn: https://www.linkedin.com/in/marcdacostanunes/ Core Timestamps00:00 - Welcome and introductions01:37 - From running a signal lab to crowdsourcing alpha04:13 - Testing for data leakage and overfitting07:09 - Why hiring the smartest person is not enough09:02 - C*ATS, FiCAD and knowing what is under the hood13:17 - LTCM, 2008 and the copula that priced risk at zero18:56 - Learning more about markets away from the trading desk21:33 - Moving from an investment bank to a hedge fund24:35 - Why alpha decays and how it travels between funds26:25 - Designing a contest that demands decorrelated signals30:22 - How far to take AI in the research workflow39:43 - Who actually wins these competitions45:43 - Why an agentic hedge fund is going backwards57:35 - Catching a competitor who deobfuscated the data59:44 - Correlation as geometry: points on a sphere1:02:05 - Weak and orthogonal versus strong and crowded1:11:40 - What surprised him about running competitions1:20:33 - Advice for quants and founders Main Topics CoveredCrowdsourced alpha versus hiring a research teamOverfitting, data leakage and competition integrityAlpha decay and why signals travel between fundsDecorrelation as the product, not raw SharpeCorrelation as geometry, and signal compressionModels, assumptions and liquidity in 1998 and 2008Agentic AI in quant research, and its limitsWho wins quant competitions, and why not the obvious peopleConnect With UsInstagram: https://instagram.com/amsterdaminvestLinkedIn: https://linkedin.com/company/amsterdam-investment-clubX: https://x.com/amsterdaminvest Subscribe for more conversations at the intersection of markets, research, and technology.

  4. Jul 21

    Robert Carver – From Managing $5B to Trading 5 Minutes a Month

    Robert Carver joins The Sophron® Network to explain what changes, and what does not, when you go from running a $5 billion fixed income book at a systematic hedge fund to trading your own futures portfolio from home. His system covers roughly 250 futures markets, runs fully automatically, and needs about five minutes of his attention a month. This conversation is about what those five minutes are actually spent on, and about the parts of systematic trading that rarely get discussed: contract rolls, reconciliation, broker APIs and the discipline of not intervening. Robert Carver is an independent systematic trader, investor and writer, and the author of Systematic Trading, Smart Portfolios, Leveraged Trading and Advanced Futures Trading Strategies, with a fifth book, The Art and Science of Trading, due in December 2026. He trades his own portfolio using pysystemtrade, the open source Python framework he built and publishes alongside his live positions, and is a visiting lecturer at Queen Mary, University of London. He previously spent seven years at AHL, part of Man Group, most recently as Head of Fixed Income, where he managed a $5 billion portfolio across roughly $1.5 billion of annualized active risk. Earlier he was a research manager at the Centre for Economic Policy Research and an exotic derivatives trader at Barclays Capital. We examine why 999,000 of his million lines of Python have nothing to do with signals, why he argues 99% of a trader's success is implementation, and why he refuses to retire strategies that appear to have stopped working. We also discuss capacity and execution at different account sizes, regime models, AI in research, the architecture of pysystemtrade, broker reconciliation, counterparty risk, and what a systematic fund actually worried about in 2008. Follow Robert CarverLinkedIn: https://www.linkedin.com/in/robert-stuart-carver/ Books and blog: https://systematicmoney.org Core Timestamps00:00 - Welcome and introductions 02:03 - Leaving institutional trading to build his own stack 04:31 - Anatomy of a trading system 06:36 - Why it is five minutes a month 09:06 - Simple signals inside a complicated system 12:31 - Why 99% of success is implementation 14:21 - Running $5 billion versus your own account 20:24 - Backtests and forward looking information 22:24 - Has a strategy really stopped working 28:17 - Return expectations, not return targets 36:07 - Leverage, overtrading, overfitting 37:24 - AI in research 38:42 - Regime models and the one finding he uses 48:13 - Inside pysystemtrade 56:47 - Reconciliation and broker APIs 1:04:08 - Build it or borrow it 1:08:17 - 2008, and counterparty risk 1:14:40 - The three lessons, and the woman in the red dress Main Topics CoveredBuilding an institutional grade trading stack as an individualWhy implementation, not signal quality, drives the outcomeCapacity, execution and contract size at different account sizesBacktesting without forward looking informationBreak tests, structural change and holding periodRegime models and where they break downAI in quantitative research and overfitting riskpysystemtrade: simulation, production and process modularityBroker integration, reconciliation and operational riskCounterparty risk in futures versus cryptoConnect With UsInstagram: https://instagram.com/amsterdaminvest LinkedIn: https://linkedin.com/company/amsterdam-investment-club X: https://x.com/amsterdaminvest Subscribe for more conversations at the intersection of markets, research, and technology.

  5. Jul 17

    Luciano França – Is Brazil A Hidden Gem For Quants?

    Luciano Boudjoukian França joins The Sophron® Network to answer a question most quants never get to ask: is Brazil a hidden gem, or a trap dressed up as one? A factor strategy backtested on Brazilian equities can show roughly three times the alpha of the same strategy in the United States. Most of it never reaches the investor. This is a conversation about the gap between the backtest and the live book, and why that gap is the entire business in an emerging market. Luciano Boudjoukian França is a founding partner, Chief Investment Officer and portfolio manager at Avantgarde Asset Management, the São Paulo firm he founded in 2015 and one of the pioneers of factor investing in Brazil. Avantgarde runs a data-driven, systematic process across a family of multifactor equity and multi-asset funds, designed to remove emotion from investment decisions. The approach traces directly to his MSc dissertation at Insper on the low-volatility anomaly in Brazilian equities. Before asset management, Luciano spent his early career in derivatives and structured products for corporate clients at Banco BBM, and in corporate and real estate finance at ABN AMRO (formerly Banco CR2), Banco Pine and Even, where he structured capital-markets instruments such as CRIs, CCIs and REITs. He has served on the boards of Inepar and Resale, where he led the first Brazilian crowdfunding exit through a strategic acquisition. He holds an MSc in Economics from Insper, a Bachelor's in Economics from the University of São Paulo, and the CFP® certification. Follow Luciano Boudjoukian FrançaLinkedIn: https://www.linkedin.com/in/lucianobfranca/ Avantgarde Asset Management: https://www.avantgardeam.com.br/ Core Timestamps00:00 - Welcome and introductions02:04 - Pioneering systematic factor investing in Brazil05:34 - Why Brazil behaves like an island market06:59 - Vale, Petrobras and a value market of 150 names10:38 - From options market making to systematic factors15:42 - Why active ETFs are banned in Brazil20:07 - Why discretionary firms struggle to become systematic23:58 - Competing with AQR for the same Brazilian spread29:13 - Factor crowding and what breaks in emerging markets34:59 - Three times the backtested alpha, eaten by costs41:11 - Is the model ever overridden?43:56 - The 2020 crash and a 30% drawdown50:16 - Why he would rather play in the minors league53:56 - What AI changed in the research process1:03:39 - Rapid fire: overrated and underrated factors1:12:34 - São Paulo, Brazil and Dutch DJs Main Topics CoveredFactor investing and the low-volatility anomaly in Brazilian equitiesWhy implementation quality, not signal discovery, drives emerging-market alphaLiquidity, spreads and hidden trading costs as the binding constraintData hygiene: point-in-time data, survivorship controls, corporate actionsPortfolio construction in a market of roughly 150 listed namesWhy discretionary managers fail to transition to systematic processesModel autonomy and the limits of human overrideDrawdown management and low-beta factor exposure through the 2020 crashCapacity constraints of a boutique systematic managerAI in the quantitative research workflowScaling a factor process into other emerging marketsConnect With UsInstagram: https://instagram.com/amsterdaminvestLinkedIn: https://linkedin.com/company/amsterdam-investment-clubX: https://x.com/amsterdaminvest Subscribe for more conversations at the intersection of markets, research, and technology.

  6. Jul 11

    Rob de Rozario – The Trillion Dollar Gap In Financial Markets

    Rob de Rozario joins The Sophron® Network to explain why the gap between a sub 3 trillion dollar digital asset market and the roughly 200 trillion dollar world of traditional finance gets closed on risk and education, not on returns. After two decades structuring exotic and FICC derivatives, Rob moved into digital assets because most participants could describe extraordinary returns but could not talk about risk. This conversation is about the bridge between those two worlds. Rob de Rozario is the Founder, CEO and Chief Trading Officer of Alphaparty, an Amsterdam based asset management and trading firm running alpha rich strategies across digital asset markets. Before Alphaparty he led trading at a digital asset firm across DeFi, derivatives and market making. His earlier career spans Lehman Brothers in Tokyo and Singapore (commodity exotics and hybrids), Nomura in Singapore (FX structured products across Asia ex Japan), and Leonteq in Zurich as Head of FICC Structuring. He holds a BSc with Honours in Applied Mathematics and Astrophysics from Monash University and pursued doctoral research in Financial Mathematics at UNSW. We get into counterparty credit risk and the FTX lesson, the education gap that keeps allocators on the sidelines, tokenized real world assets and stablecoin payments, structured products settled on chain, where alpha still hides for small funds, running risk in a 24/7 market, and how AI is reshaping who wins. We also discuss the Citi tokenization report, crypto volatility trading and Deribit, the reality of building companies, which tokens have genuine utility, and where derivatives volume sits five years out. Follow Rob de RozarioLinkedIn: https://www.linkedin.com/in/rob-d-93a43316/ Core Timestamps00:00 - Welcome and introductions02:54 - From FICC structuring to digital assets05:27 - Counterparty credit risk and the FTX lesson07:17 - A sub 3 trillion asset class next to 200 trillion in equities08:48 - The education gap and the FX University story13:00 - The Citi tokenization report and equities on chain by 203015:10 - Structured products settled by smart contract22:15 - Where alpha hides: prediction markets, Hyperliquid, smaller venues25:55 - How mature is crypto volatility trading34:19 - Running risk in a 24/7 market and three lines of defense36:30 - Inside Alphaparty: team, structure and returns42:40 - From astrophysics to markets, and where AI actually helps51:46 - Which tokens have real utility, and which go to zero57:16 - Building companies before finance65:40 - Rapid fire and the future of on chain settlement Main Topics CoveredThe trillion dollar gap between digital assets and global equitiesCounterparty credit risk as the most underpriced danger in cryptoTranslating exotic and FICC derivatives experience into digital assetsInstitutional adoption, education and the risk committeeTokenized real world assets and stablecoin paymentsStructured products settled on chainWhere durable alpha still hides for small fundsRisk management in 24/7, venue fragmented marketsAI as a tool that favors small, fast teamsUtility as the test for which tokens surviveConnect With UsInstagram: https://instagram.com/amsterdaminvestLinkedIn: https://linkedin.com/company/amsterdam-investment-clubX: https://x.com/amsterdaminvest Subscribe for more conversations at the intersection of markets, research, and technology.

  7. Jun 30

    Jeremy Kadouch – How Close Are We to Fully Autonomous Trading?

    Jeremy Kadouch, Portfolio Manager at MN Fund, joins The Sophron® Network to answer a deceptively simple question: how close are we to fully autonomous trading? MN Fund runs a 24/7 systematic volatility engine that reads the market, reasons about it, and executes on its own. But the trade decision is deliberately kept away from AI, and the honest answer on autonomy is fewer decision makers rather than none. Jeremy Kadouch is Portfolio Manager at MN Fund, an Amsterdam liquid digital asset fund, where he runs the fund's trading strategies and capital deployment alongside founders Michaël van de Poppe and Esmee Sikkens. He brings more than eight years in digital assets across trading, tokenization and product development. He also built Quorum Index, an autonomous, LLM powered market intelligence platform, and founded Cask Capital, which tokenizes cask aged spirits. He began as a retail crypto trader and has completed Yale University's Financial Markets course. We go from MN Fund's three pillar book to the move from discretionary to systematic trading, where AI does and does not belong in execution, why the fund benchmarks against Bitcoin, and how to think about risk and drawdown. We close on tokenizing real world assets and building a Bloomberg for retail out of AI agents. We also discuss realized volatility harvesting, leverage and tail risk, the maturation of crypto markets over eight years, and the discipline of not tokenizing everything. Follow Jeremy KadouchLinkedIn: https://linkedin.com/in/jeremy-kadouch Core Timestamps00:00 - Welcome and introductions03:32 - What MN Fund is, and the multi strategy single fund model06:40 - The three pillar system and allocation by regime08:39 - From manual trading to a systematic engine11:37 - The intelligence layer, and why it is not fully autonomous yet12:35 - Trading realized volatility, and why not Bitcoin or meme coins16:41 - Systematizing discretion, and AI's real role21:15 - An algorithm that reads and reasons, with no AI in execution23:52 - Eight years in crypto: maturation and regulation36:04 - Why MN Fund benchmarks against Bitcoin39:42 - Cask Capital and tokenizing cask aged spirits49:24 - The discipline of not tokenizing everything52:18 - Quorum Index and how close we are to full autonomy Main Topics CoveredSystematic vs discretionary tradingAlgorithmic volatility harvesting in cryptoRisk, drawdown control, and R multiplesWhere AI belongs in a trading stack, and where it does notAgentic systems and autonomous market intelligenceBitcoin as a benchmarkTokenization and real world assetsRegulation, liquidity, and how crypto markets have maturedConnect With UsInstagram: https://instagram.com/amsterdaminvestLinkedIn: https://linkedin.com/company/amsterdam-investment-clubX: https://x.com/amsterdaminvest Subscribe for more conversations at the intersection of markets, research, and technology.

  8. Jun 23

    Aditya Shetty – Can the Internet Replace the NASDAQ?

    Aditya Shetty joins The Sophron® Network to talk through "internet capital markets": the idea that with a phone and an internet connection you can access any asset, any market, regardless of your location, your politics or your investment size. Recorded the same week tokenized SpaceX went live, the conversation is grounded in a concrete example and an honest distinction every finance listener needs.Aditya Shetty is Lead at Superteam Global, the talent and community network for the Solana ecosystem, which runs programs such as Superteam Earn, Idea Bank and Instagrants to help builders ship and get funded. He is based in Mumbai and works across the global Solana community.We start with Solana framed as technical and financial infrastructure, an internet capital market positioned as a successor to NASDAQ. From there we get concrete with SpaceX on Solana: how Backpack built a full-stack wallet, exchange and regulated securities brokerage, why the launch partner Sunrise mattered for liquidity, and how that creates a single liquid market where price arbitrage is close to risk free. We draw the line between owning the underlying stock and holding a synthetic price tracker, separate the three layers of Solana, and close on composability, fees, Superteam's community model and the bridge between TradFi and DeFi.Follow Aditya Shetty on LinkedIn: https://www.linkedin.com/in/aditya-shetty-97ab5258/ Core Timestamps 00:00 - Welcome and introductions: Superteam and internet capital markets 01:02 - Buying SpaceX 24/7: ownership versus synthetics 02:37 - What SpaceX on Solana did differently 02:57 - Internet capital markets versus the US capital markets 03:31 - Backpack: wallet, exchange and a regulated securities brokerage 05:05 - One liquid market and risk-free arbitrage 05:17 - Decentralization and the case for investor education 06:43 - Education, KYC and DeFi trust assumptions 07:54 - Synthetics and mimicking: what you actually own 10:02 - The three layers of Solana: token, network and infrastructure 12:18 - Limited inventory, one-to-one minting and fees 14:08 - Composability: trading the weekend and borrowing against shares 15:40 - The Superteam model and community-driven investing 18:42 - Closing vision: reimagining access and the TradFi to DeFi bridge Main Topics Covered Internet capital markets as a successor to NASDAQ Tokenized equities and buying SpaceX on Solana Real ownership versus synthetic price exposure Backpack's wallet, exchange and regulated brokerage stack Single liquid markets and risk-free arbitrage Investor protection, education and KYC in an open-access model The three layers of Solana: token, network, infrastructure Composability: borrowing against and staking tokenized shares Fees across wallets, DEXs and aggregators Superteam's bottoms-up community model The bridge between TradFi and DeFi Connect With Us Instagram: https://instagram.com/amsterdaminvest LinkedIn: https://linkedin.com/company/amsterdam-investment-club X: https://x.com/amsterdaminvest Subscribe for more conversations at the intersection of markets, research, and technology.

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Direct insights from experts in the trading, quant, and finance industry.

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  • Creator
    The Sophron Network
  • Years Active
    2026
  • Episodes
    18
  • Rating
    Explicit
  • Copyright
    © The Sophron Network
  • Show Website
    The Sophron Network