Explorez tous les épisodes du podcast The Blushing Quants Podcast

Vincent Randazzo, CMT, is a portfolio manager and technical market strategist with more than 25 years of experience across firms including Morgan Stanley, UBS, ICAP, CFRA Research, and Lowry Research.

After observing that investors have access to more market data than ever but often lack clarity on how to use it, Vincent developed Defender, a quantitative, rules-based framework designed to support more objective portfolio and risk-management decisions. He is also the founder of ViewRite Advisors and manages the Defender Risk Adaptive 500 ETF, ticker SPDF.

In this episode of The Blushing Quants, Vincent joins us for a practical conversation about market breadth, regime detection, technical analysis, systematic investing, and how portfolio managers can respond when the market’s apparent strength does not reflect what is happening beneath the surface.

The central question is:

Can market breadth reveal risks that traditional market indexes fail to show?

Vincent explains why market-cap-weighted indexes can create a misleading picture when a small group of large companies drives most of the market’s performance. We discuss how breadth indicators measure participation across large-cap, mid-cap, and small-cap stocks to assess the market’s underlying health, detect fragility, and identify changing conditions.

We explore Vincent’s rules-based approach to adjusting equity exposure across different market regimes. He explains why market deterioration often happens gradually, why market bottoms can develop more quickly, and how historical evidence can help investors distinguish between healthy pullbacks and more serious changes in risk.

The conversation also covers momentum, relative strength, moving averages, trailing stops, changing correlations, and the importance of interpreting technical indicators within the correct market environment. Vincent explains why being above a moving average is not enough, why its direction also matters, and why context is essential when evaluating any signal.

Vincent also shares lessons from his own investment mistakes and from navigating the 2008 financial crisis. We discuss the danger of becoming emotionally attached to an investment thesis, why successful risk management requires both an exit and re-entry process, and how systematic rules can reduce the influence of ego and emotion.

Finally, we examine active versus passive investing, the role of technical analysis within institutional portfolio management, and how market technicians can complement fundamental portfolio managers by improving timing, risk awareness, and decision consistency.

A thoughtful and practical conversation on market breadth, portfolio management, regime detection, momentum, technical analysis, and building a systematic approach to investment risk.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Jerome Busca is a quantitative trader with more than 25 years of experience across mathematics, quantitative research, portfolio management, global futures, foreign exchange, and crypto markets.

After beginning his career in academic mathematics and applied research in France, Jerome moved into quantitative finance and later joined Citadel’s hedge fund business. Working on the mortgage desk before the 2008 financial crisis, while also helping develop a systematic CTA-style futures operation, gave him a front-row view of how institutional quantitative research, technology, and market risk evolved during a defining period in modern finance.

In this episode, Jerome joins us for a wide-ranging conversation about how quantitative trading has changed and what remains fundamentally difficult despite better data, infrastructure, and artificial intelligence.

The central question is:

As technology makes quantitative research faster and more accessible, does finding sustainable alpha become easier, or does the competition simply become more intense?

Jerome explains how global futures research was conducted before Python, modern data infrastructure, and AI transformed the industry. We discuss why technological infrastructure became a competitive advantage for firms such as Citadel, how arbitrage makes markets more efficient, and why the lifespan of many trading edges has fallen from seconds to microseconds.

We also examine portfolio construction and the limitations of traditional correlation-based optimization. Jerome shares his perspective on Markowitz optimization, regularization, Bayesian approaches, factor models, hierarchical covariance, equal-risk allocation, fat-tailed returns, conditional correlations, copulas, and why simple portfolio methods can be surprisingly difficult to outperform.

The conversation goes deeper into causality, hidden common drivers, changing market regimes, crisis correlations, and the danger of confusing statistical relationships with genuine economic mechanisms. Jerome also explains why researchers and portfolio managers should remain cautious when translating attractive academic findings into live investment decisions.

Finally, we discuss the growing influence of AI on quantitative finance, including how individual researchers can now build tools that once required institutional teams, why professional data infrastructure remains essential, and how easier backtesting can create an even greater risk of overfitting and false confidence.

We conclude by exploring emerging markets and new areas of quantitative research, including crypto, perpetual futures, prediction markets, alternative data, and even the possibility of applying systematic methods to art valuation.

A thoughtful and practical conversation on alpha decay, portfolio construction, causality, artificial intelligence, emerging markets, and the continuing evolution of quantitative finance.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Paul Chalmers, CEO of UK Trading Academy, for a raw and practical conversation about what most traders misunderstand about the markets.

Paul breaks down why trading education often fails, why theory alone is not enough, and how real market experience, risk management, psychology, and disciplined execution separate serious traders from the crowd.

We discuss how markets have changed, the role of AI and algorithms in modern trading, and why technology should support human decision-making rather than replace it. Paul explains how his team approaches dynamic algorithms, probability-based signals, market movements, and the importance of combining data with practical trading judgment.

The conversation also goes deep into geopolitical events, GBP/USD, oil, institutional traders, market makers, retail trading mistakes, trading plans, position sizing, drawdowns, and why backtesting should be used to understand risk — not just to chase beautiful profit curves.

This episode is for traders, quants, market researchers, and anyone who wants to understand the difference between learning to trade and actually learning to make decisions in live markets.

PODCAST LINKS:

UK Trading Academy: https://uktradingacademy.com/

Paul's LinkedIn: https://www.linkedin.com/in/paulchalmersuk/

PODCAST INFO:

Spotify: https://open.spotify.com/show/4jw3ouXrmbsToKtGY9q80O

Apple Podcasts: https://podcasts.apple.com/us/podcast/the-blushing-quants-podcast/id1864851089

Amazon Music: https://music.amazon.com/podcasts/cf63850e-9f1f-491d-a794-0695e85ccaa6

RSS: https://feed.podbean.com/theblushingquants/feed.xml

Full episodes playlist: https://www.youtube.com/playlist?list=PLFHtE5XlBV_VdWEnca58iQSTAXj6O-CfG

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Jonathan Davies is an economist with over 30 years of experience in financial services.

Jonathan has worked across several areas of the investment world, including fixed-income research, portfolio strategy, and portfolio management. His career has focused mainly on the macroeconomic side of markets, examining areas such as interest rates, bond yields, currency movements, equity-versus-bond allocation, regional market preferences, and multi-asset portfolio construction.

Unlike a single-stock analyst, Jonathan’s perspective comes from understanding how the broader market system works: how economies move, how asset classes interact, how portfolios are built, and how professional investors communicate strategy and risk to clients.

In this conversation, we explore one of the most important ideas in financial theory: the Efficient Market Hypothesis.

If markets already reflect available information, what does it really mean to be an active investor? Can portfolio managers consistently beat the market, or does outperformance require a clear philosophy, discipline, and a deep understanding of where market inefficiencies may still exist?

Jonathan explains why EMH is such a compelling idea, why active management is a strong claim, and why a portfolio manager needs more than past performance to build trust with clients. We also discuss what happens when an investment thesis stops working, how managers think about risk, and why different strategies may work well in some market environments and struggle in others.

This episode is a thoughtful conversation about market efficiency, active investing, macro strategy, and the real responsibility of managing capital in uncertain markets.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Eren Biri is the founder of OneEye Capital, a volatility-focused investment firm built around a strong mix of quantitative research, discretionary overlays, and deeply engineered infrastructure. With a background in computer engineering, experience at Goldman Sachs and multiple hedge funds, and a career that moved from quant research into trading and portfolio management, he brings a highly practical perspective on what it really takes to run a modern options-focused fund.

In this episode, we get into volatility trading, options markets, and the real mechanics of running a fund where risk management comes first. Eren explains how his firm combines systematic strategies with discretionary overlays, why discretionary thinking still matters even in a quant-heavy setup, and how macro awareness, cross-asset relationships, and scenario analysis shape the way he sizes, hedges, and protects positions.

We talk about how options traders think in implied probabilities, how relative value opportunities show up across equities, rates, commodities, and volatility surfaces, and why the goal is often not to predict direction but to isolate the exact risk factor you want to own. Eren breaks down delta, vega, theta, gamma, hedging, and portfolio construction, and explains how his team decomposes option markets into tradable components rather than treating them as a single undifferentiated space.

Also, explore how a small fund can compete by being engineering-heavy and infrastructure-native. Eren shares how OneEye built its own in-house stack, stores and processes massive options datasets on its own hardware, and uses AI and machine learning tools for signal calibration, regime classification, portfolio optimization, and empirical pricing, without sacrificing explainability where it matters most.

On top of that, we discuss what it looks like to run a cross-border team, how to keep a small technical organization aligned around markets, and how to position a young fund in front of investors by offering institutional-grade discipline, strong risk management, and access to strategies most allocators usually only see inside elite buy-side firms.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Nikolai Nowaczyk is a mathematician, published researcher, and quantitative risk professional with a background spanning academia, consulting, and banking. With deep experience in counterparty credit risk, model development, and validation, he brings a rare perspective on how highly technical mathematical ideas are actually implemented inside major financial institutions.

In this episode, we get into what counterparty credit risk really is, why it matters so much in derivatives markets, and how institutions measure and manage the risk that a counterparty defaults when a trade is in the money. Nikolai breaks down Monte Carlo simulation, CVA, collateralization, variation margin, initial margin, netting agreements, and the operational reality of managing risk across thousands of counterparties and massive derivatives books.

We also talk about regulation, legacy systems, model validation, and why implementing new risk requirements inside large institutions is often far more complex than it looks from the outside. On top of that, we explore machine learning in quant finance, where it can genuinely help, where traditional methods still dominate, and why explainability, documentation, and production rigor remain essential in regulated environments.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Ufuk Tasdan is a quantitative researcher with an unconventional background spanning physics, philosophy of physics, cryptocurrency trading, and energy market analytics. After studying physics and completing a PhD in philosophy of physics, he moved into applied quantitative work, first in crypto and later in European energy markets, where he focuses on price forecasting, market analysis, and model building for traders and market participants.

In this episode, we get into what it means to come into quantitative finance from a non-traditional background, and why some of the most interesting market thinkers often come from outside the usual pipeline. Ufuk shares how philosophy of science, particle physics, and critical thinking shaped the way he approaches markets, model selection, and data interpretation.

We talk about the differences between cryptocurrency and energy markets, why crypto can look simpler on the surface but remain deeply opaque, and why energy markets are more transparent in data yet far more structurally complex. Ufuk explains how he thinks about supply and demand in both worlds, why energy markets are uniquely difficult because of negative prices, physical delivery constraints, and spike behavior, and why modeling those spikes is often harder than modeling the trend itself.

We also get into economophysics, non-stationary data, analogy-based thinking, return distributions, model robustness, and the limits of standard tools like the Sharpe ratio in highly volatile markets such as crypto. Ufuk shares why he starts with the distribution of returns, how he thinks about interpreting market structure through physics-inspired analogies such as earthquakes and diffusion, and why model simplicity at the core still matters even when the surrounding structure becomes complex.

On top of that, we explore machine learning in production, why linear regression still matters, how neural networks can be useful for modeling residuals, and where human judgment remains essential, especially when regime shifts and spikes violate the system's assumptions.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Oded Shimoni is the CEO of AlphaBeta, a quantitative R&D company focused on systematic, low-correlation investment strategies across products such as mutual funds, alternative ETFs, hedge funds, and tracking funds. His work sits at the intersection of quantitative research, portfolio construction, factor investing, and the growing world of liquid alternative investment vehicles.

In this episode, we get into what it actually takes to build low-correlation strategies in practice, and how quantitative research can be used to construct broad, systematic portfolios designed to behave differently from traditional market exposure.

We talk about long-short equity, merger arbitrage, factor investing, and the challenge of turning academic ideas into investable products that can survive real market constraints. Oded explains how his team approaches weight allocation, why machine learning and deep learning can be useful for dynamically allocating across factor exposures, and why economic rationale, clean data, and point-in-time discipline still matter more than model complexity alone.

We also get into portfolio construction, explainability, missing data, normalization, correctly identifying lagging fundamentals, and the importance of working with liquid universes and reliable data providers. Beyond the research side, we explore the evolution of active and alternative ETFs, portable alpha, capital efficiency, and why the ETF wrapper is opening the door for strategies that were once mostly reserved for hedge funds.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Ben Charoenwong is a finance professor, researcher, and fund manager working at the intersection of academia, quantitative investing, and applied market practice. As an associate professor at INSEAD and co-founder of Chicago Global, he brings a rare perspective shaped by both rigorous academic training and the real constraints of building and managing investment strategies in live markets.

In this episode, we talk about what it actually means to bridge academia and industry in finance, and why that gap is both narrower and harder than most people think. Ben shares how academic research can still produce ideas that matter in practice, but also why the market is a humbling force that quickly exposes weak theories, poor signals, and overconfident models. We get into financial education, opportunity cost, critical thinking, student training, and why AI is forcing universities to rethink not just how they teach, but what they are really supposed to teach.

We also dive into fund design, market inefficiencies versus risk premia, diversification, investor fit, long-short construction, mid-frequency strategies, and the importance of building portfolios around the end client's actual risk tolerance. Ben breaks down how he approaches explainability, feature engineering, theory-driven quant research, model simplicity, alternative data, fraud signals, and geopolitical shocks, and why the best quantitative work still requires judgment, discipline, and a clear understanding of what kind of edge you are really trying to build.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Garret Brennan is the co-founder and CEO of Epoch, an AI-native quantitative research startup building tools for institutional investors who want to integrate AI into their workflows without sacrificing rigor, determinism, or trust. With a background on the fixed income desk at Bank of Montreal in New York, Garret brings both market experience and startup urgency to the problem of making quantitative research faster, more accessible, and more usable in real institutional settings.

In this episode, we talk about what it actually takes to build AI infrastructure for quantitative finance in a market that is both highly technical and deeply conservative. Garret explains why Epoch is not trying to let language models perform the computation itself, but instead uses AI as a structured interface atop a traditional institutional-grade research stack. We get into determinism, hallucination risk, backtesting infrastructure, internal libraries, multi-agent systems, and why infrastructure has to come before AI hype if you want a product that can survive real production use.

We also discuss customer workflows across the buy side and sell side, what different types of traders actually need, how to think about product-market fit in a fragmented market, and why speed to decision is one of the clearest sources of value. Garret also shares lessons from his path from sales and trading into entrepreneurship, how his market experience shaped the product, and why building for quantitative finance requires a rare mix of technical depth, workflow understanding, and obsessive attention to correctness.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Roman Isachenko is a quantitative researcher with a background in applied mathematics and rocket science who moved from engineering into finance, derivatives, and systematic trading. His experience spans risk management, derivative pricing, asset management, and small hedge fund environments, giving him a grounded view of how quant research actually works when capital, time, and market reality put every idea under pressure.

In this episode, we talk about the realities of building and rebuilding quant strategies in an environment where alpha decays quickly, and competition keeps getting tougher. Roman shares his path from engineering into quant finance, explains why derivatives first pulled him into the field, and reflects on the difference between elegant theory and what survives in live markets. We discuss pricing models, calibration, volatility, and why even strong ideas can stop working when market structure changes.

We also get into research discipline, overfitting, noise testing, Monte Carlo comparisons, strategy decay, and the hard decisions small teams face when a model starts to fail. Roman breaks down the differences between research at large institutions and at small funds, why execution and exits often matter more than entry signals, and why leadership, judgment, and genuine passion still matter as much as technical skill in quantitative finance.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Zach Marx is the Chief Investment Officer of Vineyard Quant Capital, where he works at the intersection of systematic equity investing, institutional flow, and data-driven portfolio construction.

In this episode, we get into what it actually takes to build a quantitative investment process around how institutions and retail investors make decisions, and how that can be turned into a systematic equity strategy.

We talk about Zach’s path from market data and S&P to running an investment strategy at Vineyard, why understanding how different allocators behave can be just as important as understanding the data itself, and how his team uses point-in-time information, forward expectations, and cross-sectional ranking to scale a fundamentally informed process across thousands of stocks. We also get into sector- and industry-level clustering, orthogonal factor construction, seasonality, in-sample versus out-of-sample testing, stock-level and portfolio-level risk management, and why running a successful quant strategy is as much about operations, relationships, and business building as it is about research. The conversation also touches on retail sentiment, alpha-capture frameworks, and how Zach approaches using AI to evaluate processes, without yet trusting it to make investment decisions on its own.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Mark Aron Szulyovszky is a crypto quant and an entrepreneur focused on cross-sectional alpha factors in digital assets. He works to surface crypto-native factors, build market-neutral portfolios, and turn research on derivatives, microstructure, and token-specific behavior into tradable products for both internal use and external clients.

In this episode, we get into what it actually takes to build and trade crypto-native alpha factors in a market that is volatile, fragmented, and still structurally different from traditional finance.

We talk about why Mark moved away from a more machine-learning-heavy approach toward simpler, more interpretable factor research, how he thinks about market microstructure and derivatives data in crypto, and why cross-sectional alpha remains more abundant there than in many traditional markets. He explains how his team classifies and cleans data, works with tradable universes, controls turnover, and looks for signals that can survive transaction costs rather than just look good in backtests. We also get into retail versus institutional flow, the relationship between spot and perpetual futures, portfolio construction under extreme non-stationarity, and why, in crypto, the biggest challenge is often not finding a signal but sizing and risk-managing it properly when the market regime shifts. The conversation also touches on his entrepreneurial path, including selling his first company and applying that builder mindset to quantitative finance.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Manuel Ritsch is the founder of Alpha Rho Technologies, where he is building AI-native investment infrastructure for asset management. After seeing how much of the industry still relied on outdated tools and manual processes, he set out to replicate the work of human analysts with AI and turn that into a real operating model for funds.

In this episode, we step slightly outside pure quant research to explore what it actually takes to build an AI-driven investment business from the ground up.

We talk about the gap between traditional asset managers and newer AI-native approaches, why low-frequency investing may be one of the clearest use cases for agentic systems, and how Manuel structured a fund run by AI analysts, CIOs, and investment committees. We also get into go-to-market, fundraising, bank partnerships, product positioning, client education, and the hard reality that institutional investors still want a track record, transparency, and a process they can trust. Manuel also explains why explainability matters so much in asset management, why models themselves are becoming commodities, and why the real edge increasingly comes from orchestration, usage, and product design rather than just model access.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Francisco Prack is a quant, economist, and portfolio manager with 30+ years of experience across financial markets, and a background spanning traditional finance, quantitative research, algorithmic trading, and crypto.

In this episode, we get into how a deeply model-driven way of thinking can shape an entire career in markets, from economics and traditional finance to algorithmic trading, reinforcement learning, and crypto.

We talk about why Francisco sees markets as a sequential problem rather than a static one, how he studies the tape day by day to extract patterns, and why understanding market rules and order types matters before touching the data at all. He explains how he thinks about institutional footprints, why replaying and re-reading past market sequences can be more useful than forcing generic statistical frameworks onto trading, and how reinforcement learning fits into his process by helping adapt parameter choices and position sizing across different market conditions. We also get into the practical differences between TradFi and crypto, the importance of writing conservative code for extreme market events, and why he still prefers to write the core logic himself rather than outsource the brain of the system.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Denis Lukyanov is a quantitative researcher and AI/ML practitioner working at the intersection of finance, machine learning, and agentic systems.

In this episode, we get into what it really takes to integrate agentic systems and large language models into quant workflows, and why the hard part is not generating ideas quickly, but building something structured, testable, and useful in practice.

We talk about the gap between quick AI prototypes and production-grade systems, why planning still matters more than coding speed, and how domain expertise remains the real bottleneck even as the tools improve. Denis breaks down how he thinks about combining traditional machine learning and deep learning with GenAI agents: LLMs can add real value as orchestrators, analysts, and research accelerators, but they still should not be trusted to make decisions. We also get into context windows, knowledge systems, guardrails, model-as-judge workflows, regime detection, quantitative research loops, and why serious trading systems still need explicit logic, strong data, and human control.

If you care about how AI is actually being used inside quant research today, what separates real systems from AI theater, and how to think clearly about agents, models, and market structure without losing rigor, don’t miss this one.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Toby Morris works across multi-asset trading, client coverage, sales trading, and trading desk operations, helping clients execute effectively while keeping the desk, workflow, and decision-making process aligned behind the scenes.

In this episode, we go beyond job titles to explore what the trading desk actually looks like when clients, liquidity, technology, and judgment collide in real time.

We talk about how markets evolved from phone-brokered flow to online and mobile trading, and what that shift changed for both clients and trading desks. Toby shares a grounded view on what it really takes to deliver for clients in practice: understanding their needs, choosing the right execution approach, knowing when to systematize, and knowing when discretion creates more risk than value. We also get into the hidden operational side of the business, from aligning teams across trading, operations, risk, and compliance to handling large orders, internal communication, liquidity constraints, and the uncomfortable reality that sometimes the most important skill is knowing when to say no.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Mattia Spreafico is a quant based in Switzerland with an MSc in Quant Finance and a background in Mathematical Engineering from Politecnico di Milano.

In this episode, we go beyond job titles and get into what the next generation of quants is actually dealing with day to day inside large institutions, where speed, correctness, and deployment constraints collide.

We talk about how AI is already changing the quant workflow in practice, not as hype but as a default tool for research, coding, and iteration. Mattia shares a grounded view on where LLMs genuinely help, where they still fall short in finance, and why data quality and access are still the real foundation. We also dig into the uncomfortable tradeoff nobody wants to admit: a perfect review slows you down, but moving fast increases risk, so the game becomes building better monitoring, better controls, and faster reaction when something breaks.

If you care about how quant research is evolving from idea to production, why deployment can be harder than modeling, and what skills will matter most when everyone can “code” but fewer can design robust systems, this conversation will hit.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Robert is a London-based systematic trader with 25+ years in markets. He started in the early 2000s prop trading futures, survived the no-simulator era, and evolved from discretionary trading into fully systematic research and automation. Today, he builds short-term strategies across equity indices and rates futures, and has recently helped a large institution stand up a proprietary trading team.

In this episode, we get practical about how an independent trader thinks and operates: finding edges in intermarket relationships, turning market intuition into systematic decision trees, and building portfolios that aim for strong Sharpe with positive skew. Rob also breaks down how he approaches regime awareness and robustness, in-sample vs out-of-sample work, and why walk-forward style processes are harder than they sound when you actually care about “the right trades", not just the best backtest. We also talk about the fundraising catch-22 for independents and why selling signals and research can be a smarter wedge than trying to raise a big flagship fund on day one.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Haris is a quantitative equity researcher and portfolio manager with experience across top-tier institutions, including BlackRock. Born in Greece, he studied applied computer science and applied mathematics, worked at the European Central Bank, then moved to the US for a Master's in Financial Engineering, and later built systematic equity models in the industry.

In this episode, we go into how quant research is done when time matters. Haris breaks down the prototype mindset, how to build a fast research pipeline, and what gets rejected immediately before you waste months. We talk about signal evaluation, residual momentum, sector and risk neutralization, and why correlation with your existing book can kill a "great" signal.

We also unpack execution realities, trading costs, backtest vs. realized PnL, Monte Carlo and holdouts, and the optimizer's approach to sizing, constraints, turnover, and robustness. Finally, we cover where machine learning and deep learning fit, why short horizons give data-hungry models an edge, and how LLM agents are accelerating the pace of research.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Israel Bergenstein is a quant researcher with an MSc from Oxford, focused on building hedge-fund-style systematic strategies and translating research into deployable trading models. His work bridges the gap between academic quantitative thinking and real-world market implementation, with an emphasis on rigorous research, systematic strategy development, and the practical challenges of taking models from idea to execution. He brings a perspective shaped by both strong analytical training and a deep interest in how institutional-grade trading systems are designed, tested, and refined.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Carl Wells is a systematic equity researcher and entrepreneur building an investment analytics platform focused on company quality, using CFROI and return on invested capital, along with deep accounting adjustments, to reveal the true economics behind financial statements.

In this episode, Carl shares his journey from physics to hedge funds, and how the 2008 crisis pushed him to unify fundamentals and quant through rigorous backtesting and factor research. We talk about why macro and liquidity regimes can break even great fundamental portfolios, and how systematic equity investing evolved from classic anomalies to today’s quality-driven frameworks.

Carl also explains the mechanics behind his platform: restating financials, treating R&D as investment, handling leases and intangibles, correcting recurring “one-offs", and building more stable, predictive metrics. We then dive into how he uses machine learning for forecasting while staying disciplined about interpretability, risk management, and deployment.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Paul Bilokon is a veteran quant, educator, and entrepreneur with experience across major banks and systematic trading.

In this episode, we go deep into what actually makes research deployable: building a backtesting framework you can trust, cleaning and normalizing data correctly (rolls, corporate actions, microstructure effects), and stress-testing strategies against execution lags, transaction costs, and market impact.

We also discuss how Paul thinks about critical thinking as a repeatable research process, why he prefers starting with simple baselines before escalating model complexity, and where reinforcement learning and neural networks fit in finance when explainability and production constraints matter.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

A sit-down with Raffaele Ghigliazza, a quant with a PhD background in mechanical engineering and deep work across applied math, dynamical systems, and neuroscience. He has spent about 20 years in finance, split between risk and asset management, and currently works as a macro-systematic researcher.

We discuss quant research after LLMs: what LLMs really changed, how to think about backtesting, and why robustness matters more than ever. Topics include business cycles, data limitations, overfitting, CPCV and cross-validation, ensembling, and why mixing market regimes can break a model.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Episode 8 with Orlando explores where market models work and where they fail, especially in credit markets where pricing is less observable, and data is often dirty. We cover how to find edge through data cleaning, why end-of-day pricing can mislead risk systems, and how to think about VaR and stress testing when liquidity shifts. We also discuss the limits of the Black-Scholes model for long-dated or far-from-the-money options, how Orlando builds a meritocratic research team, and what it takes to scale from an emerging fund to an institutional one.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Episode 7 features Matthias Bouquet, a quant who moved from a computer vision PhD into asset management, prop trading, banks, and hedge funds across Tokyo, London, and Singapore.

We cover why market ML is harder than vision, how overfitting shows up, and what actually helps in practice: solid validation, simpler models, better features, and strict risk management.

He also explains an options lens on volatility and skew, real-world trading frictions such as slippage, fills, and outages, and how LLMs can accelerate research without replacing discipline.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

In Episode 6, we host Meir Barak, a veteran day trader, author, and the founder and chairman of Tradenet, where he focuses on building structured training programs for traders worldwide. We discuss what his day-to-day work looks like, including turning market behavior into repeatable frameworks, prioritizing risk discipline, and developing traders through process.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Marco Santanché, founder of Unbiased Alpha, joins the show to unpack what truly matters in quantitative research. We explore the key differences between institutional and retail trading, the real risks behind CFDs and leverage, and how quants turn vague client objectives into clear, actionable KPIs. The conversation also dives into realistic backtesting, why overfitting is so common, and when machine learning genuinely adds value in trading.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

A sit-down with Jared Broad, CEO of QuantConnect, to unpack how one platform turned quant research, backtesting, and live execution into an end-to-end workflow. Jared explains why QuantConnect went open source in an industry that usually keeps everything secret, and why hedge funds waste years rebuilding the same infrastructure instead of focusing on alpha.

We break down what makes Lean “institutional-grade,” including its plugin architecture for brokerages, fees, and datasets, as well as the complex engineering behind corporate actions, ticker changes, and extended historical coverage. Jared also shares how QuantConnect validates data quality by cross-checking vendors, running automated “spiders", and using human review when needed.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

A focused conversation with Oren Tapiero, a quantitative researcher at Tidal, on how machine learning is truly used in live trading. The discussion covers why the research question matters more than the model itself, how to approach feature engineering and causality instead of simple correlation, and why walk-forward backtesting and regime awareness are essential. A clear, reality-driven perspective on ML for quants who care about what actually works.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Oz Pirvandy is a Tel Aviv-based systematic fund manager and the founder of Elevate Algo Fund. With a background across economics, political science, mathematics, and data science, Oz brings a research-driven approach to portfolio construction, shaped by both academia and real-world experience in banks, where risk management is the primary priority.

In this episode, Oz explains why the S&P 500 works as an algorithmic benchmark and what most investors miss about its mechanics: concentration, index rules, and the tradeoff between rebalancing frequency and costs. We discuss his framework for building portfolios by ranking opportunities by risk-adjusted return, then adding positions based on low correlation; why he prefers partial rebalancing; and why keeping meaningful cash reserves is essential for both protection and flexibility. We finish with his view on 2026 and his plan to launch a second, more flexible multi-strategy fund around mid-2026.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.

Ryan Ling is a London-based systematic short-term interest rate (STIR) trader. Ryan studied Mathematics and Data Science, blending statistics and computer science, and has built his career across several parts of quantitative trading. He began in banking, structuring and exotics, then moved into crypto trading, including market-making and HFT, before transitioning into interest rate futures.

In this episode, Ryan explains what market making really involves, how traders monitor high-speed algorithms in real time, and why the job often feels more like art than science when you are reacting to flow and managing adverse selection. We also discuss where data analysis and machine learning actually add value in practice, which is often after the fact through post-mortems that help teams understand what happened and improve execution. The conversation also touches on why OTC trading still matters, how competition changed crypto spreads, and a forward-looking idea Ryan finds compelling: the emergence of tradable markets for AI compute and what it might take to make them liquid.

*DISCLAIMER*

The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product.

Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.