The Blushing Quants Podcast

Eren Biri: How Volatility Traders Think and What Defines AI-Native Hedge Fund | Blushing Quants #28

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: Credit Risk and Quant Infrastructure | Blushing Quants #27

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: Physics, Crypto, and Energy Market Complexity | Blushing Quants #26

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: Low-Correlation Strategies, Research, and ETF Innovation | Blushing Quants #25

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: Academia, Hedge Funds, AI, and Applied Finance | Blushing Quants #24

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.