The Blushing Quants Podcast

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.