The Blushing Quants Podcast
- Episodios
- 42
- Se publica
- a diario
- Último episodio
- 23 Sep 2026
Sobre el podcast
The Blushing Quants is a candid look at the intersection of quantitative finance and machine learning. We discuss the hard truths of building ML-based investment systems. What works, what fails, and why. We leave the LLMs to the chatbots and focus on the heavy hitters of quantitative finance: Neural Networks, Time Series Analysis, and Statistical Learning.
*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.
Episodios 42
Eyal Neuman: Market Impact, Optimal Execution and Quantum Computing | Blushing Quants #42
Eyal Neuman is a mathematical finance researcher and co-director of the quantitative finance master’s program at Imperial College London. His work spans probability, stochastic control, market microstructure …
Notas del episodio
Eyal Neuman is a mathematical finance researcher and co-director of the quantitative finance master’s program at Imperial College London. His work spans probability, stochastic control, market microstructure, price impact, optimal execution, multi-agent systems, and quantum computing in finance.
Eyal’s path began with industrial engineering at Tel Aviv University before his interest shifted toward mathematics and probability. He completed graduate research at the Technion, followed by postdoctoral work in Hong Kong and Rochester, New York. At Imperial College, he began combining theoretical research with practical financial problems through collaborations with academics, practitioners, and Capital Fund Management.
In this episode, Eyal joins us for an in-depth conversation about connecting academic mathematics with real financial markets, constructing useful models from complex trading problems, understanding market impact, and identifying where emerging technologies may offer genuine value.
Eyal explains why meaningful quantitative research often develops at the intersection of mathematical depth and practical relevance. Industry practitioners understand the problems they face but may lack the time to build rigorous models, while academic researchers can provide mathematical structure, analytical tools, and a deeper examination of the underlying trade-offs.
We discuss how complex market behavior can be translated into a tractable model. Eyal explains why researchers must identify the most important state variables without attempting to reproduce every detail of reality. Models that are too simple may miss the essential mechanism, while models that are too complicated can become impossible to solve, difficult to interpret, and vulnerable to overfitting.
The conversation explores stochastic control and its applications to portfolio construction, market making, and optimal execution. Eyal explains how researchers define an objective, model the evolution of relevant state variables, and derive a strategy that balances expected performance, trading costs, market impact, and risk.
We then examine price impact and the problem of executing large positions. Eyal discusses propagator models, statistical estimation, liquidity, execution horizons, and how previous trades can influence future price movements. We explore how an investor can divide a large order over time to reduce costs while still completing the required transaction.
The discussion goes deeper into multi-agent markets and signal crowding. When many participants trade using the same information, their collective order flow can reinforce the signal, increase market impact, and reduce the profitability available to each trader. Eyal explains how mathematical models can make these relationships more transparent and help quantify interactions that practitioners already recognize intuitively.
We also explore passive execution through limit orders. Instead of modeling every individual market participant, researchers can study the market’s average response to an order at a particular distance from the best bid or ask. This creates a more tractable framework for deciding how much liquidity to provide, where to place orders, and how to manage inventory and execution risk.
Eyal discusses the contribution of econophysics and empirical market laws, including the square-root relationship between traded quantity and price impact. We examine the difference between observing a statistical regularity and constructing a model that explains how the behavior of traders may generate it.
The conversation also covers quantitative finance education at Imperial College. Eyal explains how the program combines foundational subjects such as stochastic processes, derivatives pricing, and interest-rate models with newer areas including machine learning, deep learning, market microstructure, and quantum computing. Practitioner lectures, industry advisors, internships, and applied research help connect academic training with the skills required in professional quantitative roles.
Finally, we discuss the current state of quantum computing in finance. Eyal explains why practical use cases remain limited and uncertain, while highlighting potential applications in derivatives pricing, high-dimensional partial differential equations, optimization, and sampling rare distributions for risk scenarios.
A technical and thoughtful conversation on mathematical finance, industry collaboration, stochastic control, market impact, optimal execution, signal crowding, quantitative education, and the developing role of quantum computing in financial 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.
Joseph Chen: Building AI-Powered Backtesting and Trading Systems | Blushing Quants #41
Joseph Chen is a quantitative finance professional with extensive experience building trading systems, backtesting frameworks, and research infrastructure for multiple financial firms. In this episode, Joseph …
Notas del episodio
Joseph Chen is a quantitative finance professional with extensive experience building trading systems, backtesting frameworks, and research infrastructure for multiple financial firms.
In this episode, Joseph joins us for a technical and practical conversation about designing institutional-grade quantitative research systems, integrating artificial intelligence into established workflows, validating financial data, and moving systematic strategies from backtesting into live trading.
Joseph explains why asking an AI coding tool to build an entire trading system from scratch can lead to missing components, inconsistent assumptions, and unreliable results. Instead, he argues that AI should operate within a modular framework containing trusted data sources, reusable libraries, clearly defined interfaces, and established backtesting procedures.
We discuss the importance of data integrity and why information from even reputable vendors must still be inspected carefully. Joseph explains how corporate-action adjustments, raw versus adjusted prices, missing observations, timestamps, exchanges, currencies, and instrument specifications can materially change the outcome of a backtest.
The conversation explores the architecture of an event-driven trading system. Joseph breaks down the roles of data loaders, bar engines, strategy modules, portfolio components, order-management systems, matching engines, reporting tools, and monitoring modules. He also explains why intermediate calculations, signals, and order events should be recorded so the entire research process can be audited and reproduced.
Joseph discusses the challenge of supporting multiple asset classes and trading frequencies within one framework. Equities, futures, foreign exchange, fixed income, and options each have different conventions, data requirements, execution rules, and risk characteristics. Similarly, infrastructure designed for daily or minute-level strategies may be unsuitable for high-frequency trading, where order-book data and millisecond-level performance become critical.
We examine the transition from backtesting to simulation and live trading. Joseph explains why the same strategy logic should remain consistent across all three environments, with only the order destination and execution mechanism changing. A realistic simulation engine should reproduce broker callbacks, partial fills, matching rules, transaction costs, and market-specific execution behavior as closely as possible.
The discussion also covers the risks of using AI to optimize strategies repeatedly on the same historical data. Faster experimentation can easily accelerate overfitting, making human supervision essential. Joseph emphasizes that researchers must evaluate whether each modification is economically reasonable instead of allowing AI to search blindly for the highest historical return.
We also explore machine learning in quantitative finance, including the trade-off between predictive power and interpretability. Joseph explains why smaller datasets may be better suited to simpler tree-based models, while deep neural networks or transformers may be appropriate only when researchers have enough data and a strategy that genuinely requires that level of complexity.
Finally, Joseph outlines how he evaluates a new systematic strategy. The process begins by confirming that the existing infrastructure can support the idea, followed by controlled backtesting, parameter-sensitivity analysis, realistic simulation, and deployment with limited capital. Exposure should increase only when live behavior remains consistent with the original research.
A detailed conversation on backtesting architecture, systematic research, AI-assisted development, data integrity, execution simulation, machine learning, strategy validation, and the infrastructure required to transform a quantitative idea into a live trading 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.
Peter Kostovčík: Why Features Matter More Than Models in Quant Trading | Blushing Quants #40
Peter Kostovčík is a quantitative trader and researcher with a background in mathematics, machine learning, systematic equities trading, and the development of live trading systems. In this episode, Peter joins …
Notas del episodio
Peter Kostovčík is a quantitative trader and researcher with a background in mathematics, machine learning, systematic equities trading, and the development of live trading systems.
In this episode, Peter joins us for a practical conversation about machine learning in finance, feature engineering, research pipelines, trading-system design, portfolio construction, position sizing, liquidity, and the growing role of AI agents in quantitative research.
Peter explains why the model itself is often the least important part of a machine-learning strategy. While researchers may focus on increasingly sophisticated algorithms, he argues that most of the real work lies in understanding the data, developing meaningful features, defining the correct objective, and forming ideas that reflect how markets actually operate.
We discuss when machine learning is useful and when it may introduce unnecessary complexity. Peter explains why it can be effective when ranking or classifying thousands of equities, but far more vulnerable to overfitting when applied to a single asset with only one historical time series.
The conversation explores how Peter uses classification, probability distributions, clustering, and other machine-learning methods to narrow large investment universes into manageable groups of potential trades. Rather than expecting a model to predict an exact return, he focuses on identifying relative opportunities that can be combined within a portfolio.
Peter also breaks down his research process, beginning with an idea and a simple statistical test before moving into feature engineering or model development. We discuss in-sample analysis, out-of-sample validation, protected backtest periods, and why repeatedly examining test results can quietly transform supposedly unseen data into another source of overfitting.
The discussion then moves from research into live implementation. Peter explains why liquidity, spreads, slippage, short-selling restrictions, turnover, market capacity, and execution constraints must be considered from the beginning. A strategy may look attractive statistically while remaining impossible to trade at meaningful size.
We also examine the role of AI agents in quantitative research and software development. Peter shares how AI has accelerated his ability to build research infrastructure and test ideas, while emphasizing that it still requires experienced supervision. Without clear constraints and market knowledge, an AI-generated system may reproduce unrealistic assumptions found in academic papers, online examples, and historical training data.
Peter discusses his preference for equities, where large and diverse universes provide more opportunities for systematic research. He also shares lessons from expanding strategies into international markets, where a valid signal can still fail because the available liquidity is insufficient.
Finally, we explore portfolio construction and position sizing. Peter explains why he begins with equal weighting before adding sector, liquidity, volatility, and concentration constraints. We compare equal weighting with probability-based sizing, discuss the limitations of universal formulas such as the Kelly criterion, and examine why the appropriate solution depends on the specific strategy, holding period, and market environment.
A practical and detailed conversation on machine learning, feature engineering, systematic trading, research validation, AI agents, liquidity, portfolio construction, and the challenges of turning a promising model into a robust live trading 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.
Julien Riposo: Building Robust Quant Models for Complex Financial Systems | Blushing Quants #39
Julien Riposo is the Head of Quantitative Research at Stewardship and Governance Associates and the founder and CEO of JR Enterprise. With a PhD in Applied Mathematics and a background spanning quantitative …
Notas del episodio
Julien Riposo is the Head of Quantitative Research at Stewardship and Governance Associates and the founder and CEO of JR Enterprise. With a PhD in Applied Mathematics and a background spanning quantitative finance, model validation, blockchain, digital assets, and market infrastructure, Julien focuses on transforming abstract mathematical ideas into practical decision-making systems.
In this episode, Julien joins us for an in-depth conversation about mathematical abstraction, complex financial systems, model robustness, asset valuation, artificial intelligence, and the process of turning research into tools that can support real decisions.
Julien explains the difference between a model that appears convincing and one that can be trusted in practice. We discuss assumptions, calibration, sensitivity analysis, stress testing, implementation constraints, and why researchers must clearly define the conditions under which a model remains reliable.
The conversation explores two dimensions of model reliability: stability through time and structural robustness at a particular moment. Julien explains why a successful backtest is not enough and how mathematics, probability, stochastic processes, and network models can help researchers understand how systems behave under uncertainty.
We also discuss the role of abstraction in quantitative research. Straightforward problems may be addressed through established optimization methods, but more complex questions often require researchers to step outside familiar frameworks, rethink the structure of the problem, and draw ideas from other disciplines.
Julien shares examples from blockchain, governance systems, biology, and finance to show how graph theory, diffusion models, and related mathematical structures can describe interactions between connected agents. He explains how similar mathematical languages may appear across very different domains, even when their practical interpretations remain distinct.
The discussion then moves to system design and the importance of properly defining a problem before selecting a model or pursuing the latest research. Julien explains why the meaning and purpose of a system must remain clear throughout the research process and why even advanced techniques provide little value when they are disconnected from the original question.
We also examine asset valuation from a deeper perspective. Market prices, accounting data, model outputs, textual signals, and AI-generated scores are all representations shaped by assumptions and constraints. Julien explores whether value can be understood as something structurally meaningful that remains consistent across multiple valid representations.
Finally, Julien discusses how he uses artificial intelligence in research and learning. Rather than treating AI as a source of automatic answers, he uses it as a rigorous analytical partner for exploring academic literature, challenging mathematical reasoning, identifying errors, and strengthening his problem-solving process.
A technical and philosophical conversation on quantitative research, mathematical modeling, complex systems, blockchain, asset valuation, artificial intelligence, and what it takes to build models that can be trusted in the real world.
*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.
Antonio Berenguer: How Options Market Makers Price Volatility and Manage Risk | Blushing Quants #38
Antonio Berenguer is an options trading and market-making professional with a background in engineering, mathematics, computational research, quantitative finance, and cryptocurrency markets. Antonio completed …
Notas del episodio
Antonio Berenguer is an options trading and market-making professional with a background in engineering, mathematics, computational research, quantitative finance, and cryptocurrency markets.
Antonio completed a PhD in computational electromagnetics before moving into quantitative finance. After earning a master’s degree in Madrid, he joined an options market-making firm in Amsterdam, where he spent approximately six years working across liquidity provision, screen and broker trading, quantitative research, signal development, and position-taking strategies. He later moved into cryptocurrency trading and market making.
In this episode, Antonio joins us for an in-depth conversation about options market making, volatility trading, liquidity, hedging, position sizing, portfolio risk, and quantitative research.
Antonio explains how market makers generate returns by quoting bid and ask prices, capturing spreads, and managing inventory risk. We discuss how they adjust quotes, use correlated assets for hedging, and sometimes cross the spread when reducing exposure becomes more important than preserving their trading edge.
The conversation explores the complexity of options portfolios across different strikes and maturities. Antonio explains how Delta, Gamma, Vega, Vanna, Volga, and other Greeks help traders summarize risk, while also highlighting the limitations of Black-Scholes and the dangers of relying too heavily on simplified measures.
We also discuss position limits, correlation risk, internal netting, centralized Delta hedging, exchange rebates, quoting obligations, and how sophisticated firms reduce transaction costs by offsetting exposures across products and trading desks.
Antonio examines the gap between academic theory and practical trading. Elegant models may fail when liquidity is limited, hedges cannot be executed, or transaction costs remove the apparent opportunity. He shares a research process built around exploring data broadly, identifying promising relationships, and focusing only on signals that can realistically be traded.
Finally, we discuss crypto options, tokenized assets, and how blockchain technology may bring traditional and digital markets closer together.
A technical and practical conversation on options, volatility, market making, portfolio risk, quantitative research, liquidity, and the infrastructure behind modern financial 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.
Gilad Bar-Ilan: Turning Crowd Sentiment into Trading Signals with AI | Blushing Quants #37
Gilad Bar-Ilan is the CEO and co-founder of Crowd Wisdom Trading, with extensive experience across proprietary trading, equities, options, futures, foreign exchange, product management, software development …
Notas del episodio
Gilad Bar-Ilan is the CEO and co-founder of Crowd Wisdom Trading, with extensive experience across proprietary trading, equities, options, futures, foreign exchange, product management, software development, and financial technology.
Gilad began his career as a day trader with remote U.S. proprietary trading firms before joining an Israeli proprietary trading firm and becoming deeply involved in options trading on the Tel Aviv Stock Exchange. He later worked as a product manager with Israeli technology startups, combining his trading experience with the ability to transform complex technical concepts into working financial products.
In this episode, Gilad joins us for an in-depth conversation about crowd intelligence, financial sentiment, alternative data, artificial intelligence, and how thousands of market opinions can be turned into structured, actionable trading signals.
Gilad explains how Crowd Wisdom Trading analyzes thousands of hours of financial content from YouTube and other online sources. Using large language models and task-specific AI agents, the platform identifies which assets traders are discussing, distinguishes between short-term and long-term views, and extracts concrete information such as direction, entry prices, targets, stop levels, and time horizons.
We discuss why identifying positive or negative sentiment is not enough. A general opinion about a stock may provide limited value, while a specific trading plan containing an entry, target, stop, and time horizon can be measured, evaluated, and compared with other predictions.
The conversation explores the challenge of separating useful information from noise. Gilad explains why aggregating every market opinion does not automatically create intelligence and why the quality, experience, and track record of the contributors matter. Instead of relying on the general public, his approach focuses on building a professional crowd of experienced traders with relevant market expertise.
Gilad connects this framework to Philip Tetlock’s research on superforecasters. Just as groups of skilled forecasters can outperform ordinary prediction groups, Gilad believes that combining the structured opinions of successful traders can produce a stronger market view than following any single analyst or commentator.
We also examine how raw information becomes actionable signals. Gilad explains why replacing unstructured noise with a larger collection of filtered opinions still leaves traders with too many decisions. The next step is therefore to rank opportunities by factors such as risk and reward, narrow the list, and present a manageable selection of potential trades.
The discussion then moves to execution and simplicity. While collecting, classifying, and aggregating data may require complex technological infrastructure, Gilad argues that the final trading plan should remain clear and understandable, with a defined entry, stop, target, and method for measuring results.
Gilad shares the entrepreneurial journey behind Crowd Wisdom Trading and explains how the emergence of ChatGPT, large language models, and AI agents made it possible to build a product he had wanted throughout his trading career. What began as a question about why traders should follow one financial commentator when technology could analyze thousands quickly developed into a scalable platform for extracting collective market intelligence.
We also explore Gilad’s experience across equities, options, futures, and foreign exchange. He explains why risk management matters more than the specific financial instrument being traded and why traders must always expect that something will eventually break. Whether the disruption is a financial crisis, natural disaster, pandemic, or unexpected market event, survival depends on controlling risk and preparing multiple contingency plans.
Finally, we discuss the future of discretionary and retail trading. Gilad expects professional tools that were once available mainly through institutional environments to become increasingly accessible on retail traders’ mobile devices. At the same time, access to more tools will not guarantee success. Traders will need to specialize, remain adaptable, understand their own decision-making, and learn how to convert expanding volumes of data into useful signals.
A practical and detailed conversation on crowd intelligence, artificial intelligence, financial sentiment, alternative data, signal construction, trading psychology, risk management, and the future of technology-assisted decision-making in financial 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.
Paul MacGregor: Building Electronic Markets and Commodity Exchanges | Blushing Quants #36
Paul MacGregor is a financial markets executive with nearly 30 years of experience across exchanges, derivatives, electronic trading, commodities, technology, and international business development. In this …
Notas del episodio
Paul MacGregor is a financial markets executive with nearly 30 years of experience across exchanges, derivatives, electronic trading, commodities, technology, and international business development.
In this episode, Paul traces his journey from strategic planning at BP to the open-outcry trading floors of LIFFE, where he witnessed the rapid transition from human execution to fully electronic markets. He explains how real-time data, systematic strategies, and execution costs accelerated that transformation, changing the structure of global markets.
Paul shares his experience developing electronic trading platforms for bonds, equities, indexes, and complex interest-rate strategies. He also discusses the expansion of exchange technology across Europe, the rise of algorithmic and proprietary trading, and the infrastructure required to support market makers and increasingly sophisticated participants.
The conversation moves into physical commodities and Paul’s time at the London Metal Exchange. He explains what makes markets such as copper, aluminium, nickel, zinc, lead, and tin different from purely financial products, including physical settlement, global supply chains, and the varied needs of producers, consumers, and investors.
Paul also explores the challenge of building effective commodity benchmarks. From battery metals to LNG, contracts must reflect the underlying physical market closely enough to help participants manage risk. When the benchmark and the actual exposure differ, basis risk can significantly reduce the effectiveness of a hedge.
The episode also examines auction mechanisms beyond traditional financial markets. Paul shares how electronic auction technology was adapted for the art market and later for climate projects, creating greater transparency and price discovery in areas that had previously relied heavily on private negotiations.
Looking internationally, Paul discusses the development of capital markets in India, China, Saudi Arabia, Singapore, and the Middle East. He reflects on India’s GIFT City, the growth of Asian commodity exchanges, and the opportunities created as financial activity becomes less concentrated in established Western markets.
Throughout the conversation, Paul emphasizes that technology alone cannot create a successful exchange. Clear strategy, stakeholder support, participant education, migration planning, and strong relationships are all essential. Even as AI, automation, and quantitative trading continue to reshape markets, people remain at the heart of building trust, liquidity, and lasting market ecosystems.
This episode offers a wide-ranging look at how exchanges evolve, how new markets are built, and how financial infrastructure connects technology with the real economy.
*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.
Roger McIntosh: Institutional Portfolios, Factor Models and Alpha Decay | Blushing Quants #35
Roger McIntosh is a Chief Investment Officer with an actuarial and quantitative background and extensive experience managing equities, fixed income, multi-asset portfolios, pension assets, index funds, and …
Notas del episodio
Roger McIntosh is a Chief Investment Officer with an actuarial and quantitative background and extensive experience managing equities, fixed income, multi-asset portfolios, pension assets, index funds, and systematic investment strategies.
Roger helped establish Vanguard’s investment team in Australia, built several of its index funds, led its equity and bond teams, and developed multi-asset portfolios for Australian investors. He later created and managed his own quantitative investment strategy, giving him a rare perspective across passive investing, active management, portfolio construction, and institutional decision-making.
In this episode, Roger joins us for an in-depth conversation about how institutional portfolios are constructed, how quantitative factors are selected and combined, and why managing an index fund is far more complex than simply purchasing every security in a benchmark.
Roger explains the difference between full replication and optimized replication. While some equity indexes can be replicated almost completely, bond indexes and broad small-cap benchmarks may contain thousands of securities that cannot all be owned efficiently. Portfolio managers must therefore reproduce the benchmark’s underlying factor exposures while controlling tracking error and active risk.
We discuss how country, industry, value, momentum, quality, duration, convexity, and other factors can be used across equity and fixed-income portfolios. Roger explains why multi-factor models are generally more robust than relying on a single factor and why the factors that influence global technology companies may differ significantly from those driving regional Asian small-cap stocks.
The conversation goes deeper into factor ranking, weighting, and signal construction. Roger shares how he uses point-in-time data, weekly model updates, ongoing ex-post testing, and percentile rankings to evaluate whether a signal remains useful or has started to decay.
We also explore alpha decay and holding periods. Roger explains why holding a position is itself an active decision, how different factors lose predictive power at different speeds, and why momentum, value, and quality signals should not always be treated in the same way.
Roger shares his experience with index rebalancing and the challenges created when large amounts of passive capital must trade simultaneously. We discuss how index additions and removals can influence price discovery, create predictable market flows, and affect the construction of both passive and active portfolios.
The discussion also covers portfolio optimization, risk budgets, benchmark-relative exposures, concentration, portfolio capacity, and why optimization methods that work across thousands of securities may be less useful for concentrated portfolios containing only 20 or 30 names.
Finally, we examine the growing abundance of financial data and the importance of data quality, interpretability, and client communication. Roger explains why quantitative models must remain understandable to investment committees and clients, how ESG requirements can affect portfolio construction, and why unconventional real-world information can sometimes provide useful signals that traditional datasets overlook.
A practical and detailed conversation on institutional investing, factor models, index construction, portfolio optimization, alpha decay, data quality, and the decisions behind managing large pools of capital.
*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.
Nam Nguyen: Sell-Side vs Buy-Side Quants, Monte Carlo and AI | Blushing Quants #34
Nam Nguyen is a career quantitative finance professional with experience across both the sell side and buy side. Based in Toronto and working across North America and Asia, Nam began his career during the …
Notas del episodio
Nam Nguyen is a career quantitative finance professional with experience across both the sell side and buy side. Based in Toronto and working across North America and Asia, Nam began his career during the global financial crisis, building models for complex financial derivatives before moving into model validation, risk management, and eventually buy-side quantitative research.
In this episode of The Blushing Quants, Nam joins us for an in-depth conversation about the differences between sell-side and buy-side quantitative finance, Monte Carlo simulation, derivatives pricing, risk modeling, backtesting, market anomalies, and the growing influence of artificial intelligence.
Nam explains how sell-side quants work with risk-neutral pricing, exotic derivatives, volatility surfaces, stochastic-volatility models, calibration, Value-at-Risk, and Expected Shortfall. He contrasts this with the buy side, where researchers focus more heavily on time series, statistical anomalies, arbitrage opportunities, portfolio construction, and the search for alpha.
We discuss the evolution of Value at Risk and why highly sophisticated models can become difficult for regulators and risk managers to audit. Nam explains why financial institutions moved toward more transparent simulation-based approaches and why Expected Shortfall may provide a better view of tail risk than traditional VaR alone.
The conversation also examines the limitations of historical simulation. We explore bootstrap scenarios, Monte Carlo methods, stress testing, and whether AI-generated scenarios could help researchers model future crises and market conditions that have never appeared in historical data.
Nam shares his perspective on building reliable backtesting environments across equities, fixed income, credit, options, and volatility products. We discuss calibration, rolling and expanding windows, out-of-sample validation, parameter stability, overfitting, and why strong historical performance does not automatically justify capital deployment.
We also explore how quantitative researchers select risk factors. Nam discusses volatility, correlation, liquidity, credit spreads, and the trade-off between interpretable models built from a small number of well-understood variables and machine-learning systems that can process a much larger feature set.
The discussion then moves to market anomalies, including seasonality, liquidity changes, volatility risk premiums, and the importance of combining practitioner knowledge with statistical testing. Nam explains why an observed anomaly must be validated before it can be treated as a reliable investment signal.
Finally, we discuss portfolio diversification, the difficulty of finding stable negative correlations, the role of options in managing downside exposure, and how AI may change market behavior itself. Nam shares why momentum, mean reversion, correlations, volatility, and market-maker hedging dynamics may evolve as more participants adopt advanced quantitative tools.
A technical and practical conversation on derivatives, quantitative risk management, Monte Carlo simulation, market anomalies, portfolio construction, and how AI is reshaping both financial research and market structure.
*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.
Antonio Marrazzo: How to Build Robust Factors with Data and Machine Learning | Blushing Quants #33
Antonio Marrazzo is a quantitative researcher with a background in economics and actuarial science, focused on factor investing, portfolio construction, market regimes, data analysis, and machine learning in …
Notas del episodio
Antonio Marrazzo is a quantitative researcher with a background in economics and actuarial science, focused on factor investing, portfolio construction, market regimes, data analysis, and machine learning in financial markets.
Originally from Argentina, Antonio began applying quantitative methods to investing before formally discovering the quant profession. He translated concepts such as Markowitz portfolio optimization into Python, built his own research pipelines, and developed a systematic approach to understanding markets through data.
In this episode, Antonio joins us for a detailed conversation about how quantitative strategies are researched, tested, and transformed into realistic investment models.
The central question is:
How do you know whether a factor is real, tradable, and persistent rather than the result of statistical chance?
Antonio explains why a factor should be supported by economic reasoning and a plausible relationship with returns. We discuss multiple-testing bias, false discoveries, factor orthogonality, risk premia, and why testing thousands of ideas will almost always produce something that appears statistically significant.
We also explore why factors must be evaluated using realistic and tradable investment universes. Antonio explains how attractive historical results can disappear after accounting for microcap exposure, liquidity constraints, bid-ask spreads, short availability, transaction costs, and other practical limitations.
The conversation goes deeper into multi-factor portfolio construction and dynamic factor allocation. Antonio shares why each factor may perform differently across market regimes, including why momentum can behave better in lower-volatility environments while short-term reversal may become more relevant when volatility increases.
We then examine one of the most important parts of quantitative research: data quality. Antonio discusses missing values, outliers, time-zone alignment, interpolation, point-in-time data, reporting dates, survivorship bias, look-ahead bias, financial statement revisions, and why researchers must understand exactly when information became available to the market.
Finally, we explore machine learning in quantitative finance. Antonio explains why feature engineering can matter more than selecting the most sophisticated model, why classification may be more practical than directly predicting returns, and how models such as logistic regression, random forests, and XGBoost can identify relationships that traditional linear methods may miss.
We also discuss expanding training windows, stationarity, realistic labeling, meta-labeling, purged cross-validation, embargo periods, and the importance of including trading costs throughout the research and validation process.
A technical and practical conversation on factor investing, regime-aware allocation, data engineering, machine learning, portfolio research, and the discipline required to avoid fooling yourself with attractive backtests.
*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.
Vincent Randazzo: Market Breadth, Risk and Systematic Portfolio Management | Blushing Quants #32
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 …
Notas del episodio
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: Inside Citadel, Alpha Decay and the Future of Quant | Blushing Quants #31
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 …
Notas del episodio
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: Trading Education Done Right - AI, Risk & Real Market Education | Blushing Quants #30
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 …
Notas del episodio
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: The Theory That Challenges Every Trader and Investor | Blushing Quants #29
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 …
Notas del episodio
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: 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 …
Notas del episodio
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 …
Notas del episodio
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 …
Notas del episodio
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 …
Notas del episodio
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 …
Notas del episodio
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: Deterministic AI for Institutional Quant Workflows | Blushing Quants #23
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 …
Notas del episodio
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: Alpha Decay, Derivatives, and the Reality of Quant | Blushing Quants #22
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 …
Notas del episodio
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: Where Retail Sentiment Meets Systematic Equities | Blushing Quants #21
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 …
Notas del episodio
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: Crypto, Alpha Factors, and Market Neutrality | Blushing Quants #20
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 …
Notas del episodio
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: AI, Asset Management, and the Business of Funds | Blushing Quants #19
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 …
Notas del episodio
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: Tape Reading, RL, and Sequential Decision-Making | Blushing Quants #18
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 …
Notas del episodio
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: Quant Research, GenAI Agents, and Trading Systems | Blushing Quants #17
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 …
Notas del episodio
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: Trading Desk Operations, Market Execution, and Sales Trading | Blushing Quants #16
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 …
Notas del episodio
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: AI Is Rewriting Quant Workflows | Blushing Quants #15
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 …
Notas del episodio
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 Tratt: 25 Years in Markets - From Prop Trader to Sharpe 4 Systems | Blushing Quants #14
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 …
Notas del episodio
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 Chalvatzis: From Fast Quant Research to Alpha, Execution, and Portfolio | Blushing Quants #13
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 …
Notas del episodio
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.
Aquí están los 40 episodios más recientes de 42. Los anteriores están en el feed del podcast.
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