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The Blushing Quants Podcast

theblushingquants
Quantitative Finance
Machine Learning
Artificial Intelligence
Cryptocurrency
Backtesting
Deep Learning
S&P 500
Genai Agents
Energy Market
Large Language Models
Epoch
Tradenet
Quantconnect
Market Making
Counterparty Credit Risk
Haris Chalvatzis
Meir Barak
AI Compute
Efficient Markets Hypothesis
Trading

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.

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The information shared o... more

PublishesTwice weeklyEpisodes30Founded6 months ago
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EducationTechnology

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Latest Episodes

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 experienc... more

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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 f... more

YouTube

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 a... more

YouTube

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... more

YouTube

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Recent Guests

Jonathan Davies
Economist with over 30 years in financial services, experience across research, strategy, and portfolio management
Independent/Various financial institutions
Episode: Jonathan Davies: The Theory That Challenges Every Trader and Investor | Blushing Quants #29
Eren Biri
CEO/Founder of OneEye Capital, hedge fund manager
OneEye Capital
Episode: Eren Biri: How Volatility Traders Think and What Defines AI-Native Hedge Fund | Blushing Quants #28
Nikolai Nowaczyk
Quantitative researcher with a background in mathematics and extensive CCR work in industry and academia
Independent researcher / industry practitioner
Episode: Nikolai Nowaczyk: Credit Risk and Quant Infrastructure | Blushing Quants #27
Ufuk Tasdan
Guest with a background in physics, philosophy of physics, and energy analytics
Montel Energy
Episode: Ufuk Tasdan: Physics, Crypto, and Energy Market Complexity | Blushing Quants #26
Oded Shimoni
CEO of AlphaBeta
AlphaBeta
Episode: Oded Shimoni: Low-Correlation Strategies, Research, and ETF Innovation | Blushing Quants #25
Ben Charoenwong
A guest on the show discussing academia-industry intersections, PhD background, and work at INSEAD
INSEAD
Episode: Ben Charoenwong: Academia, Hedge Funds, AI, and Applied Finance | Blushing Quants #24
Garret Brennan
Co-founder and CEO of Epoch
Epoch
Episode: Garret Brennan: Deterministic AI for Institutional Quant Workflows | Blushing Quants #23
Roman Isachenko
Quant researcher with experience in derivatives pricing and hedge funds
Former Moscow hedge fund / European asset management experience
Episode: Roman Isachenko: Alpha Decay, Derivatives, and the Reality of Quant | Blushing Quants #22
Zach Marx
Chief Investment Officer
Vineyard Quant Capital
Episode: Zach Marx: Where Retail Sentiment Meets Systematic Equities | Blushing Quants #21

Host

Efek
Host of the show

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Apple Podcasts
#124
Israel/Technology

Talking Points

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Oren Tapiero: How Machine Learning Works in Live Trading | Blushing Quants #3
Q: How do you approach feature engineering to align with economic storytelling?
Build features that capture the behavior of distinct market actors (long-term, mid-term, short-term) and ensure they reflect an economic story; if a feature does not fit the narrative, it should be discarded or vetted rigorously for causality and information content.
Oren Tapiero: How Machine Learning Works in Live Trading | Blushing Quants #3
Q: What is the right framing for a machine learning project in finance, and how does it affect the outcome?
Frame the problem by clearly defining the target and the research question, as this determines what the model can realistically learn and what kind of signals will be useful; misframing leads to chasing the wrong objective and poor results.
Israel Bergenstein: Systematic Strategy Design to Deployable Trading Models | Blushing Quants #12
Q: How do you approach selecting and architecting machine learning for finance?
Start with basic models, ensure orthogonal features when possible to reduce collinearity, apply regularization and dropout to control overfitting, and prioritize robust data handling and backtesting practices (e.g., walk-forward) before going live. Avoid over-reliance on complex architectures until simpler baselines meet performance targets.
Paul Bilokon: Backtesting, RL, and Robust Quant Research | Blushing Quants #10
Q: How do you approach alpha research from data to decision-making?
Begin with a reliable backtesting environment, clean and transform data carefully, test many variations, and use a sequence of models from simple baselines to more complex ones, guided by metrics aligned with business goals.
Marco Santanché: Quantitative Research in Practice - From KPIs to Live Trading | Blushing Quants #5
Q: What are the major types of criteria you consider when building a hedge fund consultancy for clients?
You assess context, timelines, regulatory or market constraints, access to instruments (e.g., long-only vs. ability to short), risk tolerance, and the discipline to manage expectations; time and honesty with clients are essential traits in delivering credible solutions.

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Frequently Asked Questions About The Blushing Quants Podcast

What is The Blushing Quants Podcast about and what kind of topics does it cover?

A rigorous look at quantitative finance powered by machine learning, with guests who are deeply technical and industry-facing. Episodes frequently center on practical risk modeling, trading systems, data quality, and how advanced analytics—from Monte Carlo simulations to agent-based AI workflows—are deployed in real markets. Expect thoughtful debates on production-ready modeling, risk controls, and the tensions between theory and execution, along with fresh takes on crypto, energy markets, and ETFs. A notable strength is the willingness to critique traditional approaches while showcasing concrete architectures, governance, and implementation details that help listeners understand what actually works in regulated, high-stakes environments.

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These podcasts share a similar audience with The Blushing Quants Podcast:

1. Top Traders Unplugged
2. Excess Returns
3. Odd Lots
4. Titans Of Tomorrow

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The Blushing Quants Podcast launched 6 months ago and published 30 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on The Blushing Quants Podcast?

Recent guests on The Blushing Quants Podcast include:

1. Jonathan Davies
2. Eren Biri
3. Nikolai Nowaczyk
4. Ufuk Tasdan
5. Oded Shimoni
6. Ben Charoenwong
7. Garret Brennan
8. Roman Isachenko

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