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

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

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

*DISCLAIMER*

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

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