Paul Bilokon is a veteran quant, educator, and entrepreneur with experience across major banks and systematic trading.

In this episode, we go deep into what actually makes research deployable: building a backtesting framework you can trust, cleaning and normalizing data correctly (rolls, corporate actions, microstructure effects), and stress-testing strategies against execution lags, transaction costs, and market impact.

We also discuss how Paul thinks about critical thinking as a repeatable research process, why he prefers starting with simple baselines before escalating model complexity, and where reinforcement learning and neural networks fit in finance when explainability and production constraints matter.

*DISCLAIMER*

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

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