Oz Pirvandy is a Tel Aviv-based systematic fund manager and the founder of Elevate Algo Fund. With a background across economics, political science, mathematics, and data science, Oz brings a research-driven approach to portfolio construction, shaped by both academia and real-world experience in banks, where risk management is the primary priority.

In this episode, Oz explains why the S&P 500 works as an algorithmic benchmark and what most investors miss about its mechanics: concentration, index rules, and the tradeoff between rebalancing frequency and costs. We discuss his framework for building portfolios by ranking opportunities by risk-adjusted return, then adding positions based on low correlation; why he prefers partial rebalancing; and why keeping meaningful cash reserves is essential for both protection and flexibility. We finish with his view on 2026 and his plan to launch a second, more flexible multi-strategy fund around mid-2026.

*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.

Ryan Ling is a London-based systematic short-term interest rate (STIR) trader. Ryan studied Mathematics and Data Science, blending statistics and computer science, and has built his career across several parts of quantitative trading. He began in banking, structuring and exotics, then moved into crypto trading, including market-making and HFT, before transitioning into interest rate futures.

In this episode, Ryan explains what market making really involves, how traders monitor high-speed algorithms in real time, and why the job often feels more like art than science when you are reacting to flow and managing adverse selection. We also discuss where data analysis and machine learning actually add value in practice, which is often after the fact through post-mortems that help teams understand what happened and improve execution. The conversation also touches on why OTC trading still matters, how competition changed crypto spreads, and a forward-looking idea Ryan finds compelling: the emergence of tradable markets for AI compute and what it might take to make them liquid.

*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.