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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 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.
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By theblushingquantsFrancisco 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.