ABOUT TPT
TPT’s mission is to bring asset management into the Age of Machine Learning (ML). We are currently engaged by clients with a combined AUM of over $1.132 trillion (as of January 31, 2020).
THOUGHT LEADERSHIP
For the past two decades, TPT’s researchers have made some of the most impactful innovations in financial machine learning, covering every stage of the investment process. These contributions include:
Feature engineering: Order imbalance sampling; the triple-barrier method; trend-scanning labels; volume-synchronized probability of informed trading
Strategy development: Stochastic flow diagrams
Bet sizing: Meta-labeling
Backtesting: Combinatorially-purged cross-validation; Monte Carlo backtests
Strategy selection: The deflated Sharpe ratio; the probability of backtest overfitting; the “false strategy” theorem
Portfolio construction: Hierarchical risk parity; nested clustered optimization
Execution: Optimal execution horizon
OUR EXPERT NETWORK
Ranking of the most-read authors in economics. Source: The Social Science Research Network.
Conventional methods yield conventional outcomes. Beating the collective wisdom of the crowds requires innovative approaches, produced by best-in-class talent. For this reason, TPT partners with domain-area experts for each specific project.
Through our connections with the leading universities and National laboratories, we assemble the right team for every particular mandate. Our network includes 30 of the best-known authors in mathematical finance, machine learning and supercomputing. We have access to hardware and software that is years ahead of what commercial services can offer.
This flexible team structure enables us to deliver answers that are novel, insightful, authoritative, and scientific.
TO LEARN MORE
Advances in Financial Machine Learning (Wiley, 2018) explains in detail how TPT’s assembly-line approach works. The assembly line organizes the investment research process in accordance with the scientific method. In particular, the goal of the research team is to formulate hypothesis, whereas the goal of the backtesting team is to prove those hypothesis wrong, by means of counterexamples, or by pointing to flaws in the discovery process.
One year after its publication, this graduate textbook has been translated into Chinese (China Citic Press), Russian (Progress Knijga), Japanese (Kinzai Institute) and Korean (Acorn Publishing), and it is taught at leading universities worldwide.
Machine Learning for Asset Managers (Cambridge University Press, 2020) demystifies the use of ML techniques in asset management. The book explains how modern statistical methods can lead to the discovery of novel investment strategies supported by sound financial theories. Covered topics include:
De-noising and de-toning
Codependence and distance metrics
Optimal clustering
Financial labels
Feature importance analysis
ML-based portfolio construction
Testing-set overfitting
These two textbooks form the academic core of the ORIE 5256 course, at Cornell University’s School of Engineering.
“Dr. López de Prado has written the first comprehensive book describing the application of modern ML to financial modeling. The book blends the latest technological developments in ML with critical life lessons learned from the author’s decades of financial experience in leading academic and industrial institutions. I highly recommend this exciting book to both prospective students of financial ML and the professors and supervisors who teach and guide them.”
“Financial problems require very distinct machine learning solutions. Dr. López de Prado’s book is the first one to characterize what makes standard machine learning tools fail when applied to the field of finance, and the first one to provide practical solutions to unique challenges faced by asset managers. Everyone who wants to understand the future of finance should read this book.”
“The first wave of quantitative innovation in finance was led by Markowitz optimization. Machine learning is the second wave and it will touch every aspect of finance. López de Prado’s Advances in Financial Machine Learning is essential for readers who want to be ahead of the technology rather than being replaced by it.”
“The complexity inherent to financial systems justifies the application of sophisticated mathematical techniques. Advances in Financial Machine Learning is an exciting book that unravels a complex subject in clear terms. I wholeheartedly recommend this book to anyone interested in the future of quantitative investments.”
“In this important book, Marcos López de Prado sets out a new paradigm for investment management built on machine learning. Far from being a ‘black box’ technique, this book clearly explains the tools and process of financial machine learning. For academics and practitioners alike, this book fills an important gap in our understanding of investment management in the machine age.”
“The author’s academic and professional first-rate credentials shine through the pages of this book— indeed, I could think of few, if any, authors better suited to explaining both the theoretical and the practical aspects of this new and (for most) unfamiliar subject. Destined to become a classic in this rapidly burgeoning field.”