Not only that, but you can predict — more precisely compute with absolute certainty — what the value of any stock will be tomorrow. Transaction fees are well below 0.05% and the market, at least in the version presented here, is fair: in other words, a zero-sum game if you play by luck.
If instead the player uses the public data and algorithm to make his bets, he will quickly become a billionaire. Actually not exactly, because the operator will go bankrupt long before it happens. In the end though, it is the operator that wins. But many players will win too, some big time. In many implementations, more than 50% of the players win on any single bet. How so?
At first glance, this sounds like fintech science fiction, or a system that must have a bug somewhere. But once you read the article, you will see why players could be interested in this new, one-of-a-kind money game. Most importantly, this technical article is about the mathematics behind the scene, the business model, and all the details (including legal ones) that make this game a viable option both for the player and the operator.
Some of the features are based on new advances in number theory. Anyone interested in cryptography, risk management, fintech, synthetic data, operations research, gaming, gambling or security laws, should read this material. It describes original, state-of-the-art technology with potential applications in the fields in question. The author may work on a real implementation.
This project started several years ago with extensive, privately funded research on the topic. An earlier version was presented at the INFORMS conference in 2019. Python code is included in the article, to process truly gigantic numbers. The author holds the world record for the number of computed digits for most quadratic irrationals, using fast algorithms. This may be the first time that massive amounts of such large sequences are used and necessary to solve a real-world problem.
The 20-page article is available on GitHub, here. The Python code is in the same folder. It is now part of my book “Gentle Introduction To Chaotic Dynamical Systems”, available here.
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About the Author
Vincent Granville is a pioneering data scientist and machine learning expert, co-founder of Data Science Central (acquired by TechTarget in 2020), founder of MLTechniques.com, former VC-funded executive, author and patent owner. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET. Vincent is also a former post-doc at Cambridge University, and the National Institute of Statistical Sciences (NISS). He published in Journal of Number Theory, Journal of the Royal Statistical Society (Series B), and IEEE Transactions on Pattern Analysis and Machine Intelligence. He is also the author of multiple books, including “Intuitive Machine Learning and Explainable AI”, available here. Vincent lives in Washington state, and enjoys doing research on spatial stochastic processes, chaotic dynamical systems, experimental math and probabilistic number theory.
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