In probability theory and intertemporal portfolio choice, the Kelly criterion (or Kelly strategy or Kelly bet), also known as the scientific gambling method, is a formula for bet sizing that leads almost surely (under the assumption of known expected returns) to higher wealth compared to any other strategy in the long run
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For an even money bet, the Kelly criterion computes the wager size percentage by multiplying the percent chance to win by two, then subtracting one-hundred percent. So, for a bet with a 70% chance to win the optimal wager size is 40% of available funds.
This repo contains my work implementing the kelly criterion, specifically the case outlined in the second paragraph above, and other betting strategies and then doing a bunch of simulations with them.
The work in this repo is done with the hy programming language, a lisp that maps pretty closely to python.
kelly.hycontains functions for implementing different betting strategies and functions for creating simulations with different those betting strategies.explore-kelly.hycontains code for exploring and visualizing betting simulations. This file is a bit messy 😅