Application of Kelly Criterion in Investing / Trading Strategies - DSA ADS Course - 2023

DSA ADS Course - 2023

This DSA ADS course is part of a series of courses that demonstrate how to use applied data science with high performance compute and high quality data to optimize decision making in real world investing / trading scenarios.

Kelly Criterion (including many different modified versions) is used by investors / traders for optimal strategies and risk management. Many elite investors and traders use a customized / modified version of the Kelly Criterion to help formulate optimal strategy in different scenarios. Goal is to determine the optimal amount to put into any one trade or investment.

Discuss investing / trading strategies, applied data science for investing / trading, probabilistic logic, information theory, and machine learning algorithms for investing / trading.

Discuss Kelly Criterion basics: applied probability theory, excess risk adjusted return, logarithm of wealth (maximize expected logarithmic utility), geometric mean maximizing portfolio strategy, long term investing, short term investing, risk reduction investing, optimal return investing, portfolio allocation theory, and capital growth theory. Review theory versus application in real world scenarios.

Discuss pros and cons of modern portfolio theory, diversification strategy, risk management and optimal investing / trading strategies in different scenario environments.

Discuss appropriate risk levels to achieve specific goals (diversification, seeking optimal returns, retirement...etc.).

Discuss diversification strategy versus seeking optimal return strategy.

Discuss benefits and risks of architecting and executing machine learning algorithms.

Discuss Kelly Criterion in relation to Kalman Filter and Black-Scholes Model in architecting and executing different strategies to estimate investment returns when key variables depend on unknown probabilities. While Kelly Criterion is used to determine the optimal size of an investment based on probability and expected size of gain or loss, the Kalman Filter is used to estimate the value of unknown variables in a dynamic state where statistical noise and uncertainties make precise measurements impossible, and the Black-Scholes Model is used to calculate the theoretical value of options contracts based upon their time to maturity and other factors.

Discuss how to customize and architect modified versions of Kelly Criterion in relation to Kalman Filter and Black-Scholes Model - and how customized Kalman filters (in addition to many different versions of Black-Scholes models) will affect application of the Kelly Criterion in real world scenarios.