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Bayesian Optimization is an efficient way to optimize model parameters, especially when evaluating different parameters is time-consuming or expensive. Trading pipelines often have many tunable configuration parameters that can have a large impact on the efficacy of the model and are notoriously expensive to train and backtest.
In traditional optimization a single metric like a Sharpe Ratio is being optimized over a potentially large set of configurations with the goal of a single, best configuration being produced. In this talk we’ll explore real world extensions to this where multiple competing objectives need to be optimized, a portfolio of multiple solutions may be required, constraints on the underlying system make certain configurations unviable, and more. We’ll present work from recent ICML and NIPS workshop papers and detailed examples.
We’ll compare the results of Bayesian Optimization to these optimization problems to standard techniques like grid search, random search, and expert tuning across several datasets.