Abstract
We present a model-free reinforcement learning (RL) framework for portfolio optimization across multiple assets and option prices. We directly model the relationship between the portfolio weights and the predictors with a network and maximize performance metrics for portfolio construction by using RL. We construct a portfolio, such that for any quantity of option that we buy or sell, we have a quasi-replicating portfolio made of a quantity of stock and a quantity of cash. We propose Cross-Asset-Option Transformers (CAOTs) to recover the interrelationships among options and their corresponding assets, which we use to construct a long-short or a bottom-up portfolio. We then maximize performance metrics of our portfolio with RL.
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