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Deep Reinforcement Learning Task for Portfolio Construction

This paper covers the analysis of contemporary portfolio management techniques, as well as the development of a backtesting trading environment and an autonomous trading agent based on state-of-the-art Reinforcement Learning algorithms with different reward functions. The scientific novelty of this study relies on the development of a scalable automated trading agent that makes periodic investment decisions autonomously. Baseline strategies include UCPR (weekly rebalancing) and UBAH (buy and hold strategy) for SPDR SP 500 ETF Trust (NYSE: SPY). Backtesting Period covers the range 2017-01-01—2020-03-30, characterized by the sustained growth of the US stock market in 2017.