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Intelligent Systematic Investment Agent: an ensemble of deep learning and evolutionary strategies

arXiv 2203.13125 ↗First posted 24 Mar 2022cs.AI cs.CY cs.NEcited by 0

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Abstract (arXiv)

Machine learning driven trading strategies have garnered a lot of interest over the past few years. There is, however, limited consensus on the ideal approach for the development of such trading strategies. Further, most literature has focused on trading strategies for short-term trading, with little or no focus on strategies that attempt to build long-term wealth. Our paper proposes a new approach for developing long-term investment strategies using an ensemble of evolutionary algorithms and a deep learning model by taking a series of short-term purchase decisions. Our methodology focuses on building long-term wealth by improving systematic investment planning (SIP) decisions on Exchange Traded Funds (ETF) over a period of time. We provide empirical evidence of superior performance (around 1% higher returns) using our ensemble approach as compared to the traditional daily systematic investment practice on a given ETF. Our results are based on live trading decisions made by our algorithm and executed on the Robinhood trading platform.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 30 references that are in the map. First posted on arXiv 24 Mar 2022.

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12 of 30 references.

References · 12

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  3. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 574
  4. 2018Learning to Walk via Deep Reinforcement Learningcited by 158
  5. 2020Phasic Policy Gradientcited by 123
  6. 2021Decoupling Value and Policy for Generalization in Reinforcement Learningcited by 82
  7. 2019Deep Reinforcement Learning for Tradingcited by 73
  8. 2018Practical Deep Reinforcement Learning Approach for Stock Tradingcited by 49
  9. 2019ABIDES: Towards High-Fidelity Market Simulation for AI Researchcited by 32
  10. 2017Multi-Period Trading via Convex Optimizationcited by 21
  11. 2021Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learningcited by 7
  12. 2018Continual Learning Augmented Investment Decisionscited by 3

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