论文标题
智能系统投资代理：深度学习和进化策略的合奏
Intelligent Systematic Investment Agent: an ensemble of deep learning and evolutionary strategies
论文作者
论文摘要
在过去的几年中，机器学习驱动的交易策略引起了很多兴趣。但是，就此类交易策略的制定理想方法的理想方法达成了有限的共识。此外，大多数文献都集中在短期交易的交易策略上，几乎没有关注试图建立长期财富的策略。我们的论文提出了一种新的方法，以使用一系列短期购买决策来制定长期投资策略和深度学习模型。我们的方法集中在一段时间内改善有关交易所交易基金（ETF）的系统投资计划（SIP）决策来建立长期财富。与给定ETF上的传统日常系统投资实践相比，我们使用我们的合奏方法提供了出色绩效（提高回报率高约1％）的经验证据。我们的结果基于我们的算法做出的实时交易决策，并在Robinhood Trupping平台上执行。
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.
