  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.
