Practical Meta-Reinforcement Learning of Evolutionary Strategy with Quantum Neural Networks for Stock Trading ()
ABSTRACT
We show
the practicality of two existing meta-learning algorithms Model- Agnostic
Meta-Learning and Fast Context Adaptation Via Meta-learning using an
evolutionary strategy for parameter optimization, as well as propose two novel
quantum adaptations of those algorithms using continuous quantum neural
networks, for learning to trade portfolios of stocks on the stock market. The
goal of meta-learning is to train a model on a variety of tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our classical approach, we trained our meta-learning models on a variety of
portfolios that contained 5 randomly sampled Consumer Cyclical stocks from a
pool of 60. In our quantum approach, we trained our quantum meta-learning models on a simulated quantum computer with portfolios containing 2 randomly sampled Consumer Cyclical stocks. Our findings
suggest that both classical models could learn a new portfolio with 0.01% of
the number of training samples to learn the original portfolios and can achieve
a comparable performance within 0.1% Return on Investment of the Buy and Hold
strategy. We also show that our much smaller quantum meta-learned models with
only 60 model parameters and 25 training epochs have a similar learning pattern to our much larger classical meta-learned models that have over 250,000 model parameters and 2500 training epochs. Given
these findings, we also discuss the benefits of scaling up our experiments from a
simulated quantum computer to a real quantum computer. To the best of our
knowledge, we are the first to apply the ideas of both classical meta-learning
as well as quantum meta-learning to enhance stock trading.
Share and Cite:
Sorensen, E. and Hu, W. (2020) Practical Meta-Reinforcement Learning of Evolutionary Strategy with Quantum Neural Networks for Stock Trading.
Journal of Quantum Information Science,
10, 43-71. doi:
10.4236/jqis.2020.103005.