Abstract
Meta-learning algorithms learn about the learning process itself so it can speed up subsequent similar learning tasks with fewer data and iterations. If achieved, these benefits expand the flexibility of traditional machine learning to areas where there are small windows of time or data available. One such area is stock trading, where the relevance of data decreases as time passes, requiring fast results on fewer data points to respond to fast-changing market trends. We, to the best of our knowledge, are the first to apply meta-learning algorithms to an evolutionary strategy for stock trading to decrease learning time by using fewer iterations and to achieve higher trading profits with fewer data points. We found that our meta-learning approach to stock trading earns profits similar to a purely evolutionary algorithm. However, it only requires 50 iterations during test, versus thousands that are typically required without meta-learning, or 50% of the training data during test.
Highlights
Accessibility to large data stores for developing effective machine learning models is a valuable source of profit and insights for numerous industries and areas of research
We found that the NES with Modal-Agnostic Meta-Learning (MAML) resulted in smoother training and that NES with Reptile led to profitable trading with a fewer number of test iterations and data
Both of these results suggest the applicability of meta-learning strategies to improve stock trading with the NES algorithm
Summary
Accessibility to large data stores for developing effective machine learning models is a valuable source of profit and insights for numerous industries and areas of research. The current requirements to have a lot of data for training deep learning models are a prevalent crux of applying machine learning to problems where little data is available or there are restrictive windows of relevant data on which to be trained. An example of this is the stock industry where the most relevant data for stock forecasting are the past few days or weeks. After building the meta-model, the algorithm can learn to solve a specific task with only a few data points and a few iterations [2]
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