At the age of machine learning and deep learning, the representation of financial data needs new approaches. Candlesticks, for example, originated from Japanese rice merchants and were first used in the 18th century. Obviously, they weren’t developed for neural networks. They are very well suited to visualize the price movement but hide possibly important information. Models process data differently, and they can “understand” features hard to apprehend in bulk by humans.
In this notebook I will share ideas about time series data transformations for neural networks.