| October 1, 2020
Project Overview
Many of the discretionary and automatic strategies highlighted in the literature on the trading are based on the notion of patterns. They bring into play both the raw price sequences and combinations of indicators. Most of the time these coded patterns created by the technical analysts rely mainly on perception bias. Those based on technical indicators do not withstand backtesting, and those based on purely technical forms are too subjective to be effectively quantified.
Machine learning-based approaches allow to build predictive models that are free of any perception bias. The hypothesis of this project is that there are many patterns in markets where only a fraction of the market has predictive capability, but where these patterns are not are probably made up of microstructures too complex to be identified by a human or by simple algorithms.
Some deep learning approaches, such as auto-encoders, allow to extract latent representations of complex structures. Only features that best characterize the variance of the data remain. These latent representations can then be used as input to other models such as classifiers.
This project consists in combining deep learning approaches based on variational auto encoders and clustering in order to extract groups of similar patterns from time series but also to identify those among them having the ability to predict the direction of the market with a sufficiently high accuracy.