time series

This is the third in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful signals from historical market data. If you haven’t read that article, it is highly recommended that you do so before proceeding, as the context it provides is important. Read Part 1 here. Part 2 provides a walk-through of setting up Keras and Tensorflow for R using either the default CPU-based configuration, or the more complex and involved (but well worth it) GPU-based configuration under the Windows environment. Read Part 2 here. Part 3 is an introduction to the model building, training and evaluation process in Keras. We train a simple feed forward network to predict the direction of a foreign exchange market over a time horizon of hour and assess its performance. [thrive_leads id='4507'] . Now that you can train your deep learning models on a GPU, the fun can really start....

[latexpage] Recently, I wrote about fitting mean-reversion time series models to financial data and using the models' predictions as the basis of a trading strategy. Continuing my exploration of time series modelling, I decided to research the autoregressive and conditionally heteroskedastic family of time series models. In particular, I wanted to understand the autogressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models, since they are referenced frequently in the quantitative finance literature, and its about time I got up to speed. What follows is a summary of what I learned about these models, a general fitting procedure and a simple trading strategy based on the forecasts of a fitted model. Several definitions are necessary to set the scene. I don't want to reproduce the theory I've been wading through; rather here is my very high level summary of what I've learned about time series modelling, in particular the ARIMA and GARCH models and how they are related to their component models: At its most basic level, fitting ARIMA and GARCH models is an exercise in uncovering the way in...