# time series

I'm a big fan of Ernie Chan's quant trading books: Quantitative Trading, Algorithmic Trading, and Machine Trading. There are some great insights in there, but the thing I like most is the simple but thorough treatment of various edges and the quant tools you might use to research and trade them. Ernie explicitly states that the examples in the books won't be tradable, but they've certainly provided fertile ground for ideas. In Machine Trading, there is an FX strategy based on an autoregressive model of intraday price data. It has a remarkably attractive pre-cost equity curve, and since I am attracted to shiny objects, I thought I'd take a closer look. Autoregressive Models 101 An autoregressive (AR) model is a time-series multiple regression where: the predictors are past values of the time series the target is the next realisation of the time series If we used a single prior value as the only predictor, the AR model would be called an [latex]AR(1)[/latex] and it would look like: [latex] y_t = \beta_0 + \beta_1 y_{t-1} + \epsilon_t [/latex] (the [latex]\beta[/latex]'s are...

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....

Recently, I wrote about fitting mean-reversion time series analysis models to financial data and using the models' predictions as the basis of a trading strategy. Continuing our exploration of time series modelling, let's research the autoregressive and conditionally heteroskedastic family of time series models. In particular, we want to understand the autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models. Why? Well, they are both referenced frequently in the quantitative finance literature, and it's about time I got up to speed so why not join me! 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. Let's get started! What are these time series analysis models? 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...