Switched from short to long at the close today, using a position size of about 1.5.
When investing strategies are back tested, the prevailing approach is to use daily closing prices both for the signal and for the entry point. This is all well in theory, but implementing such a system in practice is far from straightforward. The most obvious problem is that the close remains unknown until the end of the day, thus, we don’t know what our action needs to be at the close before the close is in place, but that’s the end of the trading day. A form of the chicken and egg problem.
In this tutorial I am going to share my R&D and trading experience using the well-known from statistics Autoregressive Moving Average Model (ARMA). There is a lot written about these models, however, I strongly recommend Introductory Time Series with R, which I find is a perfect combination between light theoretical background and practical implementations in R. Another good reading is the online e-book Forecasting: principles and practice written by Rob Hyndman, an expert in statistical forecasting and the author of the excellent forecast R package.
This web site is the next incarnation of the The Average Investor blog. Currently the new web site is the same as the old, just a blog. However, I have a few new features in mind that I have been working on over the past few months. So, enjoy and stay tuned!