This week we have experienced the day that unleashes an avalanche of tweets, posts, and air-raid sirens. It is the day the perpetual bears have been waiting for. It’s the day of the death cross! Many commentators will make a huge deal about this, they will tell us this is the beginning of the end and we are due for a long bear market correction. They could be right – but is that likely?Read more
This post is an update on some errors we have found with our testing tools. Being open about issues like this is very important to us.Read more
In Part 1 we saw how External Data Fields (EDFs) can be used throughout Optuma. This week I want to show you how EDFs can be used to setup your own mini Portfolio Manager.Read more
External Data Fields (EDFs) allow you to import, or input, custom values on a code-by-code basis, which can then be referenced throughout Optuma. This article will provide several examples of what you can do using EDFs.Read more
All too often a Technician’s first foray into being quantitative is performing a back test. Statistically, this is one of the worst places to start a quantitative process. A back test that is performed too early is based on many false assumptions and compounds many errors into the process. The result is that the back test returns are rarely repeatable in real life. In this presentation, Mathew will be explaining what these errors are, how we can avoid them (regardless of what tools you use), and reveal a new Monte Carlo method he developed which allows us to review a valid p-score for our models no matter if they are long term trend following or short term mean reversion strategies. Mathew will go on to explain how as Technicians there is a process that we can follow to rigorously test quantitative ideas.
For any Analyst who wants to research new ideas, or be able to present ideas that can stand up to rigorous quantitative scrutiny, this presentation will help you to see that not only is a quantitative approach is achievable, but as Technical Analysts, we are in the best position to drive advanced quantitative models.