Nearly 100 years ago, W.D. Gann called his 3 Day chart his Master Trend Indicator. It still stands the test of time and as one of Gann's most objective methods, it has a place in quantitative modeling. In this session Mathew will be presenting the reasons why we should be considering swing charts as a way to filter out market noise and signal generation. He will go on to explain the construction of different types of swing calculations like Point, Percent, Gann (what he called a 3 Day chart), Point & Figure, Kagi & Volatility. All of which have their pros and cons. Finally, Mathew will explain the various methods that are used to identify signals which can be derived from any swing trading method.
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. Mathew will use these methods to do a review of the various Swing Chart methods that he introduced in Session 1. He'll also show a method which mixes mean reverting signals and swing charts for some very interesting results. 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.