Forgot Login?   Sign up  
  • IFTA Speaker
  • SAMT Member

Rolf Wetzer, PhD, is past president of IFTA. He started working for IFTA in 2007, became a board member in 2009 and finally president in 2012. He redesigned the CFTe syllabus, was the editor of the IFTA journal and is heading the MFTA program since 2011. He is a member of the Swiss Association of Market Technicians (SAMT) and the prestigious German Statistical Society (DStatG).Rolf is a portfolio manager with a twenty-year track record of managing institutional and private assets, heading teams and trading successfully proprietary money within major financial institutions. Currently he is CEO and co-founder of Ghiribizzo, a company specialized in algorithmic trading of proprietary money. He designed favorable quantitative investment strategies and products and specialized in position sizing. He also lectures these topics at universities.He was awarded by the VTAD (German TA society) as best German Technical Analyst in 2006 and was runner up in 2007. As a frequent speaker on financial and management topics he presented globally in 17 countries (Europe, US and Asia)Rolf holds a PhD in Econometrics and graduate degrees in Business Administration and Management from both Technical University of Berlin (Germany) and Toulouse Business School (France).

Cycles in Trading, Empirical Mode Decomposition

Posted: 22 October, 2016 Subjects: Cycles Software_Coding&Testing
Source: C2016 IFTA Available to: Delegates 2016

The concept of cycles is very popular among technical analysts. Books and articles define them in various shapes and lengths. They are also very appealing to investors since they imply the old idea of buying low and selling high. Unfortunately, trading is not that easy and in reality, identifying and measuring cycles properly seems to be very hard. Most of the tools suggested fail since asset prices behave non-linear and most of the properties are time depending. This presentation describes a technique to identify cyclical behavior called Empirical Mode Decomposition (EMD). It is designed to split a time series into a bundle of different cyclical movements and a remaining trend. For each component, an empirical frequency could be derived in a second step. EMD is designed to deal with non-linear and non-stationary time series. The single steps to run EMD will be explained in detail. Afterwards, different ways of how traders might use this technique will be discussed.