Quantocracy’s Daily Wrap for 09/28/2016

In one of my previous posts, I wrote about my framework for choosing ETFs since I wanted a method of choosing the optimal ETFs for expressing my market views. Generally, the framework is to choose an ETF with a low expense ratio, low trading costs, low tracking error, and a tax-advantaged structure. One of the key insights I got from writing that post was that investors should weigh each of those

I've received several requests for details on the AmiBroker (AB) code and settings used for the backtest shown in my April post: Momentum Rotation 60 Day ROC System Results. That post used the AmiBroker Formula Language (AFL) code from my article in March 2015. That was a long time ago, so here is the 60 day momentum rotation system AFL again:

I was at a recent talk of the Northwest Traders and Technical Analysts group where they presented a VXX strategy with some huge return and drawdown numbers. Trading this would be very difficult. This got me thinking. If I had a strategy like this, how could I tame the numbers? Through the years, I have seen various ideas about how to do this but never looked into it. Searching the web one can find

Time series pervade financial markets and, although some embrace the so-called efficient market hypothesis, stating that current market prices reflect all available information about a security into its price, I am more inclined to think they provide us with a lot of information that we rarely know how to exploit it for our own benefit. I agree that financial time series may be damn difficult to

This is a short post to let QuantStart readers know that I'll be speaking at some events in New York and Singapore over the next couple of months: Wednesday, October 5th 2016, NYC – I'll be talking about "The Quest for Profitability", along with Seong Lee who will be talking about "Data Driven Strategies" Thursday, October 6th 2016, NYC – I'll be moderating a