Cross system money management makes the difference!
Rotational Trading: how to reduce trades and improve returns
With this post I want to share some research I’ve done in the area of rotational trading (RT). In RT systems stocks or ETFs are ranked according to one or more properties. You ride the best stocks as long as they are among the best stocks, then you change horses and go again. So far so good. Unfortunately some times you change stocks just to see that the stock you sold is doing better again and raising in your ranking. As we know the market has a certain amount of noise that can’t be predicted or modeled, hence stocks will raise or fall just because of that noise. This represent two challenges: trading cost and opportunity cost. Let me present you some ways how to reduce the impact of that volatility in ranking.
Strategy Review: Rotational Momentum Trading
It’s time for a review! A couple of month ago I had a few posts on rotational trading. The essence of these posts is summarized here. In this post I want to give you an update how the strategy has been doing since then. Furthermore I’ll release an updated version of the source code.
SPY: Usage of adaptive sector rotation model to improve returns
For a long time I’ve been intrigued by the idea of looking at specific sectors of the economy when predicting next day SPY move. Looking at sectors makes a lot of sense to me. Sectors carry specific information of how the economy is doing and what kind of positions large investors are favoring. Defensive sectors such as utilities (XLU) or healthcare (XLV) show strength in weak markets. Whereas consumer discretionary (XLY) do better in up markets. That’s at least the conventional wisdom. So if it’s that easy why not simply code that into the setup: go long when XLY is doing well and short when XLU is showing strength?
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