I wrote about Rebalancing in the Asset Allocation Process Summary post. Deciding how and when to rebalance (update the portfolio to the target mix) is one of the critical steps in the Asset Allocation Process. I want to study the portfolio performance and turnover for the following Rebalancing methods: Periodic Rebalancing: rebalance to the target 
The system described in the earlier series for ARMA trading was in fact an “extreme” version of the more common, orthodox approach prevailing in the literature. Recently I tried using R to reproduce the results of a particular paper, and that lead to a lot of new developments … How is typically ARMA trading simulated? 
I want to show how to combine various risk measures I discussed while writing the series of posts about Asset Allocation with backtesting library in the Systematic Investor Toolbox. I will use Minimum Variance portfolio as an example for this post. I recommend reading a good discussion about Minimum Variance portfolios at Minimum Variance Sector 
The end of the month effect was examined by MarketSci in the The Last Day of the Month Blahs post. The idea is simple: buy on the last day of the month and sell a few days later. This idea was put into a strategy by Quanting Dutchman in the Strategy 2 – Monthly End-of-the-Month 
I want to discuss the implementation of Rotational Trading Strategies using the backtesting library in the Systematic Investor Toolbox.The Rotational Trading strategy switches investment allocations throughout the time, betting on few top ranked assets. For example, the ranking can be based on relative strength or momentum. A few examples of the Rotational Trading Strategies (or 
I want to illustrate Backtesting with Short positions using an interesting strategy introduced by Woodshedder in the Simple, Long-Term Indicator Near to Giving Short Signal post. This strategy was also analyzed in details by MarketSci in Woodshedder’s Long-Term Indicator post. The strategy uses the 5 day rate of change (ROC5) and the 252 day rate 
I’ve been reading The Big Picture, and again, there was a discussion about seasonality in stock markets (see Fourth Quarter is Da Bomb). I’ve already discussed the two seasonal investment scenarios (Nov. to Apr VS May to Oct) in this post, and was wondering if one could break it down further into a monthly analysis.
When designing a trading strategy, I want to make sure that small changes in the strategy parameters will not transform the profitable strategy into the loosing one. I will study the strategy robustness and profitability under different parameter scenarios using a sample strategy presented by David Varadi in the Improving Trend-Following Strategies With Counter-Trend Entries 
I wrote a simple Backtesting library to evaluate and analyze Trading Strategies. I will use this library to present the performance of trading strategies that I will study in the next series of posts. It is very easy to write a simple Backtesting routine in R, for example: The code I implemented in the Systematic 
I will assume you have read through part 1 and have the csv file loaded. While we covered some basic graphing in the last post i hope to get into a little more of the data crunching. Specifically I am interested in the names which where driven by a spe...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
