Blog Archives

Minimum Correlation Algorithm Speed comparison

September 25, 2012
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Minimum Correlation Algorithm Speed comparison

The Minimum Correlation Algorithm is a heuristic method discovered by David Varadi. Below I will benchmark the execution speed of 2 versions of the Minimum Correlation Algorithm versus the traditional minimum variance optimization that relies on solving a quadratic programming problem. I have run the code above for n=10 (10 assets), n=100 (100 assets), n=500

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Minimum Correlation Algorithm Example

September 23, 2012
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Minimum Correlation Algorithm Example

Today I want to follow up with the Minimum Correlation Algorithm Paper post and show how to incorporate the Minimum Correlation Algorithm into your portfolio construction work flow and also explain why I like the Minimum Correlation Algorithm. First, let’s load the ETF’s data set used in the Minimum Correlation Algorithm Paper using the Systematic

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Minimum Correlation Algorithm Paper

September 21, 2012
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Minimum Correlation Algorithm Paper

Over summer I was busy collaborating with David Varadi on the Minimum Correlation Algorithm paper. Today I want to share the results of our collaboration: Minimum Correlation Algorithm Paper Back Test reports Supporting R code The Minimum Correlation Algorithm is fast, robust, and easy to implement. Please add it to you portfolio construction toolbox and

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Permanent Portfolio

September 17, 2012
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Permanent Portfolio

First, just a quick update: I’m moving the release date of the SIT package a few months down the road, probably in November. Now back to the post. Recently I came across a series of interesting posts about the Permanent Portfolio at the GestaltU blog. Today I want to show you how to back-test the

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Extending Gold time series

September 10, 2012
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Extending Gold time series

While back-testing trading strategies I want all assets to have long history. Unfortunately, sometimes there is no tradeable stock or ETF with sufficient history. For example, I might use GLD as a proxy for Gold allocation, but GLD is only began trading in November of 2004. We can extend the GLD’s historical returns with its

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Merging Current Stock Quotes with Historical Prices

September 4, 2012
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Merging Current Stock Quotes with Historical Prices

I got a question last week about going from the backtest to the trading. For example, if our system is based on today’s close, we can approximate the close value by the price at say 3:30pm, determine the signal and still have time enter the trade. It is not perfect, but one of possible solutions.

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Adaptive Asset Allocation – Sensitivity Analysis

August 20, 2012
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Adaptive Asset Allocation – Sensitivity Analysis

Today I want to continue with Adaptive Asset Allocation theme and examine how the strategy results are sensitive to look-back parameters used for momentum and volatility computations. I will follow the sample steps that were outlined by David Varadi on the robustness of parameters of the Adaptive Asset Allocation algorithm post. Please see my prior

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Adaptive Asset Allocation

August 13, 2012
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Adaptive Asset Allocation

Today I want to highlight a whitepaper about Adaptive Asset Allocation by Butler, Philbrick and Gordillo and the discussion by David Varadi on the robustness of parameters of the Adaptive Asset Allocation algorithm. In this post I will follow the steps of the Adaptive Asset Allocation paper, and in the next post I will show

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The New 60/40

August 6, 2012
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The New 60/40

I want to share a brilliant idea and a great example from the You’re Looking at the Wrong Number post at the GestaltU blog. Today, I will focus on the section of this post that outlines simple steps to improve a typical 60/40 stock/bond portfolio by using risk allocation instead of dollar allocation, and targeting

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Yet Another Forecast Dashboard

July 30, 2012
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Yet Another Forecast Dashboard

Recently, I came across quite a few examples of time series forecasting using R. Here are some examples: Time series cross-validation 4: forecasting the S&P 500 Holt-Winters forecast using ggplot2 Autoplot: Graphical Methods with ggplot2 Large-Scale Parallel Statistical Forecasting Computations in R (2011) by M. Stokely, F. Rohani, E. Tassone Forecasting time series data ARIMA

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