Blog Archives

2018 Volatility Recap

January 6, 2019
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2018 Volatility Recap

2018 brought more volatility to the markets, which so far has spilled into 2019. Let’s take a look at the long term volatility history picture using the Dow Jones Industrial Average: Indeed, 2018 was the most volatile year since 2011. Relatively speaking however, the volatility is on the low end for a bear market, which The post 2018 Volatility...

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The Christmas Eve Selloff was a Classic Capitulation

December 26, 2018
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The Christmas Eve Selloff was a Classic Capitulation

The selloff on Christmas eve was so bad it looked like a typical bear market capitulation. The following rally merely confirmed it. As mentioned in the last post, at the time the correction reached 16%, at the close of December 21st, the oversold indicator was not lighted. What followed was the worst Christmas eve selloff The post The Christmas...

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The Bear is Here

December 22, 2018
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The Bear is Here

October and December have been devastating for stocks. It wasn’t until Friday though that we officially reached the depths of a bear market. There are different theories, the most common is 20% pullback in an index. As readers of this blog are aware, I follow a slightly different definition, based on Jack Schannep’s work. Based The post The Bear...

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Package Paths in R

March 31, 2018
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Package Paths in R

Recently, while working on the Azure Data Lake R extension, I had to figure out a good way to create a zip file containing a package together with all its dependencies. This came down to understanding where does R store and search for packages. Despite the documentation, it did require additional reading and experimentation. First, The post Package Paths...

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The Bull Survived on Friday, but Barely

March 24, 2018
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The Bull Survived on Friday, but Barely

There are only a few well-known signals which I consider reliable. One of them is the Dow Theory. According to it, or at least to some interpretations of it, the bull market cycle almost ended this Friday. This time I am adding something new. I recorded myself while doing some of the analysis: Different people The post The Bull...

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Markets Performance after Election: Day 239

October 21, 2017
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Markets Performance after Election: Day 239

When I wrote the original post, I wasn’t planning on writing a follow-up. Certainly not the week after. But what a difference a week can make in a dynamic system like the US stock market. While re-running the computations testing the latest version of RStudio, I noticed something surprising – President Trump’s rally has advanced The post Markets Performance...

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Markets Performance after Election

October 15, 2017
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Markets Performance after Election

Coming back to markets and trading (after a while), the feeling has been that the markets, and the economy as a whole, are doing good. How good? Since I haven’t been following things closely, I had to do some forensics. Friday was the 234th trading day after the election. There were claims at different points The post Markets Performance...

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The flock Package is on CRAN

November 12, 2016
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About a couple of years ago, I rolled out the flock package to help me synchronize R processes. I have used it ever since, but it wasn’t until recently that I found the time to move the source code to GitHub, and to add the package to CRAN. The post The flock Package is on CRAN appeared first on Quintuitive.

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Better Model Selection for Evolving Models

September 25, 2016
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Better Model Selection for Evolving Models

For quite some time now I have been using R’s caret package to choose the model for forecasting time series data. The approach is satisfactory as long as the model is not an evolving model (i.e. is not re-trained), or if it evolves rarely. If the model is re-trained often – the approach has significant The post Better Model...

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Forecasting Opportunities

September 13, 2016
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Forecasting Opportunities

The previous post in this series, showed a way to identify trading opportunities. The approach I implemented used time series daily data to identify good entry points in terms of risk-reward. The natural next step is to try to make use of these opportunities using machine learning. To refresh: the output of the previous post The post Forecasting Opportunities...

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