Publications by quintuitive
Volatility and Bollinger Bands
It is a common knowledge that Bollinger Bands (price standard deviation added to a moving average of the price) are an indicator for volatility. Expanding bands – higher volatility, squeezing bands – lower volatility. A bit of googling and you get the idea. In my opinion – that’s wrong, unless, one uses a twisted definition of volatility....
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Too Much Parallelism is as Bad
The other day I run a machine learning backtest on a new data set. Once I got through the LDA and QDA initial run, I decided to try xgboost. The first thing I observed was a really bad performance. The results from the following debugging session were quite surprising to me. I have been using the same framework for a few years now. I think there...
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Loading Data with Pandas
On at least a couple of occasions lately, I realized that I may need Python in the near future. While I have amassed some limited experience with the language over the years, I never spent the time to understand Pandas, its de-facto standard data-frame library. Where does one start? For me its usually with the data. Simple stuff, loading, wrangl...
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Labeling Opportunities in Price Series
One approach to trading which has been puzzling me lately, is to sit and wait for opportunities. Sounds simplistic, but it is indeed different than, for instance, the asset allocation strategies. In order to be able to even attempt taking advantage of these opportunities, however, we must be able to identify them. Once the opportunities are iden...
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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 was a time series wh...
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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 computational overhead. Interestingly en...
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The flock Package is on CRAN
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. Related To leave a comment for the...
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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 in time that the market is the bes...
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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 to 2nd pla...
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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 interpret the Dow Theory different...
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