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.
Category Archives: Programming
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.
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.
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.
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.
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.
Convolutional Neural Network for Time Series
Neural networks have been around for a while, but it’s fair to say that many successful practical applications use at least one convolutional layer. Naturally, convolutions make sense for time series, so I went and added a few to the Walk-Forward Analysis.
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A Tensorflow Exercise
A previous post in this series, implemented the Walk Forward Loop on top of Microsoft’s CNTK. There was interest in a Google’s Tensorflow implementation, which seems to be the more popular framework in this domain, I decided to put what have already done with Tensorflow.
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Backtesting – Tool Review
Backtesting – the pillar of trading and investing. I know what all the naysayers say, but with all due respect, they got this one wrong. 🙂 In finance, and in time series in general, history repeats itself. Natural phenomena follow patterns, so does crowd behavior. Earth temperatures are not going to rise forever, so wouldn’t the current bull market. Enough rambling though.
My readers know that my main tool for backtesting has been Tradelib, a Java library which I have developed and used over the past couple of years. Working on some new (or rather old) project, I decided to take another look what’s around, and the results surprised me.
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Deep Learning for the Walk-Forward Loop
In the previous posts in these series (here, here and here) I used conventional machine learning to forecast the trading opportunities. Lately however I have been trying to move more and more towards deep learning. My first attempt was to extend the walk-forward loop to support neural networks, the building blocks of deep learning.