Publications by Intelligent Trading
Quantitative Candlestick Pattern Recognition (HMM, Baum Welch, and all that)
Fig 1. Clustering based approach to candlestick Pattern Recognition. I’ve been reading a book titled, ‘the Quants,’ that I’m sure will tantalize many traders with some of the ideas embedded within. Most notably (IMO), the notion that Renaissance’s James Simons, hired a battery of cryptographers and speech recognition experts to decipher...
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Chaos in the Financial Markets?
Over the years I’ve had quite a few interested individuals ask me about Chaos and its applications towards trading. Well, as hidden markov models and speech processing were made popular by James Simons and his team at Renaissance Technologies, one could trace much of the popularity of Chaos theory and its financial applications to ...
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Quantitative Candlestick Pattern Recognition (Part 2 — What’s this Natural Language Processing stuff?)
I wanted to briefly add one more thought regarding the temporal nature of probabilities as was alluded to in my correspondence with Adam, as well as the prior closing comments on the Chaos post (structure coalescing and dispersing).I will borrow from the field of Natural Language Processing and introduce one common visual description of how the s...
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Conditioning Systems on Regime Variables
Here is a brief and simple example of switching systems based upon regime type (sometimes called gating). I’ve brought up the idea of conditioning systems based upon regimes many times in past posts. Some texts call this filtering, although I prefer to use the term conditional gating. The simple idea is to turn on a certain system...
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Finally! A practical R book on Data Mining: "Data Mining With R, Learning with Case Studies," by Luis Torgo
I’ve been a bit busy lately with a few big things, however, I wanted to stop by and mention a fantastic book for those who have been following along the R examples. Anyone who’s followed my blog knows that I’m big on practical books with examples. There are also three main open source tools I’ve discussed with regards to p...
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Can one beat a Random Walk– IMPOSSIBLE (you say?)
Firstly, apologies for the long absence as I’ve been busy with a few things. Secondly, apologies for the horrific use of caps in the title (for the grammar monitors). Certainly, you’ll gain something useful from today’s musing, as it’s a pretty profound insight for most (was for me at the time). I’ve also considered care...
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High Low Clustering on intraday high frequency sampled data
Nothing unusually exciting on this post, but I happened to be engaged in some particle based methods recently and made some simple visual observations as I was setting up some of the sampling environment in R. I am also using Rkward and Ubuntu to generate, so I’m gathering everything from the current environment (including graphic...
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Simulating Win/Loss streaks with R rle function
The following script allows you to simulate sample runs of Win, Loss, Breakeven streaks based on a random distribution, using the run length encoding function, rle in R. Associated probabilities are entered as a vector argument in the sample function.You can view the actual sequence of trials (and consequent streaks) by looking at the...
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Pattern Recognition: forward Boxplot Trajectories using R
Although the following discussion can apply to the Quantitative Candlestick Pattern Recognition series, it is addressing the same issue as any basic conditional type system — how and when to exit. The following is one way to visualize and think about it, and is by no means optimal. ...
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Aug 4, 2011 "plunge" headlines are in the air tonight
Today’s financial headlines are littered with the word ‘plunge.’ Considering today’s (cl-cl) drop on the S&P500 was just about -5%, I don’t know that I would exactly call that a plunge. Fig 1. Historical ts plot of S&P500 returns <= -5%The following R code produced a time series pl...
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