7
$\begingroup$

Is there any software out there that currently would allow you to scan historical charts and look for a specific pattern and then show you that pattern for a list of stocks. Google finance and yahoo finance have all the charting data needed to visually detect certain patterns, but I was wondering if there was software out that I could define a particular pattern and then it would show me a bunch of real world examples of that pattern by scanning a dump of symbols. Does any such software exist or something to do something similar to this? I actually have a custom pattern I want to scan (that or I do not yet know the name of it) Not really sure how to word this but I am thinking of having it scan the recent behavior of a stock and then to scan a list of symbols for that pattern but historically back in time, not the present.

$\endgroup$
2
  • $\begingroup$ A warm welcome to Quant.SE and thank you for your question. Please see my answer below - I hope it gets you started. $\endgroup$
    – vonjd
    Jan 2, 2015 at 15:51
  • $\begingroup$ If there is anything else I could help you with please let me know :-) $\endgroup$
    – vonjd
    Jan 8, 2015 at 7:31

4 Answers 4

7
$\begingroup$

I know of two sites that promise to do that but I haven't used either of them so I cannot tell you how reliable they are:

The excellent blog systematic investor has some articles about how to do it in R:

In general this is not a trivial problem. If you are interesting in some theoretical background the following papers may be for you:

For a much broader approach you might want to check out the book Evidence Based Technical Analysis by David Aronson.

In it he applies statistical techniques to determine whether certain time series patterns have any predictive power. It's an interesting read and should equip you with some ideas on how to differentiate between folklore and statistical rigor. It also gives you ample literature references.

You can find a good overview and summary of the book on CXO Advisory.

You can also find further material on the webpage of the author.

The Automated Trading System-blog has a few posts about the book with a helpful tool at the end:

and here the conclusion with the free tool for the bootstrap-test:

$\endgroup$
3
  • 1
    $\begingroup$ This is probably one of the "best" possible answers, although the only thing that is missing is that you can generalize "patterns" to smoothed chunks of time series and match the nearest one. You can also use dynamic time warping: jstatsoft.org/v31/i07/paper $\endgroup$ Jan 2, 2015 at 17:12
  • $\begingroup$ @KKB: Thank you and thank you for the reference too, I didn't know that. $\endgroup$
    – vonjd
    Jan 2, 2015 at 17:14
  • $\begingroup$ @KKB ...I just saw that systematicinvestor uses the dtw package in the third post mentioned above but thank you again for the link $\endgroup$
    – vonjd
    Jan 2, 2015 at 17:18
2
$\begingroup$

You can try Macroaxis. Although it does not let you define your custom patterns but it can plot and recognize more than 300 technical indicator patterns on historical data. You can see example of pattern recognition for google at http://www.macroaxis.com/invest/Pattern-Recognition/GOOG

Disclaimer: I work for Macroaxis

$\endgroup$
1
$\begingroup$

Stock Analyser is a Stock Charting, Portfolio Optimization, Pairs Trade, Technical and Quantitative Analyzer. It is a free software. I am coding it.

Stock Analyst

$\endgroup$
1
1
$\begingroup$

A relatively easy reading on unsupervised pattern discovery with hierarchical clustering ("bottom-up clustering") is by Karsten Martiny. I'm not sure if his results would stand a bootstrap test, nor I take the choice of Dow Jones daily OHLC from 1932 to 2011 as a dataset too seriously.

You asked for a software. That would be e.g. R with a package for hierarchical clustering.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.