Technical analysis and its dependency on volatility, the report
Some time ago I posted about the topic of my dissertation. There were some interest in getting access to the report when it was completed, and I'm glad to say that it's finally done.
So if you want to go straight to the details, here's the link: The profitability of technical analysis in a high frequency setting and its dependency on volatility.
If on the other hand you don't feel like reading 11000 words or so, here's the 30000 ft summary:
There is a strong (significant) positive relationship between returns from these strategies and volatility. If you're a prop trader you might be doing a face palm right now. But remember that academia have spent at least 30 years or so building a framework around the assumption that markets are efficient. Technical analysis shouldn't in that context be able to produce any abnormal returns, so we need to play at that field when testing these things. I am however delighted to see an increase in fields like behavioral and emotional finance as that surely is the only decent way forward in distancing academia from its extreme views.
So what does that relationship imply? Well, since volatility is fairly easy to "predict" as it is rather persistent, this also allows us to know when you should deploy or not to deploy technical analysis. That allows us to vastly improve our trading results compared to either always using technical analysis, or worse, purely rely on a long only buy-and-hold based strategy.
To produce these results one minute OHLC bars was used, analyzing the 15 biggest (in terms of trading volume) companies currently part of the S&P 100. If you're interested in how this was analyzed feel free to contact me. Most of the software was Java based and custom made by me, deployed across a cluster of Linux servers. With over 12 years of data doing daily parameter optimization and back testing, that amounts to about 30 TB of analyzed data.
The profitability of technical analysis in a high frequency setting
UPDATE: The report has been completed, available here.
One busy month behind me, and another one up next. Currently doing exam revisions and had some rather time consuming coursework so far, which has sadly prohibited me from doing anything here on this blog. So, to make up for a month of no blog activity I'll set aside some time doing it now.
This blog post is going to be about my upcoming dissertation. I'm writing about the profitability of technical analysis in a high frequency setting. I'm also going to be looking at any possible link between this and volatility. And, I'm very much interested in any feedback, tips, information, or what ever you think you might want to contribute. Any specific papers you think I should read? Other sources of interesting information? Please post feedback as a comment here, or contact me directly.
I'm copying and pasting in some of what I've written to introduce my dissertation project. A PDF is available here with the complete content, also containing the bibliography.
HFT book review
So I've been trying to push my self into writing a book review of "High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems", written by Irene Aldridge. But I'm not really a book review kind of guy, so I've been putting it off. It's been ages since I last wrote a book review, so I thought I would link to a few reviews and then give a few notes instead.
In general I view this book as an intro book. It eases the readers journey by starting out with the evolution and business aspects of HFT, before diving into the more technical parts. It also has a metric ton of references throughout the book for those interested in a deep dive.
I do have a low frequency trading system up and running, so based on that I would recommend this book to people interested in both high and low algorithmic trading as there is definitely a lot of cross interest. Chapter 5 for instance, "Evaluating Performance of High-Frequency Strategies", covers techniques like Sharpe and Treynor ratios, Jensen's Alpha and a buch more. These are topics of both high and low frequency interest.
Another chapter I particularly enjoyed was the Risk Management chapter, giving the reader a buch of hands on and practical examples on how this can be performed.
Chapter 15 covers back-testing of trading models, basically divided into two types of strategies: Point forecasts and directional forecasts. What I find very strange about this chapter is how directional forecasts are evaluated. In essens, in addition to a stop-loss level, a take-profit level is also put into each signal/order. Now in my world, adding a take-profit to a directional forecast would change that forecast to a point forecasts. I see the benefit of having a stop-loss, but unless that is triggered I do not believe a directional forecast should be closed till the direction (trend) changes. Of course there are operational benefits of using a take-profit (in addition to a stop-loss) level for each order, basically making them a fire and forget type of order, but still, IMO, directional forecasts and their orders should be just that, directional.
All in all I would recommend this book to anyone interested in an introduction to HFT, tips and references to further reading. As said, I would also recommend this book to anyone doing algorithmic LFT. There's so much crap being said about HFT, so it's good to get some pure facts on the table every once in a while.