Is CTA trend following Dead?

                                         

This is just a very short comment related to discussions I've been having with a friend about trend following funds and a lot of the recent blogs and debates proclaiming the death of trend following.

                                                 Fig. 1 Barclay CTA Index

Using the Barclay CTA Index as a proxy, we can certainly see that there was a huge level shift of performance from around the 1990s and onwards, making the annual return appear to be on an almost exponential decay. However from about 1990 to present, the annual returns have have been steadily oscillating in a range band from around -1% to 13% (with some outliers), and although the return per decade has been dropping over the last few decades, there's not really enough data to make any strong judgements about it's demise. Just looking at the oscillatory behavior and considering the bearishness towards this class of funds-- It might just suggest a good reversion based long bet on trend-followers.

IBS reversion edge with QuantShare

Happy New Years to readers; my resolution this year is to continue delivering thoughts and ideas to others in the hopes that we all might be able to benefit somewhat from sharing observations. I'll start by describing an edge using QuantShare as the back-testing engine.

                                Fig 1. Optimized (overfit) SPY IBS Long run Performance

Have you ever had an edge that worked fairly well and then suddenly found that it was almost simultaneously revealed across several sources? This seemed to be the case with an edge I had discovered some time back via data mining. I first noticed it was divulged in a recent copy of Active Trader, 'The low-close edge,' by Nat Stewart. Later, I found the concept had also spread out into the blogosphere. Two notable write-ups can be found here and here.  The acronym IBS seems to be the buzzword floating around, so I'll continue to use it here. IBS stands for Internal Bar Strength (not to be confused with the IBS that many traders might have developed over the years). The strength indicator is described with an extremely simple equation:

$IBS = \dfrac{Close - Low}{High -Low}$

What it describes is the relative position of the close with respect to the low to high range of the period. When Jaffray Woodriff was interviewed in the latest Hedge Funds Wizards book, he described a very simple predictive indicator (based only upon transformations of the Open, High, Low, and Close of the data) that had proved remarkably stable over the years. It inspired some debate in statistical and machine learning circles, but nevertheless, sparks images of a holy grail. If there was ever a hypothesis model that came close to his description, I'd certainly consider this as a candidate for the reversion side. In addition to simplicity, one of the reasons it is so useful is that unlike many other approaches at feature transformation of raw financial series, it is scale invariant and does not require any further scaling to support non-stationary data. The results of the transformation will always be bound between 0 and 100%. So the transformed features will always be bound inside of a fixed and finite space regardless of the evolving data properties (a great property for machine learning). The algorithm runs very fast and does not require frequent readjusting of model parameters unlike many online or econometric based models.

The system simply buys at the close when the IBS indicator closes near the low end of the day and goes short when the indicator closes near the high of the day; exit is next day close.  Much of the time the indicator is neutral or no trade, allowing a good net risk adjusted return with low exposure. The thresholds, while often mentioned as being set to the 0.25 and 0.75 quartiles of the range, can be adjusted or found manually in the optimization settings.

                               Fig 2. Performance fit In Sample Optimization (to 2000).

In order to avoid hindsight bias and over-fitting error (as in Fig 1.), I show an optimization using only in sample data for SPY (yahoo data) up to the year 2000 (rank sorted by CAGR and Sharpe). One interesting thing we notice is that the higher threshold is actually optimized to 1, meaning no reversion from the high/short side. This is consistent with what we would expect with a long bias/drift market. We always have to be careful about systematic shorting with a market that has long term positive drift. 

                                 Fig 3. In Sample/ Out of Sample Performance
                                           with In Sample fitted parameters.

Fortunately, even without the short high reversion side, the system performed well for the rest of the out of sample data (Fig 3.).   A last comment is that looking at the over-fit data should gives us some insight about reversion systems and high volatility sell off regimes.


I've attached code to allow readers to repeat results.


Simulated back-test results long only. $10,000 Principle. No Slippage/Comm. incl.

Book Review: R for Business Analytics, A Ohri

      I've added a recently released book to my list of recommendations (at the amazon carousel to the right), as I've reviewed a copy provided to me via Springer Publishers. The book is R for Business Analytics, authored by A Ohri.  Mr. Ohri provides us with a brief background of his own journey as a business analytics consultant, and shares how R helped complement his work with a very low cost (time to learn the software) and very large benefits.  At the outset, he emphasizes that the book is not geared towards statisticians, but more towards practicing business analytics professionals, MBA students, and pragmatically oriented R neophytes and professionals alike. In addition, there is a focus on using GUI oriented tools towards assisting users in quickly getting up to speed and applying business analysis tools (Rattle, for example, is covered as an alternative to Weka, which has been covered here previously).  In addition, he provides numerous interviews with well known company representatives who have either successfully integrated R into their own development flow (including JMP/SAS, Google, and Oracle ), or found that large groups of customers have utilized R to augment their existing suite of tools.  The good news is that many of the large companies do not view R as a threat, but as a beneficial tool to assist their own software capabilities.

     After assisting and helping R users navigate through the dense forest of various GUI interface choices (in order to get R up and running), Mr. Ohri continues to handhold users through step by step approaches (with detailed screen captures) to run R from various simple to more advanced platforms (e.g. CLOUD, EC2) in order to gather, explore, and process data, with detailed illustrations on how to use R's powerful graphing capabilities on the back-end.

     The book has something for both beginning R users (who may be experienced in data science, but want to start learning how to apply R towards their field), and experienced R users alike (many, like myself, may find it useful to have a very broad coverage of the myriad number of packages and applications available, complemented by quickly accessible tutorial based illustrations).  In summary, the book has an extremely broad coverage of R's many packages that can be used towards business data analysis, with a very hands on approach that can help many new users quickly come up to speed and running on utilizing R's powerful capabilities. The only potential down-side is that covering so many topics, comes at a cost of sacrificing some depth and mathematical rigor (leaving the door open for readers to pursue several more specialized R texts).