Author Archives: Cesar Alvarez
Author Archives: Cesar Alvarez
Old Review below.
I am frequently asked what data provider I use. A year ago my data provider was CSI Data. Then I heard about Premium Data from Norgate Investor Services and the one feature that enticed me to look at them closer: historical S&P500 index constituent data. At that time, I was maintaining the data by hand. Each month I would have to determine which stocks had been added or deleted from the index. I would need to look for name changes in the current and historical list. Not a hard task but time consuming and easy to make mistakes. The thought of not having to do this was very enticing.
This review will focus on US Stocks and AmiBroker integration. Premium data has data for the Australian and Singapore markets, integrate with multiple other platforms and have forex and futures data. For more information go to Premium Data from Norgate Investor Services.
Continuing on from our previous posts and research, Should one trade high or low volatility stocks? , Stops and trading high vs low volatility stocks, and Low Volatility Stocks and Profit Targets, we are now testing how these results translate to a portfolio. I pick one variation from each of the tables from the Low Volatility Stocks and Profit Targets. From that one a variation we create a portfolio with a maximum of 10 stocks.
Recent articles that I found interesting.
When everyone in your field of choice is intelligent it’s much harder to separate yourself from the crowd. The paradox of skill is what makes it so difficult to beat the market over time. Finding undervalued assets isn’t as easy today as it was in the past because there are more smart people who know where to look. …
There’s a good reason why most professionals who apply models similar to trend following to stocks call them momentum models. It’s not just a clever rebranding, it’s really a very different game. To blindly cling to trend following as a religion, disregarding any real world evidence and attacking anyone presenting ideas that differ to the trend following mantra is not only unprofessional, it’s outright dangerous….
Your trading model might have a random risk element and you might not even be aware of it. In particular longer term models need special care to avoid ending up with random risk. …
What is causing the plunge in trading volume (and volatility)?
I have set up a page that contains AmiBroker code snippets I use all the time and you may find useful.
Starting on September 5, 2014, AmiBroker & Back Testing 101 is an eight week course for those of you wanting to learn AmiBroker and back testing. Contact me if you are interested in the course but the dates do not work. I am creating intermediate and advanced versions of the course.
In the two previous posts, we have looked at low volatility stocks vs. high volatility stocks with trailing stops. Overall, the data pointed to trading lower volatility stocks. In this post, the focus is on low volatility stocks but now adding profit target stops to see how they can improve the results.
Articles I enjoyed from the past week.
In any field where complexity is part of the discipline (think: finance, technology, etc.) there is a temptation to appear more sophisticated than others. More specifically, the idea is to appear to know the information that other people do not know and to have a certain cleverness about your approach and how you look at problems.
If your so-called “Absolute Returns” hedge fund crushed it over the last 18 months, I have two pieces of news for you: A) it’s not really an absolute returns vehicle after all and B) it’s going to crush you when the worm turns, regardless of what you’re counting on it to do.
Here’s an update of a 2009 post, showing how daily volatility in the S&P 500 Index varies as a function of daily volume. Specifically, we’re looking at daily true range in percentile terms as a function of hundreds of millions of shares in SPY. What we can see is that, as volume comes out of a market, movement also becomes less.
It’s been a long time now since the S&P 500 had a big change in a single day. You have to go back to November of 2011 to find the last day that the S&P 500 rose or fell by 3% in one day. Since 1950 there have been 200 trading days out of 16,202 in which the S&P changed by 3% or more, which implies that we get one 3% day for every 81 days.
Testing my next idea is taking longer than expected. Blog post next week on the test.
In my last post, Should one trade high or low volatility stocks?, we placed stocks into three volatility buckets and compared their performance. Several readers pointed out that using a fixed percentage stop made it more likely for high volatility stocks to hit the stop thus not performing as well. Readers suggested using an Average True Range stop or a time stop. We will explore those two stops and see how the volatility buckets compare.
Before we get to the tests, I need to explain a new metric I will be using. At Connors Research we use Individual Trade Quality, ITQ, when we were comparing results of non-portfolio tests, such as these tests. The simple way to understand ITQ is it analogous to Sharpe Ratio in a portfolio test. To get more details on ITQ see How to Measure the Individual Trade Quality of Your Strategy.
If one is trying to develop a multi-month hold stock strategy, is it better to focus on high or low volatility stocks? For a long time, I have wanted to add a longer term stock strategy to my basket of strategies that I trade. I do not expect this strategy to perform as well as my shorter term strategies but work as a complement to them
Low volatility or high volatility? Short term trading strategies tend to do best when they focus on high volatility stocks. Will this be true for a longer hold strategy?
Why don’t I make more frequent posts? The easy answer is backtesting is hard.
A test has three parts to it. First, coming up with the idea. I have more ideas than I can test. I have a notebook full of ideas. The hard part here is picking one. Second, writing the code and running it. This takes me a couple of hours to a couple of days to do. Writing code is the fun and mostly easy part, though sometimes it can be insanely hard. Third, is verifying the result are correct. It is the last step that can takes days to weeks to do. Then writing the post takes a couple of days.
About two years ago, I started using the Ulcer Index as another evaluation metric for portfolio backtests. I like how it captures both drawdown and drawdown length. It helps differentiate similar looking portfolios using the common metrics of Compounded Growth Rate, Share Ratio, and Maximum Drawdown. My plan was to write a blog post about it and then add it to the metrics I show on the blog. The blog Flirting With Models, found through the quant mashup Quantocracy, just made a great post on it which I highly suggest you go read: Looking into the Ulcer Index. They did a great job and saved me a post. I will show this metric on future portfolio tests.