Category Archives for "Research"
A common question I get is where do I find all my research ideas. My main source is Quantocracy. He does a great job of curating posts because the work is manually done. Then there the Better System Trader and Trend Following Radio podcasts. Usually from these sources I get a nugget of an idea to research or a simple strategy. Sometimes the post/podcast will recommend a book.
From one of these sources came the recommendation of the book “Trade Like A Stock Market Wizard” by Mark Minervini. Fortunately for me, my local library carried it. The strategy he covers in the book is a mixed of fundamentals, chart reading and technical analysis. Not something I would normally care about. I really enjoyed the chapters on risk management. In one chapter, he has very specific technical rules that all stocks must follow. On seeing this, I wondered could these rules be a basis for a trend following stock strategy? My second thought was, there are lots of rules there. Are all the rules necessary? The latter question is what I will focus on this post.
Like all traders, I am always on the lookout for any new indicators better than the ones I am using. I have been using and promoting RSI2 since 2004 for mean reversion trading. I created the ConnorsRSI in 2012. Am I married to these indicators? No. If I find something ‘better’ I will drop them. I came across this article Battle of the oscillators, I had to try it out.
One thing to understand, is that each situation is different. An indicator that works great on ETFs may work not as well on futures. Also, each person has a different metric on what is ‘better.’
I recently had someone email me about the performance of a strategy I created back in late 2005/early 2006 and traded for a few years. I remember the strategy being a daily mean reversion set up with an intraday pullback entry. I figured it probably had not done well over the last decade. I stopped trading in the middle of 2008 because I did not like how it was behaving. In the backtest it did well in bear markets but was not doing so in the middle of 2008.
I ran the strategy from 2007, using the rules as they were published and was pleasantly surprised by the results. A CAR of 25%. Overall not too bad. Wish I had still been trading it. This is an eleven year out of sample test.
Continuing from the last post, I will show how using different definitions of passing our out-of-sample test can change our results. How luck can play a role if you use only one strategy to test in out-of-sample. How you split your in-sample(IS) and out-of-sample(OOS) can change results.
As long time readers of my blog know, I often use a market timing indicator in my strategies. My favorite one, and a simple one, is using the 200 day moving average on either the SPY or S&P 500 Index. I recently ran into these posts, Using Market Breadth To Gauge Market Health (Part 5) and Matt’s Breadth Indicator. Matt’s Breadth Indicator (MBI) intrigued me because I had not seen something like this and conceptually it is simple. I also liked that it was not “easy” to test or optimize on. Therefore hopefully not many people would be using this indicator and I could potentially find better values.
In Simple ConnorsRSI Strategy on S&P500 Stocks I showed a ConnorsRSI strategy on S&P500 stocks. In ConnorsRSI Strategy: Optimization Selection, I narrowed down the optimization to three potential variations that one could consider trading. This post will explore Sensitivity Analysis (also known as: Parameter Sensitivity) to help guide us on what to expect from each variation.
In the previous post, Simple ConnorsRSI Strategy on S&P500 Stocks, I showed a simple strategy which I optimized which gave 1,300 variations. Today, I will cover various methods to choose a strategy to potentially trade.
As anyone who pays attention to the market, the S&P500 is down nine days in a row. I had several people write me about this. I was talking to a trading friend over the weekend about this. Nine days down seems bad. Let us put this in a broader context. How far have we come down in those nine days? Only 3.07%. Now that got me thinking is 3.07% in nine days that bad?
My recent research has been on the volatility Exchange Traded Products. My focus has been on long trades using VXX and XIV. Although VXX has a very strong downtrend, I am not a fan of developing short strategies on it due to the huge upside risk. I wrote about XIV here and expressed some of the dangers of trading these ETFs.
I was at a recent talk of the Northwest Traders and Technical Analysts group where they presented a VXX strategy with some huge return and drawdown numbers. Trading this would be very difficult. This got me thinking. If I had a strategy like this, how could I tame the numbers? Through the years, I have seen various ideas about how to do this but never looked into it. Searching the web one can find various volatility ETF strategies with very high returns and high drawdowns. I found one that looked interesting and had lots of potential for optimization and improvement. Then, I optimized the hell out of it searching for a variation with over 100% CAGR. I found one but I would never trade it because I over fit the data. I needed to something to work with.