I admittedly got a little distracted this week. What started as a little side project for me actually ended up consuming all of my computational resources and ultimately generated 17,485,793 trades. Yea, 17 million. Fortunately I figured out how to program macro scripts for Traders Studio recently so much of the process was automated.
The primary purpose behind this blog is to explore and test trend-following ideas that can be used in conjunction with a CANLIM approach to the markets. With that said, there has been a lot of discussion recently in part of the blogosphere I follow looking at the 2-period RSI as a short-term mean-reversion indicator. Don't let the title fool you, I am only following this “trend” with a little research of my own. Yea, I know - not that funny.
The Dogwood Report
Pennings of an Analyst
This is natural given the recent range-bound market that highlights the need for a trader to be able to adapt to various market conditions. Markets don’t always trend and knowing when to step back or change approaches is critical to stabilizing returns and minimizing drawdowns. I decided to apply my own approach to testing the RSI(2) this week to determine if it had enough meat to serve as the basis for a short-term mean-recursion trading system. I am typically not a big fan of indicators & oscillators for no other reason than I have not found or seen quantifiable evidence backing up the effectiveness of most of them. I even looked at the ADX a few weeks ago on this blog as an obvious candidate for a trend-following system. Unlike that however, the RSI(2) did impress me.
The setup, entry and exit criteria for the test this week was very simple. The setup was only a liquidity threshold requiring 10-day average volume to be above 100,000 shares. Entry was a market order to buy $1000 worth of shares at open on the day following the RSI(2) falling below a certain threshold and exits were simple 1,5 and 10-day timed exits. Not a complete “system” but simple enough to place all the burden on the RSI for the heavy lifting. I did intend to look at shorting overbought as well but simply ran out of time. The test are run for a 10-year period starting on 01/01/1996 through 12/31/2007.
The y-axis of the performance graphs are formatted with my go-to evaluation criteria, EDR (expectancy per dollar risk) which is calculated by Average Trade/Average Loss. The x-axis shows the RSI value that was used as the threshold in a <= operation. I will follow-up the graphs with the raw trade data.
So I think the results speak for themselves. It looks like an RSI <= 3 is the sweet spot so if we allow ourselves the luxury of perfect hindsight for a moment and use that as our threshold, expectancy per dollar risked on our Nasdaq portfolio, for example, was 242% higher than all trades 1-day out, 140% higher 5-days out, and still 112% higher 10-days out. Remarkable results. Combined with more refined entry, exit, risk management and position sizing rules, I’m quite certain you could increase your win percentage and expectancy much further. Not only that, as these results illustrate, the method is robust enough to apply to larget baskets of stocks which will generate plenty of oppertunities. I am a believer, atleast for now until every computer plugged into the internet starts trading it. Keeps you sharp though, when something stops working, it only means something else is starting to.