In my recent posts I’ve made reference to various swing-trade systems I’ve developed, which I’ve used as data to discuss things like correlation, diversification and the value of leveraged ETFs. I’ve had a number of people say “hey, what are these systems you’re referencing?” The short answer is: they’re not something I’m discussing publicly. “But that’s not fair!” you cry. “Everything should be free!” Yeah OK, I see your point.
I’m still not going to divulge those systems, but I do have two interesting swing-trade systems that I will share with you. They’re simple to execute using nothing more than online tools and/or spreadsheets. More importantly, they were optimized using data from 2000-2009, and perform well in out-of-sample (“OOS”) data (2010-2017).
Disclaimer: I am not your financial advisor. I advise you not to trade at all, because it’s dangerous and sometimes you lose money. Put your money in the bank instead.
Both systems trade SPY, the S&P 500 ETF. The graph below shows growth of $30,000, with the entire account invested in each trade, and no commissions or fees taken out. The drawdowns (“DD”) in the graph are on a cash basis, so they naturally get larger as the account gets larger.
I especially like how these two systems handled the Kerfluffle of 2008. Sure there was some bouncing around, but money was made none the less. In fact, the Inconvenience of 2011 actually showed worse results.
The RSI system I originally slapped together – I mean really, it took me like 10 minutes – so that I had a baseline to compare other systems with. I was fully expecting it to fall apart in OOS testing, but it didn’t. Something as long in the tooth as a short-period RSI system still had the potential to make gains. Who knew?
The only caveat is that the RSI system is somewhat flat over the past two years. You could say the same thing about the PIRDPO system in 2004-2005 though, so it happens. Doesn’t mean it’s broke.
“But Matt, what is this PIRDPO system?” you say. Looks pretty good, eh? It trades more frequently than the RSI system, and from the graph it looks more profitable but with some bigger drawdowns. How do they compare?
Before you trade either one of these systems, there’s something you should know: they make tiny profits on each trade. I mean TINY. You can not be putting on $1000 positions with a $10 commission each way and expect to make any money. You must either have a large account or a small commission fee (or both).
If you decide to use one of these systems, you probably won’t want to use BOTH of these systems. Why? They overlap quite a lot. In fact, 76.62% of all RSI days in a trade are also in play using PIRDPO. They are correlated time-wise so that you get little benefit to running them at the same time.
Here are some stats for each system:
|RSI 2000-2017||RSI 2010-2017||PIRDPO 2000-2017||PIRDPO 2010-2017|
|# of trades||250||96||327||142|
|avg. % gain/loss||0.51%||0.40%||0.48%||0.41%|
|winners||217 (86.80 %)||83 (86.46 %)||282 (86.24 %)||121 (85.21 %)|
|Max consecutive wins||25||13||22||15|
|losers||33 (13.20 %)||13 (13.54 %)||45 (13.76 %)||21 (14.79 %)|
|Max consecutive losses||2||2||3||2|
|Max trade % DD||-17.17%||-14.04%||-17.17%||-13.20%|
|Max system % DD||-17.14%||-15.19%||-17.15%||-14.10%|
A helpful tip to figuring out your position size:
First, determine your commission costs for the in/out trade. Let’s say they’re $10 each way, so $20 total. Then determine what percentage of your average profit can be eaten up by commissions. Let’s say 10%. $20 / 0.1 = $200. Your average profit per trade needs to be $200. If the average profit is 0.40% (worst case in the chart above), your position size needs to be $200 / 0.004 = $50,000.
pos size = ( commission * 2 ) / ( % of profit ) / ( average % gain per trade )
You did see the title of this post, right? It says “Part 1”. Tomorrow, the actual details of the systems….