Swing Trade System for a Financial Sector ETF

FAS Double Down

I was intrigued by this ‘rough draft’ of a swing trade system that Oddmund Grotte had mentioned on his website quantifiedstrategies.com (post link here, and check the comments for more good ideas too). It was designed to trade XLP, which is an exchange-traded fund that tracks the consumer staples sector. A quick view of the chart seemed to confirm there was profit to be had, so I set about designing a system and backtesting it in AmiBroker.

It didn’t go so well however! Variations on the theme weren’t yielding positive results. I took a look at the losing trades, of which there were many…I noticed that there were quite a few winning trades that turned to losing trades after commission was deducted. Once again, the position-size / commission size ratio rears its ugly head. A winning system can be rendered a gibbering, quivering disaster with the addition of commissions that suck away the tiny wins.

So what I needed was more bang for the buck. If I could increase my average win per trade, I could potentially create a winning system. My first thought was to use leverage. But not real leverage, just a leveraged version of the XLP ETF. I was unable to find a leveraged version of the consumer-staples sector ETF. What, no one wants to day-trade the consumer staples sector for fun and profit? WTF?

So I headed over to the financial sector, where there are 2X and 3X leverages ETFs in both directions. I settled on the Direxion Russell 1000 Financials Bullish 3X ETF, a.k.a. FAS.

My goals were to develop a system that:

– can be profitable with a $1500 position size and a commission of $4.95 each for entry and exit.

– trades roughly twice per month on average.

– has a high win rate. Minimum 60% but preferrably >70%.

– The system would only trade one position at a time, but has a hypothetical $30,000 account to absorb mild drawdowns.

– is a swing-trade system, so the holding period is relatively short, reducing risk and increasing gains due to compounding. Note that my results here do NOT include compounding. Position size has a maximum of $1500 but is usually less because whole shares must be purchased, not fractions of shares.

This ETF started trading at the end of 2008 (wow, that must have been painful). So I set my in-sample period to be 1/1/2009 to 12/31/2012. My out-of-sample period is from 1/1/13 to 04/05/15. I did a walk-forward test for the holding period and profit target, and then selected the values that appeared most often (the “mode” of the results). If you don’t like my methodology, do it yourself! 🙂

Here are the rules I came up with for my “FAS Double Down System”:

1. The closing price of the signal day must be at or below the previous day’s close.

2. Set a limit-on-open order for the next day’s open that is equal to the closing price divided by 1.002. If you can’t trade using a limit-on-open order, you could set a limit order and then delete it if the order didn’t execute at the opening bell.

3. Sell if the close exceeds either 3.5% above the trade price, 20% below the trade price, or 24 trading days have transpired since the entry (with the entry day being day 0). All trades are done at close, not intra-day. Yes the stop-loss percentage is much bigger than the profit target, but this is common with a trading system with a high percentage of wins.

The results:

– This system yielded about 20 trades a year, which meets my roughly-twice-a-month goal.

– The hit rate – AFTER deducting $4.95 for commission on both entry and exit – is over 86% on average.

– About half of the 122 trades are just held for two days or so. The maximum duration aspect is basically for when the entry turns out to be faulty, and then you hold your breath, hoping the price eventually moves in your favor. Only five trades out of 122 hit the maximum holding period.

– the system has a CAR/MaxDD ratio of 0.58. There were values that could have increased that immensely, but it brought the number of trades down into the single digits per year, which is not what I was after.Also, there were some big losses right at the beginning of the ETF’s existence which are no doubt impacting the final score.

A higher position size would probably allow for a smaller profit target and possibly more frequent trading, and thus an improved CAR/MaxDD ratio. But I don’t have the bigger position size handy at the moment.

– The Average Profit/Loss per trade is 2.63%,  and the Average Bars Held was 7.13.

– Wins: the largest win was $708.52, and the maximum consecutive wins was 22. Not a typo. Twenty two.

– Losses: the largest loss was -$586.57 and the maximum consecutive losses was 2. Not a typo. Two.

– This is a system that makes a bunch of smaller wins and then every once in awhile takes a bigger loss. The stop only helps so much, because usually there’s some apocalyptic event that can’t be “stopped”…only recovered from somewhat with a longer duration. The average profit is 6.50%, but the average loss -21.54%. Did I mention it has a high hit ratio? 🙂 Three of the four most massive losses were at the beginning of 2009, so perhaps that makes you feel better?

Here’s the equity curve over time, rather than on a per-trade basis as you saw at the beginning:

FAS Double Down equity chartThe system makes a lot of its money during the rally of 2009. But there’s a fair amount of more modest profit happening throughout. That made me want to see if that hit rate was skewed in some way chronologically. I’d hate to find out I was getting 100% hits in 2009, and 50% hits in 2014. Well that turns out not to be a concern. Here is the hit rate, broken down per year:

2009: 79%
2010: 81%
2011: 86%
2012: 90%
2013: 93%
2014: 92%

Well hey, I can live with that! The profit seems to have flattened out a little over the last few years, but it’s still steadily heading upward.

One other downside to this trading system: about half the days each month are “signal” days, because of course there are a lot of down days each month. It’s the limit-on-open order that differentiates the winners (usually). Kind of annoying if you have to set a limit order so frequently that only gets filled one or two times per month. I suppose you could set an alert and jump in within a few minutes of the opening bell, but then you’re going to get some slippage as a result.

Would I trade this? Meh. I don’t know. The frequent false alarms would be kind of annoying. It would almost make sense to set aside some cash solely to trade this system, and just sit and wait patiently.

Also, because this ETF can’t be tested back before late 2008, there is no real stress-test of a bear market. Likely this system would require an overall market-health test to be complete. But hey if the market is in collapse or the system is generating too many losing trades in a row, shut it down until it starts working again.

As always, a system that looks good in backtesting, even using in and out-of-sample data, does not guarantee future results. Disclaimers are lame. But necessary.


4 thoughts on “Swing Trade System for a Financial Sector ETF”

  1. Interesting. One metric you may want to consider for future system tests is avg. performance per trade divided by avg. standard deviation of performance per trade. I.e., a per-trade Sharpe ratio. Nothing magical about the calc., but I think it helps quickly assess what the risk/reward looks like for an individual trade.

Leave a Reply

Your email address will not be published. Required fields are marked *