Shorting the VIX

svxy-trade

Today I completed a trade using the ProShares Short VIX Short-Term Futures ETF, (ticker: SVXY). I entered at the close on 5/6/15 at $77.73 per share, And exited at the close today (05/14/15) at $83.64. That’s a profit of 7.6% before commissions, for six days of trading. Not bad!

It’s going to hurt my head to try and explain what the heck this thing is. But I’ll try anyway, and wince a little as I do. Someone correct me if I’ve screwed up.

“VIX” is the CBOE’s index of the implied short-term volatility of S&P500 futures. It’s an index that doesn’t track prices of stocks, but instead tracks the expectations of how crazy things are going to get. If the market thinks it’s in for a wild ride, volatility increases, and the VIX index goes up. Usually increased volatility is due to downward expectations in price, so the index is often called the ‘fear index’. You can read more about it here on the CBOE’s site.

Someone, or several someones, had the bright idea to create an ETF based around the VIX, so we can trade on volatility instead of price. The problem with VIX-based ETFs are two-fold though:

• They have a downward bias. I’m sure this has to do with futures expiration rollovers or contango or some other dance step…not my area of expertise. But over the long haul, they tend to drift downward. This makes any long plays harder because of the bias in the wrong direction.

• Mean-reversion systems often rely on a downward spike or significant event to act as a signal, and the return is made on the drift back to the mean. But the VIX tends to spike upward. Some bad news event triggers an increase of volatility, the VIX spikes upward, and then slowly drifts downward again. Volatility just doesn’t seem to spike downward in the same fashion.

Some other even more brilliant people decided it might be fun to have a short VIX-based ETF. This flips those two problems on their heads, turning them into features. A short-VIX ETF has both an upward bias, and spikes downward before reverting in an upward direction (usually).

I can trade that!

Below you can see two charts for the most recent time period. Note how the short-terms ups and downs are pretty similar between SVXY and SPY, but the longer term moves are different. For example the SVXY has been climbing pretty steadily over the past few months, while the SPY (S&P500 ETF) has been trading in a range. That means more opportunities for trades.

svxy-vs-spy

Unlike the VIX, which is direction-neutral in the long term, these ETFs have direction. So you really need to test with the ETF rather than the underlying VIX index. But the SVXY has only been trading since October 2011, which doesn’t provide quite the data I’d like for backtesting and developing systems.

Another thing to keep in mind: SVXY has a much greater daily percentage change than SPY. So adjust your positions accordingly, otherwise you might lose the farm. Today for example, SPY gained 1.04% from the previous day, while SVXY gained 2.04%. That’s almost twice the increase. On October 13th, 2014, SPY dropped -1.64%, while SVXY dropped -9.78%! Plan accordingly.

That said, I did come up with a nice little system that tests well. It tested so well in fact, that only one trade out of about 30 was a loser. Do I trust it? No, not completely. I don’t know how it’s going to behave during major market turmoil. But it looked sufficiently good that I was willing to put a little money on it. And the first trade was a great success.

I’m not prepared to discuss the system at the moment, but that’s not the point of this post. I love the proliferation of ETFs that has occurred over the past few years. There are many ways to make long-only trades that used to be impossible. Now if someone would come up with a triple-leveraged short corn futures ETF!

 

FAS Double Down System – First Trade Completed

fas-first-trade
That vertical line is just so I can see quickly at a glance where I entered. I forgot to remove it before the screen capture, and can’t be bothered to redo it.

Back a few weeks ago I outlined a swing-trade system using a leveraged financial-sector fund called “Direxion Russell 1000 Financials Bullish 3X ETF” (FAS). Catchy name, eh? You can read about the system’s parameters and statistics on the original post here. It has a great hit percentage and a beautiful equity curve. What’s not to love? The only real down side to this system is that the potential set ups are frequent, while the actual entry opportunities are maybe once a month or so.

On April 13th I was presented with just such an opportunity, and I dove in. What followed was a lot of “chop.” The ETF kept generating closes above and below the entry price, almost daily alternating back and forth. This was certainly on the longer side of durations for this trade: 19 days in the market, almost a full month. (50% of the trades historically have lasted 3 days or less).

But then with Friday’s booming stock market, FAS closed over my threshold and I sold. That was a gain of over 5.8% before commissions.

It’s always nice when the first trade of a new system using actual money provides a win. Although with this system and it’s 86% historical hit rate, I had high expectations.

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.