Short VXX strategy

Shorting the short-term volatility etn VXX may seem like a great idea when you look at the chart from quite a distance. Due to the contango in the volatility futures, the etn experiences quite some headwind most of the time and looses a little bit its value every day. This happens due to daily rebalancing, for more information please look into the prospect.
In an ideal world, if you hold it long enough, a profit generated by time decay in the futures and etn rebalancing is guaranteed, but in the short term, you'd have to go through some pretty heavy drawdowns. Just look back at the summer of 2011.  I have been unfortunate (or foolish) enough to hold a short VXX position just before the VIX went up. I have almost blown my account by then: 80% drawdown in just a couple of days resulting in a threat of margin call by my broker. Margin call would mean cashing the loss. This is not a situation I'd ever like to be in again. I knew it would not be easy to keep head cool at all times, but experiencing the stress and pressure of the situation was something different. Luckily I knew how VXX tends to behave, so I did not panic, but switched side to XIV to avoid a margin call. The story ends well, 8 month later  my portfolio was back at strength and I have learned a very valuable lesson.

To start with a word of warning here: do not trade volatility unless you know exactly how much risk you are taking.
Having said that, let's take a look at a strategy that minimizes some of the risks by shorting VXX only when it is appropriate.

Strategy thesis: VXX experiences most drag when the futures curve is in a steep contango. The futures curve is approximated by the VIX-VXV relationship. We will short VXX when VXV has an unusually high premium over VIX.

First, let's take a look at the VIX-VXV relationship:

The chart above shows VIX-VXV data since January 2010. Data points from last year are shown in red.
I have chosen to use a quadratic fit between the two, approximating VXV =  f(VIX) . The f(VIX) is plotted as a blue line.
The values above the line represent situation when the futures are in stronger than normal contango. Now I define a delta indicator, which is the deviation from the fit: delta = VXV-f(VIX).

Now let's take a look at the price of VXX along with delta:

Above: price of VXX on log scale. Below: delta. Green markers indicat delta > 0 , red markers delta<0.
It is apparent that green areas correspond to a negative returns in the VXX.

Let's simulate a strategy with this these assumptions:

• Short VXX when delta > 0
• Constant capital ( bet on each day is 100$)
• No slippage or transaction costs

This strategy is compared with the one that trades short every day, but does not take delta into account.

The green line represents our VXX short strategy, blue line is the dumb one.

Metrics:

          Delta>0     Dumb
Sharpe:    1.9         1.2
Max DD:    33%         114% (!!!)

Sharpe of 1.9 for a simple end-of-day strategy is not bad at all in my opinion. But even more important is that the gut-wrenching drawdowns are largely avoided by paying attention to the forward futures curve.

Building this strategy step-by-step will be discussed during the coming Trading With Python course. 

Getting short volume from BATS

In my last post I have gone through the steps needed to get the short volume data from the BATS exchange. The code provided was however of the quick-n-dirty variety. I have now packaged everything to bats.py module that can be found on google code. (you will need the rest of the TradingWithPython library to run bats.py)

Usage:

import tradingWithPython as twp # main library
import tradingWithPython.lib.bats as bats # bats module

dl = bats.BATS_Data('C:\\batsData') # init with directory to save data
dl.updateDb() # update data
s = dl.loadData() # process zip files

Building an indicator from short volume data

Price of an asset or an ETF is of course the best indicator there is, but unfortunately there is only only so much information contained in it. Some people seem to think that the more indicators (rsi, macd, moving average crossover etc) , the better, but if all of them are based at the same underlying price series, they will all contain a subset of the same limited information contained in the price.
We need more information additional to what is contained the price to make a more informed guess about what is going to happen in the near future. An excellent example of combining all sorts of info to a clever analysis can be found on the The Short Side of Long blog. Producing this kind of analysis requires a great amount of work, for which I simply don't have the time as I only trade part-time.
So I built my own 'market dashboard' that automatically collects information for me and presents it in an easily digestible form. In this post I'm going to show how to build an indicator based on short volume data. This post will illustrate the process of data gathering and processing.

Step 1: Find data source. 
BATS exchange provides daily volume data for free on their site.

Step 2: Get data manually & inspect
Short volume data of the BATS exchange is contained in a text file that is zipped. Each day has its own zip file. After downloading and unzipping the txt file, this is what's inside (first several lines):

Date|Symbol|Short Volume|Total Volume|Market Center
20111230|A|26209|71422|Z
20111230|AA|298405|487461|Z
20111230|AACC|300|3120|Z
20111230|AAN|3600|10100|Z
20111230|AAON|1875|6156|Z

....

In total a file contains around 6000 symbols.
This data is needs quite some work before it can be presented in a meaningful manner.

Step 3: Automatically get data
What I really want is not just the data for one day, but a ratio of short volume to total volume for the past several years, and I don't really feel like downloading 500+ zip files and copy-pasting them in excel manually.
Luckily, full automation is only a couple of code lines away:
First we need to dynamically create an url from which a file will be downloaded:

from string import Template

def createUrl(date):
    s = Template('http://www.batstrading.com/market_data/shortsales/$year/$month/$fName-dl?mkt=bzx')
    fName = 'BATSshvol%s.txt.zip' % date.strftime('%Y%m%d')
    
    url = s.substitute(fName=fName, year=date.year, month='%02d' % date.month)
    
    return url,fName
    

Output:

http://www.batstrading.com/market_data/shortsales/2013/08/BATSshvol20130813.txt.zip-dl?mkt=bzx

Now we can download multiple files at once:

import urllib

for i,date in enumerate(dates):
    source, fName =  createUrl(date)# create url and file name
    dest = os.path.join(dataDir,fName) 
    if not os.path.exists(dest): # don't download files that are present
        print 'Downloading [%i/%i]' %(i,len(dates)), source
        urllib.urlretrieve(source, dest)
    else:
        print 'x',

Output:

Downloading [0/657] http://www.batstrading.com/market_data/shortsales/2011/01/BATSshvol20110103.txt.zip-dl?mkt=bzx
Downloading [1/657] http://www.batstrading.com/market_data/shortsales/2011/01/BATSshvol20110104.txt.zip-dl?mkt=bzx
Downloading [2/657] http://www.batstrading.com/market_data/shortsales/2011/01/BATSshvol20110105.txt.zip-dl?mkt=bzx
Downloading [3/657] http://www.batstrading.com/market_data/shortsales/2011/01/BATSshvol20110106.txt.zip-dl?mkt=bzx

Step 4. Parse downloaded files

We can use zip and pandas libraries to parse a single file:

import datetime as dt
import zipfile
import StringIO

def readZip(fName):
    zipped = zipfile.ZipFile(fName) # open zip file 
    lines = zipped.read(zipped.namelist()[0]) # unzip and read first file 
    buf = StringIO.StringIO(lines) # create buffer
    df = pd.read_csv(buf,sep='|',index_col=1,parse_dates=False,dtype={'Date':object,'Short Volume':np.float32,'Total Volume':np.float32}) # parse to table
    s = df['Short Volume']/df['Total Volume'] # calculate ratio
    s.name = dt.datetime.strptime(df['Date'][-1],'%Y%m%d')
    
    return s

It returns a ratio of Short Volume/Total Volume for all symbols in the zip file:

Symbol
A         0.531976
AA        0.682770
AAIT      0.000000
AAME      0.000000
AAN       0.506451
AAON      0.633841
AAP       0.413083
AAPL      0.642275
AAT       0.263158
AAU       0.494845
AAV       0.407976
AAWW      0.259511
AAXJ      0.334937
AB        0.857143
ABAX      0.812500
...
ZLC       0.192725
ZLCS      0.018182
ZLTQ      0.540341
ZMH       0.413315
ZN        0.266667
ZNGA      0.636890
ZNH       0.125000
ZOLT      0.472636
ZOOM      0.000000
ZQK       0.583743
ZROZ      0.024390
ZSL       0.482461
ZTR       0.584526
ZTS       0.300384
ZUMZ      0.385345
Name: 2013-08-13 00:00:00, Length: 5859, dtype: float32

Step 5: Make a chart:

Now the only thing left is to parse all downloaded files and combine them to a single table and plot the result:

In the figure above I have plotted the average short volume ratio for the past two years. I also could have used a subset of symbols if I wanted to take a look at a specific sector or stock. Quick look at the data gives me an impression that high short volume ratios usually correspond with market bottoms and low ratios seem to be good entry points for a long position.

Starting from here, this short volume ratio can be used as a basis for strategy development.