Maybe I’m a one-trick pony. I just happen to like playing the Vix – in particular, the Vix-related derivatives. There are 2 main ways I like to do this:
- Just short it: I take advantage of a few related characteristics to short the Vix via UVXY. In particular, I like that UVXY is the prototype of a bad long-term buy, so I short the product using medium-dated options. After biting my nails during the past couple months’ market volatility, this trade finally hit pay dirt. I’m out of my latest iteration as of today, after holding about 2.5 months.
- Spread it: when the markets get really scared, the Vix futures curve looks like the red line below (from previous post):
Mmm…backwardation. Source: thinkorswim by TDAmeritrade
Today that red curve looks much more normal, as we’re back to daily all-time highs:
All back to normal. Source: thinkorswim by TDAmeritrade.
I took off the latest iteration of this trade yesterday. About 3 weeks of holding time.
What have I learned? First, it’s great to learn with successful trades. My theses were researched and executed when the time was right. Most importantly – in my mind – is patience: at one point I was looking at some pretty solid losses on the #1 trade as the Vix kept climbing higher. Nevertheless, that’s why I chose medium-term options to express the trade, and ensured I had a manageable maximum loss at initiation of the trade.
With investing, as with life… Source: Google Images
First of all, a confession: I’m becoming a Twitter junkie. Maybe it’s due to being alone most of the day, but the trading community on Twitter is fantastic. So I enjoy indulging in various conversations around market events and trading philosophy. Anyone interested can find me at @financialpiggy8
So. One of the Twitter conversations of late has been around HFT scamming, and included some pretty senior folks in the finance space. The worries are around what data HFTs get versus the ‘average’ guy, and what that might cost us as individual investors. Beyond the actual data feeds, I mused on some related issues/generalities:
- Trading strategies follow Occam’s Razor: in general, simpler strategies are more robust. At least, that’s what I have found trading at a variety of different time frames. I think many folks unaccustomed to trading believe there’s tremendous complexity in what traders’ strategies contain.
- Key to trading = discipline: I struggle with keeping discipline. I’m completely in agreement with several fellow traders on Twitter that espouse the less-interesting parts of being a trader. Namely, consistency towards rules. It’s no accident that many traders fail due to strategy neglect – trading too large/too small, ignoring entry/exit signals, etc. The prime example of a successful trader is someone with a solid routine, hopefully with no emotions attached. Hence the following point.
- Automated trading = automated discipline: seeing as I have pretty bad discipline (in my opinion), I outsource the discipline to my computer. Now my weakness is pretty much limited to turning off the system when I shouldn’t (to wit: I have turned off the system far too many times; each time has been costly, in terms of missed opportunity).
- The bigger picture – why we pay financial advisors/fund managers: people like me – interested in markets, anxious to overtrade, etc. – can also impose discipline by paying an advisor or fund manager to make all decisions. I can say with full confidence that, for many people, the 1% charged by a financial advisor is well worth the market tracking error compared to managing/overtrading their own assets. We can debate whether other options are a better deal here (e.g. robo-advisors charge less for the similar effect, as could diversified ETFs held forever), but the point is to stay away from self-destructive overtrading or overcomplicating matters.
In sum: the more complicated a trading strategy, likely the worse it will perform in future. Discipline is crucial in trading, as with investing. If you can’t keep discipline, consider hiring someone/some computer to make decisions on your behalf.
A well-defended cockroach. Source: Google images.
The latest on my reading list is A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation by Richard Bookstaber. It’s a bit dated, but right on the mark for what was to come in 2008.
The author’s experiences with risk management in a world of increasing financial complexity is the main theme of the book. Now that I’m nearing the end, I’ve found a tasty morsel of advice for systematic traders – or, indeed, just about anyone. Seek simplicity and robustness in system design/management. The author uses biological systems for his metaphors; in this case, the very simple defence mechanism of the cockroach results in lots of false positives (i.e. the roach is too conservative), but is therefore well equipped to handle the unknown unknowns of the world. His counterexample is a fish in Lake Victoria: very specialised for its environment – but gets wiped out when a foreign species is introduced. Though the fish was perfectly designed for its environment – optimal in a lot of ways – it was ill-prepared for external shocks.
This reminds me of several folks’ ideas of systematic trading: why can’t we just program the algorithm to take all the (in hindsight) obvious precautions to avoid losses? Well, aside from the observation that many of the precautions actually cost us in the long-run, a hyper-specific trading system is basically useless going forward. The markets continue to make fools of us all; therefore we need to choose systems which have more robust (read: simplistic) trading and risk management. Not only will that probably keep us profitable as markets change, they will probably be better equipped to survive external shocks.
As an example (but not meant as a plug): trend-following strategies are beautiful to me. They’re extremely simplistic trading rules, with built-in risk management. The latter is usually pretty blunt (e.g. stop-losses, or just signal reversals), but means good risk control in a variety of cases. That’s probably why trend-followers have been around for decades, quietly churning decent – though maybe not show-stopping – performance through crises and more normal times.
Happy Christmas, you quanties. Source: Google Images.
Every once in a while a look through my LinkedIn feed unearths a juicy(?) nut.
Thanks to Mr Madan for posting this quite impressive list of open-source trading platforms.
Perhaps a good time for a slight tangent: a few years ago, when I started developing quant strategies, I was pleasantly shocked to find out about open-source software, and particularly how deep these offerings were. It’s frankly AMAZING that, for just about any software developing requirement I’ve ever heard of, there’s a free open-source solution. Hence my love for languages such as Python and R, databases such as MySQL, and tools such as Spyder and Sequel Pro. Oh, and stackoverflow: not only are the tools free, but the tech support is free and generally kind. Oh, and Quandl for free data.
In sum: always look for the open-source alternative if you come across a software need.
I happened upon The Market Completionist today; Evan Jenkins, the author, writes very well on a range of more or less theoretical market concepts (e.g. efficient markets hypothesis/CAPM). I only wish he’d write more often.
Anyway, reading his older posts, I happened upon this one addressing market information efficiency. Among the ideas, the post asks why there is so much trading, when very little is required to achieve information efficiency. In sum, informed traders will look to ‘pick off’ uninformed (noise) traders, such that the latter should run away/not take the other side of the trade unless duly compensated. I imagine the end result of this is (similar to the study cited in the post) a market with very infrequent exchange, and discontinuous prices.
As an aside, this sounds a bit like the residential real estate market. When you’re looking to sell your house, you’re looking for a buyer that hopefully gets an emotional attachment to the property; that allows you to extract a surplus from the buyer based on his/her irrationality (or maybe a rational ’emotion’ premium?). Same when you’re buying a house: you want the seller to think you’re an emotionless market-maker, who demands a large discount to take the other side of the trade. Where shall the two meet??
Anyway, I’m very happy that most financial markets these days have a great deal of (over?) trading. The reason is simply price continuity. Not only do I feel very confident that my shares in AAPL are worth the latest closing price (i.e. I know the value of what I own); but I’m reasonably confident that, should the need arise for me to sell my shares, I can achieve a price very near what I’ve seen posted on Google Finance. As a house-seller in 2010, I can very much appreciate the latter point.
I guess we’re back to that old ‘market-makers/HFT as a service provider’ topic. Does there need to be so much trading to ensure information efficiency in markets? Probably not. Am I still sanguine with the idea that so much trading takes place? Sure.
I’ve already waxed lyrical about the guys at tastytrade. The group provides a lot of interesting research and fun (online) TV for traders of all types.
One of the more fun, and quality, bits of the show is ‘Where Do I Start’. The series takes host-man Tom’s daughter, Case, and teaches her to trade options from the very beginning. She has a small account, so they keep with small trades. Anyway, they’ve recently cut out the ‘fat’ of the episodes, to leave 3-minutes of good material for each topic. Worth study, if anyone is keen to learn options basics.
Just found Quantopian yesterday. Looks like they’re offering a free database of US equity prices (1-minute granularity, it seems), and an API which uses Python to create strategies. Could have fun creating/datamining a few quant strategies there!