What cockroaches can teach traders: reading list

A well-defended cockroach. Source: Google images.

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.


Autonomous agents and genetic algorithms…oh my!

Input = binary.  Output = $$.  Source: Google Images.

Input = binary. Output = $$. Source: Google Images.

The latest book I’m reading is Professional Automated Trading: Theory and Practice by Eugene Durenard.  I’m about a quarter through; in the words of my dad, it’s way cool.

The book has that certain exoticism which probably appeals to a wide range of financial geeks: lots of mentions of hard sciences; passionate disregard of prevailing ‘rule of thumb’ approaches to valuation and trading; and the creation of a robot-army, led by a robot general, to achieve trading success in a landscape filled with chaos and complexity.  If this gets your interest piqued, I suggest purchasing a copy.

OK, back to Earth for a second.  Having not read the full book yet (but the early summaries give a pretty comprehensive overview of what’s to come), I wonder how much of the theory can and does get put into practice.  From the veneered description above, I clearly want to believe the secret to endless trading profits is a fantastically-engineered army of automatons.  However, it’s been my experience from reading more practical trading literature, as well as working in various trading shops, that higher complexity = higher disappointment.  The development cycle I’m accustomed to runs a bit like:

  1. I test a basic hypothesis, such as ‘the oil price trends’.
  2. I come up with a trading rule, having verified #1.  Perhaps a basic breakout strategy.
  3. The basic strategy in #2 looks OK-ish, but I notice a few really bad apples among the backtested trades.  So I create a filter rule to ensure trades like them don’t happen.
  4. I test #3 with the same data.  Perfect.  So now I test out-of-sample data.  Guess what? #3 stinks compared with #2.
  5. Repeat #3 and #4 until I finally give up trying to find a filter rule, and stick with #2.

The idea of continuously-evolving trading robots, or a static robot army led by a continuously-evolving general, sounds a bit like an in-line version of the above sequence.  Maybe, after enough trials, the robot general will beat my logic and analysis – I’m open-minded.  Perhaps that’s why I’m keeping with the book.

In sum: the financial markets offer numerous ways to get very complicated and technical.  Sadly for yours truly, I haven’t yet found a complex ‘golden nugget’ strategy which consistently outperforms more simple trading implementations.  But I keep searching…

What trading strategy suits you? Putting the woo-woo in systematic trading…

I just finished Mechanical Trading Systems by Richard Weissman, which focuses on (who’d have thought) design and use of systematic trading strategies.  The usual mix of momentum, mean reversion and intraday strategies are all here.

What I found more interesting was the author’s pairing of the strategies with trader personality.  The thesis is that certain types of strategy suit different people.  This goes along with my earlier post about return skew: though there are several ways to make money trading, the manner in which returns come can be hard for a person to stomach.

In sum:

  • Momentum: the ‘no vacation’ strategy.  Most trades lose (a small amount of) money, but a few home runs make up for all the losses and then some.  Key is that the home runs are completely unknowable, so you must be in the market at all times.  How do you feel about losing trades 5, 6, 7, 9, etc. times in a row?  Would you keep rolling with the strategy?  How do you keep faith it’s not broken?
  • Mean reversion: made for contrarians.  Benjamin Graham’s Mr. Market becomes over exuberant and depressed without end; your job is to be greedy when others are fearful.  Going against the crowd can be tough, particularly when you’re too early to the trade.  Trade returns tend to be more winners than losers, so that’s some compensation.
  • Intraday: the ‘quick minded’ strategy.  Basically either of the two approaches above (though most go for mean reversion in the intraday context), but with a much-compressed time scale.  Same sorts of emotional issues apply, but with the added stress of needing to make multiple decisions per day (and needing plenty of caffeine).  In my mind, this is where automating the trading becomes absolutely necessary.

As I mentioned before: all these are ways to make a buck.  The question is whether you, as a person, can handle the consistent losses (momentum), the discomfort of going against the crowd (mean reversion), and/or a very stressful lifestyle (intraday).

On a completely tangential note, it was interesting to see the slippage schedule used in the 2008 book was $100 per futures contract, per side.  The market is a lot cheaper these days…

How would you like your returns skewed?

There have been several times in the past where I’m explaining ‘XYZ strategy’ to someone (hopefully they asked me beforehand), and the concept of skewness comes up.  A couple examples:

  • Several (successful) strategies lose far more frequently than they win.  It’s not always like playing the lottery…
  • Sometimes ‘the sure thing’ trade, which has made money every day, suddenly blows up.

Thus loops in the concept of skewness – how big are losses relative to gains?  On the lottery side, you’re almost certainly going to lose  USD 1 on a game with a (highly improbable) gain of USD tens of millions.  But other examples abound in financial markets:

  • Long-only (just about) anything: this is a negatively skewed strategy.  Most months/years you will have a gain, but some months will be TERRIBLE.  Don’t think about the little correction we just had…think about 2008.  It can take years to recoup the losses from long-only: for example, notice that the NASDAQ is still about 10% below its 2000 peak.
  • Venture capital: this is a positively skewed strategy, in its most basic form.  The VC fund manager selects (say) 10 companies at an early stage of development.  Financials don’t really mean much at this stage – they could do anything.  The hope is that, out of 10, there will be 1 big winner and maybe a few small winners.  The others are expected to be written off.  So, one gain outweighs the many.
  • Volatility selling: this is a classic negatively skewed strategy.  VERY negatively skewed, epitomising ‘picking up pennies in front of a steam roller’.  After premium selling funds lost about 50-70% in 2008 (or went completely bust), several actually hit high water marks in the past couple years.  So it’s a sustainable, if nerve-wracking, strategy.  By the way, insurance products and market making are roughly the same as option premium writing, in terms of performance characteristics.
  • Momentum trading: a classic positively skewed strategy.  Frequently momentum is classed as ‘long volatility’, which it is…kinda.  More long gamma…but anyway.  This is a ‘pain trade’, in that most of the time you’re losing money as markets oscillate back and forth and you’re trading with the trend.  Only occasionally do the big trends come; you can’t really forecast them, and you MUST be in the market when they come.  Otherwise this is a losing strategy.

I leave you with the following track records, harvested from Altegris’s managed futures website.  Interesting place to learn about volatility and momentum offerings.

Classic volatility selling strategy characteristics: nice, steady gains punctuated by large losses.  Source: www.managedfutures.com

Classic volatility selling strategy characteristics: nice, steady gains punctuated by large losses. Source: http://www.managedfutures.com

Screen Shot 2014-10-23 at 15.47.29

Classic example of a higher-geared momentum fund. Notice how the fund spends most of its time below high water mark; this is broken up with infrequent, large gains. Source: http://www.managedfutures.com

Extra credit: those seeking more technical info on return skewness, and particularly how the time-variance of skewness is a function of strategy design, should look at this wonkish paper.

Investing and the Death Cross

Much talk of the ‘Death Cross’ these days.  The 200-day moving average (200 DMA) is seen as the indicator for folks to start bailing on the stock market.  The Russell 2000 crossed this level a while back; the S&P 500 crossed its 200 DMA yesterday.  Market prognosticators will now side with:

  1. Trend-followers: time to get out!
  2. Value investors/contrarians: time to get in!

Which one should I be?  Well, let’s go back to our friendly Quandl report on S&P500.  Since 1950, let’s see whether it pays to be in the first or second category:


The green line simulates the easiest strategy possible – buy the S&P500 in 1950, and hold it forever.  The purple line is the same, except for the following:

  • If the closing price on date t is below the 200 DMA, sell on the opening price the next day.
  • If the closing price on date t crosses above the 200 DMA, buy back in.

I’ve used a log-scale chart, so the time frame doesn’t really matter – you could buy in 1960 or 2000, and the scale of the results aren’t really impacted (aside: do you get angry at investment performance reports showing arithmetic scales, rather than log scales; particularly when covering a long time period??  I find them very annoying, and downright misleading).

Anyway, what do you notice?  The main takeaways, for me, are the following:

  • Exiting at 200 DMA doesn’t seem to matter much in the long run.  Returns are what they are.
  • Position risk looks to be much better when you exit at 200 DMA.  I’m sure many would prefer to have sat-out the 2008/09 crash.
  • In sum: the risk manager will love you for exiting at the 200 DMA.  But your returns may or may not be negatively impacted.