Hack the market

I mentioned several weeks ago that I’ve been developing and trading a strategy that’s proven to be quite interesting and profitable. In that post, I described how I’d tried to improve the strategy through the use of a dynamic hedge. The results of that crude hedge were quite good, but just as no worthwhile software project is ever really complete, trading strategies demand constant iterative development.

Below I describe some of the steps I’ve taken to incrementally improve this strategy, discarding the relatively expensive hedge I’d developed earlier in favor of a complementary strategy. We see that when you combine two positive and uncorrelated results, you end up with a product that is literally better than the sum of its parts.

The core strategy has remained the same, though the universe of instruments it trades has been both expanded – through the inclusion of a broader set of equities & ETFs – and contracted – through the application of some filters which prevent trading of some of the instruments under various conditions. These change have yielded a few more points of annualized return with minimal impact on volatility.

As I’d mentioned, the core strategy builds a portfolio of shorts which is sold at the open and bought back at the close. One of the problems with the original hedge is that it had a cost – literally. In order to hedge my portfolio, I needed to cut-back the size of my portfolio to accommodate the cost of the hedge. I mitigated this issue to some degree by employing futures instead of a broad market ETF, but this still reduced my usable capital by approximately 10-15%. I also had my money sitting idle overnight which seemed a particularly profligate behavior.

This led to the study I described last time in which I observed the relative out-performance of the broad US equity markets overnight. If I could somehow find a way to capture some of this overnight alpha, I’d be able to both hedge my main strategy and better utilize my capital.

My first effort at such a strategy happily achieves both aims by assembling a long portfolio which is held overnight. In the chart below, I capture the returns of the two strategies independently and combined. The core daytime strategy is denoted as “OpenClose” or OC whilst the night-time strategy is “CountingSheep” or CS. Both assume an initial capitalization of $1M, employ no leverage and do not reinvest returns – each day or night they assemble a portfolio with the same $1M and profits are put aside and don’t generate interest.

The chart is nice and certainly a big improvement over the earlier hedged approach, but the real power of combining these two strategies is revealed in the two tables below. The first characterizes their risk-adjusted performance independently and then when combined. Both have a Sharpe ratio of around 2.0, but when combined they yield a new strategy which is about 25% better on a risk-adjusted basis. These cells are highlighted.

The key to their compatibility is their correlation. Or, actually, their absence of correlation. In the below table, you can see the correlations of their returns to one another and broad market ETFs. The short daytime strategy is, not surprisingly, negatively correlated with the broad market while the long night time strategy is positively correlated. The beauty of their combination lies in the lack of correlation between the two of them (highlighted) – they’re essentially uncorrelated.

I hope this post illustrates a couple of different vectors along which strategies can be evolved; in this case, to better manage risk and utilize capital.

One of the key remaining limitations of this particular strategy is its capacity. Increasing the capacity of a relatively short-term strategy like this one requires optimization of the trade executions which is its own black art but one that plenty of smart people are constantly addressing. Perhaps in a future post I’ll review some of the techniques applied to this problem for another perspective on the iterative development/evolution of trading strategies.

performance analysis, portfolio management, strategy development