A friend recently asked me what I considered to be the “axioms” of alpha-seeking trading strategies. I think there are a few, but probably the one that seems to me most important is that the atomic element of a trading strategy should always be a portfolio as opposed to a single instrument.
In a scenario of perfect knowledge, this wouldn’t be true. If you somehow *know* with certainty that crude will go up or that Citigroup will go down, then concentrating all of your resources into a position based on that belief might be reasonable. But knowledge seldom comes in such a neat package (and will frequently be illegal to act upon when it does!).
Instead, knowledge will typically come in more conditional and less certain forms: “commodities tend to rise during periods of FUD [Fear-Uncertainty-Doubt]” or “companies who announce stadium naming rights deals tend to under-perform.” In some cases, perhaps the knowledge on which you’ll base your strategy can be quantified probabilistically.
Depending on the nature and quality of the knowledge or hypothesis that forms the basis for a given strategy, one can adapt one’s portfolio construction/optimization based on customized relationships amongst the potential portfolio constituents. But one doesn’t need to be so fancy to see the concrete benefits of our first axiom. Below I detail a simple strategy I’ve put together to explore the forces involved.
Read more…
I’ve been trading an increasing amount at the open and close of the equity markets using market-on-open (MOO) and market-on-close (MOC) order types and have found that the quality of executions varies enormously between the two types and have spent a bit of time analyzing the differences which I share below.
The quick scoop is that MOC orders almost invariably fill at the exchange’s published closing price, while MOOs vary very substantially from the published open price. Below I quantify my findings in a bit greater depth.
Read more…
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.
Read more…
People have long imagined ways to make money while they slept. Happily, it’s not a pursuit I’m particularly bothered by, but as I develop trading strategies, I do make note of different market behaviors that correlate to the time of day. Or night.
In particular, I’ve been looking at various market-breadth ETFs recently as possible fodder for the little dynamic hedger I’ve described before, and I’ve noticed an interesting behavior among several of them…
Like the majority of traders, they do better when they’re not trading!
That is, they actually display better performance at night than they do during the regular trading day; there’s more profit to be had in their gaps between sessions than there is during trading sessions. Below I quantify this observation more thoroughly…
Read more…
The always excellent Wilmott Magazine has recently posted a series of articles by Ed Thorp (pictured) in which he describes his experiences developing and evolving a statistical arbitrage product. Part I provides some insights into his current operation, revealing that he maintains a dollar-neutral portfolio as I’d discussed in another post, they trade some 1.5 billion shares / year, and that they limit position sizes to 2.5% on the long side of the portfolio and 1.5% on the short side. In Part II, he explains why a stat arb system is considered an “arbitrage” and how, with the help of a talented team and led by the insights of Gerry Bamberger, they developed the first iteration of a stat arb product. Part III details the evolution of the system from a set of dollar-neutral sector-oriented portfolios to the more general sets of portfolios generated through statistical factor analysis. He concludes with some anecdotes including the emergence of David E Shaw. Very recommended.
I came across and had to share this excellent vignette by Freeman Dyson on the perils of excess model parameterization…
In desperation I asked Fermi whether he was not impressed by the agreement between our calculated numbers and his measured numbers. He replied, “How many arbitrary parameters did you use for your calculations?†I thought for a moment about our cut-off procedures and said, “Four.†He said, “I remember my friend Johnny von Neumann used to say, with four parameters I can fit an elephant, and with five I can make him wiggle his trunk.â€
I’ve recently been working-on and trading an equity strategy that has some great characteristics and some interesting challenges. The great characteristics revolve around its profitability, volatility and simplicity. The challenges start with the fact that the strategy generates alpha on the short side – thus, you are intrinsically swimming against the tide and can conceivably be ruined in a hurry. Your broker might also be unable to find inventory to short. Other challenges include the native capacity of the strategy – it’s not fundamentally scalable as a strategy and only a relatively small amount of money could be put against it without incurring increasingly onerous costs and risks. In any case, it’s been a fun strategy to develop as it’s an interesting puzzle and it makes money.
Discussing the strategy recently with a potential client, they observed that such a strategy wouldn’t be acceptable within their environment (apart the capacity issues) as their risk management practices required all strategies to maintain dollar neutrality – for any dollar of x that they used to buy something, they needed to sell a dollar of y. This led to an interesting experiment for me, the results of which I share with you below.
Read more…
I recently had a pretty visceral encounter with the forces of friction. No, I didn’t fall off my bike – I’m talking about the friction inherent in trading activities. I’ve mentioned Andrew Lo‘s market-neutral long-short algorithm before and it sees service as my blogging muse once again. I’ve modified his original algorithm such that it behaves reasonably well though, as he observes, it’s a strategy in long term decline. My recollection was that one might expect 15-20% from an unlevered deployment of the strategy.
Recently, I went to play with it and to my shock and horror it had become a wretched loser. In fact, an incredible loser. What had happened? I looked throughout my code and couldn’t find changes; this was corroborated by my CVS repository – no changes had been made to the strategy in a long while. Any coder is familiar with the natural entropy of software systems known pejoratively as “code rot” but this seemed an especially extreme case.
Read more…
Our algorithmic trading platform, StratBox, features a unique strategy component model that supports the modular development and re-use of “pieces” - we call them StratParts – of a quantitative trading strategy. StratParts expose metadata which can be manipulated by a human or software agent (e.g., a trader, an optimizer, a regime-switching protocol). A StratPart might be an entire strategy, a risk management component, a graphical or reporting component or really whatever a trader might envision. StratParts can be composed within the StratBox GUI to create a strategy which can be tested, analyzed and executed. Naturally, users can create their own StratParts which integrate seamlessly with the environment. Read more…
He didn’t look like much, but old Rocky Marciano put his back into every awkward punch he threw. More remarkably, he remains the only heavyweight champion to have ever been smart enough to get out of the game on top. He was that rarest of characters – a winner who knew when to engage and when to step away.
I’ve written a good deal about losers and ideas that might not yield the results one’s looking (hoping) for, but I haven’t written too much about life’s winners. This, of course, is absolutely par for the course amongst traders. People aren’t in the habit of giving away trade secrets, leaving sums of money on the sidewalk or revealing their trading strategies. When they do (or claim to) is likely a good time to keep an especially watchful eye on your possessions…
Read more…
