I recently spent more time doing “reading research” rather than “testing research”. As result, this post resembles a collection of links on ideas seen on the web of how to trade futures with a small account – one of the topics I have been interested in.
The Issue: Diversification with Small Account
A small account size – or starting equity – can make it difficult to achieve diversification (a “free lunch” with a high “cover charge” as described in this post – you can read more on diversification and correlation from this blog here and here).
Diversification can be achieved by trading a large number of components in a portfolio, whether “components” represent:
“Instruments” is usually the first aspect that comes to mind when thinking about diversification.
Including more assets/markets/instruments in a portfolio is often described as the “free lunch” – and this is one of the main reasons why large CTAs often include upwards of 100 markets in their portfolio selection.
A small account most likely cannot trade a portfolio of 100+ instrument. This is an issue that Dean Hoffman tries to address in this article: The Conundrum of Small Managed Futures Accounts.
Noting that most diversified trend follower CTAs have a minimum account size of at least $1M, Hoffman describes the advantages of trading larger accounts (able to trade many instruments including those with high margin requirements, more granular position sizing with contract scaling).
Hoffman then describes Dynamic Portfolio Selection as a potential solution for small accounts to achieve increased results from a “virtual high diversification”. The system monitors a large set of instruments but instead of taking all signals (as a diversified trend follower would most likely do), it evaluates and ranks each instrument relatively (based on each market’s potential on a risk-adjusted basis), resulting in about 90% of trading signals being filtered out. This naturally cuts down the number of positions held at the same time, and consequently the required account size.
As this is mostly a “marketing” article for Hoffman’s CTA offering (implementing this concept), there is not much more information on what sort of filtering is applied to select the “best” signals but the general idea is worth investigating (and you can check for yourself whether their performance seems to hold up against the theory).
This idea of filtering trades is not new: the Turtles used to use the concept decades ago, as mentioned by TB forum user sluggo in this post (which contains a link to Trading Blox code implementing similar “heat limitation” mechanism).
Systems (and Timeframes) Diversification: Swarm Behaviour
Combining several systems is also a possibility to achieve diversification. With the extra advantage that it is possible – to some extent – to design systems and control their correlations to the rest of the suite of systems (as opposed to markets, which can have a furious tendency to correlate to +1 or -1 during crisis times).
And as we all know, correlation is a key element of the “diversification benefits” equation (check this thread from user sluggo on TB forums for a good presentation/discussion on the topic).
Adding a profitable mean reversion/counter-trend system to a trend following system will, in all likelihood, reduce the volatility of the combined portfolio, thanks to the negative correlation that it brings. Adding many uncorrelated systems is likely to increase this positive effect.
However, trading a diversified suite of systems has a similar constraint to trading a large portfolio: it increases the required account equity.
A comment from Pumpernickel on a recent post from Quantum Financier (who is starting a series of posts on “signal aggregation: how we form and use an ensemble of signals isolating different pieces of information to build a profitable strategy” ) pointed to a couple of documents from Fall River Capital.
The (pdf) document (part 1 and part 2 of their white paper) describe how they tackle this issue on a large scale, by trading hundreds to thousands systems simultaneously, using the concept of swarm behaviour (which can be seen throughout the natural world, such as in the mesmerising starling flights in the English Somerset Winter, pictured above).
From the white paper (other Fall River white papers and general website are also interesting to read):
An […] approach is to assign each trading system a vote. Each model is polled for its position (long, short, or out) daily, and the total is aggregated into a tally that may be thought of as a “Vox Populi,” or crowd opinion poll. Research showed that aggregating the systems by this simple tally method was a quite workable approach, allowing us to “cheat” by holding a single position per market rather than hundreds or thousands. Regardless of the number of component models, the master strategy holds a position in accordance with the majority of the crowd.
How they choose the models/systems to be included in the portfolio is mostly driven by each system’s correlation to other systems:
The portfolio of individual candidate systems consists of between several hundred and a few thou‐ sand members that share both low correlations to one another and robust returns over many years of market history. The result is a “swarm” of trading models, each attacking the market from a different direction. This process of system development, evaluation, and selection does not prioritize superior standalone system performance, but rather seeks to uncover profitable trading rules that complement one another when implemented together.
Their testing results seem to show that this approach tracks fairly well an “equal allocation” approach with hundreds/thousands of systems, which itself benefits greatly from low correlated system diversification (reduced volatility, or increased vol-adjusted returns).
This “systems voting” strategy has also been discussed on the TB forums there (again started by user sluggo…).
These are ideas to stimulate research on how to alleviate the “futures trading diversification with a small account” issue. Other ideas can also be found on other threads from the TB forum (examples 1, 2 and 3 – search the forum for more discussions), showing that the topic is a “popular” one.
Another alternative would be to move away from trading actual futures but instead focus on “proxy” instruments such as ETFs (see this post for a quantification of how ETFs can track futures) or spread betting (they usually offer lower minimum trading lots, allowing for lower required trading equity, but can have other disadvantages, such as counterparty risk, less instruments available or cost of funding/leverage). Another trade-off to make in system/strategy design..