picture credits: WilWheaton@flickr

Several CTAs or fund managers offer a standard version of their fund, along with a leveraged version (called enhanced risk, 2x or 3x fund, etc.). However a simple performance comparison of the leveraged option against the standard option usually makes it obvious that the former does not offer a simple performance multiplier of latter.

I was looking at Conquest Capital Group and their MFS fund (based on the Trend Following benchmark used in their paper discussed here), which offers two such options: their standard fund and a 3x option.

The main fund, since inception (2004), returns 56% (net of fees) with a max drawdown of 14%. Their 3x leveraged option, over the same period, returns 85% with a max drawdown of 47%. The risk is indeed tripled, but the reward falls fairly short of it. When removing the fees on both funds (1% for the standard fund and 3% on the leveraged fund), the performances are 66% vs. 120%. This is just an example. Most CTAs are similar in that respect and it highlights some effects of leverage.

Leverage is not a magic bullet

A similar concept has become more mainstream with the recent apparition of leveraged ETFs and the realisation by some investors of the large volatility decay that they incur (when they are held on a long term-basis, i.e. anything longer than intra-day). Check the long-term charts of FAS and FAZ (3x leveraged bullish and bearish financial ETFs): the only long-term profitable trade would be to short both, to cash in on the volatility decay while being hedged (since Nov 08 they are down respectively 58% and 98%).

This is simply due to the design of these instruments, which aim to replicate and triple the daily returns of the tracked benchmark. This highlights a fact about leverage: multiplying the arithmetic returns will not result in the same multiplication of the geometric returns.

Leverage with Position Sizing

A typical way of leveraging a trading strategy is to increase the position size, and the resulting risk and return for each trade.

Assuming a fractional betting strategy for position sizing, the leverage dictated by the value of the fraction of capital risked on each trade will impact the overall average geometric return. Theories describing this phenomenon are the Kelly criterion and Ralph Vince’s Optimal f.

One of the main implications of this type of Money Management is that, for every strategy, there is an optimal fractional position size to maximize the (past) geometric return (and future if you assume that the past is a good representation of the future). Any fractional position size higher than this optimal value would result in a lower return.

Any strategy (with its resulting stream of returns) has an embedded maximum leverage, which can be defined with the optimal f.

Notional funding: another type of Leverage

Notional funding can be compared to trading with imaginary money. Typically, CTAs also offer notional funding: they trade your account based on an agreed notional amount, which is different from the actual funds in the account. For example, you could send 100k to your CTA and instruct them to trade it as if it were a 300k account. Or you could do the same thing with your own trading system.

One could intuitively think that this is exactly the same thing as tripling the position size but there is a difference.

Let’s consider this simple example with a strategy producing two consecutive trades of +10% and -5%:

The notional-funded account would close at 300 x 1.10 x 0.95 = 313.50. This would result in a gain of 13.50 on actual funds of 100: +13.50%, which is exactly triple the return one would get on a fully-funded, unleveraged account (100 x 1.10 x 0.95 = 104.50 for a 4.5% return)

Leveraging by tripling the position size on an account of 100 would result in two consecutive trades of +30% and -15%. Final balance would be: 100 x 1.30 x 0.85 = 110.50 – or a +10.50% return.

Initially, notional funding appears a better solution to the leverage issue as it does not suffer from the volatility decay or erosion of returns introduced by increasing position size.

Issues with Notional Funding: Cost

But funding always comes at a cost, pretty much in the same way as an overdraft gets charged by your bank. Another way to look at notional funding is to compare it to borrowing trading capital. The higher ratio of your notional account you borrow (higher leverage), the more the cost of borrowing will impede your trading returns.

In practice, you do not really borrow any funds (only by the power of imagination) but this practice still ends up impacting your return when you start taking into consideration the return on margin.

Impact of Margin Interest on Trend Following Returns

This paper from EDHEC Risk (PDF) looks at Trend Following/Managed Futures performance and presents an interesting breakdown of the overall return of such strategy. Assuming interest being paid at T-bill rates on the margin used for trading, it represents the bulk of the returns compared to actual Trend Following gains and rebalancing gains.

This is one of the reasons for the hidden cost of notional funding: a notional-funded account would enjoy the same Trend Following and rebalancing gains as a fully-funded account of the same size, but only a fraction of the return on margin (which is based on the actual account size).

To illustrate that point, below is a chart showing the impact of fees and margin interest on an arbitrary performance curve:

Notice the fairly sizable difference between taking return on margin into account or not (red v. blue curves). The green curve adds fees at 1% annually. The return on margin was assumed to be the T-bill rate for that month.

Risk and Margin Requirements

Another issue of Notional funding used as leverage are the constraints imposed by the underlying strategy.

Any strategy requires a minimum margin commitment to support the positions. Effectively the Margin-to-Equity ratio will dictate the maximum leverage one can use to trade a strategy.

The Drawdown is also a leverage-limiting factor: the more risk the strategy generates (higher drawdowns), the less leverage can be applied. If a strategy regularly exhibits drawdowns at the 40% level, there is a always the possibility that such drawdown appears at the beginning of trading the strategy. Based on the level of leverage, a notional-funded account might not have the time to build enough equity cushion to withstand the drawdown, which would result in the actual account funds going to 0 or a margin call.

Note that in this case, the sequence of trade returns has an impact on the outcome: if the strategy produces gains of 100% followed by a drawdown of 40% a 3x leverage via notional funding would still result in a gain of 60% (300 x 2 x 0.6 = 360, or a profit of 60 with actual funds of 100). If the 40% drawdown appears first, the notional account would shrink to 180, which would result in a negative actual account balance (trading would have to be stopped before then).

Leverage Comparisons

With these considerations and the benefit of hindsight, here is a comparison between the same arbitrary strategy as above (fees and interest included) traded with:

• no leverage
• 6x leverage using notional funding
• 6x leverage using position sizing

The notional funding appears to be the better option. However the position sizing leverage is independent of the order in the sequence of returns – as opposed to the notional funding leverage. If that nasty current drawdown (which is the largest one historically) had appeared at the beginning of trading and carried on further to reach 17%, the notional-funded account would have been wiped out.

The next chart highlights the impact of leverage on the difference between the two types of leverage for that specific strategy:

The target curve represents a simple multiplier (equal to the leverage factor) of the unleveraged performance (which, as we have shown is not attainable). The position sizing leverage clearly exhibits the behaviour discussed in Optimal f theories with the return breaking down past the optimal value.

Hopefully, this gives you a few ideas about how to work leverage in your trading strategies or when sending funds to CTAs. Taking the time to look into it has cleared up a few points for me.

Quant Fund Management meets Crowd-Sourcing

In an adaptation of the web 2.0 model, the company aims to leverage the content (quant strategies) provided by their users. In order to attract these quant users, AlgoDeal do provide you the following:

• Free backtesting software: their proprietary java-based MarketRunner
• Free infrastructure: computer grid to run backtests and optimizations and low-latency data/exchange connectivity
• Free clean historical market data: covering 80 futures markets
• Access to institutional funds

And in good ol’ 2.0 fashion, this is still a beta platform…

The Business Model

The access to the software, infrastructure and market data is completely free; but the guys at AlgoDeal are not running a charity for hedge-fund-less quant traders…

For each backtest you receive a scorecard with each metrics evaluated (CAGR, Sharpe ratio, MaxDD, etc.) and a final score. AlgoDeal selects the best scoring systems and allocates its clients’ funds to the selected strategies.

This is done via a legal agreement that ties you for a fixed-term, but the ownership of the strategy remains yours.
And you get paid: 10% of the strategy profits (which would amount to 50% of the variable part in a typical 2/20 fee arrangement).

Techie’s corner

So you’ll need java if you want to use AlgoDeal’s platform. There are 2 options, you can either edit the strategies directly on the website or download MarketRunner and run it in your favourite IDE (the only real viable option..) such as Eclipse, etc.

The whole grid aspect is hidden from you: it should just mean that the backtests and optimizations that you submit online should be parallelized and run faster.

Regarding the execution side of things for live trading, AlgoDeal have entered into an agreement with QuantHouse to provide fast access to markets (proximity hosting, exchange connectivity with proprietary fiber optic network, etc.).

Finally, the historical data is all continuous contracts, rolled automatically (no mention of what rolling algorithm is used). They currently only provide daily data going back from 1999 but expect to go down to 1-min frequency in the coming months.

Beta version

This is still a beta version and as a result things are still moving. AlgoDeal have a published Roadmap on the site which lists coming improvements over the next 3 months, such as risk management, improved money management, multi-strategy systems, etc.

The beta tag also means that this is not yet a final, stable release. One example of this last night: I could not get to run one of their canned quick start examples, simply getting the message:

Something went wrong! some backtests failed

However, the developers do seem to be responsive on the forum they maintain (they also communicate through their blog). There is also a fair deal of doc available.

Free invites

This beta release is by invite only and new registrations seem to be closed for now. However I contacted AlgoDeal directly to request a special “blogger” invite, which they kindly obliged. They also mentioned that they would allocate 100 invites for you, my readers:

We can allocate an initial set of 100 logins to referrals from your blog, once they have left their email on our website ask them to send an email to support@algodeal.com with in the subject line “On behalf of Jez’s blog”

Note: for the record, I am not associated with AlgoDeal in any way and I am not getting anything out of these invites. I just thought that sharing these might be of interest to you.

First impressions

It is obviously not as sophisticated (yet?) as a “proper” backtesting platform such as Trading Blox, and I only played with AlgoDeal and their simple canned examples on the online editor. I assume there will be lots of progress and improvements in the coming months, before their proper launch (Q2 2010?).

I think the real selling point for serious quant traders is the “easy” access to institutional money, provided that the platform is flexible enough to implement “sophisticated” strategies. I am not sure whether long-term Trend Following strategies are the best way to get selected on this platform (backtest market data only goes back to relatively recent 1999, typical higher return volatility might be an issue, etc.)

In any case, this is a “free” way to get access to a large quant fund infrastructure and capital raising without the hassles and headaches that come with setting up your own fund or working for one (and looking a much better option than covestor, KaChing or collective2). I do not know how much external capital AlgoDeal have secured for fund allocation, but each strategy could be allocated between $100k to $10M, and the 10% share of profits makes it potentially an attractive proposition.

A platform and space to watch…

Mount Everest Sunrise - picture from phobus@flickr (CC)

Being a small trader and “taking on” the big funds such as Winton Capital or Renaissance Technologies can seem as arduous as climbing the Everest. You might think: “What chance do I have to even come close to these guys and their army of PhDs? I might just send them a cheque to manage my money”.

Well, maybe it does not require rocket science to experience some success, as some simple mechanical strategies illustrate. Let’s examine the opportunities for the “small fish” trader.

The free lunch

If we consider traditional Trend Following, it pays to have deep pockets. Diversification is often referred to as “the only free lunch in finance”. The problem is that you need to have significant capital to be able to trade a wide array of futures, with possibly several different systems. This makes it hard to the small system trader to benefit from diversification.

The lunch is free only if you can spend big bucks on dinner!

New instruments on the block

However, a few new, and more accessible instruments have appeared in recent years.

Many ETFs and ETCs now exist for most markets now have their dedicated Exchange Traded Instrument. I have recently picked up a good book on the subject (A Practical Guide to ETF Trading Systems) and it does provide good ideas, especially on money management and how to increase returns without the leverage that can be found in the futures world (WARNING: I would not use leveraged ETFs for anything else than day-trading as they exhibit volatility decay).

The advent of spread betting (mostly in the UK) also allows retail traders to “bet” on financial markets with smaller starting positions than in futures trading but with similar leverage.

Finally, Forex trading has been made easier to access for retail traders, with new firms literally invading the scene in the last 10 years (OANDA even offers trading in any size starting at one dollar!).

Although not ideal, these new instruments help lower the barriers to entry and allow small traders to join the free lunch diversification party.

No footprints

Large funds have to deal with the extra complexity that their size entails. Their large orders and positions can weigh on the markets, impacting them negatively and forcing them to have dedicated efforts to execution algorithms development.

As a small trader, you are free from most of these constraints and can just get in and out of the market more easily. More importantly, this also gives you access to more markets.

Let’s check the monthly futures performance (with the new exciting tool from FinViz):

FinViz new futures tool showing some strong trends

Lumber had a particularly good month but given the size of this market, I doubt that any Trend Following Wizard significantly profited from that trend. Whereas, as a smaller trader, you could have.

Additionally I believe that these smaller, more exotic markets, exhibit less efficiency and therefore more opportunities to exploit them.

A big headstart: no fees

Even if you cannot match the performance of the large funds, the fact that you do not have to incur management/incentive fees when trading your own account is a big advantage. The difference can add up to a lot with compounding (deemed as “the most powerful force in the universe” by Albert Einstein some urban myth inventors).

No pressure

Another aspect that impedes fund managers “freedom” is the aversion of investors to drawdowns. However, reward is indissociable from risk and/or volatility. This means that managers will often tune down their strategy to offer a “less risky” but more appealing profile, at the expense of better returns. As an independent trader, you are free to decide your own tolerance level for risk and associated performance targets. You can be as aggressive as you want to be (for as long as the market keeps you alive ^_^).

a matter of personality

Finally, I believe it boils down to your personality. If you are an enterprising character, you will more likely be attracted to building and trading your own system. It might even be an irrational proposition, but I am sure some traders would prefer to invest a lot of time developing and then trading a system even if it achieved a slightly lower performance than the top Trend Following Wizards’ funds. But who said that trading in the markets was rational?…

Or are they you?…

Let’s explore the impact of fees charged by Fund Managers on the overall long-term return on your money. This is a question you might want to ask yourself before venturing in building automated trading systems (i.e. from a business point of view: would I spend a large number of man-days building a system that might barely achieve a better return than an established and succesful fund manager).

The assumption is that Trend Followers exhibit similar performance (for similar leverage). This can be verified when looking at the historical performance of the wizards. That can be explained by the fact that there is no “rocket science” approach to trend following. It boils down to getting on the trend as it starts and riding it until the end (when it bends…). For a given timeframe, any indicator you use would most likely identify the same trends.

To simplify, we will assume that a trend following fund manager achieves 20% per year and charges 1/20 fees (1% of AUM and 20% of performance).
Is it worth investing many hours in building a system yourself – or are you better off sending a cheque to John Henry, Dave Harding or Ed Seykota?

A gross return of 20%, after fees of 1% and 20% gives a net return of 0.99 x (20% x 0.8) = 14.84%
Turns out to be quite a difference!

By extrapolating and running a year by year comparison of gross return vs. net return we can see how the difference stacks up:

Net vs. Gross Return Comparison with starting capital of 100

Assuming that you have managed to build a system that produces the gross return above, we can see that keeping the fees for yourself (ie running the system) has quite an effect on the long-term return on your capital (compared to getting a fund to manage your capital):
After 3 years, you would already have gained one year compared to the managed fund approach (i.e. same capital but a year earlier). After 13 years, you are 4 years ahead!

And this is without considering you now have the option of raising external money to manage and earn your own fees (which you can only do if you run your own system)!

The example above illustrates the power of compounding and how small differences can end up making big numbers – which justifies putting the efforts to manage your own capital.