A simple job ad was placed in a handful of major newspapers calling for participants to be trained as traders. Of the applicants, a total of 23 individuals were chosen to become Turtle Traders: systematic trend followers who simply followed rules and made millions in the process.

These were average, ordinary people who were taught a system in order to determine whether trading could be taught, or if it relied on some innate skill. The system is perfect for algorithmic trading — just give a computer the rules and let it run.

The turtles were given a list of markets to trade: US treasury bonds of different maturities, cocoa, coffee, cotton, oil, sugar, gold, silver, copper, gasoline, and various global currencies. They were also given $1 million to work with.

For the typical retail investor who wants to implement a Turtle trading strategy, you’re going to have a hard time working across all of those markets without that capital.

Consider a typical oil contract. It consists of 1,000 barrels, and at say, $70/barrel, you’re going to be putting up $70k for a single contract. Most retail traders (especially younger ones) aren’t going to be able to afford that, and if they can, it doesn’t leave much else for other trades.

Commodities do provide significant leverage, so you may only need to put up 30–60% of the capital for a single contract (depending on the contract and exchange rules — which can change). But that still doesn’t leave most people with smaller accounts many options.

Enter in the wide world of ETFs!

There are ETFs for just about everything under the sun which trade like stocks. These tend to be much more affordable than commodity contracts, and you don’t have to worry about rolling futures as the contract comes closer to expiration.

With this in mind, the question is — can we build a portfolio of ETFs to replicate the Turtle trading portfolio that was so successful?

Building the Turtle Portfolio with ETFs

To start, we need a list of the instruments that the Turtles traded. Covel gives us a breakdown in his book:

  • 30-yr US Treasury
  • 10-yr US Treasury
  • 3-month US Treasury
  • Gold
  • Copper
  • Silver
  • Oil
  • Cotton
  • Coffee
  • Cocoa
  • Sugar
  • Gasoline
  • Swiss Franc
  • Deutschmark
  • French Franc
  • Japanese Yen
  • Canadian Dollar
  • Eurodollar
  • S&P 500 futures

The idea is to have a diverse set of uncorrelated markets, so if trends die down in one, they might be emerging elsewhere (long or short). There’s a slight problem with some of these markets: two of them no longer exist.

The Turtles plied their trade in the 1980’s which was a very different world. The USSR still existed and the EU with its single currency zone was still a ways off. So any modern analysis is going to have to drop the French Franc and Deutschmark in favor of the Euro. For our analysis though, we’re going to simply ignore these dormant currencies.

We want to see how well some modern ETFs stack up, so it’s time to go grab a list of ETFs for each of these instruments. Some are only going to have one, others will have multiple. Let’s go through each of these groups.


There are dozens of bond ETFs on the market to choose from, but I wasn’t able to find any that are designed to track our specific US treasuries, so I grabbed a few different ones that are described as short, medium, and long-term to see what we can use.

30-yr Treasury

10-yr Treasury

90-day US Treasury


Many major commodities (e.g. oil, gold) have multiple ETFs that track their performance. Some of the less common markets have very young ETFs as others have been taken off the market in recent years. For example coffee — despite being one of the word’s most commonly traded commodities — has a few recently defunct ETFs and a new one that launched in 2018. There are plenty of ETFs that track coffee growers, roasters, brewers, and everyone up and down the value chain, but I could only find a single ETF for the coffee contract on US exchanges.








Crude oil

RBOB gas


The ETF choices for currencies were much slimmer than for commodities, offering only one ETF for each.






  • Deutschmark and French Franc -> Euro ETF (FXE)


The Turtles traded S&P 500 and Eurodollar futures. The SPY is one of the most commonly traded and liquid instruments in the world, so it’s an obvious choice here. Unfortunately, I didn’t find a Eurodollar ETF that’s currently available on US markets, so I dropped it from the list.

S&P 500 Futures

With the list of instruments, we can go get the data. Most of the futures data isn’t available for free, so I relied on EOD and Quandl data subscriptions for those.

Trading Commodities without Futures

With our data in hand, we can see how these instruments correlate with the futures used by the Turtles.


Running correlations against the baseline contracts, we see some very strong correlations with the underlying contracts.

To build a modern, ETF-based Turtle portfolio, we can’t just take the most highly correlated gold ETF or long-term bond ETF and plug it in though. While these may be well-correlated with the underlying assets, it doesn’t follow that they maintain similar correlations with each other.

This can cause some confusion, so let’s look at a simple example. Your level of fitness and muscle mass are positively correlated. Additionally, there are many studies that show a positive effect on fitness and intelligence, so these are correlated as well. But, does that mean that muscle mass and intelligence are positively correlated? Of course not!

The same goes for our portfolio, we can’t assume because GLD is highly correlated with our gold futures and BAL is highly correlated with cotton that the correlation between GLD and BAL is the same as between gold and cotton.

What we really want, is a portfolio that has a similar overall correlation to the Turtle portfolio.


Selecting our Instruments

We have a few different portfolio selections we can make from the available instruments. If we want to match the correlations of the original turtles, we need to find out which instruments make the most sense to trade.

Let’s set up some parameters first, otherwise we could get into combinatorial explosion hell very quickly.

First, we need some metric for how closely our new portfolio matches our baseline portfolio’s correlation matrix. There are a number of distance metrics we can choose, but here we’ll go with a standard cosine similarity metric. This measures the angular distance between data, and the smaller the value, the closer they are together.

From there, we need to provide some constraints on our selections. We could trade more instruments, or drop certain markets if we’re not interested in them. But for now, we’ll constrain ourselves to trade one of each kind of market that the turtles did. Although we have multiple gold ETFs available, we’ll limit ourselves to only choosing one, and that will be it for gold. Likewise we’ll only choose one oil ETF, one sugar ETF, and so forth.

This is going to yield us 864 different portfolio combinations. We could get clever about this, but with just a few hundred possible portfolios, we can brute force our selection by trying every potential portfolio and measuring the distance between it and our baseline. If we do this, we get the following plot:


We get a few that are pretty close, but a long tail of distant portfolios. If we take the closest one, we can look at its correlation below:


This is looking at finding correlations between modern ETFs and trying to match them to the set of correlations in the futures market. We can take a cue from Ray Dalio who states that the “Holy Grail of Investing” is in diversification among uncorrelated assets.

That simple chart struck me with the same force I imagine Einstein must have felt when he discovered E=mc2: I saw that with fifteen to twenty good, uncorrelated return streams, I could dramatically reduce my risks without reducing my expected returns… I called it the “Holy Grail of Investing” because it showed the path to making a fortune.

Ray Dalio, Principles

That’s the same thing Dennis and Eckhardt were trying to do all those years ago and modern trend following traders are up to today: diversify to find uncorrelated returns.

The set of instruments we found above isn’t necessarily the most uncorrelated set of instruments to apply trend following to — just the set that’s closest in correlations to the Turtle portfolios. We can re-run our brute force optimizer again by looking to minimize the distance between our instruments and a completely uncorrelated portfolio (which sadly for us, doesn’t exist).

Swapping out these values, we get the following heat map for our least correlated set of instruments:


Interestingly, this is very close to what we had above! The only ETF we changed was BIL for SHV.

We could probably do better if we expanded our investment universe beyond the markets that the original Turtles traded in the 1980’s. There have been a plethora of new instruments and markets created in the intervening 40 years (ever heard of crypto?) which could greatly increase our diversification.

Testing the Turtle Portfolio

How well does this Turtle portfolio perform? To find out, we’d need to run a backtest using the Turtle rules and this new portfolio. Long-time Turtle traders like Jerry Parker, say that the original trend following rules should be tweaked to favor longer trends due to changes in the market over the years.

At Raposa, we’re doing the hard work of making developing and deploying an algorithmic trading strategy easy. You can check out our free demo here to learn more.