## How to Trade like a Turtle without $1,000,000 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

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
• 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.

### Bonds

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.

### Commodities

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.

### Currencies

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

### Indices

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.

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.

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.

## How to Build a Better Trading System

Most trading strategies you find online are of questionable value. So if you’re going to put your money into it, we suggest you build and test it yourself.

Thankfully, building your own strategy doesn’t have to be a daunting task – we provide tools to do just that – and outline 6 steps you can follow to build your own strategy that you can have confidence in.

## Data or Ideas?

Profitable strategies can come from a variety of sources. Most algorithmic traders break it into idea-first and data-first strategies.

Idea-first strategies starts with a hypothesis about what might be profitable. For example, you might wonder if some indicator can be applied to a given stock or currency or if there’s a way to profit on cognitive biases. So you go ahead, get the data and test your idea.

Data-first strategies start with the data and try to extract potentially profitable patterns from the data which then form your hypothesis. These can be found using black-box models (e.g. machine learning) or oddball patterns you observe in the market, even if you can’t explain why they work.

Gregory Zuckerman explains this type of thinking while asking an executive at the famous quant fund Renaissance Technologies about signals they’d trade. As long as they had the statistics to back it up, they’d trade such strange signals as “volume divided by price change three days earlier;” it doesn’t matter if they have a story about why that works or not, just go for it.

Which is better?

Well, that’s hard to say – traders have had a lot of success with both approaches. The data-first approach is often more mathematically challenging, but that doesn’t mean it’s going to be more profitable. I agree with systematic trader and author Rob Carver when he writes, “consistently profitable trading comes out of careful research, done by thoughtful and knowledgeable people, who seek to understand where their profits come from. The loss-making systematic trading I’ve seen has often been the result of haphazard data mining…That experience, combined with my preference for things I can trust and understand, means I favor the ideas-first method.”

## Test a Simple Version of Your Strategy

Now you have some idea, so you need to go test it to see if it really holds up. I suggest starting simple, without a lot of fancy position sizing, use of stop losses, checking correlations, and so forth – unless these are key parts of your idea! Just try to test the simplest version you can to see if there’s some potential.

You’re not looking for a world-beating backtest at this point, you just want to know whether or not there’s some potential in the signal you’re trying to exploit. Does it seem to do better in a bear market or bull market? What about high volatility regimes vs low volatility?

If there’s an edge in some situations, you might have something you can build and work with!

## Add the Bells and Whistles

Assuming you’ve got a trading signal that piques your interest, you can start adding in some of the key components that a live strategy is going to need. You’re going to want to work with position sizing and risk management to avoid blowing up, add stops/targets to get out of trades, and add filters or other signals to restrict entry into a trade during periods where your model will perform at its best.

A lot of traders get into trouble by over-fitting their strategies. They jump on a signal and keep tweaking parameters until they get a model with an astronomical return. Lured in by the promise of riches, they don’t realize that their model is incredibly fragile and doomed to failure.

While there are no hard and fast rules to avoid over-fitting, it’s a good idea to limit the number of parameters in your model and the number of runs you try.

Too many parameters allow you to play with a lot of combinations to find that combination that is “just right” and looks great in a backtest, but doesn’t generalize to your trading account. Each set of parameters requires a new run, so if you find yourself running “My Retire-Next-Year Strategy #149285” then it’s safe to say you should give it a break and try a new idea.

## Out-of-Sample Testing

Let’s say you have 20 years of historical data available – most novice traders are going to fit their strategy on all 20 years, then go and trade. A better approach is to split between test and training data, so you optimize your parameters on the first 15 years, then test the results on the last 5 years. The first 15 years are your in-sample data while the last 5 years comprise your out-of-sample data. This helps prevent over-fitting as discussed above because you should be able to see how much your strategy’s performance degrades during the out-of-sample test.

Some degradation is expected, so your in-sample test should have higher returns and better risk metrics than your out-of-sample test. But if in-sample is amazing and out-of-sample is horrendous, then you’ve probably over-fit your data and need to go back to the drawing board.

A lot more can be said about proper testing and optimization (we’ll go into details in future posts). One of the best techniques to use is walk forward optimization. This is where you optimize on a small, subset of in-sample data (e.g. 1 year) then run a test on the next subset of your data for out of sample testing. You can do this with a lot of different parameters and keep the top 30% every time and see what survives. This requires a lot of data and discipline, but is widely considered the gold-standard approach.

## Sanity Check

Congrats if your strategy has made it this far! Look closely at those returns though, do you really believe they’re possible in practice?

Carver argues that a single-instrument strategy should produce a realistic Sharpe Ratio of 0.3. You can get higher Sharpe Ratios using proper money and risk management as well as a broader portfolio of instruments, but if all of that gets well above 1, then you’re probably traipsing into fantasy land.

If you want to hold onto this strategy, then it may be prudent to paper trade for a bit while gathering more data to see if it performs the way you expected.

Now you should have a clearly defined, optimized, and well tested trading system that provides attractive yet reasonable returns. Looks like it’s time to trade it!

Some people build custom dashboards, spreadsheets, and all sorts of infrastructure to manage and trade their own portfolio. An easier solution would be to work with a system that will provide you automated alerts when your time to trade has come up. Even better would be one that enables you to also quickly test a variety of ideas in an easy, no-code framework like this one here.