Does Turtle Trading Still Work? What 6.7 Years Showed

The short version. On the data I have, no: the original Turtle trading rules lost 37.9% on 11 Exness markets and 57.7% on 26, from January 2020 to September 2026, with the code checked three ways. But 6.7 years is a short window for a trend system, and the result is mostly one-sided: long trades made money in every market group, short trades lost in every group. The tempting fix, dropping the shorts, is exactly the kind of decision you can't make after seeing the results. Here's what I think the test does show, what it doesn't, and what I didn't do.

This is the last of 5 parts: the 1983 experiment, the rules, the simulator, the results.

Does Turtle trading still work in 2026?

Not as published, on Exness CFDs, from 2020 to 2026. The numbers, briefly:

	11 markets	26 markets
Return	−37.9%	−57.7%
Max drawdown	58.3%	75.6%
Profit factor	0.89	0.83
Long trades	+$26,899	+$28,279
Short trades	−$64,779	−$85,989

That's a narrow claim, and I want to keep it narrow. It says these exact rules didn't make money on these markets in this period. It doesn't say trend following is dead, and it doesn't say the 1980s results were wrong.

Lesson 1: the rules are good engineering

The 2003 PDF is unusually complete. Every rule has a number, most have a worked example, and only six points are left open (I listed them in part 2). That made the system testable in a way most trading folklore isn't, and it meant I could write tests straight from the document's own examples.

The risk side held up too. Switching off the filter, the unit limits or the drawdown rule each made the result clearly worse (in part 4, the yearly loss went from −6.8% to between −10.5% and −16.2%). The layers do what they were designed to do: they cut losses when the entries aren't working.

But risk management can't create an edge that the entries don't have. It only decides how much you lose while you wait for one.

Lesson 2: more markets only help if they have an edge

Diversification is the standard answer to "the basket is too narrow", and it's what the original Turtles had. I tested it: 15 more markets across oil, gas, metals and six stock indices. The result went from −37.9% to −57.7%.

Adding uncorrelated markets smooths the curve when each market has a small positive expectation. When the extra markets lose too, you just get more losing trades, and the shared unit limits mean the good signals compete with the bad ones for the same risk budget.

Lesson 3: why I didn't just drop the shorts

This is the one I had to think about most.

Long trades made money in all five groups: forex, metals, energy, indices and bitcoin. Short trades lost in all five. 20 of the 26 markets ended the period higher than they started. So a long-only Turtle system would have looked fine on this data, and it's the obvious next step.

The problem is that I'd be choosing the rule after seeing which half of the results was good. Of course long-only looks better on a period where most markets went up: that's what the period was. Testing long-only on the same 2020–2026 data would only confirm what I'd already looked at. It proves nothing about the next six years.

A fair test of that idea needs data the rules haven't seen, and the pass criteria written down before it runs. My Exness archive starts in 2020, so there's no unseen stretch left in it. I decided not to run the long-only version at all rather than run it on data I'd already looked at.

Lesson 4: 6.7 years is short for a trend system

The rules document itself warns that Turtle traders went through losing stretches of a year or two, and that the traders who did worst were the ones who started skipping signals during them. In my data the 11-market basket had four losing years out of seven, with one +40% year (2022) in the middle.

A trend system lives on a few large moves. Six and a half years contains only so many of them, and which ones you get depends a lot on where the window starts. So I'd treat this as one sample, not a verdict on the method.

Lesson 5: CFDs aren't the 1983 futures

• Swap. Turtle positions are held for weeks. On the 11-market run, where swap is measured on just 3 markets (gold among them), swap already cost $19,499. On the other markets it's set to zero, so the real result would likely be a bit worse.
• Spread. Paid on every entry and exit. Doubling it took another 1.7% a year off the 11-market result.
• The market list. The Turtles traded Treasury bonds and notes, Eurodollars, coffee, cocoa, sugar and cotton, none of which I have as CFDs. My basket is heavier in forex crosses, which spent long stretches going sideways.

Lesson 6: shorter bars make it worse, not better

The intuitive fix for "markets are faster now" is a faster chart. The cost side says no: on gold, the spread is 0.30% of a daily N, 1.33% of an hourly N and 2.68% of a 15-minute N, and a breakout system trades much more often on short bars. Published research also finds trend effects over months, not hours. Details in part 2.

What I didn't do

• No parameter optimisation. I didn't try 30-day breakouts, 3N stops or a different add interval. With seven years of data and that many knobs, I'd find a setting that worked on 2020–2026, and it would tell me nothing about 2027.
• No long-only version, for the reasons above.
• No Whipsaw stop, the PDF's alternative half-N stop.
• No live trading. Everything here is a simulation on archive data.

The limits of this test

• Swap measured on 3 of 26 markets; commission on commodity and index CFDs unconfirmed; both set to zero where unknown.
• Some contract sizes for commodities and indices unconfirmed (with N-based sizing this mostly affects lot rounding).
• Data gaps: UKOIL and XPTUSD have missing months; XNGUSD, JP225 and XCUUSD start in 2022–2023.
• One broker, one period, CFDs instead of futures.

If anything, the costs I couldn't measure would make the results a bit worse, not better.

So what is the Turtle system good for today?

As a teaching system, a lot. It's the clearest example I know of a complete trading plan: what to trade, how much, when to enter, when to get out of losers, when to get out of winners, and how much total risk to carry. Volatility-based sizing and hard portfolio limits are worth copying into almost anything.

As a ready-made system to switch on, not on this evidence. If I come back to it, it'll be with data the rules haven't seen and the pass mark written down first.

Thanks for reading the series. There's a 7-minute video version on YouTube, with the same numbers drawn from the same data.

Research, not financial advice. Past results do not predict future results.