Richard “Doc” Ahrens has spent 30 years researching and analysing markets. He started his investing career in late September 1987, three weeks before the crash, and turned that painful lesson into a lifetime of systematic research. He’s the Director of Research at Trendline Dynamics, and the indicator at the core of his work, the Macro Trend Analyzer (MTA), took him decades to develop and refine.
In this BST Live episode, Doc walks through the MTA, explains three methods for reading it, shares quantitative results from a 19-year backtest across multiple market regimes, and goes deep on trendline analysis. There’s a lot of practical content here, including a 25-year development story behind his trailing stop that’s unlike anything most traders have encountered.
Watch the full episode below, then read on for the complete breakdown.
The Macro Trend Analyzer: what it is and where it came from
The MTA is based on Bill Williams’ Alligator indicator, which uses three Fibonacci-based moving averages. Williams used periods of 5, 8, and 13 on daily data. Doc expanded those to 13, 21, and 34 and switched to weekly data. The result is a trend-following tool that reduces noise without introducing too much lag.
The reason for weekly data: daily charts have too many gaps and too much noise. Monthly charts introduce too much lag. On a one-year chart, the weekly MTA has an average phase lag of only 2.5 days. Monthly data generates over 10 days of lag. Weekly sits in the right zone: responsive enough to catch turns, quiet enough to avoid whipsaws.
The reason for Fibonacci period ratios: using an irrational ratio (the golden mean phi) between the three averages prevents them from coming into sync with each other. When averages synchronise, you get artefacts in your signals. Fibonacci spacing avoids that problem entirely.
Three ways to read the MTA: which one actually worked
Doc didn’t just build the indicator and assume it worked. He ran a formal comparison of three different interpretation methods on the SPX index from 1998 to around 2019, a 19-year test period.
| Method | Description | Avg hold (days) | Return over 19 years |
|---|---|---|---|
| Strict test | All 3 averages must be in order (fastest on top, slowest on bottom) | 262 | 261% |
| Three-slope test | All 3 average slopes must point in the same direction | 207 | 354% |
| Simple test | A third, faster-moving interpretation | 35 | 1,500% |
The simple test’s 1,500% return looks eye-catching. But with an average holding period of 35 days, you’re watching the market every day. For most investors, that’s not practical.
Doc chose the three-slope method for his published charts. It produces a 354% return over 19 years with an average hold of 207 days. That’s roughly every other week of activity, which suits the retirement account management focus of his research. The system got users in when the market was rising and out before major damage on the way down. He then walked through every year of the 20-trade result set against real market scenarios to verify the signals looked believable before publishing it.
Why simplicity matters in trading systems
Doc makes a compelling argument for simple systems using a formula from software engineering. Ford Aerospace spent three years and millions of dollars studying what made software reliable. Their conclusion: reliability equals 1 divided by the number of paths through the software.
One path = 100% reliable. Two paths = 50% reliable. Twelve paths = nearly nothing. A NASA rocket failed a soft moon landing because a single character error was hidden inside a complex codebase, and it took dozens of engineers three months to find it.
The parallel to trading is direct. Every filter you add to a trading system is another path through the code. Every rule that interacts with another rule multiplies complexity. The market is a complex adaptive system: millions of inputs, constant adaptation, no way to predict it. Your only option is to follow it, and simpler systems follow more reliably.
Technical indicators don’t work alone
Doc references a study from the Encyclopedia of Technical Indicators by Colby and Myers, which tested every major indicator available. Their conclusion was stark: no indicator, tested in isolation and run automatically, can consistently make money.
The exception they found was a 10-day simple moving average. Not much profit, but positive.
Doc’s interpretation: it’s the interaction between the analyst and the indicator that generates results. The indicator creates a signal. The experienced analyst interprets what that signal means in context. That’s why some traders swear by RSI and others find it useless. The indicator doesn’t speak to everyone the same way.
This doesn’t mean indicators are worthless. It means systematic application of indicators in isolation, without context, will almost always fail. JP Morgan, running quant strategies, had one losing day in 1,200. But that’s not prediction. That’s very fine-grained market following.
Trendlines: three types of support and resistance
Doc breaks support and resistance into three categories.
Psychological levels: large round numbers. Prices bounce around 100, 150, 200, and multiples of 10 or 25. These levels work because market participants anchor their decisions there.
Historical levels: previous highs and lows that become future reference points. Resistance flips to support once price gets above it. This is well-established in technical analysis, and Doc treats it as fact rather than theory.
Trendlines: drawn between significant highs or lows. Doc’s approach is to start with the trendline on the losing side of the move. On an uptrend, draw the lower trendline first, connecting major lows. Then find a parallel upper trendline. Then add a centreline to get additional signals about where the trend might stall or reverse.
He references Victor Sparandio’s “Trader Vic 2” (pages 144-145) as the best explanation of how to draw trendlines he’s found in print.
Automating trendlines: 12 years of programming
Someone in the live chat asked whether trendlines could be drawn programmatically. Doc’s answer: yes, but don’t try.
He started working on automated trendline detection in 2001 and didn’t have a fully working solution until 2020. That’s approximately 12 years of full-time programming, 40 hours a week, to get the computer to draw trendlines automatically and correctly. He knew the program was starting to work well when it was finding trendlines he’d missed himself when reviewing charts manually.
The lesson here is not that it’s impossible. It’s that the problem is far harder than it looks. Manual trendline drawing requires contextual judgment that’s very difficult to reduce to code.
Persistence: 25 years to build a trailing stop
In 1995, Doc had an idea for a trailing stop that would track price closely without crashing into it like the parabolic SAR does. He spent several months on it, couldn’t get it working, set it aside. Then came back to it. Spent more months. Set it aside again. He repeated this cycle for 25 years.
In spring 2020, he finally got it working. The indicator, now called the ATS (Ahrens Trailing Stop), is visible on his published charts but he’s not yet ready to discuss the design principle behind it.
The broader point, which he frames directly: in trading, the only way you really lose is if you give up.
Related episodes
- Trading market phases: Mish Schneider
- Indicators vs price action: which is king?
- Using market noise to improve trading strategies: Martyn Tinsley
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