Most traders spend their careers building systems optimised for what has already happened. They take historical data, find patterns, build rules, and assume the future will resemble the past. Richard, “the trend following professor” as Jerry Parker called him, has spent nearly 40 years building the opposite kind of system: one designed for a future that will be more extreme than the past, not less.
Richard co-founded Aussie Turtles, is strategy ambassador for East Coast Capital Management, and runs ATS Trading Solutions. He is the host of the Algorithmic Advantage podcast. But more than his credentials, what makes this conversation unique is his intellectual framework for why markets behave the way they do – a framework built on complexity theory, fat-tail statistics, and the mathematical properties of financial market distributions. This is one of the most philosophically rigorous discussions BST has hosted on why trend following works and why most other approaches eventually fail.
The episode covers the barbell approach to trend following, the distinction between convergent and divergent traders, and the practical mechanics of building a robust trend-following portfolio designed to survive – and profit from – the most uncertain market conditions.
Watch the full episode below, then read on for the complete breakdown.
The Core Principle: Survival Takes Priority Over Performance
Richard opens with what he calls the most fundamental golden rule of trading: survival. Not returns, not Sharpe ratio, not drawdown percentage – survival. His argument is that the benefits of compounding are only available to those who remain in the game across many hundreds of trades over many years. Being knocked out by a single catastrophic event – no matter how well everything was going before it – permanently removes you from access to the long-term compounding effect.
This framing inverts the typical trader’s priority stack. Performance is what happens after survival is secured, not before. Position sizing, diversification, and risk management are not footnotes to strategy selection – they are the primary disciplines. The strategy is secondary.
The Market as a Complex Adaptive System
Richard provides a sophisticated statistical framework for why trend following works. It begins with the observation that financial market return distributions are not Gaussian (normal). When you plot the histogram of daily returns for any liquid market over a long history – equities, commodities, currencies, cryptocurrencies – you observe properties that fall outside the normal bell curve.
Specifically, you observe:
- Higher peak around zero: Markets oscillate around equilibrium more frequently than a random walk would predict.
- Fat tails: Extreme events (large positive and negative returns) occur far more often than a Gaussian distribution would predict.
Under the efficient market hypothesis, a greater-than-5-standard-deviation daily move should occur approximately once every 3.6 million trading days. In practice, it occurs multiple times per decade in major markets. Markets are not efficient. They are complex adaptive systems that display “periods of predictability interdispersed with periods of very unpredictable chaotic regime.”
This framework creates two distinct opportunities for traders: the peak of the distribution (where convergent, mean-reverting strategies work) and the tails (where divergent, trend-following strategies work). Richard specialises in the tails.
Convergent vs. Divergent Traders
The distinction Richard draws between convergent and divergent traders is one of the most useful conceptual frameworks in the episode:
- Convergent traders (mean reversion): Assume that price will return to an equilibrium after moving away from it. They are predictive – they assume something about the future based on what the past implied about the equilibrium level. They profit from the high-frequency small oscillations around the distribution’s peak. Their risk is negative skew: most trades win, but when the market enters a divergent regime (a genuine trend), the losses are large and can be catastrophic.
- Divergent traders (trend following): Don’t assume the market will return to equilibrium. They follow price and assume that wherever it is going now, it could continue far further than any probabilistic model based on historical volatility would suggest. They accept many small losses in exchange for occasional very large gains. Their trade distribution is positively skewed.
Richard’s key insight: the mean reverter’s approach contains a predictive premise. They assume the market will do something. The trend follower has no such assumption – they simply follow what the market is doing, with a rule that caps their downside and lets their profits run. In a chaotic regime (which is when the largest trades occur), the mean reverter’s model breaks catastrophically. The trend follower’s model is specifically designed for that environment.
The Barbell Approach to Trend Following
Richard’s most distinctive framework is what he calls the Barbell Approach – an adaptation of Nassim Taleb’s barbell investment philosophy applied to systematic trend following.
The barbell has two ends:
- Left barbell (capital preservation): Strict loss control at all times. No single loss is allowed to become a significant event for the portfolio. This is the conservative end – always protecting capital, always limiting downside. Richard runs tight stops. He never allows a loss to compound. This end ensures survival.
- Right barbell (unrestricted upside): When a trade enters the tail region – when it starts to run significantly in your favour – you let it run without presumptive assumptions about when it should end. This is the aggressive end. You don’t apply complex overlays, volatility adjustments, or mean-reversion logic to an open winner. You let the market dictate how far the trend extends.
The philosophical logic: markets in chaotic regimes (when the biggest trends occur) are inherently unpredictable. Adding complexity to your model in these regimes is adding overfitting, not insight. Richard deliberately keeps his models simple so they have the freedom to capture what the market delivers, rather than being constrained by assumptions about what the market should deliver.
This produces a positively skewed trade distribution – many small losses, occasional very large wins. The large wins include what he calls “100R” outcomes: positions where the gain is 100 times the initial risk. These occur rarely, but they’re far more frequent than a Gaussian distribution would predict, and they are entirely accessible to a trend follower who was positioned and let the profit run.
Maximum Diversification as a Risk Mitigation Tool
Richard’s position sizing and portfolio construction approach is built around the principle of maximum diversification across as many liquid markets as possible. The reasons are multiple:
- Individual bet size reduction: Spreading across 50+ liquid markets means any single market’s adverse move has minimal impact on the portfolio.
- Uncorrelated opportunities: By holding markets that behave differently across different regimes, you reduce the probability that all your positions go against you simultaneously.
- Maximising exposure to 100R opportunities: You never know which market will produce the next extreme tail move. Being in all liquid markets means you’re positioned to capture it wherever it occurs.
He uses path-dependent risk metrics rather than Sharpe ratio for portfolio evaluation. Specifically, the MAR ratio (Compounded Annual Growth Rate divided by Maximum Drawdown) – which focuses on the relationship between compounded returns and the worst adverse event experienced. This metric is more aligned with his inside-out philosophy of prioritising survival and the impact of extremes.
System Ensembles and Practical Portfolio Construction
At the time of the episode (early 2024, the first live show of that year), Richard was running what he calls “system ensembles” – portfolios of multiple trend-following models applied across his universe of liquid markets. Each model might use different parameters for entries, position sizing, or exit logic, but all are designed around the same barbell philosophy.
He mentions specific strategy components in his portfolio: Darvas Box breakouts (using congestion box breakout principles), swing channel models, and volatility-adjusted position sizing. These are applied across equities, futures, CFDs, and cryptocurrencies through his ATS Trading Solutions retail platform and East Coast Capital Management’s institutional portfolios.
His platform allows retail traders to implement these strategies with capital starting at levels accessible to individual traders – a deliberate choice to make systematic trend following accessible beyond the institutional space.
Get the show notes & transcript
Related episodes
- Turtle Trader Jerry Parker on 30+ Years of Trading Experience
- Brent Penfold on Trading Principles, Money Management and Risk of Ruin
- Combining Algos Using State-Based Market Design with Richard Metzger
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