Mastering Systematic Trading Strategies

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• Implementation often matters more than the strategy in determining systematic trading success.
• Different asset classes require tailored approaches as fixed income, commodities, and currencies present unique trading challenges.
• The execution of trades represents a significant but overlooked source of alpha where patient trading can preserve more value than elaborate signal optimization.

Few debates in systematic trading are as persistent as whether to specialize in a single asset class or diversify across many. This question has profound implications for portfolio construction, risk management, and performance.

This debate has become increasingly relevant as systematic trading continues to evolve, with implementation decisions often distinguishing between mediocre and exceptional returns.

In this discussion, we explore the diverse perspectives of three veterans of quantitative finance:

• Graham Robertson: With over 14 years at AHL, Graham brings institutional expertise as a veteran quantitative researcher.
• Rob Carver: Former head of fixed income at AHL and author of several influential books on systematic trading.
• Yoav Git: Previously led fixed income at AHL before founding his own specialized fixed-income CTA (Commodity Trading Advisor).

Learn why successful systematic traders focus on what to trade and how to trade it. Understanding these nuances will be key to navigating the next evolution in systematic trading.

Specialization vs. diversification debate

The fundamental question for systematic traders isn't just what markets to trade, but whether their models should be specialized for each asset class or broadly applied across diverse markets. This implementation decision can significantly impact performance and risk management.

"Different asset classes actually are very different," argues Yoav Git, making a case for specialization in systematic frameworks. He explains that while traders "can take the generalist view," doing so means "leaving money on the table" as they won't "harvest the full alpha in a particular asset class." In his view, it's important to put effort into each asset class to implement it correctly and capture that additional performance.

Rob Carver presents the case for universal models: "I've never really seen a strong, statistically significant effect in performance between different asset classes," he explains. His research suggests well-designed algorithms can account for market differences without requiring asset-specific treatment.

In summary, these contrasting approaches represent two systematic trading philosophies: deep specialization in specific market dynamics versus robust models that work across many instruments. Specialized systematic strategies may excel in certain trending environments but risk underperformance when market conditions shift. Meanwhile, diversified systematic approaches typically provide more consistent performance across varying market regimes.

Graham Robertson adds a historical observation: during difficult periods for trend following, research focused on market expansion rather than model refinement. "Markets were clearly different. Researching new markets was genuine value add." This diversification approach helped firms navigate challenging times when traditional trend-following strategies faced headwinds.

Understanding market drivers and liquidity

Digging deeper, what actually drives trends across different markets? This question reveals deeper insights into the specialization debate and helps explain why some markets trend better than others.

Yoav describes trend formation as a result of sustained imbalances in the market. When examining fixed income specifically, he focuses on the interest rate gradient between central banks that creates persistent flows driving trend formation. This gradient-driven flow pattern emerges clearly in currency markets: "In FX (Foreign Exchange), when you see Euro-USD, you see literally no trend. But when you see EM (Emerging Markets) currencies with strong carry, you see actually a very strong trend as well."

Rob takes a different approach, focusing on objective liquidity measures: Volume and open interest. These metrics manifest distinctly across markets. For example, Korean stock indices showing high volume with low open interest, while interest rate futures display the opposite pattern. "In one place, you've got huge stock in a small flow. In the other case, you've got a massive flow in a small stock."

Graham offers perhaps the most sobering assessment of market structure analysis: "Liquidity is a term that's often misused." Market structure, industry players, and transaction costs affect trading dynamics in ways simple volume metrics fail to capture—a complexity that underscores the challenge of applying universal frameworks.

Long-term performance context

A fascinating historical perspective emerges when examining which asset classes have historically contributed the most to the returns of CTA-managed strategies.

"If you look at AHL and ask them what has been your biggest earner for the last 25 years, about 70-75% of the P&L has come from fixed income," Yoav reveals. This striking concentration of returns challenges the idea that CTAs generate equal performance across all asset classes.

This performance pattern isn't merely coincidental. Yoav explains that fixed income's strong trending behavior stems from fundamental economic forces: "We've seen a secular trend. After the GFC (Global Financial Crisis), rates have really come all the way down." Even after this long-term trend ended, fixed income continued to deliver consistent returns. "For the last 15 years, fixed income has been very consistent, not just in terms of consistency over time—so different periods in time, also different speeds."

This historical context provides crucial information for allocators considering how to structure their systematic trading exposure across asset classes.

The allocation-to-CTAs challenge

Despite the maturity of systematic trend-following strategies, institutional adoption continues to face persistent headwinds. Why aren't CTAs better represented in institutional portfolios?

"We're neither fish nor fowl," Yoav says, crystallizing the dilemma. CTAs don't fit neatly into traditional asset allocation frameworks, creating an identity crisis for allocators attempting to categorize these strategies.

When positioned as a crisis hedge, CTAs face a precision problem that other hedges don't. "If I'm looking for convexity to my underlying portfolio, then CTAs are a very crude, fuzzy way to get convexity," Yoav explains. Unlike targeted hedges, CTAs don't reliably protect against specific market stresses. When they fail to deliver during crises, portfolio managers face difficult questions from investment committees who approved allocations based on promised protection that didn't materialize.

Performance metrics present another hurdle for CTAs seeking broader adoption. With Sharpe ratios hovering around 0.7 versus competitors' 1.5+, CTAs appear underwhelming in absolute return contexts. "Every other year, you have a very difficult conversation" with investment committees, Yoav notes from experience.

Graham shares a more optimistic perspective, observing that "larger investors tend to have an allocation to trend following thick and thin," suggesting institutional size influences adoption patterns. Since many CTAs primarily follow trend-based strategies, their presence in portfolios is often tied to the broader perception of trend following itself. Yet even among sophisticated investors, CTAs remain a niche allocation, often misunderstood or overlooked in broader portfolio construction discussions.

Execution as a source of alpha

Perhaps the most overlooked implementation decision involves the execution of trades—an area many managers treat as purely operational rather than strategic, potentially leaving significant returns on the table.

"Most CTAs view execution as a production line costing five basis points of Sharpe," Yoav observes. This operational view creates a contradiction in practice. "You will take a signal and sit on it for five days because you are buffering it, and then insist that the execution desk execute it within half a day," Yoav explains, highlighting the inconsistency in approach.

The mathematics reveals the opportunity cost. Crossing spreads might cost 5-30 basis points of Sharpe, while delaying execution typically costs only 1-2 basis points in alpha decay—a tradeoff many managers fail to optimize.

"Give the execution desk more time to execute. Be patient, wait for the market to come to you," Yoav suggests. "The trick is just avoiding crossing that spread." This patient approach to execution can preserve more value than elaborate signal optimization in many cases.

Rob offers a perspective on where execution improvements matter most: "There's a lot more value in doing it with the weird kind of OTC bond than there is with the bubble [highly liquid futures]," he states, referring to the contrast between less liquid over-the-counter markets and highly liquid instruments. This insight aligns with Yoav's earlier point that execution costs can be much higher in less liquid markets, making the potential benefits of careful execution more significant in those environments.

The diagnostic question that reveals a firm's true approach to execution? "Do you pay performance fees to your execution desk?" The answer reveals whether a firm truly views execution as a potential alpha source or merely an operational cost center.

Adapting systems to market structure

Moving beyond the theoretical debate, the experts highlight concrete differences in how various market types must be handled in practice.

The structural fragmentation in fixed-income markets creates unique execution challenges. "In fixed income, liquidity is very fragmented," Yoav explains. "You have multiple bonds for the same issuer, whereas, in equities, you have just one stock for a company." This fundamental difference requires specialized broker relationships and execution systems capable of handling partial fills or substituting between similar instruments.

Risk management approaches also need customization based on return distribution characteristics. "If you think about a fixed income portfolio, it's got very strong left skew," Yoav points out. This asymmetric risk profile, where extreme negative events occur more frequently than extreme positive ones, demands more conservative position sizing and tighter stop-loss mechanisms than markets with more balanced distributions.

Alternative markets present distinct properties. Yoav and his colleagues have conducted extensive research on cryptocurrencies, discovering unique trending characteristics. "Cryptocurrencies actually trend very well because there is no valuation," Yoav notes. Without fundamental valuation anchors creating mean-reversion pressures, crypto markets can sustain momentum patterns that would quickly dissipate in traditional markets where value investors counteract extreme price movements.

These practical examples demonstrate that while the underlying principle of trend following may remain consistent, the operational implementation must adapt to each market's specific structure and characteristics. The most successful managers recognize these differences and build systems flexible enough to accommodate them.

Implementation matters

As systematic trading continues to evolve, implementation decisions increasingly drive performance differentiation. The debate between specialization and diversification represents just one dimension of the complex choices systematic trading firms face in building their algorithmic trading frameworks. Whether developing specialized models for specific asset classes or creating universal systems that work across markets, the details of execution, risk management, and portfolio construction often distinguish outperforming strategies from mediocre ones.

Yoav says, "CTA’s and trend following is amazing. It's one of the only strategies which has positive skew and it's not actually harvesting liquidity premium. So it should really be allocated to a lot more than it is."

The challenge for the industry lies in making these sophisticated strategies more accessible and better understood by allocators.

These nuances may be key to better utilizing systematic strategies within their portfolios.

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This is based on an episode of Top Traders Unplugged, a bi-weekly podcast with the most interesting and experienced investors, economists, traders and thought leaders in the world. Sign up to our Newsletter or Subscribe on your preferred podcast platform so that you don’t miss out on future episodes.