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How a Turtle Evolved Trend Following

How a Turtle Evolved Trend Following

This summary is written by Rich based on a conversation in our CTA series between Brian Proctor, Managing Director at EMC Capital Advisors, and the podcast hosts, Niels and Alan.

About EMC Capital Advisors

In this episode, Niels and Alan were joined by Brian Proctor, Managing Director at EMC Capital Advisors. The discussion focused on trend following in managed futures, the investment strategy that outperformed others in 2022. 

Brian Proctor was last on the podcast in 2017, along with fellow 'turtle' Jerry and mentor Richard Dennis.

Brian updated listeners about EMC Capital Advisors, revealing its history and current strategies. 

The firm was founded by Elizabeth Cheval, one of the participants in the Turtle Program, a mentoring scheme initiated by Rich Dennis and Bill Eckhart in the 1980s. After Cheval's death, the firm was taken over by John Krautsack. The Director of Research, Dave Polli, joined the firm in 2002, while Brian joined a year later, after gaining extensive experience in trading.

Brian also discussed his personal history in trading, starting from the floor of the Board of Trade and the Merc in the 1980s and leading to him managing floor operations at both exchanges for Rich Dennis and C&D Commodities. He emphasized the benefits of the Turtle Program and the importance of managing risk, creating robust systems, and removing emotions from trading.

Brian discussed the evolution of EMC Capital Advisors' trading strategy since its early days under founder Liz Cheval. Initially, Cheval implemented more traditional breakout systems as learned in the Turtle Program. However, in the late '90s, she transitioned to different trend-following and momentum-based systems, moving away from buying and selling around breakout points.

This shift entailed an overhaul of the trading systems and a reassessment of the amount of leverage used and risk management protocols. Brian explained that the industry had changed significantly since their early days as Commodity Trading Advisors (CTAs). Back then, the risk-free rate was high, and investors sought returns approximately three times that rate, accepting higher volatility in return.

Over time, investor appetites shifted, and EMC Capital's strategies had to adapt accordingly. Although the firm continues to take on significant risk in trading, it is less than what was accepted during the late '80s and early '90s.

Trading Philosophy

Brian confirmed that EMC Capital Advisors maintains its original philosophy based on the belief that all relevant market information is reflected in the price of the market they're examining. 

The firm continues to rely on quantitative analysis to identify predictable price trends, aiming to deliver long-term positive returns with low correlation to other asset classes. 

Their models are re-optimized periodically to capture directional price movement.

When constructing portfolios, the primary focus is on generating returns, while diversification and crisis alpha are secondary considerations. 

Despite similarities between EMC Capital and other Commodity Trading Advisors (CTAs), Brian pointed out a few distinguishing aspects of their approach. Firstly, EMC Capital allocates about 45%-50% of their risk across commodity markets. Secondly, they blend systems based on trend following or range-dependent systems and momentum systems, diversifying these so they initiate and liquidate at different timeframes and under different market conditions.

The firm’s average holding period for systems ranges from 35 to 45 days, placing them in the medium-term trend following category. However, they also maintain some systems with shorter holding periods of around 15 days and a few long-term systems with holding periods exceeding 100 days.

In the discussion, Brian reflected on the lessons learned as a participant in the Turtle Program, led by Rich Dennis and Bill Eckhart. He emphasized that the first crucial lesson was proper risk management, as without it, trading success would be unlikely. The second lesson centred around trend following.

While trend following systems may not boast high winning percentages, the key is resisting the urge to liquidate positions or take profits prematurely. 

Contrary to the adage that "you never go broke taking profits," Bill Eckhart advised against it. Instead, traders should act swiftly to cut losses and allow winning trades to evolve and run over time. Properly managed, winning trades can yield returns approximately two to two and a half times larger than losing trades.

Thoughts on Volatility Sizing and Letting Profits Run

Brian discussed the nuances of EMC Capital's risk management approach regarding open equity and trading stake. He agreed with the distinction that open equity in a position should be allowed to run, in contrast to more contemporary managers who manage all dollars equally.

EMC Capital's risk management has evolved to consider open trade equity and the size of unliquidated profits when trends are in motion, and the program is heavily invested. The firm employs an algorithm with two elements: a profit element, triggered when certain thresholds are reached in terms of profit based on open trade equity and account size, and a time element, which allows trends to mature before scaling back positions.

This approach contrasts with their previous strategy, which involved letting profits run until the trade ended by reversing long-term trends. This algorithm is particularly beneficial during highly profitable periods, helping to cut back positions, secure profits, and align risk, particularly in periods of increased portfolio volatility.

Regarding trade initiation, market volatility determines the number of contracts to trade. While EMC Capital does not reduce positions solely based on increases in volatility, they differentiate between good volatility (when positions align with increasing volatility, driving trend-following returns) and bad volatility. Their algorithm gradually brings risk into alignment with current volatility, taking profits off the table to cushion against potential profit loss when trends end.

How to Improve the Rick Adjusted Return 

In the conversation, Niels and Brian discussed the role of Commodity Trading Advisor (CTA) strategies in an investment portfolio, and ways to enhance risk-adjusted returns, measured by the Sharpe ratio, without straying from trend following.

Brian highlighted the primary role of CTAs is to provide diversification to an investment portfolio by adding distinct return streams, which should improve overall returns and lower risk. To enhance risk-adjusted returns, his firm, EMC Capital, uses multiple approaches. One of their programs remains strictly aligned to the higher risk, more trend-following aspect. However, they have also developed other programs aimed at creating a smoother return stream, which incorporate long-only positions in equities, bonds, and commodities, including a persistent long position in gold.

These alternative programs are designed to target a lower risk-return profile and reduced drawdowns, offering a more appealing return stream for investors. This approach allows investors to choose the type of investment that best suits their comfort level for risk and how well each program fits into their investment objectives.

Niels and Brian further discussed the possibility of improving the risk-adjusted profile of pure long-short diversified trend following strategies.

Brian explained that they have long maintained a slight bias towards long trades in their traditional trend-following program. This has helped improve returns as their biggest winners tend to be long trades, though they've had remarkable short trades as well.

To enhance risk-adjusted returns, they re-optimize their systems, considering how markets evolve over time. Their systems take into account a mix of return metrics over varying timeframes, including a comprehensive historical price data lookback and a shorter window (e.g., 5 or 7 years), to keep the systems adaptive and evolving with changing markets. They also give more weight to long trades since historically, the most profitable trades have typically been on the long side. Niels found this approach interesting as it is rarely implemented despite the historical success of long trades.

Evolution of the EMC Program

Alan and Brian discussed the evolution and potential enhancements of a trend-following program over time.

Brian explained that while the core logic of their systems (trend following and momentum) remains fairly consistent, the parameter sets can change over time. Each system has a few parameters that determine how to initiate and liquidate a position. The systems rarely hit a hard stop, with other liquidation criteria usually kicking in instead.

He then highlighted the importance of parameters that prevent trading in certain volatile environments. Each system has a volatility filter that blocks trading when market volatility reaches a threshold, as research shows that trading in highly volatile market environments often leads to losses. The filter requires the market volatility to calm down before trading can resume.

Each system's volatility filter differs to avoid missing potential trends during periods of high volatility. Furthermore, they employ a range-dependent system where the market must be moving in the direction necessary for trade initiation or liquidation. They have a short lookback window for determining volatility, making them reactive to changes over the last few weeks in any given market. This approach, according to Brian, differentiates them from other Commodity Trading Advisors (CTAs).

Brian and Alan discussed the average hold periods of EMC’s trading systems and their approach to market volatility.

Brian explained that the longest-term trading systems often yield the highest absolute returns when big trends occur in the market. However, these returns are typically accompanied by increased deviation and larger drawdowns. They use multiple systems with varying timeframes to capture trends in different ways, aiming to improve Sharpe and Sortino numbers. While longer-term systems could yield higher returns, the associated risks could make their program less appealing to most investors. Thus, they emphasize the importance of blending different systems.

Their shortest-term system's average holding period might change over time, for instance, from an average of 18 days in a five-year period to as low as 12 to 15 days in the next one or two trading years.

Regarding volatility, Brian mentioned that they have a short lookback period, primarily influenced by their desire for prompt response to sudden shifts in volatility. Originating from their turtle trading days, this approach considers that volatility increases often signal significant market changes. If volatility is rising, they react quickly, potentially liquidating some systems if the volatility is harmful. For new initiations, trades are sized based on the new volatility rather than an extended lookback period. This way, they can manage the risk in each system across the market when volatility increases.

Trend Following Versus Momentum

Brian clarified their approach to trend following and momentum-based systems, emphasizing the differences between the two.

The trend following systems confirm the current price over a range of different timeframes, such as long-term, medium-term, and short-term lookbacks. They compare the current price to points in the past (e.g., 180 days, 40 days, 15 days ago) to confirm whether a trend is in place, which then initiates a trade, unless their filters suggest otherwise. They have different trend following systems with distinct liquidation criteria, resulting in varied holding periods, with one designed to hold a position for 35-45 days and the other for a shorter period of 15-25 days.

On the other hand, their momentum systems focus on a time-weighted analysis of the most recent daily data, specifically the last two or three weeks. One type of momentum system looks at closing prices in a market to determine if a trend is emerging, while the other type observes the speed and magnitude of day-to-day price action, particularly in instances of increasing volatility and accelerating prices.

This approach allows for diversified points of initiating and liquidating trades across the markets they're involved in. Depending on market trends, they might have one, two, three, or all systems in action. Each system carries equal risk weighting because data suggests that it's uncertain which timeframe will generate the best returns, and it seems to be quite random. This equal risk weighting helps prevent excessive risk concentration in a system that holds a position for an extended duration (e.g., 100-150 days).

Optimising Systems to a Super Value and Fitness Metric and Use of Core Logic

In the interview, Brian explained their unique system development approach, emphasizing the two key aspects: "super value" or "fitness metric" and changes in the "technical logic" of the system.

The "super value" or "fitness metric" is what each system is optimized to. This can be modified by altering various performance metrics that contribute to the algorithms that establish the parameters for each system. For instance, blending a return element with a risk-adjusted element, such as Sortino, results in a system that has a specific path in terms of return generation.

Changing the "core logic" refers to modifying what goes into these fitness metrics, thereby altering the return profile of the system. An example could be the addition of a new system to the program and reallocating risk. If efficiency (the extent to which a system captures a market trend) is added to the return element, it leads to a different set of performance data and a distinct target for that system.

Even with such alterations to the system's return profile, it still needs to be blended with other systems to gauge the overall effect on the portfolio. The changes over time are more related to how the data affects the existing system and alters the parameter sets.

Brian discussed their approach to adapting and re-optimizing their trading systems, stating that this process is performed in a staggered manner on a yearly basis, rather than all at once. The goal is to avoid making wholesale changes to all systems simultaneously, which could introduce too much risk or instability.

The re-optimization process is staggered quarterly. For example, in the first quarter, they might re-optimize a short-term trend-following system, then in the second quarter, the parameters of the long-term momentum system are re-optimized, and so on.

The forward walk out-of-sample testing methodology they utilize is crucial to maintaining reliability and reproducibility in their trading systems. After re-optimizing a trading system, they compare the actual trading results with the hypothetical out-of-sample results. If the real-time performance closely matches the out-of-sample performance, it gives them confidence that their system is reliable and reproducible.

However, if the real trading results begin to diverge from the expected out-of-sample returns, it indicates a potential issue that needs to be reviewed by the investment committee. This adaptive methodology supports their goal to be reliable, predictive, and robust in their trading approach.

Brian further discussed the idea of the core logic of their trading approach, likening it to the overall investment objective. He suggested that the core logic of their system is akin to the set of return expectations they have for each system they implement, and how they blend these systems together to create an overall portfolio.

Their goal, as a systematic quantitative Commodity Trading Advisor (CTA), is to structure their trading systems similarly to a multi-CTA fund of funds. They blend multiple systems, each with its own set of return expectations, to generate a portfolio that meets their desired rate of return, risk profile, and drawdown expectations.

Brian refered to the decisions they make during research and system blending as the "discretionary things" that differentiate their systematic quantitative approach from other CTAs. In essence, changing the core logic would mean adjusting these return expectations and how they blend their systems to meet their overall investment objectives.

Thoughts on Diversification

Brian shared his perspective on the number of markets a trading system should involve. He believes there's no correct number, with their own portfolio trading about 70 markets, and plans to add more. The first factor they consider is the liquidity of a market relative to their assets under management (AUM).

Every market in their portfolio is given a specific risk weighting, ranging from 1 for fully weighted risk markets to 0.1 for less liquid markets. They still trade in smaller, less liquid markets like lumber, orange juice, and oats because they can offer profitable opportunities.

They also consider a market's correlation to the sector it's added to. For example, if they were to add iron ore, they'd classify it in the base metals category and adjust risk allocation accordingly. If a market exhibits non-correlation and offers distinct trading opportunities, it's considered a good addition to the portfolio.

Lastly, they look at a market's historical performance and return stream when their trading systems are applied. Some markets trend better or in a more linear fashion, providing higher return expectations and warranting a higher weighting. Their goal is to add markets that improve portfolio diversification and return expectations.

The Importance of Risk Management

Brian emphasized the importance of risk management in trading, which is managed at every level of their investment program. Each trading system they use has a maximum loss per trade in every market, which is automatically generated once a trade is initiated. Systems have differing max loss thresholds, as some are designed to accept more volatility and larger drawdowns.

Risk is also managed by considering the volatility of markets. This is done through a short lookback window and by adjusting position sizes based on volatility. If market volatility increases, smaller position sizes are taken, whereas during more consolidated periods, larger numbers of contracts are initiated.

Risk is also limited by controlling the amount of risk taken in any one market or sector. The maximum risk in the portfolio is calculated by adding the risk weighting of each market in a sector and dividing it by the total risk allocated across the portfolio. This gives a percentage of risk that can be allocated to that sector.

In periods of market instability, volatility can increase drastically, which also increases the overall risk in the program. In such cases, a scaling mechanism is used to bring positions back in line with the current market risk.

Finally, Brian talked about the concept of utility and marginal utility in investing. As an investor's profits increase, their satisfaction or "utility" also increases, but at a decreasing rate (marginal utility). Hence, the emphasis is often on preserving gains and avoiding big drawdowns. This concept influences their risk management at the portfolio level and dictates when they start scaling back.

One potential downside of this approach is that it could limit upside potential in explosive periods and might lead to underperformance compared to peers. However, in reversal periods, this risk management mechanism could lead to outperformance. Brian suggested that this differentiated risk management strategy is one of the things that sets their firm apart.

Views on Liquidity

Brian suggested that markets are more liquid now than they were in the past, largely due to the advent of electronic trading. The flow of information is also much faster today than it was in the '80s and '90s, and markets react more quickly to news. However, this can sometimes lead to negative consequences, such as when unexpected news leads to a cascade of liquidation orders that push trades in the wrong direction.

To mitigate this, Brian's firm employs a blend of trading systems, some of which are shorter-term in nature. This allows them to be nimble and liquidate at least part of their position swiftly in case of a sudden market reversal. Waiting for longer timeframes to react could lead to larger drawdowns. They have learned from experience that if many market participants are positioned the wrong way, liquidations tend to continue over extended periods, which can be painful.

Misconceptions of Trend Following

Brian refuted the notion that all trend followers do the same thing, stating it's incorrect and oversimplifying the complexity of their strategies. He also disagreed with claims that trend following is dead or that it's too volatile for certain portfolios, pointing out that it adds long-term positive returns and provides diversification benefits.

Niels and Brian further discussed the amount of work, testing, and idea development that goes into trend following. Brian illustrated this by sharing their unsuccessful attempts to develop a counter-trend system, a task they found extremely challenging. Instead, they created an algorithm that scales back positions, showcasing the continuous process of innovation and improvement in their approach.

While some traders may stick to what they've always done and remain successful, Brian's firm is committed to continually evaluating and improving their strategies.

Outlook for 2023

Brian expressed a combination of excitement and nervousness about the rest of 2023, due to potential uncertainties like a resurgence of inflation or worldwide debt issues. However, he noted the benefits of having a diverse portfolio that includes commodities, interest rates, and the ability to short equities.

Despite the uncertainty, Brian prefered the quantitative approach they take as it allows for better risk management and helps him sleep at night. He highlighted that their daily internal Value-at-Risk (VaR) is currently quite high historically due to various positions across sectors, indicating a significant amount of ongoing trends and volatility.

However, Brian observed a renewed interest in systematic commodity trading strategies among investors. Despite the uncertainties, he remains cautiously optimistic about the future.

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.