Why Use A Portfolio Of Systems

In the ever-evolving world of financial markets, most traders continually search for the "holy grail" – that one perfect system that works in all conditions. After more than two decades in the markets, I've learned a fundamental truth: such a system doesn't exist. Instead, the real edge comes from building a portfolio of complementary trading systems, each designed to capitalize on different market behaviors and regimes.

Think of market participation like conducting an orchestra. A single instrument, no matter how beautifully played, cannot replicate the depth, complexity, and resilience of a full symphony. Similarly, a single trading strategy – regardless of its sophistication – cannot optimally capture the myriad opportunities that markets present across different regimes.

Today's newsletter outlines how combining slow trend following, fast trend following, momentum, mean reversion, carry, pairs trading, day trading, and swing trading strategies creates a robust trading framework capable of weathering any market storm. More importantly, we'll explore how this approach dramatically reduces drawdowns while maintaining (and often improving) overall returns.

Understanding Market Regimes: The Foundation of Strategic Diversification

Before diving into specific strategies, we need to establish a framework for understanding market conditions. In my work, I categorize markets into five distinct regimes, each with its own behavioral characteristics and optimal trading approaches:

The Five Market Regimes

1. Bull Volatile: Characterized by strong upward momentum with significant daily price swings. Think of the early days of a new bull market after a capitulation event, or the final euphoric stage before a major top.

2. Bull Quiet: Steady upward progression with lower volatility. This often represents the middle stage of bull markets where gains come consistently but less dramatically.

3. Neutral: Sideways movement with no clear directional bias. Prices often oscillate within a defined range, frustrating both bulls and bears.

4. Bear Quiet: Steady downward progression with controlled volatility. Often seen in the early to mid-stages of bear markets when participants have accepted the downtrend but panic hasn't set in.

5. Bear Volatile: Sharp, emotional selling with high volatility. This occurs during capitulation events or panic selling scenarios, often near major market bottoms.

Understanding these regimes is crucial because each one demands a different set of trading strategies. What works brilliantly in Bull Quiet may bleed capital in Bear Volatile.

Key Insight: The most significant trading losses typically occur when applying strategies optimized for one regime to a market operating in a different regime. System diversification addresses this exact problem.

The Strategy Portfolio: Understanding Each Component

Let's break down each strategy type and understand its strengths, weaknesses, and optimal market conditions:

1. Slow Trend Following

Core Characteristics:

• Identifies and captures major market moves lasting months to years
• Typically uses longer-term moving averages (50-200 days) or breakouts of multi-month ranges
• Employs wide stops to accommodate normal market noise
• Aims to capture the "meat" of major trends rather than precise entries and exits

Optimal Regimes: Bull Quiet, Bear Quiet

Example Implementation: A simple implementation might enter long positions when price crosses above its 200-day moving average and exit when it falls below. Position sizing is typically conservative due to wider stops.

Performance Profile:

• Win Rate: 30-40%
• Profit Factor: 1.8-2.5
• Max Drawdown: 15-25%
• Average Holding Period: 3-6 months

Historical Note: Many of the most successful hedge funds in history (like Trend-Following CTAs) have built their fortunes primarily on variations of slow trend following. The consistency of this approach across centuries of market history is remarkable.

2. Fast Trend Following

Core Characteristics:

• Captures shorter-term directional moves lasting days to weeks
• Uses shorter-term moving averages (5-20 days) or momentum indicators
• Features tighter stops and more responsive entries/exits
• Aims to quickly identify and capitalize on emerging trends

Optimal Regimes: Bull Volatile, Bear Volatile

Example Implementation: Fast trend systems might use 5-day breakouts with trailing stops based on Average True Range (ATR). They're particularly effective when combined with volume confirmation.

Performance Profile:

• Win Rate: 40-50%
• Profit Factor: 1.5-2.0
• Max Drawdown: 10-20%
• Average Holding Period: 5-15 days

3. Momentum

Core Characteristics:

• Focuses on rate of change rather than absolute direction
• Typically ranks assets by relative strength measures
• Often includes rotation among sectors or asset classes
• Capitalizes on continuation of strong price action

Optimal Regimes: Bull Volatile, Bull Quiet

Example Implementation: A typical momentum strategy might rank stocks, ETFs, or commodities based on their 3-6 month performance, then go long the top performers and potentially short the bottom performers.

Performance Profile:

• Win Rate: 55-65%
• Profit Factor: 1.7-2.2
• Max Drawdown: 15-30%
• Average Holding Period: 2-8 weeks

4. Mean Reversion

Core Characteristics:

• Capitalizes on price's tendency to return to average values
• Often uses statistical measures like standard deviations from moving averages
• Works best in range-bound or overextended markets
• Essentially "buys dips" and "sells rips"

Optimal Regimes: Neutral, transitions between other regimes

Example Implementation: A basic mean reversion system might buy when price reaches 2 standard deviations below its 20-day moving average and sell when it returns to the average.

Performance Profile:

• Win Rate: 65-75%
• Profit Factor: 1.3-1.8
• Max Drawdown: 10-20%
• Average Holding Period: 2-10 days

5. Carry

Core Characteristics:

• Exploits interest rate or yield differentials
• Common in forex, fixed income, and commodity futures markets
• Generates returns through "positive carry" (earning more in interest/yield than paying)
• Often combines carry with trend or momentum filters

Optimal Regimes: Bull Quiet, Neutral

Example Implementation: In currency markets, a carry trade might involve borrowing in low-interest-rate currencies (like JPY historically) and investing in higher-yielding currencies (like AUD or emerging markets).

Performance Profile:

• Win Rate: 60-70%
• Profit Factor: 1.4-1.9
• Max Drawdown: 15-25%
• Average Holding Period: 1-3 months

6. Pairs Trading

Core Characteristics:

• Market-neutral approach that simultaneously goes long one asset and short a correlated asset
• Based on the principle that correlated assets will maintain their historical relationship
• Profits from convergence of temporary price divergences
• Reduces exposure to overall market movements

Optimal Regimes: All regimes, particularly effective during Neutral and Bear Quiet

Example Implementation: A pairs trader might go long Coca-Cola and short Pepsi when their price ratio deviates significantly from historical norms, expecting the relationship to revert.

Performance Profile:

• Win Rate: 55-65%
• Profit Factor: 1.3-1.7
• Max Drawdown: 8-15%
• Average Holding Period: 5-15 days

7. Day Trading

Core Characteristics:

• Opens and closes positions within the same trading day
• Focuses on intraday patterns, support/resistance, and volume analysis
• Eliminates overnight risk exposure
• Typically employs tight risk management with defined intraday stops

Optimal Regimes: All regimes, but strategy selection varies by regime

Example Implementation: Day traders might focus on opening range breakouts, fading extreme moves, or trading around key technical levels, with all positions cleared by market close.

Performance Profile:

• Win Rate: 50-60%
• Profit Factor: 1.3-1.8
• Max Drawdown: 5-15%
• Average Holding Period: Minutes to hours

8. Swing Trading

Core Characteristics:

• Aims to capture "swings" in price movement over days to weeks
• Combines elements of trend following and mean reversion
• Often uses technical patterns like flags, channels, and consolidations
• Typically manages moderate time horizons with defined exits

Optimal Regimes: Bull Quiet, Bear Quiet, transitions between regimes

Example Implementation: Swing traders might buy pullbacks in uptrends or sell rallies in downtrends, targeting moves that last 3-10 days on average.

Performance Profile:

• Win Rate: 45-55%
• Profit Factor: 1.5-2.0
• Max Drawdown: 10-20%
• Average Holding Period: 3-10 days

The Magic of Combination: Portfolio Effects

Now that we've outlined each strategy type, let's explore what happens when we combine them into a comprehensive trading portfolio. The benefits emerge through three primary mechanisms:

1. Drawdown Reduction Through Uncorrelated Returns

When properly constructed, these different strategy types produce returns with low correlation to each other. This means that when one strategy is experiencing a drawdown, others are often performing well.

Many of these strategies have low or even negative correlations with each other. This is the foundation of modern portfolio theory – combining assets with low correlation reduces portfolio volatility without necessarily reducing returns.

2. Regime-Optimized Performance

Each market regime favors different strategy types. By maintaining exposure across multiple strategies, you ensure that some portion of your portfolio is always aligned with current market conditions.

This is why strategy diversification is so powerful. No single column (strategy) performs well across all rows (regimes), but a portfolio incorporating all strategies maintains positive performance across all regimes.

3. Psychological Sustainability

Perhaps the most underappreciated benefit of strategy diversification is psychological. Trading a single system through its inevitable drawdowns is emotionally challenging. Even systems with excellent long-term performance experience periods of underperformance that test a trader's resolve.

A portfolio approach creates more consistent equity curves, making it easier to maintain discipline and avoid the destructive cycle of system-hopping during drawdowns.

Building Your System Portfolio: Practical Steps

Now let's discuss how to practically implement this portfolio approach:

Step 1: Start with Your Core Competency

Most traders have a natural inclination toward certain strategy types. Begin with what resonates with your personality and trading style. For some, this might be trend following; for others, it might be mean reversion or day trading.

Master this approach first, developing a robust system with clear rules, position sizing guidelines, and risk management parameters.

Step 2: Add Complementary Systems

Once your core system is established, gradually introduce complementary strategies with low correlation to your existing approach. If you started with trend following, consider adding a mean reversion or pairs trading component.

The key is to add systems that tend to perform well when your core strategy struggles.

Step 3: Allocate Capital Strategically

Capital allocation among strategies should reflect:

• Your confidence in each system (based on backtesting and live performance)
• The volatility of each system (higher volatility systems generally receive less capital)
• Current market conditions (slightly overweight strategies favored by the current regime)

A sample allocation might look like:

• Slow Trend Following: 20%
• Fast Trend Following: 15%
• Momentum: 15%
• Mean Reversion: 15%
• Carry: 10%
• Pairs Trading: 10%
• Day Trading: 5%
• Swing Trading: 10%

Step 4: Implement Appropriate Position Sizing

Each system should have its own position sizing methodology based on its specific characteristics. For example:

• Trend following systems might use ATR-based position sizing to account for volatility
• Mean reversion systems might employ fixed fractional sizing based on stop distance
• Pairs trading might use more complex statistical measures like standard deviation of spread

Step 5: Monitor and Rebalance

Regularly review the performance of each system component. When one strategy has significantly outperformed others, consider rebalancing to maintain your target allocation.

This disciplined rebalancing process often results in "selling high and buying low" across your strategy portfolio.

Case Study: Performance Across Market Regimes

To illustrate the power of this approach, let's examine how a hypothetical portfolio of systems might have performed during specific market periods representing different regimes:

Bull Volatile (Blended): March 2020 - June 2020 (Post-COVID Crash Recovery)

During this period of explosive recovery from the COVID crash, markets displayed classic Bull Volatile characteristics. Our hypothetical portfolio performance:

• Slow Trend Following: +8.2%
• Fast Trend Following: +21.4%
• Momentum: +18.7%
• Mean Reversion: +5.3%
• Carry: -2.1%
• Pairs Trading: +3.8%
• Day Trading: +12.5%
• Swing Trading: +16.9%
• Combined Portfolio: +11.8%
• S&P 500: +25.5%

While the portfolio underperformed the explosive equity market, it captured significant upside with far less volatility.

Bull Quiet: January 2017 - January 2018

This period represented a classic Bull Quiet regime with steady gains and low volatility.

• Slow Trend Following: +15.8%
• Fast Trend Following: +8.3%
• Momentum: +19.6%
• Mean Reversion: +6.2%
• Carry: +12.7%
• Pairs Trading: +5.5%
• Day Trading: +9.3%
• Swing Trading: +13.1%
• Combined Portfolio: +11.3%
• S&P 500: +23.9%

Neutral: January 2015 - December 2015

Markets moved sideways for much of 2015, creating a challenging environment for directional strategies.

• Slow Trend Following: -6.2%
• Fast Trend Following: -3.5%
• Momentum: +4.1%
• Mean Reversion: +11.7%
• Carry: +7.3%
• Pairs Trading: +9.2%
• Day Trading: +6.5%
• Swing Trading: +2.8%
• Combined Portfolio: +4.0%
• S&P 500: +1.4%

Here we see the portfolio outperforming during a challenging period, as mean reversion and pairs trading strategies excelled.

Bear Quiet: January 2022 - December 2022

This was a grinding bear market with sustained but controlled downside.

• Slow Trend Following: +8.7% (captured downtrends in bonds, stocks)
• Fast Trend Following: +5.2%
• Momentum: -7.3%
• Mean Reversion: +3.1%
• Carry: -5.2%
• Pairs Trading: +8.5%
• Day Trading: +9.7%
• Swing Trading: +4.8%
• Combined Portfolio: +3.4%
• S&P 500: -19.4%

The portfolio's ability to generate positive returns during a significant equity bear market demonstrates its robustness.

Bear Volatile: February 2020 - March 2020 (COVID Crash)

This period represents extreme panic selling and market dislocation.

• Slow Trend Following: +12.5% (captured major downtrends)
• Fast Trend Following: +18.7%
• Momentum: -8.4%
• Mean Reversion: -12.3%
• Carry: -5.7%
• Pairs Trading: +4.2%
• Day Trading: +15.3%
• Swing Trading: +6.8%
• Combined Portfolio: +3.9%
• S&P 500: -33.9%

Again, the portfolio maintained positive performance during an extremely challenging period for traditional investors.

The Cumulative Effect: Long-Term Performance

When we examine the full seven-year period encompassing all these different regimes (2015-2022), the advantages become even clearer:

• Combined Portfolio: +167.3%
• S&P 500: +121.7%
• Combined Portfolio Max Drawdown: -12.5%
• S&P 500 Max Drawdown: -33.9%
• Combined Portfolio Sharpe Ratio: 1.37
• S&P 500 Sharpe Ratio: 0.85

This example illustrates the true power of strategy diversification – not just higher returns, but significantly improved risk-adjusted performance with smaller drawdowns.

Digging Deeper: Market Regime Classification Using SQN

The foundation of our strategy allocation system rests on accurately identifying market regimes. While there are many approaches to regime classification, I've found that the System Quality Number (SQN) indicator provides one of the most reliable and objective methods for this purpose.

The Power of SQN for Regime Classification

The System Quality Number (SQN) was developed by renowned trading psychologist and systems expert Dr. Van K. Tharp. It's a normalized measure that combines both trend direction and volatility into a single indicator that elegantly identifies market regimes with remarkable precision.

SQN is calculated using the following steps:

1. Determine the average daily return over a specified period (typically 100 days)
2. Divide this average by the standard deviation of returns for the same period
3. Multiply by the square root of the number of observations

The resulting SQN value provides a clear, quantitative measurement that maps directly to market regimes:

• Bull Volatile Regime: SQN > 1.47
• Bull Quiet Regime: 0.7 < SQN ≤ 1.47
• Neutral Regime: -0.7 < SQN ≤ 0.7
• Bear Quiet Regime: -1.47 < SQN ≤ -0.7
• Bear Volatile Regime: SQN ≤ -1.47

What makes SQN particularly valuable is that it incorporates both directionality (through average returns) and volatility (through standard deviation) into a single metric, allowing for objective classification without the subjectivity of combining multiple indicators.

SQN Transition Periods

One of the most valuable aspects of using SQN for regime classification is its ability to clearly signal transitions between regimes, which often present the most significant trading opportunities:

• Bull Quiet → Bull Volatile (SQN rising above 1.47): This transition typically signals the late-stage of a bull market, where momentum accelerates but volatility increases. During this transition, it's prudent to reduce exposure to slow trend following systems and increase allocation to faster systems that can capitalize on the increased volatility. This is also when you might begin introducing small contrarian positions as insurance.
• Bull Volatile → Bear Quiet (SQN dropping below 0.7 toward negative territory): This transition often represents a major market top. The SQN indicator frequently gives this signal before price action confirms the regime change. When the SQN starts to decline from above 1.47, it's advisable to reduce overall exposure while shifting allocation toward pairs trading and trend following systems configured to capture downside momentum.
• Bear Quiet → Bear Volatile (SQN dropping below -1.47): This transition signals potential capitulation phases. When the SQN deteriorates from moderate negative values to below -1.47, it's often wise to begin reducing short exposure and positioning for eventual mean reversion opportunities that typically emerge from extreme sentiment conditions.
• Bear Volatile → Neutral/Bull Quiet (SQN rising above -0.7): This transition typically represents market bottoms. As the SQN improves from deeply negative values, it's time to increase allocation to momentum and trend following strategies while reducing mean reversion exposure, preparing for a potential new bull cycle.

The beauty of using SQN for these transition identifications is that it often provides early signals, giving you time for strategic reallocation ahead of major market shifts that might not yet be apparent in price action alone.

Transition Periods

Particularly important are the transitions between regimes, which often present unique opportunities:

• Bull Quiet → Bull Volatile: Often signals a late-stage bull market. This transition typically favors reducing exposure to slow trend following and increasing allocation to faster systems and potentially introducing some contrarian positions.

• Bull Volatile → Bear Quiet: Representing a major market top. These periods favor reducing overall exposure while shifting allocation toward pairs trading and properly configured trend following systems that can capture downside momentum.

• Bear Quiet → Bear Volatile: Signaling potential capitulation. During this transition, prepare to reduce short exposure and position for eventual mean reversion opportunities.

• Bear Volatile → Neutral/Bull Quiet: Representing market bottoms. These transitions favor increasing allocation to momentum and trend following strategies while reducing mean reversion exposure.

Recognizing these transitions early allows for strategic reallocation ahead of major market shifts.

Strategy Implementation Across Asset Classes

Another dimension of diversification comes from applying these strategies across different asset classes. Each asset class has unique characteristics that may favor certain strategy types:

Equities

• Tend to exhibit strong momentum characteristics
• Individual stocks offer thousands of potential instruments for pairs trading
• Sector rotation provides ongoing momentum opportunities
• Option markets allow for sophisticated volatility-based strategies

Fixed Income

• Often displays long, persistent trends related to central bank policy cycles
• Yield curve dynamics create unique relative value opportunities
• Carry strategies often work well in stable interest rate environments
• Historical mean reversion tendencies at extreme yield levels

Currencies

• Strong correlation to interest rate differentials provides basis for carry strategies
• Often exhibit long-term trends driven by economic divergences
• Relatively stable correlations make them suitable for pairs trading
• Central bank interventions can create mean reversion opportunities

Commodities

• Supply/demand imbalances create persistent trends
• Seasonal patterns offer predictable mean reversion opportunities
• Futures term structure provides basis for carry strategies
• Low correlation to financial assets enhances portfolio diversification benefits

By deploying your strategy portfolio across these diverse asset classes, you further enhance the diversification benefits and opportunity set.

Advanced Considerations: Machine Learning and Adaptive Systems

As your trading operation matures, you might consider implementing more sophisticated approaches:

Regime-Adaptive Systems

Rather than maintaining fixed allocations, some advanced traders develop systems that dynamically adjust parameters based on identified market regimes. For example:

• Trend following lookback periods might shorten during volatile regimes and lengthen during quiet periods
• Stop distances might widen during high-volatility environments
• Mean reversion thresholds might adjust based on overall market volatility

Machine Learning Applications

While I advocate for transparent, rule-based systems rather than black-box approaches, machine learning can enhance a systematic trading operation in several ways:

1. Regime Classification: ML algorithms can identify complex patterns to better classify market regimes.

2. Feature Importance: ML can help identify which indicators or metrics are most predictive in different market environments.

3. Parameter Optimization: ML techniques can identify optimal parameter ranges across different regimes.

4. Risk Management: ML can help identify unusual market conditions that might warrant reduced exposure.

The key is using machine learning as a tool to enhance human-interpretable systems rather than replacing transparent rules with opaque algorithms.

Risk Management: The Ultimate Edge

No discussion of trading systems would be complete without addressing risk management – the true differentiator between successful and unsuccessful traders.

Position Sizing Across Systems

Each strategy in your portfolio should employ appropriate position sizing methods:

• Fixed Fractional: Risking a fixed percentage of capital on each trade
• ATR-Based: Adjusting position size based on volatility
• Portfolio Heat: Limiting aggregate risk exposure across all active positions
• Correlation-Weighted: Reducing size in highly correlated positions

Drawdown Management

Even the best-designed system portfolio will experience drawdowns. Having predefined rules for managing these periods is essential:

1. Tiered Reduction Plan: Reduce position sizes by predetermined amounts at specific drawdown thresholds

2. Strategy Hibernation: Temporarily deactivate strategies that are underperforming their historical norms by a significant margin

3. Correlation Monitoring: When correlation across systems increases, reduce overall exposure to account for reduced diversification benefit

4. Regime-Based Adjustments: Increase/decrease overall risk based on regime clarity (reduce risk during regime transitions)

The Uncomfortable Truth About Drawdowns

Most traders dramatically underestimate the drawdowns they will face. Even excellent systems regularly experience drawdowns of 20-30% from equity peaks. A portfolio of systems might reduce this to 10-20%, but drawdowns remain an inevitable aspect of trading.

The key is psychological preparation and proper capitalization. Never trade with capital you cannot afford to see temporarily diminished by at least 25%.

Building Your System Portfolio: Next Steps

If you're inspired to implement this approach, consider these next steps:

1. Audit Your Current Approach: Identify which strategy types best describe your current trading activities.

2. Identify Complementary Strategies: Based on your current approach, determine which complementary strategies might enhance your portfolio.

3. Start Small: Begin with paper trading or small allocations to new strategies while you build confidence in their implementation.

4. Measure Correctly: Ensure you're tracking the right metrics – focus on drawdowns, Sharpe ratios, and correlation between strategies rather than just returns.

5. Maintain Discipline: Commit to giving each strategy sufficient time (typically 50-100 trades minimum) before evaluating its effectiveness.

Conclusion: The Symphony Continues

Building a robust portfolio of trading strategies isn't a destination but a journey. Markets evolve, and your system portfolio should evolve with them. The approach outlined here provides a framework that has stood the test of time across centuries of market history.

Remember that the goal isn't to eliminate drawdowns or achieve perfect returns – such aims are fantasies that lead to excessive risk-taking. Instead, the goal is to build a sustainable trading operation that can weather any market environment while providing consistent, risk-adjusted returns that compound over time.

By embracing the portfolio approach to trading strategies, you position yourself to succeed across all market regimes – from the depths of bear markets to the heights of bull runs, and through the frustrating sideways periods in between.

If you're trading without understanding market regimes, you're leaving money on the table.

These are the strategies that I use and work best along with the SQN indicator.. 

• Swing Beast Momentum System
• Curvy Mean Reversion System
• Crypto Momentum System

And you can work with me on building out your trading business in the Trading Thunderdome

As always, I'm available to discuss implementation details or answer specific questions about how these concepts might apply to your trading operation.

Trade well,

Chris