CTA Strategies

Commodity Trading Advisors (CTAs) have been a prominent force in the financial markets since the 1970s. 

These specialized traders focus on trading futures contracts and other derivative instruments across a wide range of asset classes. 

Their strategies, known as managed futures, offer a unique approach to diversification and potential returns.

Key Takeaways – CTA (Commodity Trader Advisors) Managed Futures Strategies

Logic Behind the Positions (General CTA Principles):

1. Systematic Trend Identification – The core logic for a CTA is the systematic identification of price trends. Proprietary algorithms analyze historical price data, volatility, and potentially other quantitative inputs (like volume or open interest) to determine the existence, direction, and strength of a trend.
2. Diversification – The portfolio is diversified across multiple uncorrelated or low-correlation asset classes (equities, fixed income, currencies, commodities). This is a key risk management feature, as trends can emerge in different markets at different times.
3. Risk Management:
   • Position Sizing – The percentage allocated to each position is carefully determined based on its volatility and its contribution to overall portfolio risk. Higher volatility assets typically receive smaller allocations.
   • Stop-Loss Orders – CTAs systematically use stop-loss orders or other mechanisms to cut losses if a trend reverses.
   • Market Neutrality (Directional Agnosticism) – CTAs are generally indifferent to whether markets go up or down. Their goal is to capture trends in either direction.
4. Rebalancing and Adaptation – The portfolio is dynamic. As trends change, positions are adjusted, closed, or new ones initiated according to the signals from the trading system. If a trend weakens or reverses, the system will signal an exit or a new position in the opposite direction.
5. Objectivity – Decisions are driven by the model’s output, removing emotional biases from trading decisions.

Key Considerations for this Hypothetical Portfolio:

• Time Horizon – CTA models can operate on various time horizons, from short-term systematic day trading (days/weeks) to long-term (months/quarters). The example portfolio we use in the article implicitly assumes a medium to longer-term trend focus.
• Model Specificity – Different CTAs use vastly different models. Some might incorporate more sophisticated pattern recognition, machine learning, or alternative data sources. The example portfolio we use is a simplified representation.
• Cost of Carry & Roll Yield – For futures contracts, considerations like the cost of carry and roll yield (when contracts expire and positions are rolled to the next contract month) are integral to the strategy and profitability.

How Can Individual Traders Setup a CTA Strategy?

• We cover that below.

What are CTAs?

CTAs are professional money managers registered with the Commodity Futures Trading Commission (CFTC).

They specialize in trading futures contracts and other derivatives.

Unlike traditional fund managers, CTAs have the flexibility to take both long and short positions in various markets.

This versatility allows them to potentially profit in both rising and falling markets (or no matter what direction the market is going).

Many traders are interested in studying the CTA business model due to its focus on making the best decisions rather than having systematic bias (i.e., always being long).

Growth in Managed Futures

Managed futures strategies have grown a lot over the past few decades.

Today, the industry manages hundreds of billions of dollars in assets.

Institutional investors, high-net-worth individuals, and even some retail investors have embraced these strategies.

Core Principles of CTA Strategies

CTA strategies are built on several fundamental principles that set them apart from traditional trading or investment approaches.

Trend Following

It’s much more nuanced than saying that CTAs are simply “trend following algos.”

But… the majority of CTAs do use trend-following strategies.

These approaches try to identify and capitalize on persistent price movements across various markets.

Trend followers use algorithms to detect trends early, ride them for as long as possible, and exit when the trend shows signs of reversal.

This systematic approach removes much of the emotional bias often associated with discretionary trading decisions.

And machines are simply better than humans in a lot of ways – data processing, brute-force calculation, following directions, trade execution.

They simply do it faster, more accurately, and less emotionally than any human could hope to.

Diversification Across Markets

CTAs typically trade in a wide range of futures markets.

These may include:

• Commodities (e.g., energy, metals, agriculture)
• Currencies
• Interest rates
• Stock indices (single-stock trading may occur but isn’t necessarily common given CTAs focus on futures markets)

Spreading their exposure across various uncorrelated or mostly uncorrelated markets enables CTAs to try to reduce risk and better smooth out returns over time.

This broad diversification is a key selling point for investors looking to complement their traditional stock and bond portfolios.

Systematic Trading Approaches

Most CTAs rely on quantitative models and computer-driven trading systems.

These systems analyze vast amounts of market data and derive outputs based on however the algorithms are designed.

Systematic approaches try to execute trades consistently and efficiently across multiple markets simultaneously.

How Do CTAs Identify Trends?

CTAs identify trends through a combination of technical analysis and quantitative models. 

Key techniques include:

• Moving averages – Comparing short-term and long-term moving averages to spot trend directions.
• Breakout analysis – Identifying when prices breach significant support or resistance levels.
• Momentum indicators – Using indicators like the Relative Strength Index (RSI) or Moving Average Convergence Divergence (MACD) to gauge trend strength.
• Volume analysis – Assessing trading volume to confirm trend validity.
• Time series analysis – Applying statistical methods to detect persistent price patterns.
• Machine learning algorithms – Using AI to recognize complex trend formations.
• Volatility filters – Adjusting trend signals based on market volatility levels.
• Multi-timeframe analysis – Confirming trends across different time horizons.
• Intermarket analysis – Examining correlations between related markets (i.e., diversification value, what’s discounted across markets).
• Sentiment indicators – Incorporating data on market positioning or investor sentiment.

CTAs often combine these methods, using proprietary weightings and filters. 

For instance, a CTA might combine ~15 different variables based on their understanding of how things work as it relates to their goals, then use that for testing.

As mentioned, they typically execute these trades systematically – executing trades automatically when predefined trend criteria are met. 

Then refining and iterating as they learn and understand more.

Continuous backtesting and optimization on past data and lots of synthetic data help improve these models so that they work well on forward data.

Types of CTA Strategies

Trend following dominates the CTA family, but there are several other approaches used by managers in this space.

Momentum Strategies

Momentum strategies are closely related to trend following but focus on shorter-term price movements.

These approaches try to capitalize on the tendency of assets that have performed well (or poorly) in the recent past to continue that performance in the near future.

Of course, this is not so easy to trade momentum in a discretionary way, so CTAs develop systematic ways of doing it.

Momentum traders may hold positions for days or weeks, as opposed to the months-long holds typical of trend followers.

Momentum tends to align more closely with day trading.

Mean Reversion Strategies

In contrast to trend following, mean reversion strategies bet on prices returning to their historical averages.

These CTAs identify assets that have moved significantly away from their long-term means and take positions anticipating a reversal.

Mean reversion can be effective in range-bound markets where clear trends are absent.

This can also mean that CTAs can be more versatile and not so dependent on a certain environment for making money.

Relative Value Strategies

Some CTAs specialize in relative value trades, which involve simultaneously buying and selling related instruments.

For example, a manager might go long on one commodity future while shorting another in the same sector (e.g., long oil, short gasoline).

These strategies try to profit from price discrepancies while minimizing exposure to overall market movements.

Also important for some institutional traders offering products to investors looking for uncorrelated returns streams.

Option-Based Strategies

A subset of CTAs focuses on options trading within futures markets.

These strategies can include writing options to collect premiums, creating complex spread positions, or using options to hedge other futures positions.

Option-based approaches often try to generate more consistent returns with lower volatility than pure directional strategies.

For those with the expertise, it’s easier to customize returns streams with options/derivatives.

Global Macro Strategies

Global macro CTAs analyze macroeconomic trends and geopolitical events to make directional, non-directional, and relative value bets across asset classes like commodities, currencies, and interest rates.

They use fundamental indicators such as GDP growth, inflation, and central bank policies to predict market movements.

Positions are generally held over medium to long terms.

Volatility Arbitrage Strategies

Focusing on discrepancies between implied and expected future volatility, these CTAs exploit mispricing in options markets.

Strategies include buying undervalued options and selling overvalued ones to profit from volatility differences.

This helps generate returns regardless of market direction.

Machine Learning-Based Strategies

Leveraging machine learning and AI, some CTAs analyze vast data to identify complex patterns and make predictive decisions in real-time.

Seasonality Strategies

Seasonality strategies exploit predictable patterns in commodity prices (mostly, though potentially other assets too) due to factors like weather, agricultural cycles, or consumer demand. 

An example would be CTAs analyzing historical data to identify seasonal trends and positioning accordingly, providing trading opportunities based on established cyclical behaviors and knowing them better than other traders.

Risk Management in CTA Strategies

Managers use various techniques to control risk and protect capital.

Position Sizing and Leverage

CTAs carefully calibrate the size of their positions based on market volatility and the overall risk profile of their portfolio.

Some use risk parity approaches to allocate capital across different markets.

Leverage is often used to improve returns (generally 10-20% annual returns are targeted).

Stop-Loss Orders

Most systematic CTA strategies use predefined stop-loss levels for each trade.

These automatic exit points help limit potential losses on individual positions.

The specific placement of stops varies by strategy, with some managers using fixed percentage stops while others use more dynamic approaches based on market volatility.

Volatility Targeting

Many CTAs try to maintain a consistent level of portfolio volatility over time – for example, 12%, 15%, and 18% are popular targets (15% is roughly the long-run average of the S&P 500).

During periods of high market turbulence, they may reduce position sizes or increase cash holdings.

Conversely, in calmer markets, they might increase leverage to maintain their target risk level.

The general goal is to keep risk and the expected distribution of returns steady over time, which can help smooth out returns and manage drawdowns.

Correlation Management

CTAs try to benefit from the power of uncorrelated returns by trading across markets and without directional bias.

Managers closely monitor the correlations between different positions in their portfolio, adjusting allocations to maintain diversification benefits.

This helps reduce the risk of large drawdowns caused by correlated market moves.

Example CTA Portfolio & Rationale

Here’s an example of a diversified CTA managed futures portfolio, including long and short positions across different asset classes (commodities, currencies, fixed income, equities), with clear logic for each trade.

This reflects a systematic, trend-following model, i.e., the most common CTA strategy.

Assumed Macroeconomic Context (as of the writing of this article)

• Inflation – Persistently moderate to high in several key economies, though potentially showing signs of peaking in some developed markets due to aggressive prior monetary tightening.
• Interest Rates – Central banks in developed markets (e.g., US Federal Reserve, ECB) are holding rates at elevated levels, with some cautious signaling about potential future easing if inflation continues to recede and growth falters. Divergence in policy may exist, with some smaller economies still tightening or others beginning to cut.
• Economic Growth – A mixed global picture. The US economy might be showing signs of a slowdown but remains relatively resilient. Europe could be facing more significant growth headwinds. Emerging markets present a varied landscape, with some benefiting from commodity prices or shifting supply chains.
• Geopolitical Climate – Ongoing geopolitical tensions in specific regions continue to influence energy and agricultural markets.
• Currency Markets – The US Dollar may have seen some pullback from previous highs if the Fed signals a peak in its tightening cycle, but relative economic strength and interest rate differentials still play a key role.

This helps give better context to our portfolio.

Example CTA Managed Futures Portfolio

Here’s the general portfolio logic and construction:

1. Diversification Across Asset Classes

CTA portfolios try to capture uncorrelated returns by trading across:

• Commodities (e.g., oil, gold, soybeans)
• Currencies (G10 FX pairs; EM FX is generally less liquid)
• Fixed Income (sovereign bond futures)
• Equities (global index futures)

This reduces portfolio volatility and smooths the equity curve.

2. Trend Following – Core Philosophy

Most CTA models are based on systematic trend-following algorithms:

• Signal filters like moving averages (e.g., 50/200-day cross), breakout systems, or Donchian channels identify directional bias.
• Momentum confirmation avoids counter-trend noise.
• Volatility adjustments scale position sizing based on market conditions.

3. Long/Short Balance for Convexity

This portfolio includes both long and short positions:

• Long crude oil and copper (commodity inflation trades)
• Short soybeans (oversupply play)
• Long USD/JPY, short EUR/USD (relative macro strength divergence)
• Short fixed income (rising yield trends)
• Long equities in S&P 500 and Nikkei (equity momentum exposure)

This allows the strategy to perform in rising or falling markets – no directional bias required.

4. Risk Budgeting and Volatility Scaling

Positions are typically risk-parity weighted:

• Each trade is sized to contribute equally to overall portfolio risk (e.g., using ATR or implied volatility).
• Correlations are monitored to avoid overexposure to thematic clusters (e.g., not overloading on inflation-sensitive assets).
  

There is nuance to this as positions can be structured to be weighted by risk regime and not the individual positions.

For example, weighting positions to not have bias to rising growth or rising inflation, but neutral.

5. Rolling & Liquidity

All instruments are highly liquid futures contracts, rolled monthly or quarterly as needed (e.g., front-month crude oil or Euro FX futures).

This ensures low slippage and efficient execution.

Performance Characteristics of CTA Strategies

CTA strategies have several distinct performance characteristics that attract those looking for diversification and downside protection.

Low Correlation to Traditional Assets

One of the primary appeals of managed futures is their historically low correlation to stocks and bonds.

This characteristic can make CTAs valuable portfolio diversifiers, potentially improving risk-adjusted returns when combined with traditional investments.

Potential for Crisis Alpha

Some CTAs have demonstrated an ability to generate positive returns during periods of market stress.

This “crisis alpha” potential stems from their ability to go short and their focus on liquid futures markets.

Notable examples include CTA performance during the 2008 financial crisis and the COVID-19 market turmoil in early 2020.

Return Profile and Drawdowns

CTA returns can be characterized by long periods of modest gains punctuated by occasional large winning trades.

This return profile, sometimes described as a “long right tail,” reflects the nature of trend-following strategies.

However, CTAs can also experience prolonged drawdowns, particularly during choppy, trendless markets.

Fee Structure

CTAs typically charge both management and performance fees.

Management fees usually range from 1% to 2% of assets under management, while performance fees often fall in the 15% to 20% range.

Some managers use high-water marks or hurdle rates (e.g., needs to outperform cash) to align their interests more closely with those of investors.

How an Individual Trader Can Do a CTA Strategy, Step-by-Step

Choose Your Markets

Select from a diverse range of futures markets to trade.

Include a mix of commodities, currencies, interest rates, and stock indices.

Start with one market, then up to 5-10 markets to keep things manageable.

Develop a Trend-Following System

Create a simple trend-following algorithm.

For example:

• Use two moving averages (e.g., 20-day and 50-day)
• Go long when the short-term average crosses above the long-term
• Go short when it crosses below
• Exit when the trend reverses

First get the hang of writing algorithms and the idea of writing out the logic of your decisions.

Risk Management

Set position sizes based on volatility (e.g., risk 0.5% of your capital per trade)

Use stop-losses (e.g., 2 times the average true range)

Aim for a consistent portfolio volatility (e.g., 15% annualized)

Automate Your Strategy

Use a trading platform that allows automated execution (e.g., TradeStation, NinjaTrader, QuantConnect).

Code your strategy and backtest it thoroughly.

Start Paper Trading

Run your strategy in a simulated environment for at least 3-6 months.

This helps you identify potential issues without risking real capital.

If you can, run synthetic data against it.

Allocate Capital Wisely

When ready for live trading, start small.

Consider using only 20-30% of your trading capital initially.

Monitor and Adjust

Track your strategy’s performance daily.

Calculate key metrics like Sharpe ratio, maximum drawdown, and correlation to major indices.

Adjust position sizes or exit rules if necessary.

Diversify Your Approach

As you gain experience, consider adding:

• Multiple timeframes (e.g., combining daily and weekly signals)
• Other technical indicators (e.g., RSI, MACD)
• Volatility filters to adapt to changing market conditions

Continuous Learning

Regularly review academic research on trend following and CTAs.

Network with other systematic traders to share insights.

Always be testing and refining.

Consider Additional Strategies

Once comfortable with trend following, explore other CTA approaches like:

• Mean reversion trades
• Relative value strategies
• Simple option writing for income

Successful CTA-style trading requires a long-term perspective. 

It’s not about hitting home runs, but consistently capturing small edges across multiple markets. 

Always be prepared for extended drawdowns and recognize that variance is part of it.

Invest in CTA ETFs

There are CTA ETFs.

These include:

• Simplify Managed Futures Strategy ETF (CTA) – Seeks absolute returns with low correlation to equities. Single-manager fund.
• iMGP DBi Managed Futures Strategy ETF (DBMF) – Among the first CTA ETFs, coming out in 2019. Closer to an index of CTA managers.
• WisdomTree Managed Futures Strategy Fund (WTMF) – Aims for positive total returns in rising or falling markets.
• KFA Mount Lucas Managed Futures Index Strategy ETF (KMLM) – Tracks an index designed to emulate CTA performance.
• iShares Managed Futures Active ETF (ISMF) – An actively managed ETF targeting total return from non-traditional asset classes.
• First Trust Managed Futures Strategy Fund (FMF) – Another option offering access to managed futures strategies.
• American Beacon AHL Trend ETF (AHLT) and Blueprint Chesapeake Multi-Asset Trend ETF (TFPN) also provide exposure to trend-following.

These ETFs generally use futures contracts to gain their exposures.

Challenges & Criticisms of CTA Strategies

CTA strategies face several challenges and criticisms.

Crowding and Capacity Issues

As the managed futures industry has grown, some observers worry about crowding in popular trades.

With many CTAs following similar trend-following approaches, there’s a risk that large capital flows could impact markets and reduce strategy effectiveness.

This is generally true everywhere.

Periods of Underperformance

CTAs can struggle during extended periods of trendless or choppy markets.

The years following the 2008 financial crisis saw many CTAs underperform as central bank interventions disrupted traditional market trends (e.g., volatility was generally suppressed).

Complexity and Transparency Concerns

The quantitative nature of many CTA strategies can make them challenging for some to understand.

The “black box” perception persists.

This complexity and abstruse nature of what CTAs do can also make it difficult for investors to differentiate between skilled managers and those relying on statistical flukes.

High Fees

The fee structure of many CTA programs has come under scrutiny, particularly during periods of underperformance.

Some investors question whether the potential benefits justify the costs, especially when compared to lower-cost alternative risk premia products that try to capture similar return factors.

The Future of CTA Strategies

As financial markets evolve, CTA strategies continue to adapt and innovate.

Machine Learning and AI

Many CTAs are looking at the use of machine learning and artificial intelligence to improve their trading models.

These techniques may help identify more subtle patterns in market data and improve strategy performance.

Alternative Data Sources

CTAs are increasingly incorporating alternative data sources into their models.

Satellite imagery, social media sentiment, and other non-traditional datasets may provide an edge in predicting market movements.

The challenge is in integrating this information without overfitting models to past data.

Expansion into New Markets

As traditional futures markets become more crowded, some CTAs are exploring opportunities in new areas.

Cryptocurrency futures, for example, have attracted attention from some managers looking for potentially new sources of returns and higher volatility.

Others are looking at less liquid markets or more exotic derivatives to gain an edge.

Customization and Risk Premia Approaches

Investors are demanding more tailored solutions from CTAs.

This has led to an increase in customized managed accounts and the development of risk premia products* that try to isolate specific return factors associated with CTA strategies.

These approaches may offer lower fees and greater transparency than traditional CTA funds.

*Risk premia products in the CTA context are investment vehicles designed to capture specific return factors traditionally associated with managed futures strategies, but in a more systematic and transparent way.

These products typically isolate and replicate key drivers of CTA returns, such as momentum across various asset classes, without relying on discretionary (human) trading decisions.

They often use rules-based approaches to provide exposure to trends in equities, bonds, commodities, and currencies.

Risk premia products usually offer lower fees compared to traditional CTA funds and try to deliver similar diversification benefits.

They’re designed to give investors more targeted exposure to the sources of return in CTA strategies.

FAQ – CTA Strategies

How Do CTAs Make Money?

CTAs make money by capturing trends across a diversified set of futures markets.

Most use systematic, rules-based strategies to identify persistent price movements (whether upward or downward) and profit from directional bets. 

By going long in rising markets and short in falling ones, they can generate returns independent of equity or bond benchmarks. 

Their strength lies in diversification, risk-adjusted sizing, and the ability to thrive in volatility, dislocations, or macro shifts that trigger extended moves.

How Do CTAs Profitably Trade Futures? Isn’t That Zero-Sum in Nature?

While futures are zero-sum at the trade level (every long has a short), CTAs profit by identifying behavioral inefficiencies and persistent trends that arise from macro forces, crowded positioning, or delayed institutional reactions. 

Their advantage is systematic discipline: they ride trends that others miss or exit prematurely. 

Profits often come from commercial hedgers, discretionary traders, or liquidity-seeking flows that react emotionally or slowly to macro changes, allowing CTAs to harvest alpha in repeatable, non-random ways.

What is the primary, sustainable source of alpha CTAs try to capture across various market regimes?

The primary source of alpha for CTAs is price persistence, often referred to as trend-following momentum. 

This arises from investor herding, slow institutional rebalancing, and the staggered digestion of macroeconomic information. (Many algorithmic systems can’t handle certain types of information well.)

CTAs exploit these slow-moving inefficiencies systematically, using quant models to detect directional bias early and ride it until reversal.

Because these behaviors recur across asset classes and regimes, the strategy remains strong, even if short-term performance can be cyclical.

How do CTAs manage inter-asset correlations?

CTAs constantly monitor cross-asset correlations to avoid concentrated risk exposure. 

Most use risk parity or volatility targeting to size positions, ensuring no single trend or theme (e.g., all commodities rising) dominates the portfolio. 

Some use dynamic correlation matrices, stress-testing, or principal component analysis (PCA) to reduce overlap and isolate independent sources of return. 

The goal is to preserve diversification benefits, especially when markets become highly correlated during risk-on or risk-off environments.

How do CTAs adapt to evolving market structures, such as increased algorithmic trading, shifts in central bank policies, or changing liquidity profiles across futures contracts?

CTAs adjust by continuously updating models to reflect new volatility regimes, microstructure shifts, and macro policy changes. 

For example, increased algorithmic trading may prompt faster signal decay, requiring quicker entry/exit logic. 

Central bank policy shifts might reduce fixed income trend strength, leading to adaptive filters. 

Liquidity screens ensure only tradable contracts are used. 

Some CTAs incorporate machine learning or ensemble models to increase adaptability without overfitting to noise.

What is the ongoing research and development process for model refinement and to reduce model decay?

Ongoing R&D involves out-of-sample testing, walk-forward analysis, and stress testing across decades of data. 

Synthetic data is often used, given financial data tends to be limited (especially when considering how markets change over time).

Teams evaluate parameter sensitivity, market regime dependency, and structural breaks. 

New models are introduced gradually in a parallel sandbox environment before full deployment. 

To prevent model decay, CTAs monitor live performance versus historical simulations and conduct attribution analysis. 

Emerging techniques – such as ensemble learning and regime-switching frameworks – help maintain edge.

What is the estimated AUM capacity for CTA strategies before potential performance degradation due to market impact or liquidity constraints? How do they monitor this, and what measures are in place if capacity limits are approached?

CTA capacity varies by strategy granularity and liquidity of target markets. 

Large, highly liquid futures (like S&P 500, EUR/USD, crude oil) can absorb billions in AUM. 

However, niche or thinly traded contracts may show slippage with just a few hundred million. 

Firms monitor execution slippage, bid-ask spread changes, and participation ratios. 

If degradation is detected, they may rebalance weightings toward deeper markets, limit inflows, or cap AUM at a strategy level.

How do CTAs try to minimize transaction costs, slippage, and market impact?

CTAs minimize costs using algorithmic execution, volume-weighted average price (VWAP) strategies, and trade scheduling across multiple sessions to avoid crowding.

They also avoid trading around economic releases or during illiquid hours. 

Many CTAs use smart order routers and proprietary execution algos to break trades into smaller slices. 

Frequent backtesting of fill assumptions versus actual slippage helps recalibrate execution logic. 

Managing position sizing relative to market depth is also critical.

How do CTA strategies typically behave in relation to various macroeconomic scenarios (e.g., inflationary vs. deflationary environments, rising vs. falling interest rates, varying growth outlooks)?

CTAs perform best during transitional or volatile macro environments – such as inflation shocks, yield curve shifts, or currency crises – when trends emerge across assets. 

In inflationary or rising-rate regimes, commodity and short-duration bond trends often dominate. 

During deflationary or falling-rate environments, long bonds and defensives may trend. 

In stable or range-bound markets, CTA returns may stall or become flat. 

Their strength is convexity: the ability to gain in either direction when clear trends arise.

How do CTAs account for roll yield and the cost of carry?

CTAs account for roll yield and cost of carry by integrating them into their signal filters, contract selection, and position sizing. 

Roll yield – the gain or loss from rolling a futures contract forward – is especially relevant in markets with steep contango or backwardation (e.g., crude oil, VIX, or higher rate environments). 

Systematic CTAs may favor contracts with more favorable roll dynamics or reduce exposure when negative carry erodes profitability. 

Cost of carry (such as interest rates or storage costs) is baked into pricing models, particularly in currency and commodity strategies. 

Some CTAs optimize entry points and roll timing based on calendar spreads or curve shape. 

Ultimately, while trend is the core driver, adjusting for carry-related distortions improves net performance and reduces unwanted drag on returns.

Conclusion

CTA managed futures strategies offer a unique approach to trading that can provide diversification benefits and potential downside protection. 

Their ability to trade across a wide range of markets and take both long and short positions sets them apart from traditional trading and investment strategies.