Growing LP Optionality: How to Master a New Fundamental Strategy Launch

As limited partners (LPs) and qualified investors consider allocating capital to digital assets, they face critical questions: Which strategies should they invest in? Directional long-only, long-biased, long/short, or market-neutral strategies?

These questions become especially pertinent during periods of market volatility, such as the dramatic swings observed in early August, where digital assets experienced fluctuations of over 15% in a single day. For portfolio managers, the challenge lies in balancing market views, research, expertise, and effectively positioning themselves to current and prospective LPs.

To address these challenges, many digital asset funds are increasingly launching multi-strategy platforms. Some funds are even offering LPs the flexibility to subscribe to individual strategies rather than the entire fund.

This article is the first in a series aimed at providing fund managers with a structured framework on how to think about launching and managing new strategies. While specific evaluation metrics will vary depending on the fund and strategy, there are some universal best practices that portfolio managers can follow. This series will focus on answering three pivotal questions:

1. What is the objective of launching a new strategy within the context of my fund?

2. What are the key steps in strategy construction and testing?

3. How can I effectively monitor and evaluate new strategies on an ongoing basis?

Give’s expertise, we will emphasize the role of technology and how purpose-built tools can simplify the complex and costly process of launching and managing a new strategy.

In this first article, we’ll focus on fundamental digital asset funds—those that dedicate significant resources to researching and analyzing on-chain and other fundamental data to form views on individual tokens and specific sectors within the digital asset space.

1. Setting Strategy Objectives for Fundamental Funds

The first step in launching a new fundamental strategy is to articulate a clear and specific objective. A new strategy requires significant extra costs in terms of portfolio manager time and effort, making a strong strategic rationale essential. The objectives must go beyond the generic goal of “increasing returns” and can include:

- Thematic Diversification: For fundamental funds, a common objective is to offer LPs more options through thematic strategies that focus on specific investment themes or sectors within digital assets. Themes such as AI, decentralized physical infrastructure networks (DePIN), cross-chain interoperability, or gaming can serve as the basis for dedicated fundamental strategies. Thematic diversification is familiar to institutional investors, particularly those with experience in thematic ETFs.

- Performance Enhancement: Portfolio managers may choose to launch a new strategy when they identify a growing trend or a return-generating opportunity that is outside the scope of their current mandate. For example, a yield-generating strategy leveraging decentralized protocols may present additional technology and liquidity risks that require careful disclosures and discussions with investors.

- Risk Management: Fundamental managers might launch new strategies to balance the risks of their core strategies. For instance, thesis-driven funds may develop strategies based on market and sentiment indicators to mitigate the volatility of long-only strategies. This approach can help reduce drawdowns and reassure LPs that, despite a long-biased stance, the portfolio manager has the flexibility to manage exposure in response to market volatility or news.

2. Strategy Construction and Testing

Idea Generation: Generating investment ideas in the digital asset space requires a multifaceted approach that combines traditional financial analysis, technological expertise, and strategic decision-making around key investment parameters.

- Token Selection: Selecting the right tokens is a critical component of idea generation. Fundamental managers must carefully evaluate each token's underlying technology, use case, and team to determine its long-term viability. Additionally, factors like tokenomics, governance structure, and community support play a significant role in the selection process.

- Position Sizing and Exposure Goals: Once tokens are selected, determining the appropriate position size for each token is crucial. Position sizing should align with the strategy’s overall exposure goals and risk tolerance. Managers must consider the potential impact of each position on the fund’s performance, ensuring that no single position disproportionately affects the portfolio.

- Allocation Constraints and Limits: Effective strategy construction involves setting allocation constraints and limits to manage risk. This might include setting maximum exposure limits for individual tokens, sectors, or themes to prevent over-concentration. These constraints ensure a balanced and diversified portfolio that can weather market fluctuations.

- Rebalancing Criteria and Target Frequency: Rebalancing is essential for maintaining the desired portfolio structure over time. Managers need to establish clear rebalancing criteria—whether based on time intervals, market conditions, or specific triggers—and decide on the target frequency for rebalancing. Regular rebalancing helps manage risk and aligns the portfolio with the strategy’s strategic objectives.

- Execution Dynamics: Execution is a critical aspect of strategy implementation, particularly in the digital asset space where liquidity can vary significantly. Managers must account for token availability and liquidity when executing trades. Understanding market depth, order book dynamics, and slippage is vital to executing trades efficiently without negatively impacting the market. Additionally, the choice of trading venues and the timing of trades can influence execution quality and costs.

Backtesting and Qualitative Assessment: After generating investment ideas, the next critical step is constructing and backtesting multiple variations of the portfolio that correspond to the strategies identified during the idea generation phase. Constructing several model portfolios allows managers to test the performance of different approaches under various market scenarios, helping to refine and validate the strategy before implementation.

- Portfolio Variations: By creating multiple portfolio variations, managers can assess the impact of different token selections, position sizes, and allocation strategies. This process is crucial for understanding how each variation performs across a range of market conditions, including bull, bear, and sideways markets. The ability to compare these variations provides deeper insights into which strategies are most resilient and aligned with the fund’s objectives.

- Scenario Analysis: Fundamental digital asset investors should conduct scenario analysis to evaluate how their model portfolios would perform under specific conditions, such as market dislocations, regulatory changes, or technological breakthroughs. This involves simulating different market environments and stress-testing the portfolios to understand their potential vulnerabilities and strengths.

- Risk and Return Metrics: Key metrics to consider during backtesting include risk-adjusted returns (such as Sharpe and Sortino ratios), drawdowns, volatility, and Value-at-Risk (VaR). Additionally, metrics specific to digital assets, like on-chain activity, token liquidity, circulating supply, and inflows/outflows, can provide further insights into how the portfolio might behave in the actual market.

- Correlation and Beta Analysis: Understanding the correlation between the portfolio and various market benchmarks or other assets is essential for managing diversification and reducing systemic risk. Beta analysis, particularly over time, can help assess how the portfolio's exposure to specific factors or assets evolves, allowing managers to fine-tune their strategies to align with their risk tolerance.

- Performance Attribution: Managers should also perform performance attribution to identify the primary drivers of returns within the model portfolios. This analysis helps in pinpointing which tokens or sectors contribute most to performance, allowing for more informed decision-making when selecting the final strategy.

- Qualitative Assessment: Beyond quantitative analysis, qualitative assessment plays a vital role in backtesting. This involves reviewing the assumptions underlying each model portfolio and considering factors such as market sentiment, regulatory outlook, and technological advancements that could impact the strategy’s success. By combining both qualitative and quantitative insights, portfolio managers can develop a more robust and adaptable strategy.

3. Ongoing Monitoring and Evaluation

After successfully launching a new strategy, the work is far from over. Continuous monitoring and evaluation are essential to ensure that the strategy remains aligned with its objectives and adapts to changing market conditions. This phase involves regularly assessing performance, managing risks, and making necessary adjustments based on market dynamics. Some key steps in this phase include:

Performance Tracking and Risk Management: Ongoing performance tracking should be directly linked to the objectives set during the idea generation and backtesting stages. This includes monitoring how well the strategy adheres to its exposure goals, position sizing, and rebalancing criteria. Utilizing live portfolio monitoring tools like Genie's, managers can continuously assess performance against benchmarks, ensuring that the strategy is delivering the intended outcomes. Risk management also plays a critical role here, with managers needing to evaluate real-time data on market conditions, sentiment shifts, and liquidity to make informed decisions on rebalancing or adjusting allocations. For example, if a token that was selected based on specific fundamental metrics starts showing signs of declining liquidity or increasing volatility, proactive adjustments may be necessary to protect the portfolio.

Adaptive Rebalancing: As market conditions change, the need for adaptive rebalancing becomes evident. Managers must monitor triggers that were established during the strategy’s design phase, such as significant price movements or changes in liquidity. The rebalancing process should be flexible enough to accommodate these shifts while remaining true to the strategy’s core objectives. Automated systems can assist in identifying when these triggers are met and suggest optimal rebalancing actions, helping to maintain the portfolio’s desired exposure and risk profile.

Conclusion: Leveraging Genie to Automate Strategy Development and Enhance Insights

The steps outlined above consist of a robust framework that can be repeated and scaled as fundamental funds consider launching new strategies. The work involved might seem overwhelming and quite resource-intensive – and it is. For these reasons, an advanced Portfolio and Risk Management System like Genie can streamline the strategy development process, offering a range of automation and analytical capabilities that enhance both efficiency and insightfulness. More specifically:

- Automation of Key Processes: Genie’s platform can automate many of the repetitive tasks involved in strategy development, such as data collection, backtesting, and continuous monitoring. By integrating real-time data feeds with predefined allocation constraints, rebalancing criteria, and risk management protocols, Genie enables portfolio managers to focus on strategic decision-making rather than operational details. This automation not only saves time but also reduces the likelihood of human error in executing complex strategies.

- Enhanced Analytical Capabilities: Genie’s robust analytical tools provide deeper insights into portfolio performance and risk exposures. With advanced AI/ML capabilities, the platform can identify patterns and correlations across a wide range of fundamental and market data, offering predictive analytics that help managers anticipate market shifts and adjust their strategies proactively. This level of analysis allows for more informed decision-making, giving portfolio managers the confidence to explore new strategies and refine existing ones.

- Confidence in Strategy Development: By automating routine tasks and enhancing the quality of insights, Genie empowers portfolio managers to develop, test, and implement new strategies with greater confidence. The platform’s ability to simulate various market scenarios, monitor live portfolio performance, and provide real-time risk assessments ensures that managers are well-equipped to navigate the complexities of the digital assets market. Ultimately, this leads to more robust strategy development efforts, enabling fundamental investors to focus their time and energy on their unique research perspectives.

For fundamental digital asset funds, it’s vital to marry deep analysis with state-of-the-art technology. By setting clear objectives, rigorously testing strategies, and continuously monitoring performance with the help of advanced tools, these funds can more effectively navigate the complexities of the digital assets market. As the industry evolves, the funds that can adapt and leverage new technologies like Genie’s will be best positioned to thrive in the digital assets world.

About GenieAI:

GenieAI is the premier AI-powered Portfolio and Risk Management System for digital assets, leading the industry towards smarter, data-driven decisions and automated workflows. The technology enables investors to anticipate market trends and navigate risks in ways that were once unimaginable—all while managing portfolios with unprecedented precision.

GenieAI helps leading fund managers to:

1. Enhance portfolio metrics

2. Predict market trends

3. Improve data-driven decisions

4. Automate analysis and reporting

5. Boost model performance

GenieAI's expert team of industry leaders brings unmatched AI and ML experience to investment management. GenieAI’s clients include some of the world’s most successful and well-respected digital asset funds.

GenieAI is backed by leading investors, including Coinbase Ventures, Bain Capital Ventures, Goodwater Capital, Sierra Ventures, Fasanara Digital, Arca, and other prominent firms.