Alpha Decay: When to Launch a New Quant Strategy

Coinbase released its 2024 Allocator's Guide to Digital Asset Hedge Funds, where quant funds are taking a big share of the spotlight. With that in mind, it's the perfect time to dive into quant strategies as part of our ongoing series on essential frameworks for launching new digital asset strategies. In this article, we’ll break down the key steps to develop and test a quant strategy in the digital assets space. While each step deserves its own deep dive, we’ll focus on the big picture—what portfolio managers need to know to get started and stay ahead. And we’ll emphasize how AI and ML tools can supercharge a portfolio manager’s work.

Step #1: Why Launch a New Quant Strategy?

Market cycles can create major dislocations—prime opportunities for quant portfolio managers to capture value. But before diving in, it’s crucial to understand why a portfolio manager is launching a new strategy. Here’s what to consider:

1. Diversification: As existing quant strategies become more correlated, the risks of concentration increase. When everything starts moving together, it’s time to develop new strategies that aren’t as closely linked, spreading out the risk.

2. New Data: In the world of digital assets, data is scattered across centralized exchanges, on-chain transactions, and even social media. One of the key roles of quant researchers and portfolio managers is to gather these fragmented datasets, test them, and see how they correlate with market movements. The right data can unlock significant alpha.

3. New Technology: Advancements in AI and machine learning can be game-changers. These technologies can uncover hidden relationships between indicators, providing the foundation for a new quant strategy.

4. Changing Market Regimes: No strategy stays fresh forever. As markets evolve, strategies can suffer from alpha decay. For instance, a trend-following model that worked last year might need a tweak to account for new macro factors. Keeping strategies updated is essential.

5. New Opportunities: New markets and shifting liquidity dynamics create fresh opportunities. For example, while Bitcoin and Ethereum have dominated options markets, the rise of altcoin options is opening doors for quant traders.

Step #2: Narrowing Down the Idea

It all starts with identifying opportunities in the market—whether it's a pattern, inefficiency, or trend that could be capitalized on. PMs can then formulate a hypothesis about how the market might behave under certain conditions. Then, it's time to gather data—anything from traditional prices and volumes to derivative markets data like open interest, on-chain data like network activity, as well as market sentiment—that'll help validate or debunk the initial hypothesis. AI can be a game-changer here by sifting through massive datasets, helping to process vast amounts of data to uncover relationships that might not be obvious at first glance.

Step #3: Building the Model

Next, data is turned into useful insights by transforming it into features the model can work with. Then, it's time to choose an approach—whether going with a statistical model will be preferable versus, for example, a machine learning or simply a rule-based strategy. Once the portfolio manager has picked the right tools, the model coding phase kicks off. At this stage, AI can help streamline the process by fine-tuning feature selection and boosting the model's accuracy.

Step #4: (Back)Testing the Waters

After building the model, it’s time to see how it would have performed in the past. This is called backtesting, where historical data is used to test the strategy. Portfolio managers will typically  evaluate key metrics like the Sharpe ratio, drawdown, time to recover from maximum drawdown, and cumulative returns net of trading fees.

Step #5: Managing Risk

With the strategy built, it’s time to identify potential risks—whether market risk, liquidity risk, or risks inherent to the model itself. Once these risks have been outlined, quants can set specific rules for position sizes, stop-losses, and drawdown limits to protect against big losses. AI can add value here by adjusting risk settings dynamically based on real-time data, thus acting in a preventative way.

Step #6: Fine-Tuning

At this stage, quants will focus on refining their model’s parameters to enhance performance. While doing this, it’s critical to be mindful of overfitting—making sure that the model is not just optimized for past data but capable of adapting to future conditions. AI can assist with parameter optimization, helping to strike the right balance between performance and robustness without overcomplicating the model.

Step #7: Making It Real

Once the strategy is ready, it’s time to make it operational. This often means integrating the model with a live trading system that supports the trading frequency and agility required by the model. Ensuring that it can handle real-time data and place trades automatically is crucial. AI-driven execution algorithms can give traders a competitive edge by optimizing trade timing and minimizing market impact.

Step #8: Keeping an Eye on Things

As the new quant strategy runs, continuous monitoring is essential. PMs will want to track its performance and risk in real-time, ensuring everything’s going as expected. If market dynamics change, adjustments might have to be made on the fly. AI can help here by providing predictive insights into changing market conditions, allowing PMs to make proactive adjustments before problems arise.

Step #9: Staying Compliant

It’s important to ensure that the newly launched strategy complies with all relevant regulations, especially if trading is taking place across multiple jurisdictions. Equally crucial is thorough documentation of every step, from strategy development to any changes made along the way.

Step #10: Going Live

Finally, when everything’s in place, it’s time to deploy the strategy in live markets. Portfolio managers typically start with a small allocation, gradually increasing the capital allocated as they gain confidence in how the strategy performs in real-world conditions. AI can continue to assist by analyzing real-time performance, providing insights on scaling decisions by taking into account evolving liquidity dynamics.

By following this process and incorporating AI along the way, portfolio managers can develop, test, and deploy quant strategies more efficiently and with greater confidence, gaining essential accuracy and operational velocity.

Conclusion

Developing a quant strategy is a blend of creativity, rigorous testing, and constant refinement. In modern finance, AI integration is crucial, enabling smarter data analysis, refined models, and precise strategy deployment. Reach out to our team to learn more about how Genie AI helps top-performing quant teams globally. With Genie AI, the future of quant strategy isn’t just promising—it’s here.

About GenieAI

GenieAI is the premier AI-powered Portfolio and Risk Management System for digital assets. At GenieAI, we empower investors to anticipate market trends, navigate risks, and manage portfolios with unmatched precision. With Genie AI, smarter, data-driven decisions and automated workflows are at your fingertips, making the once unimaginable possible.

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.

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