Broadening the Horizons: The Art of Diversification in Quantitative Trading

Exploring the Practice of Employing Multiple Models for Risk Mitigation and Revenue Growth

Section I: A New Approach to System Risk

As quantitative traders, we know that over-reliance on a specific model can be dangerous. If a particular edge loses its viability, we might suddenly find ourselves without a revenue stream. So, how do we counteract this threat? One solution I've discovered is deploying a variety of models, both uncorrelated and correlated, to disperse the system risk over multiple parameters. This approach can help mitigate several risks:

• Decay of an individual system's edge

• Overfitting of a particular model

• Market regime risk

Section II: The Retail Analogy: Diversification in Practice

Let's imagine you're opening a store. Would you exclusively sell one product? Likely not, as that could jeopardize your business stability. This is similar to our situation as quants. We need a diverse range of "products," or in our case, models.

However, over-diversification can lead to another issue: becoming too scattered. You wouldn't open a store selling food, toys, and hardware all at once; similarly, we shouldn't stretch ourselves too thin by trading crypto, futures, stocks, bonds, and currencies simultaneously. Moderation is key here: we need just the right amount of diversification to remain competitive and mitigate portfolio-level risk.

Section III: Overcoming the Challenges of Diversification

Diversifying our portfolio comes with its unique set of challenges, many of which require a good understanding of computer programming. We need to handle tasks such as:

• Determining when to go long/short

• Managing multiple open positions

• Keeping track of systems requiring updates on stops, trails, tps, and those without stops

• Connecting with multiple brokers

• Storing data for every order sent

• Regularly updating all tasks

• Retrieving data

These tasks may seem overwhelming from an outsider's perspective, and indeed, the complexity only grows as we delve deeper. But once it's built and actively trading, it's a system that continues to give back.

Section IV: Embracing Errors as Stepping Stones

During the initial deployment of my models, I encountered several errors that only became apparent once the models began to trade actively. But these mistakes are not roadblocks; instead, they're stepping stones leading us closer to our final product. Discouragement won't help; every error corrected gets us closer to an optimized and effective system.

In the world of quantitative trading, we're constantly learning, and with risk comes the potential for growth. Embrace this journey and relish in the discovery that unfolds.