Model Determination

Proprietary Models

All models used to determine optimal portfolio allocations at Markov Labs are maintained as proprietary intellectual property. By keeping these models confidential, we are able to capture alpha while minimizing the risk of strategies being replicated by competitors. The secrecy of our models also allows for more dynamic and adaptive portfolio construction, as our algorithms can continuously integrate new market data, liquidity conditions, and risk factors without exposing the methodology. This approach ensures that our strategies remain both resilient and competitively advantageous, while protecting the underlying research and analytics that drive performance.

Model Research

Model research is the foundation of Markov Labs’ strategy development process for designing optimal portfolio allocation models. We construct comprehensive models to identify key relationships, structural dynamics, and inefficiencies across credit markets. This research provides a deep understanding of how different assets interact under varying conditions, allowing us to uncover opportunities that are not immediately apparent from surface-level market data.

The goal of this research is to design strategies capable of outperforming existing market benchmarks and competing vaults. Once a potential model is identified, it undergoes rigorous evaluation, including simulations, historical backtesting, and, in some cases, limited live deployment. These steps allow us to validate the model’s robustness, stress-test its assumptions, and ensure that it behaves as expected under both normal and extreme market conditions before being considered for full production.

Each model is designed to understand the dynamics that impact yield for each market. This analysis incorporates both external factors, such as asset price volatility, market liquidity, interest rate shifts, and macroeconomic trends, and internal factors, which examine how the vault’s own actions—like allocation changes, rebalances, and liquidations—affect performance. Modeling these interactions allows Markov Labs to anticipate potential feedback effects and optimize strategies to minimize unintended consequences on yield and risk.

Based on these insights, we compute asset weights using either traditional optimizer functions or, in more complex market environments, agent-based simulations. These simulations capture multi-dimensional interactions across assets, markets, and liquidity pools, providing a more nuanced allocation strategy that accounts for the real-world dynamics of DeFi markets. By combining rigorous research, iterative testing, and dynamic allocation modeling, Markov Labs ensures that its strategies remain adaptive, resilient, and capable of generating alpha in diverse market conditions.