OpenGradient Research

OpenGradient Research explores useful applications of DeAI, including analysis of and models for DeFi, chain, risk, tokenomics, TradFi, anomaly detection, etc. Check back regularly to see updates, new research, and new models.

Coming soon:

Mitigating Risk and Loss in AMM Liquidity Pools: A Dynamic Fee System based on Risk Prediction

In decentralized finance, constant function market makers have transformed asset trading by enabling automated market making. However, the nature of static trading fees in CFMM models raises concerns about the long-term profitability of liquidity providers because LPs are not compensated for increased risk during volatile environments. We introduce a dynamic fee system that leverages a simple machine-learned model to forecast volatility and adjust fees in real time based on market conditions. Historical simulations show that a predictive dynamic fee model generates higher total collected fees for LPs compared to static fee structures. By adjusting fees according to market volatility, the model compensates LPs for increased risk during volatile periods, thereby enhancing their profitability and the attractiveness of liquidity provision in DeFi ecosystems.

Anticipating Volatility and its Dynamics Over Varying Horizons

Information about future volatility can be useful in options pricing, risk management, portfolio optimization, and volatility sensitive trading. There is often a siloing of analysis of low, medium, and high frequency volatility. We analyze dynamics of volatility in crypto assets over multiple timeframes and demonstrate forward-looking volatility ANN models that that outperform simple current volatility and other common models over multiple risk horizons.