Multi-Modal High-frequency Forecasting for the WETH/USDC Uniswap v3 Pool: Integrating on-chain DEX Metrics with advanced Machine Learning Models

Abstract

The rapid expansion of decentralized finance (DeFi) has generated rich, transparent on-chain data that remains largely underutilized in high-frequency trading models. Most existing studies rely primarily on centralized exchange (CEX) price feeds, which often suffer from low signal-to-noise ratios (Lim et al., 2021; Lee et al., 2025). This study develops a multi-modal forecasting framework for the WETH/USDC 0.05% fee tier pool on Uniswap v3. We integrate Binance CEX market microstructure data with granular onchain DEX metrics, including swap imbalance, on-chain volume, liquidity depth, tick velocity, and pool liquidity utilization. An XGBoost classifier was trained on synchronized 15-minute bars, with realistic cost-aware backtesting incorporating pool fees and slippage. The model achieved a directional accuracy of 63.26% on out-of-sample data. Feature importance analysis revealed that on-chain variables, particularly volume_usd, liquidity_usd, imbalance, and tick_velocity, ranked among the top predictors. In cost-aware backtesting, the strategy outperformed Buy & Hold by 2.49 percentage points, although absolute returns remained modestly negative due to transaction costs. This research demonstrates the incremental predictive value of integrating Uniswap v3 on-chain DEX metrics with CEX data for high-frequency forecasting. While transaction costs remain a significant challenge, the findings highlight the potential of multi-modal approaches in DeFi markets and provide a foundation for future work using more advanced architectures such as the Temporal Fusion Transformer..