Funding-Rate Driven Routing in Financial Prediction Systems

Abstract

Financial forecasting models tend to be based on past market action trends. While such methods are very good under steady-state conditions, financial markets tend to change abruptly due to a change in liquidity, macroeconomic news, or leverage imbalances. Such models trained on past price action are too sluggish in responding to regime changes and therefore make the wrong and not to be relied upon predictions at the worst possible moment when accurate advice is most required. To address this challenge, this paper proposes a funding-rate driven routing model that dynamically switches between forecasting models depending on inferred market states. Funding rates and spot–perpetual basis signs are used to detect volatility and sentiment changes that cause shock regimes. In ordinary times, a light univariate LSTM is used to ensure forecasting efficacy. As volatility rises, the system pushes forecasting to a multi-source deep learning model that incorporates price, volume, and sentiment background. The architecture uses an edge–cloud hybrid execution strategy that compromises on latency, privacy, and scalability. This work demonstrates that, through the integration of regime detection and model switching, one can obtain more accurate forecasting without sacrificing computational responsibility. The model introduces an additional more adaptive, context-aware methodology for real-world financial forecasting environments that must operate under continuously varying risk levels.