Funding Rate Arb: ML Models & Production Deployment
LightGBM prediction models for funding rate direction, composable strategy shell for entry/sizing/exit, TUI simulator, and live signal generation deployed to Vercel.
LightGBM Quantile Regression (86% Direction Accuracy across 46 Features): Predicts next-interval funding direction and magnitude quantiles while keeping entry, sizing, and exit rules modular.
Shifted from pure backtesting to ML-driven prediction and production deployment. Built a LightGBM quantile regression model achieving 86% direction accuracy across 46 features. The composable strategy shell separated ML predictions (signal) from trading decisions (entry/sizing/exit), enabling rapid experimentation — 21 variants tested with real model predictions. Key learning: data-based exits dominated prediction-based exits by 40+ percentage points. Deployed a FastAPI production pipeline on Vercel with cron-driven signal generation, plus a TUI simulator for live strategy monitoring. Found and fixed 4 critical ML pipeline bugs where the trained model was never actually being used.