Development of an Event-Driven Portfolio Management Algorithm for Funding Rate Arbitrage Strategies to Enhance Sharpe Ratio in Cryptocurrency Markets
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Abstract
Using the Funding Rate Arbitrage strategies of cryptocurrency Perpetual Futures markets brings many of its limitations to light. When faced with market volatility, a static equal-weight solution is unable to adjust immediately. On the other hand, Machine Learning approaches rely on forecasting Funding Rate values, which are characterized by mean-reversion, and this makes it difficult to predict profitably. This research thus develops an Event-Driven Portfolio Management Algorithm using Mean-Variance Optimization and compares it with three baseline strategies: Static Equal-Weight, Ridge Regression, and XGBoost with Walk-Forward Validation. Backtesting was done on actual data from Binance Futures for eight digital assets over a four-year period. Test results show that our method achieves an annualized return of 10.97% with a Sharpe Ratio of 30.47, outpacing Static by 22.4%, while both Machine Learning baselines fail to beat Static (Cohen's d less than 0.2). Our method never lost in any single month (43 out of 43 months) with Max Drawdown of only 0.18%. Statistical significance is confirmed through 8 methods, with Monte Carlo Bootstrap P (>0) equal to 99.63%. Sensitivity analysis confirms robustness and the Breakeven Fee ranges from 5.4 to 7.3 bps at least twice the actual transaction fee.
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