The Statistical Pair Arbitrage Strategy

Statistical arbitrage, a strategy widely used in traditional finance, is now increasingly being implemented in the rapidly growing world of crypto. By coupling statistical arbitrage with XGBoost, a powerful machine learning algorithm, we are taking advantage of price inefficiencies with even more precision.

Combination of Several Methods

  • Classical Statistical Arbitrage – a strategy that exploits temporary price inefficiencies between two cryptocurrencies that statistically co-integrated or typically move in tandem. When the price relationship between these assets diverges from the norm, the algorithm detects this anomaly and executes trades to capitalize on the anticipated correction.
  • XGBoost: A powerful gradient boosting machine learning framework utilized for its speed and model performance. Using XGBoost increases the probability of identifying profitable arbitrage opportunities by leveraging historical data patterns.
  • ARIMA: A statistical method used for time-series forecasting, enabling the prediction of cryptocurrency prices based on their own past behavior. This allows us to anticipate possible future movements in the cryptocurrency pairs we trade.

The strategy was also subjected to rigorous backtesting, a process of applying models to historical data to assess their performance. Backtesting allows us to test and refine the strategies and configure parameters before they are launched into the real-world trading environment.

Statistical Significance

It’s easy to design a strategy that works brilliantly for a short time frame or a specific set of conditions. Backtesting over longer periods or various market conditions ensures that the strategy’s performance isn’t a result of mere luck or overfitting.

Risk Management

Correct backtesting helps in understanding the potential drawdowns (losses) a strategy might face. By examining these drawdowns, we can tailor our risk management techniques, ensuring that we aren’t overexposing ourselves to undue risks.