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Efficient Crypto Mean Reversion: Vectorized OU Backtesting in Python
Cryptocurrency markets are notorious for their wild swings. While this volatility scares many, it also presents opportunities. One classic approach to potentially profit from these oscillations is mean reversion — the idea that prices tend to revert to an average level over time. I remember first encountering this concept and thinking about how to apply it systematically, especially in a market moving as fast as crypto. The Ornstein-Uhlenbeck (OU) process provides a mathematical framework specifically designed to model such mean-reverting behavior, making it a compelling candidate for strategy development.
However, developing a strategy is only the first step. Rigorous testing is crucial. Performing backtests iteration by iteration can be painfully slow, especially with high-frequency data common in crypto. This is where vectorized backtesting shines. By leveraging the power of libraries like NumPy and Pandas, we can evaluate strategy performance across historical data incredibly quickly, allowing for faster iteration and analysis. This tutorial guides you through building and evaluating an OU-based mean reversion strategy for cryptocurrencies using precisely this efficient, vectorized approach. We’ll go from theory and data acquisition to signal generation, backtesting implementation and detailed performance analysis.
