Pairs Trading with Cointegration: Statsmodels
What if your trading strategy could identify profitable setups by mathematically proving two assets are linked, rather than just guessing they move toge...
What if your trading strategy could identify profitable setups by mathematically proving two assets are linked, rather than just guessing they move together? Building a robust pairs trading python system requires moving beyond simple correlation to verify cointegration, ensuring the spread between assets actually reverts to a mean. Most traders confuse correlation with cointegration, leading to strategies that fail when the underlying relationship breaks down. While correlation measures if two stocks move in the same direction, cointegration proves a stable, long-term equilibrium exists between them. This distinction is the foundation of statistical arbitrage, a strategy pioneered by Morgan Stanley in the 1980s to profit from relative price movements regardless of market direction. Key fact: A study in 2019 showed that around 92% of trading in the Forex market was performed by trading algorithms rather than humans, highlighting the shift toward automated, rule-based execution. In this guide, we explore how to implement this strategy using statsmodels for rigorous statistical testing and vectorbt for high-performance backtesting.