Statistical arbitrage is a class of quantitative trading strategies that aim to profit from temporary price discrepancies between statistically related financial instruments. Unlike pure arbitrage, which seeks risk-free profits from identical assets trading at different prices, statistical arbitrage involves taking on some market risk, as the relationship between assets is not perfectly guaranteed to converge. The core idea is to identify two or more assets whose prices tend to move together or have a stable spread (e.g., pairs trading where one asset is long and another is short) and then trade when this relationship deviates from its historical norm, expecting it to revert. Common strategies include pairs trading (buying an underperforming stock and selling an outperforming one within a correlated pair), basket trading (identifying undervalued/overvalued groups of stocks), and cross-asset arbitrage (exploiting relationships between different asset classes). These strategies heavily rely on statistical models, econometrics, and robust backtesting to identify these mean-reverting relationships. While offering potential for consistent returns, statistical arbitrage requires significant computational resources, real-time data analysis, and sophisticated risk management due to the inherent statistical risk.