Investment Thesis

Mean reversion is a financial theory suggesting that asset prices and historical returns revert to their long-term average levels, implying periods of above-average returns are likely followed by below-average returns, and vice versa.

This strategy is a mean reversion trading algorithm focusing on daily trading strategies for top U.S. equities with the highest market capitalization. The algorithm exploits significant overnight price movements by shorting stocks with substantial overnight gains the following trading day, assuming they will revert toward their mean prices. 

Implementation involves storing previous close prices, calculating overnight changes, identifying top movers, and executing trades by liquidating existing positions and opening short positions for identified stocks. This systematic approach leverages historical price data to generate returns through price reversion, with potential future enhancements like incorporating volatility and trading volume to refine the selection criteria and improve performance.