In 1985, behavioral economists Werner De Bondt and Richard Thaler published a landmark paper arguing that stock markets systematically overreact to news — that "Losers" get punished too harshly and "Winners" get rewarded too generously, and that both eventually snap back toward fair value. This mean-reversion effect, driven by human emotion rather than rational pricing, became one of the most cited anomalies in academic finance. The question worth asking in 2024 is: does it still work? This article implements a full contrarian backtesting framework in Python. We use a rolling Z-score engine to detect statistically extreme price moves across 40+ S&P 500 constituents, then simulate a long-short strategy — buying oversold "Losers" and shorting overbought "Winners" — with a 60-day recovery window. The result is a clean, reproducible research notebook benchmarked against SPY that you can extend with your own parameters. Most algo trading content gives you theory. This gives you the code. 3 Python strategies.…