Are most retail quant strategies just overfit regime bets?

I’ve been thinking a lot about how many retail algos look amazing in backtests but fall apart the moment market structure shifts. A lot of strategies I see shared here rely heavily on a specific regime, whether that’s low rates, persistent trends, high liquidity, or tight spreads. They perform beautifully on in-sample data, survive a short out-of-sample window, and then decay once volatility clustering or correlations change. It makes me wonder whether the real edge isn’t in signal generation, but in regime detection and adaptive sizing. Most retail quants focus on optimizing entry logic with dozens of parameters, yet very few seem to model structural changes explicitly. We talk a lot about Sharpe and drawdown, but less about robustness across macro regimes or microstructure shifts. For those running live systems, how are you dealing with regime dependency? Are you incorporating volatility state models, HMMs, rolling retraining, or just accepting that strategies have expiration dates? I’m curious how people here think about durability versus pure backtest performance.