The single biggest gap between my backtests and live PnL was midpoint fills

Spent a year wondering why my backtests printed nicely and my live PnL kept underperforming by 20-50%. Most of it traced back to one assumption I hadn't realized my backtester was making: every trade was filling at the midpoint of the bid-ask spread. That price doesn't exist in the real market. When you enter a long, you cross the spread and pay the ask. When you exit, you hit the bid. The gap is the spread, and you pay it every round trip. Most retail backtesters (TradingView default, custom Python builds, some commercial platforms) silently assume midpoint fills unless you explicitly model otherwise. That's a free 0.5-2 bps per trade on liquid US equities, and much more on small-caps, low-volume futures, and options. Quick worked example: intraday mean reversion, 200 trades/year, 8 bp edge per trade. Midpoint fills: 200 × 8 = 1,600 bps = 16% annualized. Realistic fills (1 bp half-spread each side, 1 bp slippage round-trip = 3 bp total cost): 200 × (8 - 3) = 10%. Push up the frequency, or thin the edge, and the gap widens. A 4 bp / 500-trade strategy goes from 20% to 5% once you stop filling at the mid. Sharpe gets hit harder than return does, and costs shrink the numerator while leaving volatility mostly untouched. A backtest Sharpe of 1.8 often lands closer to 0.9 once spreads are modeled honestly. Curious what the sub does on this. Flat bp assumption, regime-dependent costs, historical bid-ask data, or something else? And has anyone found a fill model that tracks live execution closely?